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Talking Numbers: Technical versus fundamental investment recommendations
Accepted Manuscript
Talking Numbers: Technical versus Fundamental Investment Recommendations Doron Avramov , Guy Kaplanski , Haim Levy PII: DOI: Reference:
S0378-4266(18)30099-2 10.1016/j.jbankfin.2018.05.005 JBF 5347
To appear in:
Journal of Banking and Finance
Received date: Revised date: Accepted date:
10 October 2017 24 April 2018 6 May 2018
Please cite this article as: Doron Avramov , Guy Kaplanski , Haim Levy , Talking Numbers: Technical versus Fundamental Investment Recommendations , Journal of Banking and Finance (2018), doi: 10.1016/j.jbankfin.2018.05.005
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We examine simultaneous fundamental and technical forecasts from the TV show “Talking Numbers.” Technicians are able to predict individual stocks, while fundamental analysts provide no
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value.
Technicians generate significant Alpha of 13.6% that withstands reasonable transaction
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costs.
Technicians implement toolkits that go beyond well-known anomalies.
Market efficiency hypothesis is not rejected for indexes, equity sectors, bonds, or
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commodities.
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Talking Numbers:
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Technical versus Fundamental Investment Recommendations
Doron Avramov*, Guy Kaplanski**, Haim Levy***
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Dr. Guy Kaplanski Head of Financial Studies School of Business Administration Bar Ilan University Tel: 050-2262962 Homepage: http://mba.biu.ac.il/en/kaplanski
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Kaplanski Guy | The Graduate School of Business ... mba.biu.ac.il Mark Hulbert, Technicians vs. analysts in a CNBC stock-picking slapdown: Who wins? MarketWatch (Wall Street Journal), Sept 4, 2015 ; Jacob Bettany, Guy Kaplanski talks Behavioural Economics and the impact of the World Cup on Stock Market Performance, Money Science, June 20, 2014.
*
Chinese University of Hong Kong and Hebrew University of Jerusalem, email: [email protected]. Bar-Ilan University, Israel, email: [email protected]. Phone: 972-50-2262962, Address: Bar Ilan Univeristy, Ramat Gan, Israel. Corresponding Author. *** The Hebrew University of Jerusalem, email: [email protected]. **
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Abstract:
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Market efficiency is often evaluated through the ability of fundamental analysis or technical trading rules to exploit predictable patterns in asset prices. The evidence following decades of empirical research is mixed. This paper reexamines the evidence using a novel database from the TV
show
―Talking
Numbers.‖
We
assess
the
performance
of
1,599
investment
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recommendations, where each recommendation features a fundamental and a technical forecast. We show that technicians are able to predict individual stock returns to economically significant degrees up to a one-year horizon. Beyond that, the null hypothesis of market efficiency is not
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rejected for market-wide indices, equity sectors, bonds, or commodities.
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Keywords: fundamental analysis; technical rules; market efficiency; market anomalies
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JEL Codes: G10, G14, G24
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1. Introduction Whether financial markets are informationally efficient has long been controversial among researchers. Perhaps the most convincing testimony that this controversy is here to stay is the fact that Eugene Fama and Robert Shiller shared the 2013 Nobel Prize in Economics despite the
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fact that they represent two polar opposite views on market efficiency. The null hypothesis of market efficiency is often evaluated through the ability of fundamental analysis or technical trading rules to exploit predictable patterns in asset prices. The evidence following decades of empirical research is mixed. To illustrate, focusing on fundamental analysis, Stickel (1992) and
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Womack (1996) document value in revisions of consensus recommendations, while Jaffe and Mahoney (1999) and Metrick (1999) demonstrate the lack of value in investment newsletters. Considering technical rules, Brock, Lakonishock, and LeBaron (1992), Lo Mamaysky, and
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Wang (2000), Zhu and Zhou (2009), Menkhoff (2010), and Han Yang, and Zhou (2013) advocate in their favor, while Allen and Karjalainen (1999) and Yamamoto (2012), for example,
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provide evidence to the contrary.
This paper reexamines the evidence on market efficiency using a novel database from the
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TV show ―Talking Numbers.‖ This show was hosted by CNBC and Yahoo Finance, and it
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featured simultaneous dual recommendations on single stocks, indices, treasuries, and commodities. Each dual recommendation contains a fundamental and a technical forecast on the
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same asset and over a similar investment horizon. Our empirical experiments consider 1,599 such dual recommendations (1,000 recommendations for 262 individual stocks and 599 for general assets) featuring the most prominent stocks (e.g., Apple, Google, Facebook, Microsoft, and Boeing), most liquid commodities (e.g., gold, oil), major bonds (e.g., the U.S. ten-year
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notes), and major equity indices (e.g., the various Dow Jones and Nasdaq indices, and the energy, real estate, and pharmaceutical sectors). The show is well suited to exploring market efficiency. First, as both technicians and fundamental analysts are exposed to the same public information during the broadcast, their
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simultaneous recommendations reveal the extent to which they possess private information. In addition, as the show covers virtually all asset classes, our experiments are general enough and robust to liquidity concerns and investment capacity. The evidence is also robust to data-mining concerns. Indeed, to our knowledge, we are the first to explore the ―Talking Numbers‖ forecasts
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and the first to directly examine the performance of technical recommendations as opposed to publicized technical rules.
Figure 1 highlights our major findings. The upper panel plots the average cumulative
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return in excess of the return on the S&P 500 index, following buy and sell technical and buy and sell fundamental recommendations. The lower panel plots return spreads by considering
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buy-minus-sell technical and fundamental portfolios. Stocks are held for a twelve-month period from the recommendation broadcast in equal proportions, and we record excess returns (Panel A)
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and investment payoffs (Panel B) using the closing price of the recommendation date.
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Evidently, technicians display impressive stock-picking skills, recommending, on average, the purchase of undervalued stocks along with the sale of overvalued stocks. To
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illustrate, observe from Panel A that the excess return on technical buy recommended stocks peaks at 5.60% (105.60% of the initial endowment). In addition, the excess return on technical sell recommended stocks troughs at minus 2.56% (97.44% of the initial endowment). The corresponding figures for fundamental buy and sell recommendations are 3.21% and 1.90% after six months. It should be noted, however, that the curves often cross each other, suggesting that
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fundamental recommendations are unable to reliably predict future stock returns. Similar patterns are obtained upon considering returns adjusted to Fama-French and momentum factors. Panel B depicts calendar-time portfolios constructed daily from contemporaneous recommendations, as in Mitchell and Stafford (2000). Here, the return spread corresponding to
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the buy-minus-sell technical portfolio is positive and increases over the entire sample period, reaching 44.04% in December 2014. The buy-minus-sell fundamental portfolio payoff is either negative or very close to zero. The overall difference between technical and fundamental spreads tops 43.15%. Moreover, the buy-minus-sell technical portfolio yields a significant Jensen-Alpha
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of 0.136 (t = 2.13), which further increases to 0.44 (t = 3.16) upon considering only the most extreme ―strong‖ buy and ―strong‖ sell recommendations. Strikingly, the break-even transaction costs that would eliminate the abnormal performance are 2.1% for all buy and all sell technical
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recommendations and 6.3% for strong buy and strong sell technical recommendations. [Please insert Figure 1 here]
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The success of technicians in picking individual stocks is robust to controlling for common risk factors (including time-series and cross-section momentum factors) as well as
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controlling for firm-level size, book-to-market ratio, volatility, turnover, illiquidity, price trends,
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and consensus forecasts. Technical recommendations also produce robust predictions for all subsamples explored, grouped by industry, size, book-to-market ratio, past returns, and volatility.
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In contrast, the inability of fundamental analysts to predict future stock returns is uniform across all industries as well as size, book-to-market ratio, past returns, and volatility groups. Moreover, neither the abovementioned characteristics nor analysts‘ coverage and consensus, institutional holdings, earnings surprises or time elapsed from the release of accounting reports is associated with technicians‘ outperformance. This reduces the possibility that the success of technicians is
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related to a certain type of stock and might be driven by a selection bias of stocks in our sample in favor of technicians. While technicians are able to predict individual stock returns to economically significant degrees, further evidence shows that neither technicians nor fundamental analysts are able to
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predict returns on the broader asset classes: Treasuries, market indices, commodities, and equity sector indices. Our overall evidence is in line with the common wisdom asserting that the ability to possess and process private information applies mainly to individual stocks. Indeed, Blume, Easley, and O‘Hara (1994) and Zhu and Zhou (2009) argue that technicians trade on private
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signals, Kavajecz and Odder-White (2004) link technical rules to liquidity provisions, while Elder (2014) notes in his bestseller The New Trading for Living (page 36) that ―… Technical analysis can help you detect insider buying and selling.‖ In all these potential explanations, i.e.,
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liquidity, private signal, and inside information, the success of technicians in predicting individual stock returns need not translate into robust predictions of broader asset returns.
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General asset classes (beyond individual stocks) are less prone to private information, illiquidity, or insider trading.
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Upon comparing fundamental and technical approaches, a valid concern is that our
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sample does not represent the top performing fundamental analysts in the industry. Nevertheless, our result that a select group of technicians can beat the market establishes a nontrivial challenge
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to the foundation of market efficiency. Beyond that, we note that technicians and fundamental analysts are of the same quality based on various characteristics, such as education and experience, as described in Section 2 below. One may also argue that our sample of technical and fundamental recommendations is not essentially representative of fundamental and technical approaches. Notably, we do show that fundamental recommendations are correlated with
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fundamental factors such as earnings consensus, while technical recommendations are correlated with technical indicators such as trends. As a final remark, technical and fundamental approaches are associated with market anomalies that have been studied extensively.1 Prominent findings in the field are that the
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profitability of market anomalies is attributable to the short leg of the trade, i.e., short selling of stocks according to the anomaly (Stambaugh, Yu, and Yuan, 2012; Avramov, Chordia, Jostova, and Philipov, 2013) and that anomalies attenuate following their discovery (Schwert 2003; Chordia, Subrahmanyam, and Tong, 2014; McLean and Pontiff, 2016). Our dual
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recommendations pertain to the ―out-of-sample‖ period spanning Nov 8, 2011 through Dec 31, 2014, or the period that follows the publication of anomalies in academic journals. As technicians‘ forecasts also generate abnormal returns in the long-leg of the trade, i.e., also from
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buying stocks according to their forecasts, their success cannot be attributed to those well-known anomalies. Essentially, technicians implement toolkits that go beyond well-known anomalies.
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Moreover, their forecast outperformance—also in the short leg of the trade—stands out particularly when considering the bull market characterizing our sample period.
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The remainder of this paper is organized as follows. Section 2 explains the nature of the
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―Talking Numbers‖ broadcast and the participating analysts and describes the methodology used to convert the content of the show into investment recommendations. Section 3 describes the
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data. Section 4 reports the empirical findings corresponding to individual stocks. Section 5 1
To name a few, anomalies related to past returns (Jegadeesh, 1990, Lehmann, 1990, Jegadeesh and Titman, 1993),
trading volume and volatility (e.g., Haugen and Baker, 1996) are associated with technical signals, while the accruals (Sloan, 1996), credit risk (Dichev, 1998; Campbell, Hilscher, and Szilagyi, 2008; Avramov, Chordia, Jostova, and Philipov, 2009), value (Fama and French, 1992), and post-earnings-announcement-drift (Ball and Brown, 1968) are based on firm fundamentals.
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extends the analysis to other asset classes. Section 6 concludes. A list of the assets covered in the show and the data sources are detailed in the appendix.
2.
The investment recommendations and participating analysts
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The two major schools of thought in investment management are technical and fundamental analyses. To formulate expectations about future asset prices, technicians analyze historical price movements and trading volume, whereas fundamental analysts consider market, industry, and firm-level economic factors. In this paper, we examine whether investment
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recommendations based on the two approaches provide economic value. The analysis is based on dual recommendations extracted from the TV show ―Talking Numbers.‖ According to Yahoo, the broadcast ―takes a 360° approach to trading – highlighting the best investment opportunities
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by analyzing stocks both from a technical and a fundamental point of view...‖ The sample spans November 8, 2011 through December 31, 2014. November 8, 2011
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featured the first comparison between technical and fundamental points of view. Previously, ―Talking Numbers‖ was a rather different show. It was part of the CNBC broadcast ―Closing
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Bell‖ and usually featured the view of a single analyst, who mainly discussed the S&P 500
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index. In April 2015, the original program was discontinued, and CNBC initiated a new broadcast titled ―Trading Nation.‖ This new show exhibits several similarities to ―Talking
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Numbers,‖ including the same host. In addition, several analysts who participated in ―Talking Numbers‖ also take part in ―Trading Nation.‖ However, the format of ―Trading Nation‖ does not formally confront technical and fundamental points of view. Prior to May 2013, ―Talking Numbers‖ was exclusively hosted by the CNBC television network. For that period, we used two main sources: the CNBC archive at video.cnbc.com and
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The Internet Archive‘s TV news research service at archive.org. We also used several netsearching practices to detect programs that were missing from the main data archives. Since May 2013, CNBC and Yahoo Finance have jointly hosted the show. The main source of programs after the merger is Yahoo Finance at finance.yahoo.com. This source is organized
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chronologically and contains all the post-merger programs. Overall, we were able to cover the vast majority, possibly all, of the ―Talking Numbers‖ recommendations during the sample period.
In the first year of the sample, ―Talking Numbers‖ was broadcast once every trading day,
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typically featuring two technical and two fundamental recommendations about two distinct assets. Then, the program was usually broadcast several times per day, and in most cases each program covered a single asset. In a few cases, the program has featured only one analyst
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delivering either the technical or fundamental point of view, without a comparison. Those single recommendations are excluded from the primary analysis but considered later when examining
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the robustness of the results.
Prior to analyzing investment recommendations, we note that the resumes of show
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participants indicate a similar quality level of technicians and fundamental analysts. Both groups
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have vast professional experience, a position as managing director or chief officer, and nominations in ranking tables for best analysts, portfolio managers, or technicians. Such high-
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profile analysts are less prone to experience and reputation concerns (Graham, 1999; Sorescu and Subrahmanyam, 2006), career concerns (Hong Kubik, and Solomon, 2000; Clement and Tse, 2005), and conflicts of interest (Fang and Yasuda, 2009). Moreover, analysts belonging to both groups serve as the heads of research or the investment division of prominent banks, investment management funds, and research companies,
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such as Deutsche Bank, Piper Jaffray, Stifel Financial Corp, and Standard & Poor‘s Investment Advisory Services. Remarkably, 100% of the participants are ―senior‖ analysts with more than ten years of experience (95% with more than 20 years), while 74% of the fundamental analysts and 91% of the technicians have an additional title of managing director, chief officer, or head of
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department.
Additionally, 13% of the fundamental analysts and 15% of the technicians are from the buy-side industry, actively managing wealth. Of the fundamental analysts, 13% are founders of their own business, while the figure for technicians is 21%. In terms of education, 53% of
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fundamental analysts and 26% of technicians hold a graduate degree (mainly an MBA or MA in economics). Of them, 40% of fundamental analysts and 15% of technicians have graduated from ―Top 20‖ U.S. universities. Interestingly, the correlation between the fundamental and technical
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stock recommendations turns out to be practically zero. The overall evidence suggests that the outperformance of technicians emerges from the investment toolkits applied rather than the
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unique traits of the participating analysts.
The fundamental analysis typically starts with a macroeconomic outlook, followed by a
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recommendation, along with supporting discussions focusing on the economic forces underlying
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the market, industry, and firm. The technician usually describes a chart of historical prices and then discusses the main technical characteristics underlying the recommendation. Often, there
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are additional supporting charts and graphical exposition of technical characteristics, such as moving average and volume. It is common for the technician, the fundamental analyst, and the show hosts to debate about the recommendations. Throughout the show, recommendations are referred to as ―investment‖ recommendations corresponding to periods that range from one to several months. On rare occasions, the analysts
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also mention a separate recommendation referred to as a ―trading‖ recommendation for one day or a few days, or a long-term recommendation for horizons longer than one year. Such recommendations are exceptional items that always accompany the main investment recommendations and are thus excluded from the analysis.
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In the main analysis, we classify technical and fundamental recommendations into the three conventional categories: ―buy,‖ ―hold,‖ and ―sell.‖ In a second stage, we refine the classification and split buy recommendations into ―strong buy‖ and ―buy‖ and sell recommendations into ―sell‖ and ―strong sell.‖ In approximately 20–30% of the cases
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(depending on the asset class), the analyst‘s formal rating is explicitly stated verbally or in a caption. The classification in those cases adheres to the explicit ratings. Otherwise, if the recommendation is not explicit we systematically extract the recommendation category based on
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the content of the show.
While the differences between strong buy and buy and between strong sell and sell
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recommendations could be subtle, the distinction between buy and sell groups is clear and well defined. It is unlikely that a positive recommendation would be classified as sell or a negative
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recommendation classified as buy. Therefore, to avoid classification errors, the main analysis
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applies a two-category scale: a buy category, which includes both buy and strong buy recommendations, and a sell category, which includes both sell and strong sell recommendations.
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The results with the five-category scale (available upon request) are qualitatively similar and are briefly discussed in the robustness tests. Table 1 summarizes the descriptive statistics of the broad set of recommendations for
single stocks, the market index, particular equity sectors, bonds, and commodities. A full list of all the individual stocks featured in ―Talking Numbers‖ as well as all the other assets are listed in
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the appendix. Altogether, we capture 1,599 dual recommendations. There are 1,000 technical recommendations and 1,000 fundamental recommendations (1,000 dual recommendations) featuring 227 individual stocks. There are 149 dual recommendations covering the S&P 500 index (the NYSE composite index in one case); 256 dual recommendations corresponding to 58
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indices and ETFs, such as the Nasdaq 100, the Dow Jones Industrial/Utilities/Transportation and particular sectors, including banking, retail, homebuilders, miners, and biotechnology, as well as non-U.S. markets, including emerging markets, frontier markets, and the Nikkei 225; 50 dual recommendations featuring bond yields (mostly 10-year Treasuries but also municipal bonds);
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and 144 dual recommendations covering 17 commodities (especially gold and crude oil). On 370 shows, a single recommendation records no corresponding comparison because either only one analyst participated in the show or one of the analysts did not discuss the relevant asset. As noted
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in the previous section, such recommendations are excluded from the main analysis but are later considered in the robustness tests. A total of 49 observations are excluded because the
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underlying asset is unique (e.g., Bitcoin, VIX, luxury houses) with only a few observations. [Please insert Table 1 here]
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Table 1 shows that while the number of technicians and fundamental analysts is quite
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similar among the general asset classes, it is markedly distinct among single stocks. There are 34 technicians versus 159 fundamental analysts. As noted earlier, while this difference may imply
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that technicians could potentially be of higher quality, this does not affect our main finding that a select group of technicians is able to predict individual stock returns to economically significant degrees, which is at odds with the notion that investment recommendations provide no value. Also notable is the relatively small number of fundamental and technical female analysts—
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approximately 10% across all the various asset classes. As shown below, for both types of analysts, we find similar predicting abilities across gender and education. Investment recommendations populate all categories with a tendency toward buy recommendations in the case of stocks. The Spearman rank correlation coefficient, which
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measures the correlation between figures ranging from 1 (sell) to 3 (buy) corresponding to the fundamental and technical recommendations, is typically small. It is -0.07 for single stocks, 0.12 for the market index, 0.10 for equity sectors and non-U.S. indices, 0.25 for bonds, and 0.10 for commodities. As the core of our analysis pertains to individual stocks, it is evident that technical
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and fundamental recommendations are not mutually related, either positively or negatively. We further explore the determinants of the fundamental versus technical stock recommendations. Table 2 reports the results of the Probit regression
REC i γ0 j 1 1 j FIRM i j j 1 2 jTRENDi j j 1 3 j CONSENSUSi j ּεi , 5
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(1)
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where i is the stock-specific subscript and REC describes the fundamental or technical
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recommendation category (1–sell, 2–buy). In line with Womack (1996) and Jegadeesh, Kim,
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Krische, and Lee (2004), among others, we consider three firm-level variables: previous year log of market capitalization, book-to-market ratio if the previous year book value is positive and zero
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otherwise, and volatility over the year prior to the recommendation broadcast. The TREND variables include the return over the previous month and previous two
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through six months to account for the one-month reversal of Jegadeesh (1990) and the momentum of Jegadeesh and Titman (1993). It also includes the previous month‘s volatility, turnover (volume / number of outstanding shares) and the Amihud (2002) illiquidity measure. The last two variables account for the possibility that technical traders are liquidity providers (Kavajecz and Odder-White, 2004).
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We consider eight CONSENSUS variables to examine the association between recommendations and the consensus among analysts, corporate insiders, and institutional investors. Specifically, the first variable is the number of outstanding recommendations, which accounts for information diffusion frictions in stocks that are less covered by analysts (e.g.,
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Hong, Lim, and Stein, 2000). The next two variables measure the mean and standard deviation of analysts‘ recommendations (1–strong sell, 5–strong buy) standing for market consensus and its dispersion,
respectively.
Recommendation
upgrades
minus
downgrades
account
for
recommendation revisions (Stickel, 1992; Womack, 1996). The next two variables are the
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corporate insider public trading index, which is a proxy for the consensus among corporate insiders, and the percentage of institutional ownership of the company stocks, which is a proxy for the consensus among institutions. Considering institutional investors accounts for the
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possibility that the investment recommendations are different due to price biases attributable to institutional trades (Gompers and Metrick, 2011). The corporate insider public trading index is
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defined as purchases less sales of corporate insiders scaled by their total trades in the previous quarter. The last two variables are the days elapsed from the last release of accounting reports
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and the percentage surprise in earnings per share (EPS) in the last quarter, which account for
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possible post-earnings-announcement-drift (Ball and Brown, 1968). [Please insert Table 2 here]
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The dependent variable in Test 1 (2) in Table 2 is the fundamental (technical) recommendation. Starting from fundamental recommendations, we note that only the coefficient corresponding to analysts‘ mean recommendation is positive and significant, implying that fundamental recommendations align with analysts‘ consensus. This does not imply that fundamental recommendations are subject to herding, as the documented positive relation could
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be attributable to utilization of similar toolkits in establishing earnings forecasts and investment recommendations. Different economic factors underlie technical recommendations. The coefficients corresponding to size and returns over the previous month and two to six months are positive and
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significant. Thus, while fundamental recommendations are associated with market consensus in particular, technical recommendations are correlated with recent trends in past returns. These results are also consistent with the view that fundamental analysts and technicians employ
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distinct investment toolkits associated with distinct and unique economic forces.
3. Individual stocks: The empirical evidence
This section focuses exclusively on single-stock recommendations. Investment
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recommendations featuring the other asset classes will be analyzed in the next section. Figure 2 depicts the average stock returns for the three recommendation categories. We consider
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investment horizons of one, three, six, nine, and twelve months following the broadcasts. To be on the conservative side, here as well as in all the follow-up analyses, investment returns start
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accumulating based on the recommendation day‘s closing price.
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The left (right) plot in Figure 2 pertains to fundamental (technical) recommendations. Top figures display raw returns, while bottom figures display returns adjusted to the three Fama-
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French and momentum factors, i.e., the raw returns less fitted returns based on the four-factor model. As technicians are often trend chasers, we also employ the time-series momentum factor of Moskowitz, Ooi, and Pedersen (2012) along with the Fama-French three factors. The results with the cross-section momentum are very similar and available upon request. The parallel lines on both sides of each diagram represent the confidence intervals at a 5% significance level.
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Consistent with the findings in the introduction, it is evident from Figure 2 that fundamental analysts do not deliver valuable forecasts for future stock returns. The mean raw returns following buy and sell recommendations are quite similar, while for nine months the mean return following sell recommendations is actually higher (17%) than the mean return
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following buy recommendations (15.8%). For the twelve-month horizon, the mean returns associated with buy and sell recommendations are 21.7% and 20.4%, respectively. The corresponding risk-adjusted figures are slightly more appealing, 3.0% and 0.9%. In contrast, technicians reveal a rather strong return-recommendation relation. The mean returns for all
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horizons following buy recommendations are higher than those following sell recommendations. For the twelve-month horizon, the mean returns associated with buy and sell recommendations are 24.9% and 18.5%, respectively. The corresponding risk-adjusted returns are 4.7% and 0.1%.
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Even more appealing, for the six-month horizon there is no overlapping of confidence intervals, as the upper bound of 9.2% for sell recommendations is below the lower bound of 10.6% for buy
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recommendations. The corresponding bounds for risk-adjusted returns are -0.1% and 1.2%. [Please insert Figure 2 here]
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Table 3 reports the difference between buy and sell average returns for the various
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investment horizons. Starting with fundamental analysts, the return spreads between buy and sell fundamental recommendations are relatively small, given by 0.1%, 1.4%, 0.3%, -1.2%, and
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1.3%, respectively. Indeed, for all horizons, the null hypothesis of equal means is not rejected. The same holds for risk-adjusted returns. In contrast, technicians display impressive stockpicking skills. Their buy recommendations yield uniformly higher average returns, both raw and risk-adjusted, than their sell recommendations. The return spreads between buy and sell recommendations are equal to 1.9%, 2.3%, 6.2%, 5.8%, and 6.4% for the five horizons
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considered. The null hypothesis of equal mean returns is rejected for all horizons besides three months. Consistent with confidence intervals in Figure 2, the six-month horizon exhibits the highest t statistic (3.62). Similar evidence emerges on the basis of risk-adjusted returns. The investment returns following buy recommendations are uniformly larger than those following
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sell recommendations. For example, the corresponding nine-month returns are -0.7% for sell and 4.3% for buy, and the null hypothesis of equal mean returns is rejected for all horizons. The superiority of technicians over fundamental analysts appears in all five investment horizons. In all cases, the average returns on sell technical recommendations are lower than the corresponding
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fundamental recommendations, and the average returns on buy technical recommendations are higher than the corresponding fundamental recommendations.
[Please insert Table 3 here]
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The differences between mean returns or risk-adjusted returns following buy and sell recommendations are of the same size order, where the t-statistics for equal means are only
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slightly larger for risk-adjusted returns and horizons shorter than twelve months. To further investigate whether the performance of the two competing groups is associated with risk factors
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we define a new variable for the buy-minus-sell portfolios in Figure 1b: Z t Rt Rtadj , where R
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and Radj stand for returns and risk-adjusted returns, respectively, on either fundamental or technical buy-minus-sell portfolio. For both portfolios, the average value of Z is negative, small
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(|Z| < 10-4) and insignificantly different from zero (t-values of -0.28 and -1.02, respectively). Thus, we conclude that analysts‘ performance is not significantly associated with risk factors, and the success of technicians is robust to employing raw and risk-adjusted returns. In unreported tests, we find that technical recommendations outperform in two dimensions: the number of correct recommendations and the quality of recommendations
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manifested through gains and losses. Technicians recommend higher proportions of correct recommendations, whereby a correct recommendation amounts to a buy (sell) recommendation followed by an advancing (diminishing) stock price. Their recommendations also yield higher gains following correct recommendations and incur lower losses following incorrect
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recommendations.
3.1 Cross-section analysis
We employ regression analysis to further study the quality of recommendations. In the
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context of analysts‘ recommendations, it has been shown that firm size (Womack, 1996), past return, volume, book-to-market ratio (Jegadeesh et al. 2004), and industry affiliation (Boni and Womack, 2006) are all associated with the performance of recommendations. In response, we run the following cross-section regression 3
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Ri γ0 γ1 REC i j 1 2 j FIRM i j j 1 3 jTRENDi j 4 BROADi εּ i , 5
(2)
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where i is the stock-specific subscript, R is the stock return over the investment horizon, and REC describes the fundamental or technical recommendation category (1–sell, 2–buy). The
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FIRM variables are market capitalization, book-to-market ratio, and previous year volatility. The
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TREND variables are returns over the previous month and previous two through six months, attempting to capture short-term reversal and momentum effects, and the one-month volatility,
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turnover, and illiquidity, as have been already defined in Equation (1). Equation (2) also includes an immediate broadcast impact variable (BROAD), which is equal to the return over two days following the recommendation broadcast, intended to control for the impact of the media exposure to the broadcast.
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Table 4 reports the regression evidence from eight distinct tests. The dependent variable in all the tests is the six-month return following the broadcast. Test 1 excludes all the control variables. Here, consistent with the previous analyses, the fundamental recommendations‘ (REC) coefficient is insignificant, while its technical counterpart is significantly positive (t = 4.97).
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In cross-section regressions, we control for size, book-to-market ratio, volatility, past returns, the prior month volatility, turnover and illiquidity, and the immediate impact of the broadcast itself. The evidence supporting technical recommendations stands out, as the technical recommendations‘ (REC) coefficient is significantly positive (t = 3.31). The coefficient for
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returns immediately after the show broadcast is also positive and highly significant (t = 9.28). Although Table 2 shows that technical recommendations are correlated with past returns, it seems that technical recommendations do not capture the momentum effect, as the coefficient for
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returns over the previous two to six months is also positive and significant. We explore this issue in the coming sections. Note that our sample consists of large firms mostly belonging to the
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upper-size decile, and low-mid book-to-market deciles (see the appendix for the list of stocks). Thus, it may not be surprising that our sample of stocks does not exhibit distinct effects related to
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size, volatility, or book-to-market ratio. Neither firm type nor volatility, turnover, or illiquidity
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captures the predictive power of technical recommendations. The return-recommendation relation is also not attributable to the direct short-term impact of the broadcast on the stock price.
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While there is a significant immediate impact of the broadcast on the stock price, the predictive ability of technical recommendations persists long afterwards. [Please insert Table 4 here]
To avoid classification errors of recommendations, we employ a three-category scale. In separate robustness tests, we repeat all the reported tests with a five-category scale in which buy
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and sell recommendations are further classified into strong buy and buy and strong sell and sell categories. The results with the refined scale are even more supportive of technical recommendations and are available upon request. Test 3 summarizes the main results of this analysis, where the recommendation variable employs a five-category scale. As with the three-
technical counterpart is highly significantly positive (t = 3.82).
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category scale, the fundamental recommendations‘ (REC) coefficient is insignificant, while its
Kumar (2010) shows that female analysts display superior forecast abilities due to the self-selection process. Presumably, those females who have superb abilities as analysts pursue a
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career in a male-dominated industry. Our sample contains approximately 90% male analysts among the fundamentalists and technicians across all the asset classes (see Table 1). Thus, gender does not seem to represent a potential source of systematic bias.
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Test 4 formally accounts for analyst gender. The fundamental recommendations‘ coefficients are near zero and insignificant regardless of the analyst‘s gender. The technical
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recommendations‘ coefficients are larger and significant (t = 3.23 for males and 2.79 for females). While the coefficient corresponding to female analysts is slightly larger (5.27 10-2
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versus 4.91 10-2), the F-statistic does not reject the null hypothesis of no gender effect.
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Namely, the success of technical recommendations in predicting returns on individual stocks is not captured by an analyst‘s gender. Moreover, female technicians or fundamental analysts do
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not outperform their male counterparts. Tests 5 and 6 compare recommendations conditional on undergraduate versus graduate
analysts and analysts from ―Top 20‖ U.S. universities versus other analysts. In both tests, the fundamental recommendations‘ coefficients are not significant, while the technical
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recommendations‘ coefficients are larger and significant (t = 3.54 and 3.13, and 2.91 and 3.52, respectively). Test 7 compares the buy-side and sell-side analysts. While the fundamental recommendations‘ coefficients remain insignificant, the technical recommendations‘ coefficients
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are, once again, highly significant (t = 3.18 for sell-side analysts and 3.74 for buy-side analysts). Moreover, the buy-side analysts‘ coefficient (8.26 10-2) is almost twice as large as that of sellside coefficient (4.74 10-2), and the difference is significant (p = 0.05). Altogether, both sell-
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side and buy-side technicians are successful in predicting stock returns.
The dependent variable in Test 8 is the six-month return adjusted to the Fama-French and time-series momentum factors. Evidently, consistent with the previous results that the success of technicians is similar whether we employ raw or risk-adjusted returns, the predictive ability of
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technical recommendations is unexplained by common risk factors that could simultaneously affect stock prices and the recommendation category.
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While the dependent variable in Tests 1 through 8 is the stock return (raw or riskadjusted) over six months following the recommendation broadcast, in unreported tests, we also
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examine returns over one, three, nine, and twelve months following the broadcast. For all
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investment horizons, the fundamental recommendations‘ coefficients are indistinguishable from zero, while their technical counterparts are positively significant.
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Finally, in two unreported tests, we verify that the main results are neither sensitive to
outliers nor to the omission of single recommendation, either fundamental or technical, that was reported on a few programs with no comparison. To check for outliers, we run Equation (2) with the dependent variable winsorized at 2.5%. To test for single recommendations, we include those recommendations in the regression. In both tests, the overall evidence is unchanged.
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To summarize, the cross-section regressions confirm the strong predictive ability of technical recommendations, as demonstrated in Figure 1. That predictive ability is uncaptured by the size, book-to-market ratio, volatility, turnover, illiquidity, past stock trends, common risk factors, analyst‘s gender and education, and the apparent immediate impact of the
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recommendation broadcast. Both sell-side and buy-side analysts display robust predictive ability, which seems to be more profound in the latter group. The results are also robust to outlier returns as well as to potential classification errors. Fundamental recommendations, in contrast, do not
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show any clear and consistent relation with subsequent returns.
3.2 Examining industry and style effects
Boni and Womack (2006) argue that the economic value of financial analysts relates to
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their status as industry specialists. To explore the potential effects of industry affiliation and firm attributes on recommendations, we run the following cross-section regression:
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Rie γ0 γ1 j ( REC i )( FIRM ij ) ּεi ,
(3)
j
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where Rie is the six-month stock return (we consider both raw and risk-adjusted returns) in excess of average return corresponding to the matching industry or characteristic. RECi describes the
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recommendation category (1–sell, 2–buy). We consider two specifications. In one, FIRMij (j =
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1,2,…,6) are dummies for six industries: mining, construction and manufacturing, utilities, trade, financial and administration, and services. The industry division is made according to the Standard Industrial Classification (SIC) code with the exception that the construction and the wholesale trade and administration sectors, for which we record fewer than ten observations, are merged with their closest-matching industries. In the second specification, FIRMij (j = 1,2,3) are dummies for firms belonging to the bottom 30%, core 40%, and top 30% of either firm‘s size,
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book-to-market ratio, volatility, or past returns over the previous month and previous two through six months. As evident from the top of Table 5, firms are not evenly spread across industries, and the same holds for firm characteristics, as the sample is biased toward large companies with a moderate book-to-market ratio. Furthermore, analysts may specialize in a
return less the industry or style average return. [Please insert Table 5 here]
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certain style or a sector. To control for that, the dependent variable in Equation (3) considers
Table 5 reports the results. Starting with the fundamental analysis, all the
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recommendation coefficients are insignificant, regardless of whether raw returns (upper row) or risk-adjusted returns (lower row) are employed. Moving to the technical front, the recommendations produce robust predictions based on raw and risk-adjusted returns. Comparing
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the coefficients and t-statistics, we find a similar success across industries. The slightly less significant mining stock returns may be attributed to the small number of mining stocks or the
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prominent inability of both technicians and fundamental analysts to predict commodity prices, as we show below. Nevertheless, the almost identical magnitude of mining coefficient versus other
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industry coefficients indicates that technicians are still able to successfully differentiate between
period.
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firms within the mining industry that altogether realized negative performance during the sample
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Panel B of Table 5 reports the effect of firm characteristics on recommendations. The fundamental recommendations‘ coefficients corresponding to bottom, core and top size, book-tomarket, volatility, and past returns are all insignificant. This is consistent with the notion that fundamental recommendations display low power in predicting future returns across all equity styles. In contrast, almost all technical recommendations‘ coefficients are positive and highly
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significant. The only exception is the segment of bottom small firms, which consists of only 19 firms. The intensity of the success of technicians across all other styles is similar to the results across industries. In all cases, the null hypothesis of equal coefficients across categories is not
3.3 Abnormal performance and break-even transaction costs
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rejected.
We construct zero-cost buy-minus-sell portfolios for both fundamental and technical recommendations. We assume that trading occurs at the closing price of the day of
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recommendation and that it lasts for 12 months. All buy and sell recommended stocks are equally weighted, and the amount invested in buy recommended stocks is equal to the amount of shorts in sell recommended stocks. The evidence shows that a $1 position in the technical buy-
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minus-sell portfolio at the beginning of the period (November 2011) yields positive profits during the entire period, which tends to increase over time, reaching $0.44 in March 2015 with
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break-even transaction costs of 2.1% per recommendation. Indeed, regressing portfolio excess returns on the market factor yields a positive and significant Jensen-Alpha of 0.136 (t = 2.13) for
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the technical portfolio in comparison to -0.023 (t = -0.37) for the fundamental portfolio. Overall,
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our findings show that technical recommendations establish sizable and implementable
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investment rules at both the long and short legs of the trade.
3.4 Characterizing technicians‘ outperformance In this section, we attempt to characterize the stocks of the technicians who outperform
fundamental analysts. This analysis sheds more light on the source of technicians‘ outperformance and allows us to generalize the sample results.
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Table 6 compares average characteristics of stocks in disagreement and agreement among the two types of analysts. As outperformance of technicians is only attributable to stocks in disagreement, we attempt to identify the characteristics of stocks that technicians evaluate more successfully than fundamental analysts. In Panel A of Table 6, we first confirm that technicians‘
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outperformance exists when recommendations disagree. The first row reports average riskadjusted returns when the technical recommendation is buy and the fundamental recommendation is sell. For all time horizons, average risk-adjusted returns are positive, topping 5.71% after nine months. The second row considers technical sell and fundamental buy
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recommendations. Here, average risk-adjusted returns are negative for all time horizons. Indeed, the differences in risk-adjusted returns across buy and sell are large, ranging between 2.35% to 8.91%, and significant for one, six, and nine-month horizons.
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Panel B of Table 6 compares the 18 characteristics described in Table 2 that are conditional on agreement or disagreement among the two types of analysts. The comparison is
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made separately for technical buy and fundamental sell and for technical sell and fundamental buy. When the technical recommendation is buy, stocks in disagreement exhibit significantly
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higher book-to-market, previous month returns and insider trading, and lower previous month
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volatility. Note that the association with previous month returns is in the opposite sign to the short-term reversal of Jegadeesh (1990). Namely, higher one-month returns, which, according to
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the reversal effect indicate a sell signal, are actually associated with successful technical buy recommendations. When a technical recommendation is sell, only the average number of days elapsed since the release of accounting reports differs significantly across agreement and disagreement. However, this small difference (64.4 days versus 69.83 days) only implies that, in both cases, there is still about one month before the next release of accounting reports.
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The correlations between technicians‘ outperformance and certain stock characteristics may raise the concern that the stocks that appeared in the show were chosen because they are more suited for technical analysis. Note, however, that a selection bias would not change our main outcome, which is that in contradiction to the market efficiency hypothesis, technicians
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display valuable forecast ability. Moreover, according to Table 6, most of the stocks‘ characteristics are uncorrelated with technicians‘ outperformance. Similarly, in Table 5, we have already shown that technicians‘ success exists for stocks belonging to bottom, core, and top of book-to-market, volatility, and past returns. Therefore, these characteristics are also not expected
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to considerably affect technicians‘ outperformance. The same holds for the very small difference in days elapsed from accounting reports. Altogether, the correlation with one-month returns, volatility, and insider trading mainly support the technicians‘ theme that they rely on short-term
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price trends in making their recommendations.
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[Please insert Table 6 here]
4. Examining recommendations among broader asset classes
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Why are technical recommendations successful in predicting returns on individual
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stocks? One possibility is that technicians trade on private signals, as suggested by Blume, Easley, and O‘Hara (1994), and Zhu and Zhou (2009). According to Elder (2014), technicians
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can infer insider buying and selling activities from the charts. Kavajecz and Odder-White (2004) link technical rules to liquidity provisions. In either case, the essential hypothesis is that the success of technicians in predicting individual stock returns does not translate into robust predictions of broader asset returns, as those assets are substantially more liquid, less prone to private information, and more likely to attract capital that trades away all arbitrage mispricing.
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Table 7 reports mean returns following buy and sell recommendations for various asset classes: the S&P 500 index, equity sectors and non-U.S. equity indices, U.S. bonds and commodities. In all cases, there is no clear association between the recommendations and subsequent returns. The null hypothesis of equal mean returns following buy and sell
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recommendations is not rejected for all assets, all time horizons, and both fundamental and technical recommendations. These findings are consistent with Kiseok, Kenneth and Quentin (2005) and Taylor (2014), who cast doubt on the usefulness of technical analysis of market indices.
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[Please insert Table 7 here]
In sum, the apparent success of technicians to forecast individual stock returns is the exception rather the rule. In all other asset classes, both technicians and fundamental analysts do
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not display predictive ability. Our findings thus indicate that the markets corresponding to virtually all assets are efficient, yet inefficiency appears to exist among individual stocks.
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Prior to concluding the paper, let us note that our study complements prior work examining the immediate impact of media-publicized recommendations. Liu, Smith, and Syed
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(1990), Mathur and Waheed (1995), and Kerl and Walter (2007) document abnormal returns
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shortly after the publication of recommendations in the newspaper, and Hirschey, Richardson, and Scholz (2000) report abnormal returns on the day after the recommendations are posted on
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the Internet. However, Dewally (2003) detects no market reaction to recommendations posted by a newsgroup on the Internet. Neumann and Peppi (2007) find that the recommendations made by Jim Cramer, the host of the CNBC ―Mad Money‖ program, are followed by abnormal payoffs during the following day, and Busse and Green (2002) find that recommendations broadcast on
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the CNBC ―Morning Call‖ and ―Midday Call‖ programs produce abnormal immediate profits within 15 seconds. Differently from these studies, we examine the long-term value rather than the immediate impact of recommendations. Eventually, technical stock recommendations provide value not
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only for immediate trading but also up to a full year following their broadcast.
5. Conclusion
This study employs a novel database from a TV broadcast to compare between
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fundamental and technical analysts. Our experiments are based on simultaneous fundamental and technical recommendations for the same underlying assets and investment horizon. The unique setup of the show, featuring synchronized dual recommendations, a great variety of asset classes,
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and the presence of leading professionals, offers an ideal laboratory to assess the value of financial analysis in general and market efficiency in particular. Ultimately, both technicians and
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fundamental analysts are exposed to the same public information, while their recommendations differ due to the distinct toolkits applied.
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The simultaneous broadcast guarantees identical public information exposure, eliminates
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time gap biases, such as cross-herding among the participating analysts, and allows one to control for the immediate short-term effect of the broadcast itself. The high profile of the
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participating analysts levels the playing field and mitigates biases related to analysts‘ quality, experience, and career concerns. The broad focus of the program and the comprehensive list of assets covered make our findings general and mitigate the concerns about illiquidity biases and exceptional observations.
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Consistent with the semi-strong market efficiency hypothesis, the fundamental analysis reveals little ability to predict future returns on all assets examined. Surprisingly, technicians display a significant predicting ability of individual stock returns, pointing to market inefficiency even among the universe of the largest and most-traded stocks. For a start, trading individual
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stocks based on technical recommendations yields significant payoffs. Moreover, such stockpicking ability withstands controlling for common risk factors, firm characteristics, industry effects, the analyst‘s gender, education, affiliation type (sell-side versus buy-side), and the potential immediate impact of the broadcast. Focusing on technicians‘ outperformance, no clear
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tendency toward certain firms is found, which mitigates the concern that the success of technicians is confined to the specific stocks in our sample. The only found association between technicians‘ outperformance and recent price trends supports technicians‘ main theme that they
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rely on short-term price trends in making their recommendations.
The predictive ability of technicians characterizes returns on individual stocks only. In
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contrast, the null hypothesis of market efficiency is intact for general asset classes, including the market indices, equity sectors and non-U.S. equity indices, bonds, and commodities. One
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appealing explanation is that technicians trade on private signals, as advocated by economic
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theory. Technicians potentially extract insiders trading from the charts. Either way, predictable patterns are most likely to apply to individual stocks and not to broader asset classes that are less
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prone to insider trading, private information, or illiquidity.
Acknowledgment The authors acknowledge the helpful comments and suggestions of the two anonymous referees that helped to improve this paper.
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Table 1: Overview of recommendation categories for various asset classes The table displays the frequency of recommendation categories for various asset classes. The sample period is November 8, 2011 (the day when the first simultaneous fundamental–technical comparison was broadcasted) through December, 31 2014. The Spearman‘s correlation coefficient is between the numerical values of fundamental
58 Equity sectors and non-U.S. indices
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11
28
4
3
19
11
3
3
2
11
17
2
3
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17 Commodities
4
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3 Bonds
20
Spearman’s Sell Hold Buy Total Correlation 130 54 151 335 43 31 100 174 179 94 218 491 352 179 469 1000 -0.07 16 11 21 48
10 8 21 39
13 9 40 62
39 28 82 149
0.12
Sell Hold Buy Total
51 19 33 103
23 20 29 72
25 15 41 81
99 54 103 256
0.10
Sell Hold Buy Total
7 5 8 20
3 5 4 12
4 3 11 18
14 13 23 50
0.25
Sell Hold Buy Total
61 13 9 83
9 5 10 24
18 4 15 37
88 22 34 144
0.10
Sell Hold Buy Total
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The U.S. Market (S&P 500)
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227 Stocks
Fundamental Analysts Technical Analysts Fundamental Male Female Male Female Technical Sell 142 17 31 3 Hold Buy Total
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Single Assets
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and technical recommendations (sell–1, hold–2, buy–3).
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Table 2: Determinants of individual stock recommendations The table reports the coefficients slopes (multiplied by 10) of the Probit regression RECi γ0
3
FIRM i j 1 1 j
j
5
TRENDi j 1 2 j
j
8
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CONSENSUSi j 1 3 j
j
εּi ,
where i is the stock-specific subscript and REC describes the recommendation main category (1–sell, 2–buy). The FIRM variables are the previous-year log of
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market capitalization, book-to-market ratio if the previous-year book value is positive and zero otherwise, and volatility measured by the daily returns over the year prior to the recommendation broadcast. The TREND variables are the previous month return, return for months two through six prior to the recommendation broadcast, previous month volatility, turnover estimated as previous month volume divided by the number of outstanding shares, and illiquidity estimated as the previous month average Amihud illiquidity measure 108. The CONSENSUS variables are the number of analysts covering the stock, mean and standard deviation of all analysts‘ recommendations (1–strong sell, 5–strong buy), percentage upgrades minus downgrades of outstanding recommendations, corporate
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insiders trading index, which is defined as purchases less sales of corporate insiders divided by their total trades in the previous quarter, the percentage of institutional ownership of the company stocks, percentage surprise in earnings per share (EPS) during the past quarter, and calendar days elapsed from last release of quarterly accounting reports. The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets)
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corresponding to heteroskedasticity-consistent Huber–White standard errors. One and two asterisks indicate a significance level of 5% and 1%, respectively.
Firm Factors
0.02 (0.42)
42.66 (0.45)
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1.32** (2.73)
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Technical Recommendations
Trend Factors
1M 2-6M BE/ME 1Y Vol. Returns Returns 0.72 -161.67 1.77 1.93 (0.41) (-1.81) (0.42) (1.17)
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Dependent Variable: ME 0.67 Fundamental Recommendations (1.47)
20.51** (3.89)
4.68* (2.41)
Market Consensus Factors
1M Vol. 0.40 (0.22)
1M Turn. 0.00 (0.07)
1M Number Rec. illiquid. of Rec. Mean -478.84 -0.04 3.73** (-1.29) (-0.67) (2.73)
-2.60 (-1.42)
-0.05 2436.16 -0.03 (-0.86) (1.02) (-0.60)
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1.90 (1.46)
Rec. Change Insiders Inst. EPS Post STD in Rec. Trading Owner. Surprise Acc. -3.41 -13.89 -1.18 2.14 0.14 0.01 (-1.25) (-0.82) (-1.15) (0.82) (0.31) (0.63) 2.44 (0.90)
-7.44 (-0.45)
1.27 (1.30)
3.92 (1.57)
-0.03 (-0.07)
-0.02 (-0.90)
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Table 3: Stock returns per recommendation category The table describes the relation between the return and the recommendation category. The investment horizons are one, three, six, nine, and twelve months following the recommendations. Upper (lower) panel considers the raw (risk-adjusted) average returns, with risk adjustment pertaining to the Fama–French and momentum factors. The T-
two asterisks indicate a significance level of 5% and 1%, respectively.
Fundamental Recommendations
3
6
9
12
1.2 1.5 1.3 0.1 (0.10)
4.1 6.4 5.5 1.4 (1.13)
10.3 10.5 10.6 0.3 (0.19)
17.0 18.5 15.8 -1.2 (0.50)
20.4 26.5 21.7 1.3 (0.44)
Sell -0.3 Hold 0.5 Buy -0.2 0.1 (0.18)
-0.6 2.3 0.7 1.3 (1.16)
0.7 2.0 1.2 0.6 (0.36)
2.2 4.1 1.7 -0.5 (0.24)
0.9 5.7 3.0 2.1 (0.93)
Sell Hold Buy
Mean Return (%) Buy minus Sell t-statistic
Mean RiskAdjusted Return (%)
AC
CE
PT
ED
Buy minus Sell t-statistic
Technical Recommendations
1
38
3
6
9
12
0.0 3.5 6.5 13.3 18.5 2.4 6.3 11.8 16.8 21.1 1.9 5.9 12.7 19.0 24.9 1.9** 2.3 6.2** 5.8* 6.4* (2.57) (1.87) (3.62) (2.39) (2.19)
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1
M
Investment Horizon (months):
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statistics correspond to null hypothesis that buy and sell recommendations exhibit the same mean returns. One and
-1.4 -1.2 -2.4 -0.7 0.1 0.2 1.9 2.8 2.6 2.6 0.6 1.2 3.0 4.3 4.7 2.0** 2.3* 5.4** 5.1** 4.6* (3.03) (2.08) (3.63) (2.60) (2.02)
Table 4: Individual stock recommendations: cross-section regressions The table reports the coefficients slopes (multiplied by 10 2) of the cross-section regression Ri γ0 γ1 RECi
3
FIRM i j 1 2 j
j
5
TRENDi j 1 3 j
j
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4 BROADi εּi ,
where i is the stock-specific subscript, R is the stock return over a six-month period, and REC describes the recommendation category (1–sell, 2–buy). The FIRM
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variables are the previous-year log of market capitalization, book-to-market ratio if the previous-year book value is positive and zero otherwise, and daily returns volatility over the year prior to the recommendation broadcast. The TREND control variables are the previous month return and return for months two through six prior to the recommendation broadcast, aimed to account for momentum and reversal, and the one-month volatility, turnover, and illiquidity described in Table 2. The broadcast immediate impact control variable (BROAD) is the return over two days following the recommendation broadcast. The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets) corresponding to heteroskedasticity- and
AC
CE
PT
ED
M
autocorrelation-consistent (HAC) standard errors sorted on analysts. One and two asterisks indicate a significance level of 5% and 1%, respectively.
39
Recommendation
2. Impact of Recommendation Broadcast
0.13 (0.07)
3. Recommendations Consist of Five Categories
0.60 (0.82) Male 0.23 (0.13)
4. Male vs. Female Analysts
6. Analysts‘ University
7. Sell-Side vs. Buy-Side Analysts
Underg. Grad. 1.41 -0.95 (0.75) (-0.50) Top 20 -0.72 (-0.38) S-side 0.72 (0.39)
4.93** (3.31)
**
2.19 (3.82) Male Female 4.91** (3.23) Underg.
5.27** (2.79) Grad.
4.67** 5.21** (3.54) (3.13) Other U. Top 20 Other U. 0.65 (0.36) 5.03** 4.86** (2.91) (3.52) B-side S-side B-side -1.28 (-0.67) 4.74** 8.26** (3.18) (3.74)
0.22 (0.16)
0.40 (0.48) 0.04 (0.05)
4.55** (3.42)
-0.01 (-0.60) -0.02 (-0.97)
AC
427.60 (1.56) 243.42 (1.01)
6.62 (0.59) -1.46 (-0.14)
5.88 (1.29) 6.36** (3.60)
-0.68 (-0.20) 0.71 (0.23)
-0.17* (-2.31) -0.06 (-0.52)
0.24 (1.26) 0.23 (1.25)
94.56** (4.86) 114.10** (9.28)
-0.02* (-1.97) -0.02* (-2.03)
499.37 (1.94) 491.84 (1.94)
1.66 (0.17) -2.53 (-0.27)
6.61 (1.71) 5.84** (3.51)
0.48 (0.16) 1.00 (0.39)
-0.18* (-2.24) -0.18 (-1.61)
0.24 (1.33) 0.20 (1.16)
139.54** (4.46) 136.80** (4.55)
-0.01 (-0.60) -0.02 (-0.97)
425.62 (1.56) 241.26 (0.96)
6.59 (0.59) -1.51 (-0.15)
5.88 (1.29) 6.37** (3.60)
-0.67 (-0.20) 0.72 (0.23)
-0.17* (-2.31) -0.06 (-0.51)
0.23 (1.20) 0.23 (1.25)
94.40** 0.13 (4.89) (p = 0.71) ** 113.95 0.03 (9.01) (p = 0.86)
-0.02 (-1.03) -0.02 (-1.01)
427.69 (1.56) 248.79 (1.02)
5.72 (0.52) -1.68 (-0.16)
5.61 (1.24) 6.28** (3.40)
-0.44 (-0.13) 0.70 (0.23)
-0.17* (-2.33) -0.06 (-0.54)
0.17 (0.92) 0.23 (1.32)
96.79** 7.22 (4.99) (p < 0.01) ** 114.51 0.33 (9.22) (p = 0.57)
0.36 (0.43) 0.04 (0.05)
-0.01 (-0.58) -0.02 (-1.02)
426.52 (1.56) 244.99 (1.00)
6.47 (0.58) -1.51 (-0.14)
5.67 (1.25) 6.34** (3.47)
-0.61 (-0.18) 0.72 (0.23)
-0.17* (-2.34) -0.06 (-0.52)
0.26 (1.27) 0.24 (1.25)
94.22** 2.34 (4.81) (p = 0.13) ** 114.16 0.03 (9.16) (p = 0.85)
0.48 (0.58) 0.06 (0.08)
-0.02 (-0.77) -0.02 (-0.91)
423.48 (1.57) 245.24 (1.02)
6.54 (0.59) -1.35 (-0.13)
5.74 (1.27) 6.47** (3.70)
-0.51 (-0.15) 0.74 (0.23)
-0.17* (-2.21) -0.06 (-0.53)
0.21 (1.08) 0.14 (1.08)
96.44** 5.17 (4.99) (p = 0.02) 114.19** 3.70 (9.35) (p = 0.05)
0.04 (0.06) -0.20 (-0.27)
-0.01 (-0.68) -0.02 (-0.88)
266.70 (1.10) 95.99 (0.45)
7.54 (0.82) -0.80 (-0.10)
4.96 (1.19) 4.95** (3.25)
-0.36 (-0.11) 0.73 (0.26)
-0.14* (-2.04) -0.02 (-0.26)
0.38 (1.96) 0.38* (2.28)
84.11** (5.69) 97.50** (9.64)
0.11 (0.13) 0.04 (0.05) 0.39 (0.47) 0.04 (0.05) 0.34 (0.41) 0.05 (0.06)
CE
8. Dependent Variable is Risk-Adjusted Returns
0.62** (4.97)
PT
5. Graduate vs. Undergraduate Analysts
Female -0.10 (-0.05)
ME
Trend Factors 2-6M 1M 1M 1M Broadcast F for Return Volatility Turnover illiquidity Impact equality
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1. Base Case
Technical
1Y 1M BE/ME Volatility Return
M
Fundamental 0.03 (0.19)
ED
Test
Firm Factors
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Table 5. Industry and style effects The table reports the coefficients slopes (multiplied by 10) of the following regression: where
Rie
Rie γ 0
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γ
( REC i j 1j
)( FIRM ij ) εּi ,
is the stock return or return adjusted for the Fama–French and momentum factors (Radj) in excess of the average return corresponding to the stock
industry or style over six months following the recommendation broadcast; RECi describes the recommendation category (1–sell, 2–buy); FIRMij are firm characteristics‘ dummies: six industry dummies in Panel A and three dummies in Panel B corresponding to the bottom 30%, core 40%, and top 30% of size,
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book-to-market, volatility, previous month return, or past return over months two through six prior to the recommendation broadcast.
The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets) corresponding to heteroskedasticity- and autocorrelation-consistent (HAC) standard errors sorted by analysts. One and two asterisks indicate a significance level of 5% and 1%, respectively. A. Industry
Radj R Technical Radj
0.23 (1.09) 0.27 (1.54)
-0.01 (-0.06) 0.02 (0.15)
0.65* (2.11) 0.62 (1.70)
0.59** (3.43) 0.53** (3.78)
Transportation & Wholesale & Public Utilities Retail Trade 25 42 67 238
Finance, Insurance, Real Estate & Public Administration 26 71
Services 37 227
F for Equal Industries
0.02 (0.08) 0.05 (0.29)
0.04 (0.20) 0.07 (0.41)
-0.13 (-0.69) -0.06 (-0.38)
0.14 (0.86) 0.011 (0.79)
0.81 (p = 0.55) 0.91 (p = 0.48)
0.59** (5.76) 0.52** (5.46)
0.66* (3.33) 0.59** (3.71)
0.54** (3.21) 0.47** (3.37)
0.77** (3.71) 0.68** (3.90)
0.45 (p = 0.82) 0.61 (p = 0.69)
M
R Fundamental
Mining 11 15
ED
Recommendations Number of firms Number of observations
Manufacturing & Construction 86 382
B. Firm‘s Characteristics
Top
Volatility Fundamental Technical R Radj R Radj -0.13 -0.09 0.75** 0.64** (-0.43) (0.71) (3.81) (3.78) -0.01 -0.01 0.57** 0.50** (-0.04) (0.97) (3.42) (3.43) -0.04 -0.04 0.61** 0.53** (-0.21) (-0.27) (4.00) (4.56)
Previous Month Return Fundamental Technical R Radj R Radj 0.03 0.05 0.52** 0.46** (0.13) (0.30) (4.19) (3.96) 0.08 1.16 0.62** 0.55** (0.38) (0.62) (2.60) (2.79) -0.03 0.01 0.72** 0.62** (-0.18) (0.06) (5.53) (5.46)
Return over Months Two through Six Fundamental Technical R Radj R Radj -0.06 -0.02 0.64** 0.57** (-0.23) (-0.10) (3.57) (3.82) 0.00 0.03 0.65** 0.47** (0.00) (0.25) (3.70) (2.99) -0.02 0.01 0.63** 0.52** (-0.09) (0.07) (3.61) (4.63)
1.06 0.78 3.40 2.53 0.26 0.19 0.05 0.08 0.10 0.15 0.64 0.57 1.41 1.26 0.28 0.26 0.06 0.04 0.48 0.54 (0.35) (0.46) (0.03) (0.08) (0.77) (0.83) (0.95) (0.92) (0.91) (0.87) (0.53) (0.57) (0.25) (0.28) (0.76) (0.77) (0.93) (0.97) (0.62) (0.58)
AC
F all equal (p-value)
Book-to-Market Fundamental Technical R Radj R Radj 0.02 0.03 0.64** 0.56** (0.09) (0.20) (4.25) (4.75) 0.01 0.03 0.62** 0.54** (0.06) (0.14) (3.97) (4.27) -0.08 -0.04 0.64** 0.53** (-0.32) (-0.20) (4.78) (4.55)
PT
Core
CE
Bottom
Size Fundamental Technical R Radj R Radj -0.53 -0.34 -0.14 -0.09 (-1.08) (-0.76) (-0.31) (-0.19) -0.05 -0.03 0.89** 0.77** (-0.21) (-0.16) (4.66) (4.72) 0.09 0.11 0.61** 0.53** (0.43) (0.66) (4.80) (4.70)
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Table 6. Technician outperformance and stock characteristics
Panel A compares average returns adjusted to Fama–French and momentum factors when technical and fundamental recommendations are in disagreement. Investment horizons are one, three, six, nine, and twelve months following the recommendations. Panel B compares average stock characteristics when analysts
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are in disagreement or agreement. The characteristics are defined in Table 2. One and two asterisks indicate a significance level of 5% and 1%, respectively.
A. Average Returns when Analysts are in Disagreement
Fundamental Sell Buy
Technical Buy Sell
1 1.27 -1.54 2.81** (2.62)
Difference t-statistic
3 0.55 -1.80 2.35 (1.64)
Investment Horizon (months) 6 2.70 -4.23 6.93** (2.83)
9 5.71 -3.20 8.91** (2.86)
12 4.56 -1.13 5.69 (1.79)
Trend Factors
Technical Buy 10.27 Disagreement Agreement 10.18 Difference 0.09 t-statistic (1.01)
0.50 0.32 0.18** (3.83)
2.48 2.42 0.06 (0.88)
Technical Sell Disagreement 10.83 Agreement 10.67 Difference 0.16 t-statistic (1.32)
0.35 0.36 -0.01 (-0.84)
1.90 2.03 -0.13 (-1.56)
Market Consensus Factors
1M Vol.
1M Turn.
2.08 2.17 -0.09** (-6.94)
22.91 21.92 0.99 (0.87)
0.13 0.00 0.13 (1.41)
24.81 25.52 -0.71 (-1.02)
1.75 1.86 -0.11 (0.82)
15.88 22.81 -6.93 (-1.86)
0.28 0.00 0.28 (1.76)
25.51 26.54 -1.03 (-1.23)
7.49 -0.09 7.58** (4.40)
15.56 12.81 2.75 (-0.68)
4.09 2.13 1.96 (1.79)
16.27 9.47 6.80 (0.87)
1M Number Rec. illiquid. of Rec. Mean
AC
CE
ME
1M 2-6M Return Return
PT
BE/ME
1Y Vol.
ED
Firm Factors
M
B. Technician Outperformance and Stock Characteristics
42
Rec. STD
Change Insiders Institut. EPS in Rec. Trading Owner. Surprise
Post Acc.
2.42 2.48 -0.06 (1.48)
0.83 0.79 0.04 (1.56)
0.02 0.19 -0.17 (-0.91)
0.43 0.29 0.14* (2.09)
0.54 0.54 0.00 (0.72)
0.93 0.84 0.09 (-1.27)
65.25 68.88 -3.63 (-1.47)
2.28 2.33 -0.05 (1.35)
0.79 0.78 0.01 (0.75)
-0.13 0.03 -0.16 (-1.01)
0.28 0.31 -0.03 (-0.95)
0.56 0.55 0.01 (1.07)
0.89 0.89 0.00 (1.59)
64.40 69.83 -5.43* (-2.05)
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Table 7. Average returns per recommendation category on broader asset classes The table reports the average returns on various asset classes for each recommendation category over one, three, six, nine and twelve months following the recommendations broadcast. The asset classes are the S&P 500 index, equity sectors and non-U.S. equity indices, U.S. bonds and commodities. The t-statistics null hypothesis asserts equal returns across buy and sell recommendations. One and two asterisks indicate a significance level of 5% and 1%,
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respectively.
Fundamental Recommendations
Investment Horizon (months):
Buy minus sell t-statistic Equity sectors and non-U.S. equity indices
Mean Return (%) Buy minus sell t-statistic
U.S. bonds
Mean Return (%)
Mean Return (%)
PT
Commodities
9
12
Sell 2.31 Hold 0.99 Buy 1.68 -0.63 (1.29)
4.28 4.84 4.28 0.01 (0.01)
8.46 7.66 7.52 -0.94 (1.60)
11.16 11.10 9.51 -1.65 (1.43)
14.01 12.04 15.10 1.09 (0.71)
Sell 1.07 Hold 1.58 Buy 1.63 0.56 (0.73)
3.43 4.41 3.98 0.55 (0.44)
5.65 6.13 4.66 -0.99 (0.64)
8.41 9.90 7.77 -0.64 (0.30)
Sell 0.59 Hold 0.55 Buy -0.50 -1.08 (1.71)
0.99 1.76 -0.44 -1.44 (1.70)
2.37 2.94 0.45 -1.92 (1.75)
Sell -1.49 -4.55 -8.97 Hold -4.49 -6.63 -10.73 Buy -2.13 -2.40 -8.37 -0.64 2.15 0.60 (0.48) (0.89) (0.23)
ED
Buy minus sell t-statistic
6
AC
CE
Buy minus sell t-statistic
9
12
2.20 4.28 8.43 2.50 5.01 8.02 1.20 4.29 7.53 -1.00 0.01 -0.90 (1.89) (0.02) (1.44)
11.18 11.53 9.74 -1.44 (1.27)
14.26 14.45 13.47 -0.80 (0.46)
9.58 12.00 12.00 2.43 (0.81)
1.45 4.62 6.01 1.63 4.06 6.17 1.21 3.07 4.59 -0.24 -1.55 -1.43 (0.34) (1.48) (0.99)
8.63 10.27 7.76 -0.87 (0.46)
10.59 12.72 10.91 0.33 (0.12) -2.37
2.54 2.85 2.23 -0.31 (0.24)
2.60 5.48 2.64 0.04 (0.04)
0.56 1.38 3.10 0.77 2.00 2.61 -0.37 -0.54 0.58 -0.93 -1.91* -2.52* (1.39) (2.39) (2.26)
3.46 2.48 1.93 -1.53 (1.12)
4.14 4.79 1.77 -2.37* (2.08)
-11.75 -14.26 -10.20 1.55 (0.51)
-12.18 -14.11 -10.18 2.00 (0.62)
-2.01 -4.67 -9.27 -2.15 -1.64 -8.32 -2.53 -5.25 -9.19 -0.52 -0.58 0.08 (0.35) (0.23) (0.03)
-11.35 -12.45 -12.42 -1.07 (0.33)
-11.12 -13.57 -12.56 -1.44 (0.42)
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Mean Return (%)
3
M
S&P 500
1
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Technical Recommendations
1
3
6
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1a. Average cumulative excess returns
AC
CE
PT
ED
M
1b. The spread in returns on buy-minus-sell portfolios
Figure 1. Returns and recommendations The top panel depicts the average excess return, defined as return minus the return on the S&P 500 index, for technical and fundamental buy and sell recommendations. The bottom panel depicts the return spreads on buyminus-sell technical and fundamental portfolios. Stocks are bought for a twelve-month period starting at the closing price of the recommendation broadcast‘s day. Stocks in portfolios are equally weighted.
44
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2a. Raw returns
ED
M
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2b. Risk-adjusted returns
Figure 2. Average returns per recommendation category for various investment horizons
PT
The figure depicts the average returns on stocks for the buy, hold, and sell recommendations over one, three, six, nine and twelve months following the recommendation broadcast. The left (right) figures pertain to fundamental
CE
(technical) analysis. The top figures exhibit raw returns, while bottom figures display the returns adjusted for the Fama–French
factors
(Rm-rf,
SMB,
and
HLM)
and
time-series
momentum,
MOM,
i.e.,
AC
Ritadj Rti ˆim ( Rtm r ft ) ˆis SMBt ˆih HLM t ˆiM MOM t r ft where betas are estimated from time-series regressions over the studied period with stock daily returns.
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Appendix. List of assets and data sources This appendix lists all assets and describes their data source. The stock return and trading volume figures are from the Center of Research in Security Prices (CRSP). The firm accounting variables are from COMPUSTAT. The earnings surprises and analysts‘ consensus variables are
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from I/B/E/S. Thomson-Reuters Institutional Holdings (13F) Database provides institutional holdings, and Insider Filings (Form 4) Database captures changes in ownership of corporate insiders. The Fama–French (1993) factors are provided by Kenneth R. French‘s library. The time-series momentum factor of Moskowitz, Ooi, and Pedersen (2012) is taken from Pedersen‘s
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website. The stock indexes covered by ―Talking Numbers‖ are provided by the S&P Dow Jones Indexes, NASDAQ OMX Global Indexes, Nikkei, Moscow Exchange, Bucharest Stock Exchange, and International Securities Exchange. The prices of precious metals, natural gas, and
M
copper are from the London Bullion Market Association, the U.S. Energy Information Administration (EIA), and New York Mercantile Exchange, respectively. Agriculture prices are
ED
from CME Group and Intercontinental Exchange (ICE) and the source for the CRB Index is Thomson Reuters. All the other commodity prices are from the Federal Reserve Bank of St.
PT
Louis.
CE
The interest rates are from the Federal Reserve Bank of St. Louis. The 90-day Treasury bill rate serves as a proxy for the risk-free rate. To measure the performance of 10-year
AC
Treasuries recommendations we employed two methods. First, the ten-year Treasury Constant Maturity Rates were used to calculate the price of a notional zero-coupon 10-year bond. Second, we employed the price of the iShares 7–10 Year Treasury Bond ETF. As the empirical evidence for the two methods is similar, we report the findings for the first approach. Below is the full list of all assets organized in groups according to their type.
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U.S. Market. S&P 500, NYSE Composite Index. Equity Sectors. S&P100, Dow Industrial, Dow Utilities, Dow Transportation, Nasdaq Composite, Nasdaq 100, Russel2000, Guggenheim Shipping ETF (SEA), KBW Bank Index (BKX), PHLX Housing Sector Index (HGX), MSCI REIT Index (RMZ), Alerian MLP (AMLP),
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Gold Miners ETF (GDX), Junior Gold Miner ETF (GDXJ), Broker Dealers ETF (IAI), IShares Nasdaq Biotech (IBB), Russel 2000 ETF (IWM), IShares US Real Estate ETF (IYR), IShares DJ Transportation AVR (IYT), SPDR KBW REG Banking (KRE), S&P400 MICAP (MDY), Oil Service Holders (OIH), Market Vectors Retails (RTH), ISE Homebuilders Index (RUF), Market
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Vectors Steal (SLX), Social Media ETF (SOCL), Vanguard REIT (VNQ),
NYSE
ARCA
Airline Index (XAL), S&P Aerospace Defense (XAR), SPDR S&P Homebuilders (XHB), Energy SPDR (XLE), SPDR Financial ETF (XLF), Industrial Select Sector SPDR (XLI),
M
Technology SPDR (XLK), Consumer Staple SPDR (XLP), Utilities SPDR ETF (XLU), Health Care Sector SPDR ETF (XLV), Consumer Dictionary (XLY), SPDR S&P MTLS&MNG ETF
ED
(XME), SPDR S&P Retail (XRT), IShares DJ US Home (ITB). Non-U.S. Equity Indexes. Nikkei 225, Shanghai Composite, S&P BSE Sensex, IShares MSCI
PT
India ETF (INDA), Hang Sang, Nigeria ETF (NGE), Romanian BET, Vietnam ETF (VNM),
CE
Wisdomtree (DXJ), IShares MSCI Emerging Markets (EEM), IShares MSCI Mexico (EWW), IShares MSCI Brazil (EWZ), IShares FTSE China 25 (FXI), Market Vector Russia (RSX), RTS
AC
Moscow (RTS), IShares MSCI Turkey ETF (TUR), Vanguard MSCI Europe (VGK). U.S. Stocks. Alcoa (AA), Auto Parts (AAP), Apple (AAPL), Abbot Laboratories (ABT), Automatic Data Processing (ADP), American Eagle (AEO), Aflac (AFL), AIG (AIG), Allstate (ALL), Advanced Micro (AMD), Amgen (AMGN), Amazon (AMZN), Autonation (AN), Abercrombie & Fitch (ANF), AOL (AOL), Apache (APA), Anadarko Petroleum (APC), Apollo
47
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Group (APOL), Aeropostale (ARO), Athenahealth (ATHN), Activision Blizzard (ATVI), American Express (AXP), AstraZeneca (AZN), AutoZone (AZO), Boeing (BA), Bank OF America (BAC), Bed Bath & Beyond (BBBY), Blackberry (BBRY), Best Buy (BBY), Barclays (BCS), Sotheby‘s (BID), Biogen Idec (BIIB), Barnes & Noble (BKS), Burger King (BKW),
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Bristol Myers (BMY), British Petroleum (BP), Buffalo Wild Wings (BWLD), Citi Group (C), Cabela's (CAB), Conagra (CAG), Cheesecake Factory (CAKE), Caterpillar (CAT), Chubb (CB), CBS Corp (CBS), Carnival (CCL), Chesapeake Energy (CHK), Cliff Natural (CLF), Colony Financial (CLNY), Comcast (CMCSA), Chipotle (CMG), Cabot Oil and Gas (COG), Coach
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(COH), ConocoPhillips (COP), Costco (COST), Campbell Soup (CPB), Carter's (CRI), Salesforce (CRM), Cisco Systems (CSCO), Cintas (CTAS), CVS Caremark (CVS), Chevron (CVX), Caesars (CZR), Dominion Resources (D), Delta Air Lines (DAL), DuPont (DD), 3D
M
Systems (DDD), Deere (DE), Dell (DELL), Diageo (DEO), Dollar General (DG), D.R. Horton (DHI), Walt Disney (DIS), Dish Network (DISH), Dunkin Brands (DNKN), Diamond Offshore
ED
(DO), DR Pepper (DPS), Domino's (DPZ), Darden Restaurant (DRI), DirecTV (DTV), Devon Energy (DVN), DreamWorks (DWA), Electronics ART (EA), EBAY (EBAY), Consolidated
PT
Edison (ED), Enterprise Products (EPD), Equity Residential (EQR), Expedia (EXPE), Ford (F),
CE
Facebook (FB), Freeport McMorran (FCX), Family Dollar (FDO), FEDEX (FDX), Freddie Mac (FMCC), Fannie Mae (FNMA), Fossil Group (FOSL), Twenty-First Century Fox (FOXA), First
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Solar (FSLR), General Dynamic (GD), General Electric (GE), Gilead Sciences (GILD), General Mills (GIS), General Motors (GM), Green Mountain (GMCR), Randgold Resources (GOLD), Google (GOOG), GoPro (GPRO), Gap (GPS), Garmin (GRMN), Groupon (GRPN), Goldman Sachs (GS), Halliburton (HAL), Home Depot (HD), Herbalife (HLF), Harley-Davidson (HOG), Hovnanian (HOV), Hewlett Packard (HPQ), H&R Block (HRB), Hertz Global (HTZ), Humana
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(HUM), IBM (IBM), Icahn Enterprises (IEP), Imax (IMAX), Intel (INTC), Invensense (INVN), Intuitive Surgical (ISRG), JetBlue (JBLU), J.C. Penney (JCP), Johnson & Johnson (JNJ), Juniper Networks (JNPR), Jos A Bank (JOSB), JPMorgan (JPM), Nordstrom (JWN), KB Home (KBH), Krispy Kreme (KKD), Coca-Cola (KO), Michael Kors (KORS), Kansas City Southern (KSU),
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Liberty Global (LBTYA), Lennar (LEN), Lions Gate (LGF), Lockheed Martin (LMT), LinkedIn (LNKD), Lorillard INC (LO), Lowe's (LOW), Lufkin Industries (LUFK), Lululemon (LULU), Southwest Airlines (LUV), Las Vegas Sands (LVS), Macy's (M), MasterCard (MA), Macerich (MAC), Mattel (MAT), McDonald‘s (MCD), Kraft (KFT/MDLZ), MGM Resorts (MGM),
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Monster Beverage (MNST), Altria (MO), Marathon Petroleum (MPC), Merck (MRK), Morgan Stanley (MS), Microsoft (MSFT), Madison Square (MSG), Micron Technology (MU), Murphy Oil (MUR), Navistar (NAV), Nasdaq OMX (NDAQ), Noodles (NDLS), Newmont Mining
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(NEM), Netflix (NFLX), Nice Systems (NICE), Nike (NKE), Nokia (NOK), Norfolk Southern (NSC), Nuance Communications (NUAN), NYSE Euronext (NYX), Old Dominion Freight
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(ODFL), Omnicom Group (OMC), Oracle (ORCL), Outerwall (OUTR), Orbitz (OWW), Occidental Petroleum (OXY), Pandora (P), Priceline (PCLN), PepsiCo (PEP), Pfizer (PFE),
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Proctor & Gamble (PG), PulteGroup (PHM), PVH (PVH), Qualcomm (QCOM), Royal
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Caribbean (RCL), Royal Dutch Shell (RDS-A), Revlon (REV), Transocean (RIG), Ralph Lauren (RL), Realigy Holdings (RLGY), Ross Stores (ROST), Sprint (S), Starbucks (SBUX), Solarcity
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(SCTY), SeaWorld (SEAS), Sears (SHLD), Sherwin Williams (SHW), Sirius XM Radio (SIRI), Six Flags (SIX), Saks (SKS), Sketchers (SKX), Schlumberger (SLB), SanDisk (SNDK), Sony (SNE), Sodastream (SODA), Sonic (SONC), Staples (SPLS), Constellation (STZ), AT&T (T), Molson Coors (TAP), Taser International (TASR), Taubman Centers (TCO), Target (TGT), Tiffany (TIF), Toyota (TM), Toll Brothers (TOL), TripAdvisor (TRIP), Trinity Industry (TRN),
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Travelers (TRV), Tesla (TSLA), Tesora (TSO), Take Two Inter (TTWO), Time Warner Cable (TWC), Twitter (TWTR), Time Warner (TWX), Texas Instruments (TXN), Under Armor (UA), United Continental (UAL), UBS (UBS), United Healthcare (UNH), Ultra Petroleum (UPL), United Parcel Service (UPS), Urban Outfitters (URBN), USB (USB), United Technologies
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(UTX), Visa (V), Viacom (VIAB), Valero Energy (VLO), Vodaphone (VOD), Verizon (VZ), Webmd Health (WBMD), Wendy's (WEN), Wells Fargo (WFC), Whole Foods (WFM), Antm (WLP), Wal-Mart (WMT), Weingarten REALITY Investors (WRI), World Wrestling (WWE), Wynn Resorts (WYNN), US Steal (X), Exxon Mobil (XOM), Yelp (YELP), Yahoo (YHOO),
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Yum Brands (YUM), Zillow (Z), Zynga (ZNGA).
U.S. Bonds. 10-YR T-Note (IShares 7-10 Year Treasury bond ETF –IEF (94US10Y), IShares S&P National Muni (MUB), Barclays Muni Bond (TFI).
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Commodities. Gold (GCZ4), Silver (SIZ4), Copper (HGZ4), Natural Gas (NGV14), Palladium (PAL), Brent Crude Oil (BRENT), RBOB Gasoline (GASOLINE), WTI Crude Oil (WTI), Corn
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(CORN), Orange Juice (ORNG), Wheat (WHEAT), Deutsche Bank Commodities ETF (DBC), SPDR Gold ETF (GLD), IPATH DJ-UBS Coffee (JO), Silver ETF (SLV), Natural Gas Fund
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(UNG), CRP Index.
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Others. VIX, Renaissance IPO ETF (IPO), Bitcoin, NYC Real Estate, Luxury Houses, Junk Bonds ETF, Alibaba IPO, Mortgage Rates, Dollar Index, Yen-Dollar, Dollar-Euro, Dollar-
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Rupee.
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Talking Numbers: Technical versus Fundamental Investment Recommendations Doron Avramov , Guy Kaplanski , Haim Levy PII: DOI: Reference:
S0378-4266(18)30099-2 10.1016/j.jbankfin.2018.05.005 JBF 5347
To appear in:
Journal of Banking and Finance
Received date: Revised date: Accepted date:
10 October 2017 24 April 2018 6 May 2018
Please cite this article as: Doron Avramov , Guy Kaplanski , Haim Levy , Talking Numbers: Technical versus Fundamental Investment Recommendations , Journal of Banking and Finance (2018), doi: 10.1016/j.jbankfin.2018.05.005
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Highlight:
We examine simultaneous fundamental and technical forecasts from the TV show “Talking Numbers.” Technicians are able to predict individual stocks, while fundamental analysts provide no
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value.
Technicians generate significant Alpha of 13.6% that withstands reasonable transaction
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costs.
Technicians implement toolkits that go beyond well-known anomalies.
Market efficiency hypothesis is not rejected for indexes, equity sectors, bonds, or
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commodities.
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Talking Numbers:
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Technical versus Fundamental Investment Recommendations
Doron Avramov*, Guy Kaplanski**, Haim Levy***
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Dr. Guy Kaplanski Head of Financial Studies School of Business Administration Bar Ilan University Tel: 050-2262962 Homepage: http://mba.biu.ac.il/en/kaplanski
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Kaplanski Guy | The Graduate School of Business ...
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Chinese University of Hong Kong and Hebrew University of Jerusalem, email: [email protected]. Bar-Ilan University, Israel, email: [email protected]. Phone: 972-50-2262962, Address: Bar Ilan Univeristy, Ramat Gan, Israel. Corresponding Author. *** The Hebrew University of Jerusalem, email: [email protected]. **
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Abstract:
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Market efficiency is often evaluated through the ability of fundamental analysis or technical trading rules to exploit predictable patterns in asset prices. The evidence following decades of empirical research is mixed. This paper reexamines the evidence using a novel database from the TV
show
―Talking
Numbers.‖
We
assess
the
performance
of
1,599
investment
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recommendations, where each recommendation features a fundamental and a technical forecast. We show that technicians are able to predict individual stock returns to economically significant degrees up to a one-year horizon. Beyond that, the null hypothesis of market efficiency is not
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rejected for market-wide indices, equity sectors, bonds, or commodities.
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Keywords: fundamental analysis; technical rules; market efficiency; market anomalies
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JEL Codes: G10, G14, G24
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1. Introduction Whether financial markets are informationally efficient has long been controversial among researchers. Perhaps the most convincing testimony that this controversy is here to stay is the fact that Eugene Fama and Robert Shiller shared the 2013 Nobel Prize in Economics despite the
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fact that they represent two polar opposite views on market efficiency. The null hypothesis of market efficiency is often evaluated through the ability of fundamental analysis or technical trading rules to exploit predictable patterns in asset prices. The evidence following decades of empirical research is mixed. To illustrate, focusing on fundamental analysis, Stickel (1992) and
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Womack (1996) document value in revisions of consensus recommendations, while Jaffe and Mahoney (1999) and Metrick (1999) demonstrate the lack of value in investment newsletters. Considering technical rules, Brock, Lakonishock, and LeBaron (1992), Lo Mamaysky, and
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Wang (2000), Zhu and Zhou (2009), Menkhoff (2010), and Han Yang, and Zhou (2013) advocate in their favor, while Allen and Karjalainen (1999) and Yamamoto (2012), for example,
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provide evidence to the contrary.
This paper reexamines the evidence on market efficiency using a novel database from the
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TV show ―Talking Numbers.‖ This show was hosted by CNBC and Yahoo Finance, and it
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featured simultaneous dual recommendations on single stocks, indices, treasuries, and commodities. Each dual recommendation contains a fundamental and a technical forecast on the
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same asset and over a similar investment horizon. Our empirical experiments consider 1,599 such dual recommendations (1,000 recommendations for 262 individual stocks and 599 for general assets) featuring the most prominent stocks (e.g., Apple, Google, Facebook, Microsoft, and Boeing), most liquid commodities (e.g., gold, oil), major bonds (e.g., the U.S. ten-year
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notes), and major equity indices (e.g., the various Dow Jones and Nasdaq indices, and the energy, real estate, and pharmaceutical sectors). The show is well suited to exploring market efficiency. First, as both technicians and fundamental analysts are exposed to the same public information during the broadcast, their
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simultaneous recommendations reveal the extent to which they possess private information. In addition, as the show covers virtually all asset classes, our experiments are general enough and robust to liquidity concerns and investment capacity. The evidence is also robust to data-mining concerns. Indeed, to our knowledge, we are the first to explore the ―Talking Numbers‖ forecasts
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and the first to directly examine the performance of technical recommendations as opposed to publicized technical rules.
Figure 1 highlights our major findings. The upper panel plots the average cumulative
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return in excess of the return on the S&P 500 index, following buy and sell technical and buy and sell fundamental recommendations. The lower panel plots return spreads by considering
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buy-minus-sell technical and fundamental portfolios. Stocks are held for a twelve-month period from the recommendation broadcast in equal proportions, and we record excess returns (Panel A)
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and investment payoffs (Panel B) using the closing price of the recommendation date.
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Evidently, technicians display impressive stock-picking skills, recommending, on average, the purchase of undervalued stocks along with the sale of overvalued stocks. To
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illustrate, observe from Panel A that the excess return on technical buy recommended stocks peaks at 5.60% (105.60% of the initial endowment). In addition, the excess return on technical sell recommended stocks troughs at minus 2.56% (97.44% of the initial endowment). The corresponding figures for fundamental buy and sell recommendations are 3.21% and 1.90% after six months. It should be noted, however, that the curves often cross each other, suggesting that
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fundamental recommendations are unable to reliably predict future stock returns. Similar patterns are obtained upon considering returns adjusted to Fama-French and momentum factors. Panel B depicts calendar-time portfolios constructed daily from contemporaneous recommendations, as in Mitchell and Stafford (2000). Here, the return spread corresponding to
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the buy-minus-sell technical portfolio is positive and increases over the entire sample period, reaching 44.04% in December 2014. The buy-minus-sell fundamental portfolio payoff is either negative or very close to zero. The overall difference between technical and fundamental spreads tops 43.15%. Moreover, the buy-minus-sell technical portfolio yields a significant Jensen-Alpha
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of 0.136 (t = 2.13), which further increases to 0.44 (t = 3.16) upon considering only the most extreme ―strong‖ buy and ―strong‖ sell recommendations. Strikingly, the break-even transaction costs that would eliminate the abnormal performance are 2.1% for all buy and all sell technical
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recommendations and 6.3% for strong buy and strong sell technical recommendations. [Please insert Figure 1 here]
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The success of technicians in picking individual stocks is robust to controlling for common risk factors (including time-series and cross-section momentum factors) as well as
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controlling for firm-level size, book-to-market ratio, volatility, turnover, illiquidity, price trends,
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and consensus forecasts. Technical recommendations also produce robust predictions for all subsamples explored, grouped by industry, size, book-to-market ratio, past returns, and volatility.
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In contrast, the inability of fundamental analysts to predict future stock returns is uniform across all industries as well as size, book-to-market ratio, past returns, and volatility groups. Moreover, neither the abovementioned characteristics nor analysts‘ coverage and consensus, institutional holdings, earnings surprises or time elapsed from the release of accounting reports is associated with technicians‘ outperformance. This reduces the possibility that the success of technicians is
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related to a certain type of stock and might be driven by a selection bias of stocks in our sample in favor of technicians. While technicians are able to predict individual stock returns to economically significant degrees, further evidence shows that neither technicians nor fundamental analysts are able to
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predict returns on the broader asset classes: Treasuries, market indices, commodities, and equity sector indices. Our overall evidence is in line with the common wisdom asserting that the ability to possess and process private information applies mainly to individual stocks. Indeed, Blume, Easley, and O‘Hara (1994) and Zhu and Zhou (2009) argue that technicians trade on private
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signals, Kavajecz and Odder-White (2004) link technical rules to liquidity provisions, while Elder (2014) notes in his bestseller The New Trading for Living (page 36) that ―… Technical analysis can help you detect insider buying and selling.‖ In all these potential explanations, i.e.,
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liquidity, private signal, and inside information, the success of technicians in predicting individual stock returns need not translate into robust predictions of broader asset returns.
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General asset classes (beyond individual stocks) are less prone to private information, illiquidity, or insider trading.
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Upon comparing fundamental and technical approaches, a valid concern is that our
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sample does not represent the top performing fundamental analysts in the industry. Nevertheless, our result that a select group of technicians can beat the market establishes a nontrivial challenge
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to the foundation of market efficiency. Beyond that, we note that technicians and fundamental analysts are of the same quality based on various characteristics, such as education and experience, as described in Section 2 below. One may also argue that our sample of technical and fundamental recommendations is not essentially representative of fundamental and technical approaches. Notably, we do show that fundamental recommendations are correlated with
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fundamental factors such as earnings consensus, while technical recommendations are correlated with technical indicators such as trends. As a final remark, technical and fundamental approaches are associated with market anomalies that have been studied extensively.1 Prominent findings in the field are that the
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profitability of market anomalies is attributable to the short leg of the trade, i.e., short selling of stocks according to the anomaly (Stambaugh, Yu, and Yuan, 2012; Avramov, Chordia, Jostova, and Philipov, 2013) and that anomalies attenuate following their discovery (Schwert 2003; Chordia, Subrahmanyam, and Tong, 2014; McLean and Pontiff, 2016). Our dual
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recommendations pertain to the ―out-of-sample‖ period spanning Nov 8, 2011 through Dec 31, 2014, or the period that follows the publication of anomalies in academic journals. As technicians‘ forecasts also generate abnormal returns in the long-leg of the trade, i.e., also from
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buying stocks according to their forecasts, their success cannot be attributed to those well-known anomalies. Essentially, technicians implement toolkits that go beyond well-known anomalies.
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Moreover, their forecast outperformance—also in the short leg of the trade—stands out particularly when considering the bull market characterizing our sample period.
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The remainder of this paper is organized as follows. Section 2 explains the nature of the
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―Talking Numbers‖ broadcast and the participating analysts and describes the methodology used to convert the content of the show into investment recommendations. Section 3 describes the
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data. Section 4 reports the empirical findings corresponding to individual stocks. Section 5 1
To name a few, anomalies related to past returns (Jegadeesh, 1990, Lehmann, 1990, Jegadeesh and Titman, 1993),
trading volume and volatility (e.g., Haugen and Baker, 1996) are associated with technical signals, while the accruals (Sloan, 1996), credit risk (Dichev, 1998; Campbell, Hilscher, and Szilagyi, 2008; Avramov, Chordia, Jostova, and Philipov, 2009), value (Fama and French, 1992), and post-earnings-announcement-drift (Ball and Brown, 1968) are based on firm fundamentals.
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extends the analysis to other asset classes. Section 6 concludes. A list of the assets covered in the show and the data sources are detailed in the appendix.
2.
The investment recommendations and participating analysts
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The two major schools of thought in investment management are technical and fundamental analyses. To formulate expectations about future asset prices, technicians analyze historical price movements and trading volume, whereas fundamental analysts consider market, industry, and firm-level economic factors. In this paper, we examine whether investment
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recommendations based on the two approaches provide economic value. The analysis is based on dual recommendations extracted from the TV show ―Talking Numbers.‖ According to Yahoo, the broadcast ―takes a 360° approach to trading – highlighting the best investment opportunities
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by analyzing stocks both from a technical and a fundamental point of view...‖ The sample spans November 8, 2011 through December 31, 2014. November 8, 2011
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featured the first comparison between technical and fundamental points of view. Previously, ―Talking Numbers‖ was a rather different show. It was part of the CNBC broadcast ―Closing
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Bell‖ and usually featured the view of a single analyst, who mainly discussed the S&P 500
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index. In April 2015, the original program was discontinued, and CNBC initiated a new broadcast titled ―Trading Nation.‖ This new show exhibits several similarities to ―Talking
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Numbers,‖ including the same host. In addition, several analysts who participated in ―Talking Numbers‖ also take part in ―Trading Nation.‖ However, the format of ―Trading Nation‖ does not formally confront technical and fundamental points of view. Prior to May 2013, ―Talking Numbers‖ was exclusively hosted by the CNBC television network. For that period, we used two main sources: the CNBC archive at video.cnbc.com and
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The Internet Archive‘s TV news research service at archive.org. We also used several netsearching practices to detect programs that were missing from the main data archives. Since May 2013, CNBC and Yahoo Finance have jointly hosted the show. The main source of programs after the merger is Yahoo Finance at finance.yahoo.com. This source is organized
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chronologically and contains all the post-merger programs. Overall, we were able to cover the vast majority, possibly all, of the ―Talking Numbers‖ recommendations during the sample period.
In the first year of the sample, ―Talking Numbers‖ was broadcast once every trading day,
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typically featuring two technical and two fundamental recommendations about two distinct assets. Then, the program was usually broadcast several times per day, and in most cases each program covered a single asset. In a few cases, the program has featured only one analyst
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delivering either the technical or fundamental point of view, without a comparison. Those single recommendations are excluded from the primary analysis but considered later when examining
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the robustness of the results.
Prior to analyzing investment recommendations, we note that the resumes of show
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participants indicate a similar quality level of technicians and fundamental analysts. Both groups
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have vast professional experience, a position as managing director or chief officer, and nominations in ranking tables for best analysts, portfolio managers, or technicians. Such high-
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profile analysts are less prone to experience and reputation concerns (Graham, 1999; Sorescu and Subrahmanyam, 2006), career concerns (Hong Kubik, and Solomon, 2000; Clement and Tse, 2005), and conflicts of interest (Fang and Yasuda, 2009). Moreover, analysts belonging to both groups serve as the heads of research or the investment division of prominent banks, investment management funds, and research companies,
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such as Deutsche Bank, Piper Jaffray, Stifel Financial Corp, and Standard & Poor‘s Investment Advisory Services. Remarkably, 100% of the participants are ―senior‖ analysts with more than ten years of experience (95% with more than 20 years), while 74% of the fundamental analysts and 91% of the technicians have an additional title of managing director, chief officer, or head of
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department.
Additionally, 13% of the fundamental analysts and 15% of the technicians are from the buy-side industry, actively managing wealth. Of the fundamental analysts, 13% are founders of their own business, while the figure for technicians is 21%. In terms of education, 53% of
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fundamental analysts and 26% of technicians hold a graduate degree (mainly an MBA or MA in economics). Of them, 40% of fundamental analysts and 15% of technicians have graduated from ―Top 20‖ U.S. universities. Interestingly, the correlation between the fundamental and technical
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stock recommendations turns out to be practically zero. The overall evidence suggests that the outperformance of technicians emerges from the investment toolkits applied rather than the
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unique traits of the participating analysts.
The fundamental analysis typically starts with a macroeconomic outlook, followed by a
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recommendation, along with supporting discussions focusing on the economic forces underlying
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the market, industry, and firm. The technician usually describes a chart of historical prices and then discusses the main technical characteristics underlying the recommendation. Often, there
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are additional supporting charts and graphical exposition of technical characteristics, such as moving average and volume. It is common for the technician, the fundamental analyst, and the show hosts to debate about the recommendations. Throughout the show, recommendations are referred to as ―investment‖ recommendations corresponding to periods that range from one to several months. On rare occasions, the analysts
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also mention a separate recommendation referred to as a ―trading‖ recommendation for one day or a few days, or a long-term recommendation for horizons longer than one year. Such recommendations are exceptional items that always accompany the main investment recommendations and are thus excluded from the analysis.
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In the main analysis, we classify technical and fundamental recommendations into the three conventional categories: ―buy,‖ ―hold,‖ and ―sell.‖ In a second stage, we refine the classification and split buy recommendations into ―strong buy‖ and ―buy‖ and sell recommendations into ―sell‖ and ―strong sell.‖ In approximately 20–30% of the cases
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(depending on the asset class), the analyst‘s formal rating is explicitly stated verbally or in a caption. The classification in those cases adheres to the explicit ratings. Otherwise, if the recommendation is not explicit we systematically extract the recommendation category based on
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the content of the show.
While the differences between strong buy and buy and between strong sell and sell
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recommendations could be subtle, the distinction between buy and sell groups is clear and well defined. It is unlikely that a positive recommendation would be classified as sell or a negative
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recommendation classified as buy. Therefore, to avoid classification errors, the main analysis
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applies a two-category scale: a buy category, which includes both buy and strong buy recommendations, and a sell category, which includes both sell and strong sell recommendations.
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The results with the five-category scale (available upon request) are qualitatively similar and are briefly discussed in the robustness tests. Table 1 summarizes the descriptive statistics of the broad set of recommendations for
single stocks, the market index, particular equity sectors, bonds, and commodities. A full list of all the individual stocks featured in ―Talking Numbers‖ as well as all the other assets are listed in
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the appendix. Altogether, we capture 1,599 dual recommendations. There are 1,000 technical recommendations and 1,000 fundamental recommendations (1,000 dual recommendations) featuring 227 individual stocks. There are 149 dual recommendations covering the S&P 500 index (the NYSE composite index in one case); 256 dual recommendations corresponding to 58
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indices and ETFs, such as the Nasdaq 100, the Dow Jones Industrial/Utilities/Transportation and particular sectors, including banking, retail, homebuilders, miners, and biotechnology, as well as non-U.S. markets, including emerging markets, frontier markets, and the Nikkei 225; 50 dual recommendations featuring bond yields (mostly 10-year Treasuries but also municipal bonds);
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and 144 dual recommendations covering 17 commodities (especially gold and crude oil). On 370 shows, a single recommendation records no corresponding comparison because either only one analyst participated in the show or one of the analysts did not discuss the relevant asset. As noted
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in the previous section, such recommendations are excluded from the main analysis but are later considered in the robustness tests. A total of 49 observations are excluded because the
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underlying asset is unique (e.g., Bitcoin, VIX, luxury houses) with only a few observations. [Please insert Table 1 here]
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Table 1 shows that while the number of technicians and fundamental analysts is quite
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similar among the general asset classes, it is markedly distinct among single stocks. There are 34 technicians versus 159 fundamental analysts. As noted earlier, while this difference may imply
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that technicians could potentially be of higher quality, this does not affect our main finding that a select group of technicians is able to predict individual stock returns to economically significant degrees, which is at odds with the notion that investment recommendations provide no value. Also notable is the relatively small number of fundamental and technical female analysts—
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approximately 10% across all the various asset classes. As shown below, for both types of analysts, we find similar predicting abilities across gender and education. Investment recommendations populate all categories with a tendency toward buy recommendations in the case of stocks. The Spearman rank correlation coefficient, which
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measures the correlation between figures ranging from 1 (sell) to 3 (buy) corresponding to the fundamental and technical recommendations, is typically small. It is -0.07 for single stocks, 0.12 for the market index, 0.10 for equity sectors and non-U.S. indices, 0.25 for bonds, and 0.10 for commodities. As the core of our analysis pertains to individual stocks, it is evident that technical
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and fundamental recommendations are not mutually related, either positively or negatively. We further explore the determinants of the fundamental versus technical stock recommendations. Table 2 reports the results of the Probit regression
REC i γ0 j 1 1 j FIRM i j j 1 2 jTRENDi j j 1 3 j CONSENSUSi j ּεi , 5
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(1)
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3
where i is the stock-specific subscript and REC describes the fundamental or technical
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recommendation category (1–sell, 2–buy). In line with Womack (1996) and Jegadeesh, Kim,
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Krische, and Lee (2004), among others, we consider three firm-level variables: previous year log of market capitalization, book-to-market ratio if the previous year book value is positive and zero
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otherwise, and volatility over the year prior to the recommendation broadcast. The TREND variables include the return over the previous month and previous two
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through six months to account for the one-month reversal of Jegadeesh (1990) and the momentum of Jegadeesh and Titman (1993). It also includes the previous month‘s volatility, turnover (volume / number of outstanding shares) and the Amihud (2002) illiquidity measure. The last two variables account for the possibility that technical traders are liquidity providers (Kavajecz and Odder-White, 2004).
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We consider eight CONSENSUS variables to examine the association between recommendations and the consensus among analysts, corporate insiders, and institutional investors. Specifically, the first variable is the number of outstanding recommendations, which accounts for information diffusion frictions in stocks that are less covered by analysts (e.g.,
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Hong, Lim, and Stein, 2000). The next two variables measure the mean and standard deviation of analysts‘ recommendations (1–strong sell, 5–strong buy) standing for market consensus and its dispersion,
respectively.
Recommendation
upgrades
minus
downgrades
account
for
recommendation revisions (Stickel, 1992; Womack, 1996). The next two variables are the
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corporate insider public trading index, which is a proxy for the consensus among corporate insiders, and the percentage of institutional ownership of the company stocks, which is a proxy for the consensus among institutions. Considering institutional investors accounts for the
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possibility that the investment recommendations are different due to price biases attributable to institutional trades (Gompers and Metrick, 2011). The corporate insider public trading index is
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defined as purchases less sales of corporate insiders scaled by their total trades in the previous quarter. The last two variables are the days elapsed from the last release of accounting reports
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and the percentage surprise in earnings per share (EPS) in the last quarter, which account for
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possible post-earnings-announcement-drift (Ball and Brown, 1968). [Please insert Table 2 here]
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The dependent variable in Test 1 (2) in Table 2 is the fundamental (technical) recommendation. Starting from fundamental recommendations, we note that only the coefficient corresponding to analysts‘ mean recommendation is positive and significant, implying that fundamental recommendations align with analysts‘ consensus. This does not imply that fundamental recommendations are subject to herding, as the documented positive relation could
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be attributable to utilization of similar toolkits in establishing earnings forecasts and investment recommendations. Different economic factors underlie technical recommendations. The coefficients corresponding to size and returns over the previous month and two to six months are positive and
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significant. Thus, while fundamental recommendations are associated with market consensus in particular, technical recommendations are correlated with recent trends in past returns. These results are also consistent with the view that fundamental analysts and technicians employ
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distinct investment toolkits associated with distinct and unique economic forces.
3. Individual stocks: The empirical evidence
This section focuses exclusively on single-stock recommendations. Investment
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recommendations featuring the other asset classes will be analyzed in the next section. Figure 2 depicts the average stock returns for the three recommendation categories. We consider
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investment horizons of one, three, six, nine, and twelve months following the broadcasts. To be on the conservative side, here as well as in all the follow-up analyses, investment returns start
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accumulating based on the recommendation day‘s closing price.
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The left (right) plot in Figure 2 pertains to fundamental (technical) recommendations. Top figures display raw returns, while bottom figures display returns adjusted to the three Fama-
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French and momentum factors, i.e., the raw returns less fitted returns based on the four-factor model. As technicians are often trend chasers, we also employ the time-series momentum factor of Moskowitz, Ooi, and Pedersen (2012) along with the Fama-French three factors. The results with the cross-section momentum are very similar and available upon request. The parallel lines on both sides of each diagram represent the confidence intervals at a 5% significance level.
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Consistent with the findings in the introduction, it is evident from Figure 2 that fundamental analysts do not deliver valuable forecasts for future stock returns. The mean raw returns following buy and sell recommendations are quite similar, while for nine months the mean return following sell recommendations is actually higher (17%) than the mean return
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following buy recommendations (15.8%). For the twelve-month horizon, the mean returns associated with buy and sell recommendations are 21.7% and 20.4%, respectively. The corresponding risk-adjusted figures are slightly more appealing, 3.0% and 0.9%. In contrast, technicians reveal a rather strong return-recommendation relation. The mean returns for all
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horizons following buy recommendations are higher than those following sell recommendations. For the twelve-month horizon, the mean returns associated with buy and sell recommendations are 24.9% and 18.5%, respectively. The corresponding risk-adjusted returns are 4.7% and 0.1%.
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Even more appealing, for the six-month horizon there is no overlapping of confidence intervals, as the upper bound of 9.2% for sell recommendations is below the lower bound of 10.6% for buy
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recommendations. The corresponding bounds for risk-adjusted returns are -0.1% and 1.2%. [Please insert Figure 2 here]
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Table 3 reports the difference between buy and sell average returns for the various
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investment horizons. Starting with fundamental analysts, the return spreads between buy and sell fundamental recommendations are relatively small, given by 0.1%, 1.4%, 0.3%, -1.2%, and
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1.3%, respectively. Indeed, for all horizons, the null hypothesis of equal means is not rejected. The same holds for risk-adjusted returns. In contrast, technicians display impressive stockpicking skills. Their buy recommendations yield uniformly higher average returns, both raw and risk-adjusted, than their sell recommendations. The return spreads between buy and sell recommendations are equal to 1.9%, 2.3%, 6.2%, 5.8%, and 6.4% for the five horizons
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considered. The null hypothesis of equal mean returns is rejected for all horizons besides three months. Consistent with confidence intervals in Figure 2, the six-month horizon exhibits the highest t statistic (3.62). Similar evidence emerges on the basis of risk-adjusted returns. The investment returns following buy recommendations are uniformly larger than those following
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sell recommendations. For example, the corresponding nine-month returns are -0.7% for sell and 4.3% for buy, and the null hypothesis of equal mean returns is rejected for all horizons. The superiority of technicians over fundamental analysts appears in all five investment horizons. In all cases, the average returns on sell technical recommendations are lower than the corresponding
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fundamental recommendations, and the average returns on buy technical recommendations are higher than the corresponding fundamental recommendations.
[Please insert Table 3 here]
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The differences between mean returns or risk-adjusted returns following buy and sell recommendations are of the same size order, where the t-statistics for equal means are only
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slightly larger for risk-adjusted returns and horizons shorter than twelve months. To further investigate whether the performance of the two competing groups is associated with risk factors
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we define a new variable for the buy-minus-sell portfolios in Figure 1b: Z t Rt Rtadj , where R
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and Radj stand for returns and risk-adjusted returns, respectively, on either fundamental or technical buy-minus-sell portfolio. For both portfolios, the average value of Z is negative, small
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(|Z| < 10-4) and insignificantly different from zero (t-values of -0.28 and -1.02, respectively). Thus, we conclude that analysts‘ performance is not significantly associated with risk factors, and the success of technicians is robust to employing raw and risk-adjusted returns. In unreported tests, we find that technical recommendations outperform in two dimensions: the number of correct recommendations and the quality of recommendations
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manifested through gains and losses. Technicians recommend higher proportions of correct recommendations, whereby a correct recommendation amounts to a buy (sell) recommendation followed by an advancing (diminishing) stock price. Their recommendations also yield higher gains following correct recommendations and incur lower losses following incorrect
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recommendations.
3.1 Cross-section analysis
We employ regression analysis to further study the quality of recommendations. In the
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context of analysts‘ recommendations, it has been shown that firm size (Womack, 1996), past return, volume, book-to-market ratio (Jegadeesh et al. 2004), and industry affiliation (Boni and Womack, 2006) are all associated with the performance of recommendations. In response, we run the following cross-section regression 3
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Ri γ0 γ1 REC i j 1 2 j FIRM i j j 1 3 jTRENDi j 4 BROADi εּ i , 5
(2)
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where i is the stock-specific subscript, R is the stock return over the investment horizon, and REC describes the fundamental or technical recommendation category (1–sell, 2–buy). The
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FIRM variables are market capitalization, book-to-market ratio, and previous year volatility. The
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TREND variables are returns over the previous month and previous two through six months, attempting to capture short-term reversal and momentum effects, and the one-month volatility,
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turnover, and illiquidity, as have been already defined in Equation (1). Equation (2) also includes an immediate broadcast impact variable (BROAD), which is equal to the return over two days following the recommendation broadcast, intended to control for the impact of the media exposure to the broadcast.
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Table 4 reports the regression evidence from eight distinct tests. The dependent variable in all the tests is the six-month return following the broadcast. Test 1 excludes all the control variables. Here, consistent with the previous analyses, the fundamental recommendations‘ (REC) coefficient is insignificant, while its technical counterpart is significantly positive (t = 4.97).
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In cross-section regressions, we control for size, book-to-market ratio, volatility, past returns, the prior month volatility, turnover and illiquidity, and the immediate impact of the broadcast itself. The evidence supporting technical recommendations stands out, as the technical recommendations‘ (REC) coefficient is significantly positive (t = 3.31). The coefficient for
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returns immediately after the show broadcast is also positive and highly significant (t = 9.28). Although Table 2 shows that technical recommendations are correlated with past returns, it seems that technical recommendations do not capture the momentum effect, as the coefficient for
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returns over the previous two to six months is also positive and significant. We explore this issue in the coming sections. Note that our sample consists of large firms mostly belonging to the
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upper-size decile, and low-mid book-to-market deciles (see the appendix for the list of stocks). Thus, it may not be surprising that our sample of stocks does not exhibit distinct effects related to
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size, volatility, or book-to-market ratio. Neither firm type nor volatility, turnover, or illiquidity
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captures the predictive power of technical recommendations. The return-recommendation relation is also not attributable to the direct short-term impact of the broadcast on the stock price.
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While there is a significant immediate impact of the broadcast on the stock price, the predictive ability of technical recommendations persists long afterwards. [Please insert Table 4 here]
To avoid classification errors of recommendations, we employ a three-category scale. In separate robustness tests, we repeat all the reported tests with a five-category scale in which buy
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and sell recommendations are further classified into strong buy and buy and strong sell and sell categories. The results with the refined scale are even more supportive of technical recommendations and are available upon request. Test 3 summarizes the main results of this analysis, where the recommendation variable employs a five-category scale. As with the three-
technical counterpart is highly significantly positive (t = 3.82).
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category scale, the fundamental recommendations‘ (REC) coefficient is insignificant, while its
Kumar (2010) shows that female analysts display superior forecast abilities due to the self-selection process. Presumably, those females who have superb abilities as analysts pursue a
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career in a male-dominated industry. Our sample contains approximately 90% male analysts among the fundamentalists and technicians across all the asset classes (see Table 1). Thus, gender does not seem to represent a potential source of systematic bias.
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Test 4 formally accounts for analyst gender. The fundamental recommendations‘ coefficients are near zero and insignificant regardless of the analyst‘s gender. The technical
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recommendations‘ coefficients are larger and significant (t = 3.23 for males and 2.79 for females). While the coefficient corresponding to female analysts is slightly larger (5.27 10-2
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versus 4.91 10-2), the F-statistic does not reject the null hypothesis of no gender effect.
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Namely, the success of technical recommendations in predicting returns on individual stocks is not captured by an analyst‘s gender. Moreover, female technicians or fundamental analysts do
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not outperform their male counterparts. Tests 5 and 6 compare recommendations conditional on undergraduate versus graduate
analysts and analysts from ―Top 20‖ U.S. universities versus other analysts. In both tests, the fundamental recommendations‘ coefficients are not significant, while the technical
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recommendations‘ coefficients are larger and significant (t = 3.54 and 3.13, and 2.91 and 3.52, respectively). Test 7 compares the buy-side and sell-side analysts. While the fundamental recommendations‘ coefficients remain insignificant, the technical recommendations‘ coefficients
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are, once again, highly significant (t = 3.18 for sell-side analysts and 3.74 for buy-side analysts). Moreover, the buy-side analysts‘ coefficient (8.26 10-2) is almost twice as large as that of sellside coefficient (4.74 10-2), and the difference is significant (p = 0.05). Altogether, both sell-
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side and buy-side technicians are successful in predicting stock returns.
The dependent variable in Test 8 is the six-month return adjusted to the Fama-French and time-series momentum factors. Evidently, consistent with the previous results that the success of technicians is similar whether we employ raw or risk-adjusted returns, the predictive ability of
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technical recommendations is unexplained by common risk factors that could simultaneously affect stock prices and the recommendation category.
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While the dependent variable in Tests 1 through 8 is the stock return (raw or riskadjusted) over six months following the recommendation broadcast, in unreported tests, we also
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examine returns over one, three, nine, and twelve months following the broadcast. For all
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investment horizons, the fundamental recommendations‘ coefficients are indistinguishable from zero, while their technical counterparts are positively significant.
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Finally, in two unreported tests, we verify that the main results are neither sensitive to
outliers nor to the omission of single recommendation, either fundamental or technical, that was reported on a few programs with no comparison. To check for outliers, we run Equation (2) with the dependent variable winsorized at 2.5%. To test for single recommendations, we include those recommendations in the regression. In both tests, the overall evidence is unchanged.
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To summarize, the cross-section regressions confirm the strong predictive ability of technical recommendations, as demonstrated in Figure 1. That predictive ability is uncaptured by the size, book-to-market ratio, volatility, turnover, illiquidity, past stock trends, common risk factors, analyst‘s gender and education, and the apparent immediate impact of the
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recommendation broadcast. Both sell-side and buy-side analysts display robust predictive ability, which seems to be more profound in the latter group. The results are also robust to outlier returns as well as to potential classification errors. Fundamental recommendations, in contrast, do not
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show any clear and consistent relation with subsequent returns.
3.2 Examining industry and style effects
Boni and Womack (2006) argue that the economic value of financial analysts relates to
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their status as industry specialists. To explore the potential effects of industry affiliation and firm attributes on recommendations, we run the following cross-section regression:
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Rie γ0 γ1 j ( REC i )( FIRM ij ) ּεi ,
(3)
j
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where Rie is the six-month stock return (we consider both raw and risk-adjusted returns) in excess of average return corresponding to the matching industry or characteristic. RECi describes the
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recommendation category (1–sell, 2–buy). We consider two specifications. In one, FIRMij (j =
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1,2,…,6) are dummies for six industries: mining, construction and manufacturing, utilities, trade, financial and administration, and services. The industry division is made according to the Standard Industrial Classification (SIC) code with the exception that the construction and the wholesale trade and administration sectors, for which we record fewer than ten observations, are merged with their closest-matching industries. In the second specification, FIRMij (j = 1,2,3) are dummies for firms belonging to the bottom 30%, core 40%, and top 30% of either firm‘s size,
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book-to-market ratio, volatility, or past returns over the previous month and previous two through six months. As evident from the top of Table 5, firms are not evenly spread across industries, and the same holds for firm characteristics, as the sample is biased toward large companies with a moderate book-to-market ratio. Furthermore, analysts may specialize in a
return less the industry or style average return. [Please insert Table 5 here]
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certain style or a sector. To control for that, the dependent variable in Equation (3) considers
Table 5 reports the results. Starting with the fundamental analysis, all the
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recommendation coefficients are insignificant, regardless of whether raw returns (upper row) or risk-adjusted returns (lower row) are employed. Moving to the technical front, the recommendations produce robust predictions based on raw and risk-adjusted returns. Comparing
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the coefficients and t-statistics, we find a similar success across industries. The slightly less significant mining stock returns may be attributed to the small number of mining stocks or the
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prominent inability of both technicians and fundamental analysts to predict commodity prices, as we show below. Nevertheless, the almost identical magnitude of mining coefficient versus other
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industry coefficients indicates that technicians are still able to successfully differentiate between
period.
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firms within the mining industry that altogether realized negative performance during the sample
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Panel B of Table 5 reports the effect of firm characteristics on recommendations. The fundamental recommendations‘ coefficients corresponding to bottom, core and top size, book-tomarket, volatility, and past returns are all insignificant. This is consistent with the notion that fundamental recommendations display low power in predicting future returns across all equity styles. In contrast, almost all technical recommendations‘ coefficients are positive and highly
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significant. The only exception is the segment of bottom small firms, which consists of only 19 firms. The intensity of the success of technicians across all other styles is similar to the results across industries. In all cases, the null hypothesis of equal coefficients across categories is not
3.3 Abnormal performance and break-even transaction costs
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rejected.
We construct zero-cost buy-minus-sell portfolios for both fundamental and technical recommendations. We assume that trading occurs at the closing price of the day of
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recommendation and that it lasts for 12 months. All buy and sell recommended stocks are equally weighted, and the amount invested in buy recommended stocks is equal to the amount of shorts in sell recommended stocks. The evidence shows that a $1 position in the technical buy-
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minus-sell portfolio at the beginning of the period (November 2011) yields positive profits during the entire period, which tends to increase over time, reaching $0.44 in March 2015 with
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break-even transaction costs of 2.1% per recommendation. Indeed, regressing portfolio excess returns on the market factor yields a positive and significant Jensen-Alpha of 0.136 (t = 2.13) for
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the technical portfolio in comparison to -0.023 (t = -0.37) for the fundamental portfolio. Overall,
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our findings show that technical recommendations establish sizable and implementable
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investment rules at both the long and short legs of the trade.
3.4 Characterizing technicians‘ outperformance In this section, we attempt to characterize the stocks of the technicians who outperform
fundamental analysts. This analysis sheds more light on the source of technicians‘ outperformance and allows us to generalize the sample results.
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Table 6 compares average characteristics of stocks in disagreement and agreement among the two types of analysts. As outperformance of technicians is only attributable to stocks in disagreement, we attempt to identify the characteristics of stocks that technicians evaluate more successfully than fundamental analysts. In Panel A of Table 6, we first confirm that technicians‘
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outperformance exists when recommendations disagree. The first row reports average riskadjusted returns when the technical recommendation is buy and the fundamental recommendation is sell. For all time horizons, average risk-adjusted returns are positive, topping 5.71% after nine months. The second row considers technical sell and fundamental buy
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recommendations. Here, average risk-adjusted returns are negative for all time horizons. Indeed, the differences in risk-adjusted returns across buy and sell are large, ranging between 2.35% to 8.91%, and significant for one, six, and nine-month horizons.
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Panel B of Table 6 compares the 18 characteristics described in Table 2 that are conditional on agreement or disagreement among the two types of analysts. The comparison is
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made separately for technical buy and fundamental sell and for technical sell and fundamental buy. When the technical recommendation is buy, stocks in disagreement exhibit significantly
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higher book-to-market, previous month returns and insider trading, and lower previous month
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volatility. Note that the association with previous month returns is in the opposite sign to the short-term reversal of Jegadeesh (1990). Namely, higher one-month returns, which, according to
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the reversal effect indicate a sell signal, are actually associated with successful technical buy recommendations. When a technical recommendation is sell, only the average number of days elapsed since the release of accounting reports differs significantly across agreement and disagreement. However, this small difference (64.4 days versus 69.83 days) only implies that, in both cases, there is still about one month before the next release of accounting reports.
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The correlations between technicians‘ outperformance and certain stock characteristics may raise the concern that the stocks that appeared in the show were chosen because they are more suited for technical analysis. Note, however, that a selection bias would not change our main outcome, which is that in contradiction to the market efficiency hypothesis, technicians
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display valuable forecast ability. Moreover, according to Table 6, most of the stocks‘ characteristics are uncorrelated with technicians‘ outperformance. Similarly, in Table 5, we have already shown that technicians‘ success exists for stocks belonging to bottom, core, and top of book-to-market, volatility, and past returns. Therefore, these characteristics are also not expected
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to considerably affect technicians‘ outperformance. The same holds for the very small difference in days elapsed from accounting reports. Altogether, the correlation with one-month returns, volatility, and insider trading mainly support the technicians‘ theme that they rely on short-term
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price trends in making their recommendations.
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[Please insert Table 6 here]
4. Examining recommendations among broader asset classes
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Why are technical recommendations successful in predicting returns on individual
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stocks? One possibility is that technicians trade on private signals, as suggested by Blume, Easley, and O‘Hara (1994), and Zhu and Zhou (2009). According to Elder (2014), technicians
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can infer insider buying and selling activities from the charts. Kavajecz and Odder-White (2004) link technical rules to liquidity provisions. In either case, the essential hypothesis is that the success of technicians in predicting individual stock returns does not translate into robust predictions of broader asset returns, as those assets are substantially more liquid, less prone to private information, and more likely to attract capital that trades away all arbitrage mispricing.
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Table 7 reports mean returns following buy and sell recommendations for various asset classes: the S&P 500 index, equity sectors and non-U.S. equity indices, U.S. bonds and commodities. In all cases, there is no clear association between the recommendations and subsequent returns. The null hypothesis of equal mean returns following buy and sell
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recommendations is not rejected for all assets, all time horizons, and both fundamental and technical recommendations. These findings are consistent with Kiseok, Kenneth and Quentin (2005) and Taylor (2014), who cast doubt on the usefulness of technical analysis of market indices.
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[Please insert Table 7 here]
In sum, the apparent success of technicians to forecast individual stock returns is the exception rather the rule. In all other asset classes, both technicians and fundamental analysts do
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not display predictive ability. Our findings thus indicate that the markets corresponding to virtually all assets are efficient, yet inefficiency appears to exist among individual stocks.
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Prior to concluding the paper, let us note that our study complements prior work examining the immediate impact of media-publicized recommendations. Liu, Smith, and Syed
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(1990), Mathur and Waheed (1995), and Kerl and Walter (2007) document abnormal returns
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shortly after the publication of recommendations in the newspaper, and Hirschey, Richardson, and Scholz (2000) report abnormal returns on the day after the recommendations are posted on
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the Internet. However, Dewally (2003) detects no market reaction to recommendations posted by a newsgroup on the Internet. Neumann and Peppi (2007) find that the recommendations made by Jim Cramer, the host of the CNBC ―Mad Money‖ program, are followed by abnormal payoffs during the following day, and Busse and Green (2002) find that recommendations broadcast on
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the CNBC ―Morning Call‖ and ―Midday Call‖ programs produce abnormal immediate profits within 15 seconds. Differently from these studies, we examine the long-term value rather than the immediate impact of recommendations. Eventually, technical stock recommendations provide value not
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only for immediate trading but also up to a full year following their broadcast.
5. Conclusion
This study employs a novel database from a TV broadcast to compare between
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fundamental and technical analysts. Our experiments are based on simultaneous fundamental and technical recommendations for the same underlying assets and investment horizon. The unique setup of the show, featuring synchronized dual recommendations, a great variety of asset classes,
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and the presence of leading professionals, offers an ideal laboratory to assess the value of financial analysis in general and market efficiency in particular. Ultimately, both technicians and
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fundamental analysts are exposed to the same public information, while their recommendations differ due to the distinct toolkits applied.
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The simultaneous broadcast guarantees identical public information exposure, eliminates
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time gap biases, such as cross-herding among the participating analysts, and allows one to control for the immediate short-term effect of the broadcast itself. The high profile of the
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participating analysts levels the playing field and mitigates biases related to analysts‘ quality, experience, and career concerns. The broad focus of the program and the comprehensive list of assets covered make our findings general and mitigate the concerns about illiquidity biases and exceptional observations.
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Consistent with the semi-strong market efficiency hypothesis, the fundamental analysis reveals little ability to predict future returns on all assets examined. Surprisingly, technicians display a significant predicting ability of individual stock returns, pointing to market inefficiency even among the universe of the largest and most-traded stocks. For a start, trading individual
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stocks based on technical recommendations yields significant payoffs. Moreover, such stockpicking ability withstands controlling for common risk factors, firm characteristics, industry effects, the analyst‘s gender, education, affiliation type (sell-side versus buy-side), and the potential immediate impact of the broadcast. Focusing on technicians‘ outperformance, no clear
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tendency toward certain firms is found, which mitigates the concern that the success of technicians is confined to the specific stocks in our sample. The only found association between technicians‘ outperformance and recent price trends supports technicians‘ main theme that they
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rely on short-term price trends in making their recommendations.
The predictive ability of technicians characterizes returns on individual stocks only. In
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contrast, the null hypothesis of market efficiency is intact for general asset classes, including the market indices, equity sectors and non-U.S. equity indices, bonds, and commodities. One
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appealing explanation is that technicians trade on private signals, as advocated by economic
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theory. Technicians potentially extract insiders trading from the charts. Either way, predictable patterns are most likely to apply to individual stocks and not to broader asset classes that are less
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prone to insider trading, private information, or illiquidity.
Acknowledgment The authors acknowledge the helpful comments and suggestions of the two anonymous referees that helped to improve this paper.
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Table 1: Overview of recommendation categories for various asset classes The table displays the frequency of recommendation categories for various asset classes. The sample period is November 8, 2011 (the day when the first simultaneous fundamental–technical comparison was broadcasted) through December, 31 2014. The Spearman‘s correlation coefficient is between the numerical values of fundamental
58 Equity sectors and non-U.S. indices
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11
28
4
3
19
11
3
3
2
11
17
2
3
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17 Commodities
4
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3 Bonds
20
Spearman’s Sell Hold Buy Total Correlation 130 54 151 335 43 31 100 174 179 94 218 491 352 179 469 1000 -0.07 16 11 21 48
10 8 21 39
13 9 40 62
39 28 82 149
0.12
Sell Hold Buy Total
51 19 33 103
23 20 29 72
25 15 41 81
99 54 103 256
0.10
Sell Hold Buy Total
7 5 8 20
3 5 4 12
4 3 11 18
14 13 23 50
0.25
Sell Hold Buy Total
61 13 9 83
9 5 10 24
18 4 15 37
88 22 34 144
0.10
Sell Hold Buy Total
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The U.S. Market (S&P 500)
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227 Stocks
Fundamental Analysts Technical Analysts Fundamental Male Female Male Female Technical Sell 142 17 31 3 Hold Buy Total
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Single Assets
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and technical recommendations (sell–1, hold–2, buy–3).
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Table 2: Determinants of individual stock recommendations The table reports the coefficients slopes (multiplied by 10) of the Probit regression RECi γ0
3
FIRM i j 1 1 j
j
5
TRENDi j 1 2 j
j
8
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CONSENSUSi j 1 3 j
j
εּi ,
where i is the stock-specific subscript and REC describes the recommendation main category (1–sell, 2–buy). The FIRM variables are the previous-year log of
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market capitalization, book-to-market ratio if the previous-year book value is positive and zero otherwise, and volatility measured by the daily returns over the year prior to the recommendation broadcast. The TREND variables are the previous month return, return for months two through six prior to the recommendation broadcast, previous month volatility, turnover estimated as previous month volume divided by the number of outstanding shares, and illiquidity estimated as the previous month average Amihud illiquidity measure 108. The CONSENSUS variables are the number of analysts covering the stock, mean and standard deviation of all analysts‘ recommendations (1–strong sell, 5–strong buy), percentage upgrades minus downgrades of outstanding recommendations, corporate
M
insiders trading index, which is defined as purchases less sales of corporate insiders divided by their total trades in the previous quarter, the percentage of institutional ownership of the company stocks, percentage surprise in earnings per share (EPS) during the past quarter, and calendar days elapsed from last release of quarterly accounting reports. The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets)
ED
corresponding to heteroskedasticity-consistent Huber–White standard errors. One and two asterisks indicate a significance level of 5% and 1%, respectively.
Firm Factors
0.02 (0.42)
42.66 (0.45)
CE
1.32** (2.73)
AC
Technical Recommendations
Trend Factors
1M 2-6M BE/ME 1Y Vol. Returns Returns 0.72 -161.67 1.77 1.93 (0.41) (-1.81) (0.42) (1.17)
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Dependent Variable: ME 0.67 Fundamental Recommendations (1.47)
20.51** (3.89)
4.68* (2.41)
Market Consensus Factors
1M Vol. 0.40 (0.22)
1M Turn. 0.00 (0.07)
1M Number Rec. illiquid. of Rec. Mean -478.84 -0.04 3.73** (-1.29) (-0.67) (2.73)
-2.60 (-1.42)
-0.05 2436.16 -0.03 (-0.86) (1.02) (-0.60)
37
1.90 (1.46)
Rec. Change Insiders Inst. EPS Post STD in Rec. Trading Owner. Surprise Acc. -3.41 -13.89 -1.18 2.14 0.14 0.01 (-1.25) (-0.82) (-1.15) (0.82) (0.31) (0.63) 2.44 (0.90)
-7.44 (-0.45)
1.27 (1.30)
3.92 (1.57)
-0.03 (-0.07)
-0.02 (-0.90)
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Table 3: Stock returns per recommendation category The table describes the relation between the return and the recommendation category. The investment horizons are one, three, six, nine, and twelve months following the recommendations. Upper (lower) panel considers the raw (risk-adjusted) average returns, with risk adjustment pertaining to the Fama–French and momentum factors. The T-
two asterisks indicate a significance level of 5% and 1%, respectively.
Fundamental Recommendations
3
6
9
12
1.2 1.5 1.3 0.1 (0.10)
4.1 6.4 5.5 1.4 (1.13)
10.3 10.5 10.6 0.3 (0.19)
17.0 18.5 15.8 -1.2 (0.50)
20.4 26.5 21.7 1.3 (0.44)
Sell -0.3 Hold 0.5 Buy -0.2 0.1 (0.18)
-0.6 2.3 0.7 1.3 (1.16)
0.7 2.0 1.2 0.6 (0.36)
2.2 4.1 1.7 -0.5 (0.24)
0.9 5.7 3.0 2.1 (0.93)
Sell Hold Buy
Mean Return (%) Buy minus Sell t-statistic
Mean RiskAdjusted Return (%)
AC
CE
PT
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Buy minus Sell t-statistic
Technical Recommendations
1
38
3
6
9
12
0.0 3.5 6.5 13.3 18.5 2.4 6.3 11.8 16.8 21.1 1.9 5.9 12.7 19.0 24.9 1.9** 2.3 6.2** 5.8* 6.4* (2.57) (1.87) (3.62) (2.39) (2.19)
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1
M
Investment Horizon (months):
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statistics correspond to null hypothesis that buy and sell recommendations exhibit the same mean returns. One and
-1.4 -1.2 -2.4 -0.7 0.1 0.2 1.9 2.8 2.6 2.6 0.6 1.2 3.0 4.3 4.7 2.0** 2.3* 5.4** 5.1** 4.6* (3.03) (2.08) (3.63) (2.60) (2.02)
Table 4: Individual stock recommendations: cross-section regressions The table reports the coefficients slopes (multiplied by 10 2) of the cross-section regression Ri γ0 γ1 RECi
3
FIRM i j 1 2 j
j
5
TRENDi j 1 3 j
j
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4 BROADi εּi ,
where i is the stock-specific subscript, R is the stock return over a six-month period, and REC describes the recommendation category (1–sell, 2–buy). The FIRM
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variables are the previous-year log of market capitalization, book-to-market ratio if the previous-year book value is positive and zero otherwise, and daily returns volatility over the year prior to the recommendation broadcast. The TREND control variables are the previous month return and return for months two through six prior to the recommendation broadcast, aimed to account for momentum and reversal, and the one-month volatility, turnover, and illiquidity described in Table 2. The broadcast immediate impact control variable (BROAD) is the return over two days following the recommendation broadcast. The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets) corresponding to heteroskedasticity- and
AC
CE
PT
ED
M
autocorrelation-consistent (HAC) standard errors sorted on analysts. One and two asterisks indicate a significance level of 5% and 1%, respectively.
39
Recommendation
2. Impact of Recommendation Broadcast
0.13 (0.07)
3. Recommendations Consist of Five Categories
0.60 (0.82) Male 0.23 (0.13)
4. Male vs. Female Analysts
6. Analysts‘ University
7. Sell-Side vs. Buy-Side Analysts
Underg. Grad. 1.41 -0.95 (0.75) (-0.50) Top 20 -0.72 (-0.38) S-side 0.72 (0.39)
4.93** (3.31)
**
2.19 (3.82) Male Female 4.91** (3.23) Underg.
5.27** (2.79) Grad.
4.67** 5.21** (3.54) (3.13) Other U. Top 20 Other U. 0.65 (0.36) 5.03** 4.86** (2.91) (3.52) B-side S-side B-side -1.28 (-0.67) 4.74** 8.26** (3.18) (3.74)
0.22 (0.16)
0.40 (0.48) 0.04 (0.05)
4.55** (3.42)
-0.01 (-0.60) -0.02 (-0.97)
AC
427.60 (1.56) 243.42 (1.01)
6.62 (0.59) -1.46 (-0.14)
5.88 (1.29) 6.36** (3.60)
-0.68 (-0.20) 0.71 (0.23)
-0.17* (-2.31) -0.06 (-0.52)
0.24 (1.26) 0.23 (1.25)
94.56** (4.86) 114.10** (9.28)
-0.02* (-1.97) -0.02* (-2.03)
499.37 (1.94) 491.84 (1.94)
1.66 (0.17) -2.53 (-0.27)
6.61 (1.71) 5.84** (3.51)
0.48 (0.16) 1.00 (0.39)
-0.18* (-2.24) -0.18 (-1.61)
0.24 (1.33) 0.20 (1.16)
139.54** (4.46) 136.80** (4.55)
-0.01 (-0.60) -0.02 (-0.97)
425.62 (1.56) 241.26 (0.96)
6.59 (0.59) -1.51 (-0.15)
5.88 (1.29) 6.37** (3.60)
-0.67 (-0.20) 0.72 (0.23)
-0.17* (-2.31) -0.06 (-0.51)
0.23 (1.20) 0.23 (1.25)
94.40** 0.13 (4.89) (p = 0.71) ** 113.95 0.03 (9.01) (p = 0.86)
-0.02 (-1.03) -0.02 (-1.01)
427.69 (1.56) 248.79 (1.02)
5.72 (0.52) -1.68 (-0.16)
5.61 (1.24) 6.28** (3.40)
-0.44 (-0.13) 0.70 (0.23)
-0.17* (-2.33) -0.06 (-0.54)
0.17 (0.92) 0.23 (1.32)
96.79** 7.22 (4.99) (p < 0.01) ** 114.51 0.33 (9.22) (p = 0.57)
0.36 (0.43) 0.04 (0.05)
-0.01 (-0.58) -0.02 (-1.02)
426.52 (1.56) 244.99 (1.00)
6.47 (0.58) -1.51 (-0.14)
5.67 (1.25) 6.34** (3.47)
-0.61 (-0.18) 0.72 (0.23)
-0.17* (-2.34) -0.06 (-0.52)
0.26 (1.27) 0.24 (1.25)
94.22** 2.34 (4.81) (p = 0.13) ** 114.16 0.03 (9.16) (p = 0.85)
0.48 (0.58) 0.06 (0.08)
-0.02 (-0.77) -0.02 (-0.91)
423.48 (1.57) 245.24 (1.02)
6.54 (0.59) -1.35 (-0.13)
5.74 (1.27) 6.47** (3.70)
-0.51 (-0.15) 0.74 (0.23)
-0.17* (-2.21) -0.06 (-0.53)
0.21 (1.08) 0.14 (1.08)
96.44** 5.17 (4.99) (p = 0.02) 114.19** 3.70 (9.35) (p = 0.05)
0.04 (0.06) -0.20 (-0.27)
-0.01 (-0.68) -0.02 (-0.88)
266.70 (1.10) 95.99 (0.45)
7.54 (0.82) -0.80 (-0.10)
4.96 (1.19) 4.95** (3.25)
-0.36 (-0.11) 0.73 (0.26)
-0.14* (-2.04) -0.02 (-0.26)
0.38 (1.96) 0.38* (2.28)
84.11** (5.69) 97.50** (9.64)
0.11 (0.13) 0.04 (0.05) 0.39 (0.47) 0.04 (0.05) 0.34 (0.41) 0.05 (0.06)
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8. Dependent Variable is Risk-Adjusted Returns
0.62** (4.97)
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5. Graduate vs. Undergraduate Analysts
Female -0.10 (-0.05)
ME
Trend Factors 2-6M 1M 1M 1M Broadcast F for Return Volatility Turnover illiquidity Impact equality
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1. Base Case
Technical
1Y 1M BE/ME Volatility Return
M
Fundamental 0.03 (0.19)
ED
Test
Firm Factors
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Table 5. Industry and style effects The table reports the coefficients slopes (multiplied by 10) of the following regression: where
Rie
Rie γ 0
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γ
( REC i j 1j
)( FIRM ij ) εּi ,
is the stock return or return adjusted for the Fama–French and momentum factors (Radj) in excess of the average return corresponding to the stock
industry or style over six months following the recommendation broadcast; RECi describes the recommendation category (1–sell, 2–buy); FIRMij are firm characteristics‘ dummies: six industry dummies in Panel A and three dummies in Panel B corresponding to the bottom 30%, core 40%, and top 30% of size,
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book-to-market, volatility, previous month return, or past return over months two through six prior to the recommendation broadcast.
The first line in each test reports the coefficient value, while the second line reports the t-value (in brackets) corresponding to heteroskedasticity- and autocorrelation-consistent (HAC) standard errors sorted by analysts. One and two asterisks indicate a significance level of 5% and 1%, respectively. A. Industry
Radj R Technical Radj
0.23 (1.09) 0.27 (1.54)
-0.01 (-0.06) 0.02 (0.15)
0.65* (2.11) 0.62 (1.70)
0.59** (3.43) 0.53** (3.78)
Transportation & Wholesale & Public Utilities Retail Trade 25 42 67 238
Finance, Insurance, Real Estate & Public Administration 26 71
Services 37 227
F for Equal Industries
0.02 (0.08) 0.05 (0.29)
0.04 (0.20) 0.07 (0.41)
-0.13 (-0.69) -0.06 (-0.38)
0.14 (0.86) 0.011 (0.79)
0.81 (p = 0.55) 0.91 (p = 0.48)
0.59** (5.76) 0.52** (5.46)
0.66* (3.33) 0.59** (3.71)
0.54** (3.21) 0.47** (3.37)
0.77** (3.71) 0.68** (3.90)
0.45 (p = 0.82) 0.61 (p = 0.69)
M
R Fundamental
Mining 11 15
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Recommendations Number of firms Number of observations
Manufacturing & Construction 86 382
B. Firm‘s Characteristics
Top
Volatility Fundamental Technical R Radj R Radj -0.13 -0.09 0.75** 0.64** (-0.43) (0.71) (3.81) (3.78) -0.01 -0.01 0.57** 0.50** (-0.04) (0.97) (3.42) (3.43) -0.04 -0.04 0.61** 0.53** (-0.21) (-0.27) (4.00) (4.56)
Previous Month Return Fundamental Technical R Radj R Radj 0.03 0.05 0.52** 0.46** (0.13) (0.30) (4.19) (3.96) 0.08 1.16 0.62** 0.55** (0.38) (0.62) (2.60) (2.79) -0.03 0.01 0.72** 0.62** (-0.18) (0.06) (5.53) (5.46)
Return over Months Two through Six Fundamental Technical R Radj R Radj -0.06 -0.02 0.64** 0.57** (-0.23) (-0.10) (3.57) (3.82) 0.00 0.03 0.65** 0.47** (0.00) (0.25) (3.70) (2.99) -0.02 0.01 0.63** 0.52** (-0.09) (0.07) (3.61) (4.63)
1.06 0.78 3.40 2.53 0.26 0.19 0.05 0.08 0.10 0.15 0.64 0.57 1.41 1.26 0.28 0.26 0.06 0.04 0.48 0.54 (0.35) (0.46) (0.03) (0.08) (0.77) (0.83) (0.95) (0.92) (0.91) (0.87) (0.53) (0.57) (0.25) (0.28) (0.76) (0.77) (0.93) (0.97) (0.62) (0.58)
AC
F all equal (p-value)
Book-to-Market Fundamental Technical R Radj R Radj 0.02 0.03 0.64** 0.56** (0.09) (0.20) (4.25) (4.75) 0.01 0.03 0.62** 0.54** (0.06) (0.14) (3.97) (4.27) -0.08 -0.04 0.64** 0.53** (-0.32) (-0.20) (4.78) (4.55)
PT
Core
CE
Bottom
Size Fundamental Technical R Radj R Radj -0.53 -0.34 -0.14 -0.09 (-1.08) (-0.76) (-0.31) (-0.19) -0.05 -0.03 0.89** 0.77** (-0.21) (-0.16) (4.66) (4.72) 0.09 0.11 0.61** 0.53** (0.43) (0.66) (4.80) (4.70)
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Table 6. Technician outperformance and stock characteristics
Panel A compares average returns adjusted to Fama–French and momentum factors when technical and fundamental recommendations are in disagreement. Investment horizons are one, three, six, nine, and twelve months following the recommendations. Panel B compares average stock characteristics when analysts
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are in disagreement or agreement. The characteristics are defined in Table 2. One and two asterisks indicate a significance level of 5% and 1%, respectively.
A. Average Returns when Analysts are in Disagreement
Fundamental Sell Buy
Technical Buy Sell
1 1.27 -1.54 2.81** (2.62)
Difference t-statistic
3 0.55 -1.80 2.35 (1.64)
Investment Horizon (months) 6 2.70 -4.23 6.93** (2.83)
9 5.71 -3.20 8.91** (2.86)
12 4.56 -1.13 5.69 (1.79)
Trend Factors
Technical Buy 10.27 Disagreement Agreement 10.18 Difference 0.09 t-statistic (1.01)
0.50 0.32 0.18** (3.83)
2.48 2.42 0.06 (0.88)
Technical Sell Disagreement 10.83 Agreement 10.67 Difference 0.16 t-statistic (1.32)
0.35 0.36 -0.01 (-0.84)
1.90 2.03 -0.13 (-1.56)
Market Consensus Factors
1M Vol.
1M Turn.
2.08 2.17 -0.09** (-6.94)
22.91 21.92 0.99 (0.87)
0.13 0.00 0.13 (1.41)
24.81 25.52 -0.71 (-1.02)
1.75 1.86 -0.11 (0.82)
15.88 22.81 -6.93 (-1.86)
0.28 0.00 0.28 (1.76)
25.51 26.54 -1.03 (-1.23)
7.49 -0.09 7.58** (4.40)
15.56 12.81 2.75 (-0.68)
4.09 2.13 1.96 (1.79)
16.27 9.47 6.80 (0.87)
1M Number Rec. illiquid. of Rec. Mean
AC
CE
ME
1M 2-6M Return Return
PT
BE/ME
1Y Vol.
ED
Firm Factors
M
B. Technician Outperformance and Stock Characteristics
42
Rec. STD
Change Insiders Institut. EPS in Rec. Trading Owner. Surprise
Post Acc.
2.42 2.48 -0.06 (1.48)
0.83 0.79 0.04 (1.56)
0.02 0.19 -0.17 (-0.91)
0.43 0.29 0.14* (2.09)
0.54 0.54 0.00 (0.72)
0.93 0.84 0.09 (-1.27)
65.25 68.88 -3.63 (-1.47)
2.28 2.33 -0.05 (1.35)
0.79 0.78 0.01 (0.75)
-0.13 0.03 -0.16 (-1.01)
0.28 0.31 -0.03 (-0.95)
0.56 0.55 0.01 (1.07)
0.89 0.89 0.00 (1.59)
64.40 69.83 -5.43* (-2.05)
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Table 7. Average returns per recommendation category on broader asset classes The table reports the average returns on various asset classes for each recommendation category over one, three, six, nine and twelve months following the recommendations broadcast. The asset classes are the S&P 500 index, equity sectors and non-U.S. equity indices, U.S. bonds and commodities. The t-statistics null hypothesis asserts equal returns across buy and sell recommendations. One and two asterisks indicate a significance level of 5% and 1%,
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respectively.
Fundamental Recommendations
Investment Horizon (months):
Buy minus sell t-statistic Equity sectors and non-U.S. equity indices
Mean Return (%) Buy minus sell t-statistic
U.S. bonds
Mean Return (%)
Mean Return (%)
PT
Commodities
9
12
Sell 2.31 Hold 0.99 Buy 1.68 -0.63 (1.29)
4.28 4.84 4.28 0.01 (0.01)
8.46 7.66 7.52 -0.94 (1.60)
11.16 11.10 9.51 -1.65 (1.43)
14.01 12.04 15.10 1.09 (0.71)
Sell 1.07 Hold 1.58 Buy 1.63 0.56 (0.73)
3.43 4.41 3.98 0.55 (0.44)
5.65 6.13 4.66 -0.99 (0.64)
8.41 9.90 7.77 -0.64 (0.30)
Sell 0.59 Hold 0.55 Buy -0.50 -1.08 (1.71)
0.99 1.76 -0.44 -1.44 (1.70)
2.37 2.94 0.45 -1.92 (1.75)
Sell -1.49 -4.55 -8.97 Hold -4.49 -6.63 -10.73 Buy -2.13 -2.40 -8.37 -0.64 2.15 0.60 (0.48) (0.89) (0.23)
ED
Buy minus sell t-statistic
6
AC
CE
Buy minus sell t-statistic
9
12
2.20 4.28 8.43 2.50 5.01 8.02 1.20 4.29 7.53 -1.00 0.01 -0.90 (1.89) (0.02) (1.44)
11.18 11.53 9.74 -1.44 (1.27)
14.26 14.45 13.47 -0.80 (0.46)
9.58 12.00 12.00 2.43 (0.81)
1.45 4.62 6.01 1.63 4.06 6.17 1.21 3.07 4.59 -0.24 -1.55 -1.43 (0.34) (1.48) (0.99)
8.63 10.27 7.76 -0.87 (0.46)
10.59 12.72 10.91 0.33 (0.12) -2.37
2.54 2.85 2.23 -0.31 (0.24)
2.60 5.48 2.64 0.04 (0.04)
0.56 1.38 3.10 0.77 2.00 2.61 -0.37 -0.54 0.58 -0.93 -1.91* -2.52* (1.39) (2.39) (2.26)
3.46 2.48 1.93 -1.53 (1.12)
4.14 4.79 1.77 -2.37* (2.08)
-11.75 -14.26 -10.20 1.55 (0.51)
-12.18 -14.11 -10.18 2.00 (0.62)
-2.01 -4.67 -9.27 -2.15 -1.64 -8.32 -2.53 -5.25 -9.19 -0.52 -0.58 0.08 (0.35) (0.23) (0.03)
-11.35 -12.45 -12.42 -1.07 (0.33)
-11.12 -13.57 -12.56 -1.44 (0.42)
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Mean Return (%)
3
M
S&P 500
1
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Technical Recommendations
1
3
6
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1a. Average cumulative excess returns
AC
CE
PT
ED
M
1b. The spread in returns on buy-minus-sell portfolios
Figure 1. Returns and recommendations The top panel depicts the average excess return, defined as return minus the return on the S&P 500 index, for technical and fundamental buy and sell recommendations. The bottom panel depicts the return spreads on buyminus-sell technical and fundamental portfolios. Stocks are bought for a twelve-month period starting at the closing price of the recommendation broadcast‘s day. Stocks in portfolios are equally weighted.
44
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2a. Raw returns
ED
M
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2b. Risk-adjusted returns
Figure 2. Average returns per recommendation category for various investment horizons
PT
The figure depicts the average returns on stocks for the buy, hold, and sell recommendations over one, three, six, nine and twelve months following the recommendation broadcast. The left (right) figures pertain to fundamental
CE
(technical) analysis. The top figures exhibit raw returns, while bottom figures display the returns adjusted for the Fama–French
factors
(Rm-rf,
SMB,
and
HLM)
and
time-series
momentum,
MOM,
i.e.,
AC
Ritadj Rti ˆim ( Rtm r ft ) ˆis SMBt ˆih HLM t ˆiM MOM t r ft where betas are estimated from time-series regressions over the studied period with stock daily returns.
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Appendix. List of assets and data sources This appendix lists all assets and describes their data source. The stock return and trading volume figures are from the Center of Research in Security Prices (CRSP). The firm accounting variables are from COMPUSTAT. The earnings surprises and analysts‘ consensus variables are
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from I/B/E/S. Thomson-Reuters Institutional Holdings (13F) Database provides institutional holdings, and Insider Filings (Form 4) Database captures changes in ownership of corporate insiders. The Fama–French (1993) factors are provided by Kenneth R. French‘s library. The time-series momentum factor of Moskowitz, Ooi, and Pedersen (2012) is taken from Pedersen‘s
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website. The stock indexes covered by ―Talking Numbers‖ are provided by the S&P Dow Jones Indexes, NASDAQ OMX Global Indexes, Nikkei, Moscow Exchange, Bucharest Stock Exchange, and International Securities Exchange. The prices of precious metals, natural gas, and
M
copper are from the London Bullion Market Association, the U.S. Energy Information Administration (EIA), and New York Mercantile Exchange, respectively. Agriculture prices are
ED
from CME Group and Intercontinental Exchange (ICE) and the source for the CRB Index is Thomson Reuters. All the other commodity prices are from the Federal Reserve Bank of St.
PT
Louis.
CE
The interest rates are from the Federal Reserve Bank of St. Louis. The 90-day Treasury bill rate serves as a proxy for the risk-free rate. To measure the performance of 10-year
AC
Treasuries recommendations we employed two methods. First, the ten-year Treasury Constant Maturity Rates were used to calculate the price of a notional zero-coupon 10-year bond. Second, we employed the price of the iShares 7–10 Year Treasury Bond ETF. As the empirical evidence for the two methods is similar, we report the findings for the first approach. Below is the full list of all assets organized in groups according to their type.
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ACCEPTED MANUSCRIPT
U.S. Market. S&P 500, NYSE Composite Index. Equity Sectors. S&P100, Dow Industrial, Dow Utilities, Dow Transportation, Nasdaq Composite, Nasdaq 100, Russel2000, Guggenheim Shipping ETF (SEA), KBW Bank Index (BKX), PHLX Housing Sector Index (HGX), MSCI REIT Index (RMZ), Alerian MLP (AMLP),
CR IP T
Gold Miners ETF (GDX), Junior Gold Miner ETF (GDXJ), Broker Dealers ETF (IAI), IShares Nasdaq Biotech (IBB), Russel 2000 ETF (IWM), IShares US Real Estate ETF (IYR), IShares DJ Transportation AVR (IYT), SPDR KBW REG Banking (KRE), S&P400 MICAP (MDY), Oil Service Holders (OIH), Market Vectors Retails (RTH), ISE Homebuilders Index (RUF), Market
AN US
Vectors Steal (SLX), Social Media ETF (SOCL), Vanguard REIT (VNQ),
NYSE
ARCA
Airline Index (XAL), S&P Aerospace Defense (XAR), SPDR S&P Homebuilders (XHB), Energy SPDR (XLE), SPDR Financial ETF (XLF), Industrial Select Sector SPDR (XLI),
M
Technology SPDR (XLK), Consumer Staple SPDR (XLP), Utilities SPDR ETF (XLU), Health Care Sector SPDR ETF (XLV), Consumer Dictionary (XLY), SPDR S&P MTLS&MNG ETF
ED
(XME), SPDR S&P Retail (XRT), IShares DJ US Home (ITB). Non-U.S. Equity Indexes. Nikkei 225, Shanghai Composite, S&P BSE Sensex, IShares MSCI
PT
India ETF (INDA), Hang Sang, Nigeria ETF (NGE), Romanian BET, Vietnam ETF (VNM),
CE
Wisdomtree (DXJ), IShares MSCI Emerging Markets (EEM), IShares MSCI Mexico (EWW), IShares MSCI Brazil (EWZ), IShares FTSE China 25 (FXI), Market Vector Russia (RSX), RTS
AC
Moscow (RTS), IShares MSCI Turkey ETF (TUR), Vanguard MSCI Europe (VGK). U.S. Stocks. Alcoa (AA), Auto Parts (AAP), Apple (AAPL), Abbot Laboratories (ABT), Automatic Data Processing (ADP), American Eagle (AEO), Aflac (AFL), AIG (AIG), Allstate (ALL), Advanced Micro (AMD), Amgen (AMGN), Amazon (AMZN), Autonation (AN), Abercrombie & Fitch (ANF), AOL (AOL), Apache (APA), Anadarko Petroleum (APC), Apollo
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ACCEPTED MANUSCRIPT
Group (APOL), Aeropostale (ARO), Athenahealth (ATHN), Activision Blizzard (ATVI), American Express (AXP), AstraZeneca (AZN), AutoZone (AZO), Boeing (BA), Bank OF America (BAC), Bed Bath & Beyond (BBBY), Blackberry (BBRY), Best Buy (BBY), Barclays (BCS), Sotheby‘s (BID), Biogen Idec (BIIB), Barnes & Noble (BKS), Burger King (BKW),
CR IP T
Bristol Myers (BMY), British Petroleum (BP), Buffalo Wild Wings (BWLD), Citi Group (C), Cabela's (CAB), Conagra (CAG), Cheesecake Factory (CAKE), Caterpillar (CAT), Chubb (CB), CBS Corp (CBS), Carnival (CCL), Chesapeake Energy (CHK), Cliff Natural (CLF), Colony Financial (CLNY), Comcast (CMCSA), Chipotle (CMG), Cabot Oil and Gas (COG), Coach
AN US
(COH), ConocoPhillips (COP), Costco (COST), Campbell Soup (CPB), Carter's (CRI), Salesforce (CRM), Cisco Systems (CSCO), Cintas (CTAS), CVS Caremark (CVS), Chevron (CVX), Caesars (CZR), Dominion Resources (D), Delta Air Lines (DAL), DuPont (DD), 3D
M
Systems (DDD), Deere (DE), Dell (DELL), Diageo (DEO), Dollar General (DG), D.R. Horton (DHI), Walt Disney (DIS), Dish Network (DISH), Dunkin Brands (DNKN), Diamond Offshore
ED
(DO), DR Pepper (DPS), Domino's (DPZ), Darden Restaurant (DRI), DirecTV (DTV), Devon Energy (DVN), DreamWorks (DWA), Electronics ART (EA), EBAY (EBAY), Consolidated
PT
Edison (ED), Enterprise Products (EPD), Equity Residential (EQR), Expedia (EXPE), Ford (F),
CE
Facebook (FB), Freeport McMorran (FCX), Family Dollar (FDO), FEDEX (FDX), Freddie Mac (FMCC), Fannie Mae (FNMA), Fossil Group (FOSL), Twenty-First Century Fox (FOXA), First
AC
Solar (FSLR), General Dynamic (GD), General Electric (GE), Gilead Sciences (GILD), General Mills (GIS), General Motors (GM), Green Mountain (GMCR), Randgold Resources (GOLD), Google (GOOG), GoPro (GPRO), Gap (GPS), Garmin (GRMN), Groupon (GRPN), Goldman Sachs (GS), Halliburton (HAL), Home Depot (HD), Herbalife (HLF), Harley-Davidson (HOG), Hovnanian (HOV), Hewlett Packard (HPQ), H&R Block (HRB), Hertz Global (HTZ), Humana
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ACCEPTED MANUSCRIPT
(HUM), IBM (IBM), Icahn Enterprises (IEP), Imax (IMAX), Intel (INTC), Invensense (INVN), Intuitive Surgical (ISRG), JetBlue (JBLU), J.C. Penney (JCP), Johnson & Johnson (JNJ), Juniper Networks (JNPR), Jos A Bank (JOSB), JPMorgan (JPM), Nordstrom (JWN), KB Home (KBH), Krispy Kreme (KKD), Coca-Cola (KO), Michael Kors (KORS), Kansas City Southern (KSU),
CR IP T
Liberty Global (LBTYA), Lennar (LEN), Lions Gate (LGF), Lockheed Martin (LMT), LinkedIn (LNKD), Lorillard INC (LO), Lowe's (LOW), Lufkin Industries (LUFK), Lululemon (LULU), Southwest Airlines (LUV), Las Vegas Sands (LVS), Macy's (M), MasterCard (MA), Macerich (MAC), Mattel (MAT), McDonald‘s (MCD), Kraft (KFT/MDLZ), MGM Resorts (MGM),
AN US
Monster Beverage (MNST), Altria (MO), Marathon Petroleum (MPC), Merck (MRK), Morgan Stanley (MS), Microsoft (MSFT), Madison Square (MSG), Micron Technology (MU), Murphy Oil (MUR), Navistar (NAV), Nasdaq OMX (NDAQ), Noodles (NDLS), Newmont Mining
M
(NEM), Netflix (NFLX), Nice Systems (NICE), Nike (NKE), Nokia (NOK), Norfolk Southern (NSC), Nuance Communications (NUAN), NYSE Euronext (NYX), Old Dominion Freight
ED
(ODFL), Omnicom Group (OMC), Oracle (ORCL), Outerwall (OUTR), Orbitz (OWW), Occidental Petroleum (OXY), Pandora (P), Priceline (PCLN), PepsiCo (PEP), Pfizer (PFE),
PT
Proctor & Gamble (PG), PulteGroup (PHM), PVH (PVH), Qualcomm (QCOM), Royal
CE
Caribbean (RCL), Royal Dutch Shell (RDS-A), Revlon (REV), Transocean (RIG), Ralph Lauren (RL), Realigy Holdings (RLGY), Ross Stores (ROST), Sprint (S), Starbucks (SBUX), Solarcity
AC
(SCTY), SeaWorld (SEAS), Sears (SHLD), Sherwin Williams (SHW), Sirius XM Radio (SIRI), Six Flags (SIX), Saks (SKS), Sketchers (SKX), Schlumberger (SLB), SanDisk (SNDK), Sony (SNE), Sodastream (SODA), Sonic (SONC), Staples (SPLS), Constellation (STZ), AT&T (T), Molson Coors (TAP), Taser International (TASR), Taubman Centers (TCO), Target (TGT), Tiffany (TIF), Toyota (TM), Toll Brothers (TOL), TripAdvisor (TRIP), Trinity Industry (TRN),
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ACCEPTED MANUSCRIPT
Travelers (TRV), Tesla (TSLA), Tesora (TSO), Take Two Inter (TTWO), Time Warner Cable (TWC), Twitter (TWTR), Time Warner (TWX), Texas Instruments (TXN), Under Armor (UA), United Continental (UAL), UBS (UBS), United Healthcare (UNH), Ultra Petroleum (UPL), United Parcel Service (UPS), Urban Outfitters (URBN), USB (USB), United Technologies
CR IP T
(UTX), Visa (V), Viacom (VIAB), Valero Energy (VLO), Vodaphone (VOD), Verizon (VZ), Webmd Health (WBMD), Wendy's (WEN), Wells Fargo (WFC), Whole Foods (WFM), Antm (WLP), Wal-Mart (WMT), Weingarten REALITY Investors (WRI), World Wrestling (WWE), Wynn Resorts (WYNN), US Steal (X), Exxon Mobil (XOM), Yelp (YELP), Yahoo (YHOO),
AN US
Yum Brands (YUM), Zillow (Z), Zynga (ZNGA).
U.S. Bonds. 10-YR T-Note (IShares 7-10 Year Treasury bond ETF –IEF (94US10Y), IShares S&P National Muni (MUB), Barclays Muni Bond (TFI).
M
Commodities. Gold (GCZ4), Silver (SIZ4), Copper (HGZ4), Natural Gas (NGV14), Palladium (PAL), Brent Crude Oil (BRENT), RBOB Gasoline (GASOLINE), WTI Crude Oil (WTI), Corn
ED
(CORN), Orange Juice (ORNG), Wheat (WHEAT), Deutsche Bank Commodities ETF (DBC), SPDR Gold ETF (GLD), IPATH DJ-UBS Coffee (JO), Silver ETF (SLV), Natural Gas Fund
PT
(UNG), CRP Index.
CE
Others. VIX, Renaissance IPO ETF (IPO), Bitcoin, NYC Real Estate, Luxury Houses, Junk Bonds ETF, Alibaba IPO, Mortgage Rates, Dollar Index, Yen-Dollar, Dollar-Euro, Dollar-
AC
Rupee.
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