Beta as a determinant of investor activity in sector exchange-traded funds

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Beta as a determinant of investor activity in sector exchange-traded funds夽 Jarkko Peltomäki ∗ Stockholm Business School, Stockholm University, Room 15:419, house 15/Kräftriket, 106 91 Stockholm, Sweden

a r t i c l e

i n f o

Article history: Received 2 December 2015 Received in revised form 22 June 2016 Accepted 28 June 2016 Available online xxx JEL classification: G11 G23

a b s t r a c t This study investigates the role of beta along with an extended set of risk characteristics as determinants of ETF flow and ETF trading in sector exchange-traded funds (ETFs). The results reveal that the relation between beta and ETF trading (ETF flow) is decreasing (increasing) and U-shaped (inverse U-shaped). These findings imply, in line with the documented low-risk anomaly, that investors may perceive lowbeta ETFs as less desirable alternatives than high-beta ETFs. The shape of the relation between beta and investor activity indicates that it is more important for investors to avoid low beta than to achieve high beta. © 2016 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

Keywords: Exchange-traded fund Investor activity Investment decisions Beta

1. Introduction Exchange traded funds (ETFs) have become popular investment vehicles among investors wishing to invest in tradable and liquid investment products made to track benchmark indexes. As ETFs are creatable and redeemable, but also traded like stocks, investor activity in these investment products can be observed from their net asset flows, henceforth ETF flow, and their share turnover, henceforth ETF trading. Consequently, a very decisive characteristic of ETFs is that they combine the ability to trade and the possibility to create and redeem shares, meaning they are a mix of what investors can do with mutual funds and stocks. The increasing popularity of ETFs points to the importance of a comprehensive understanding of investor behavior in ETFs. As the trading activity and behavior of ETF investors may affect the underlying securities through redemptions and creations, several studies have investigated the association between investor activity in ETFs and its potential market impact on the underlying securities.

夽 Jarkko Peltomäki thank the Finnish Foundation for the Advancement of Securities Markets for generous financial support. I thank Kenny Siaw, anonymous referees and seminar participants at the 2013 MFA Annual Meeting for their helpful comments. ∗ Tel.: +46 8 163022. E-mail address: [email protected]

Ben-David, Franzoni, and Moussawi (2014), Da and Shive (2013), and Staer (2014) raise concerns over the short-term behavior of ETF investors that may affect the underlying securities. Clifford, Fulkerson, and Jordan (2014) find that ETF investors exhibit return chasing behavior similar to mutual fund investors, investment decisions in ETFs may be driven by a naïve extrapolation bias; investors see past good performers as good investment opportunities, and vice versa. However, investors’ preferences for high volatility (see Baker, Bradley, & Wurgler, 2011), and not just return chasing behavior, may drive investor activity in such investment vehicles. The purpose of this study is to address the role of beta along with an extended set of risk characteristics in determining ETF activity; hence we examine whether different fund variables, the variables of fund risk and financial stress variables, and explain ETF flow and ETF trading in the nine select sector SPDR funds using a sample period from May 31, 2006 to March 6, 2012. The nine sector ETFs inhere in the SPDR S&P 500 index ETF that will be considered as a benchmark of the overall market performance. Sector ETFs suit the analysis of the dependence of investor activity and systematic risk better since sectors have more consistent and intuitive high or low systematic exposure to the market portfolio by their nature. For example, the industrials sector is cyclical in nature and carries high systematic risk. Therefore, the chosen small sample of sector ETFs is particularly well-suited for addressing the research problem of this study. Following the evidence of Hong and Sraer (2014), Frazzani and Pedersen (2014), and Christoffersson

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Please cite this article in press as: Peltomäki, J. Beta as a determinant of investor activity in sector exchange-traded funds. The Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.06.006

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and Simutin (2014) suggesting that investors have irrational and rational reasons for preferring high-beta stocks, it can be expected that investors invest more money in high-beta ETFs. This study contributes to the existing knowledge in the extant literature by aiming to ascertain if investors demonstrably allocate more funds to high-beta investments causing the low-risk anomaly. This aspect of the study is motivated specifically by the understanding that investors prefer to invest in high-beta stocks because of their irrational preferences (see Baker et al., 2011; Hong & Sraer, 2014) or funding constraints (see Blitz, Falkenstein, & van Pliet, 2014; Frazzani & Pedersen, 2014). Thus, testing the relation between beta and fund flows in ETFs sheds light on this understanding from a new perspective and helps to ascertain why investors wish to invest in leveraged exchange-traded products (ETPs), which by design have high betas. Additionally, this study contributes to the literature by extending the evidence on the drivers of ETF flows and ETF trading. Clifford et al. (2014) find that prior returns drive ETF flow, Broman and Shum (2015) find that liquidity is an important determinant of fund flows, and Box, Davis, and Fuller (2016) show evidence that ETF flow decreases with a higher expense ratio. Regarding ETF trading, Nadler and Schmidt (2015) find that major macroeconomic announcements impact on ETF trading, increasing daily ETF trading volumes. None of these studies, however, addresses the role of beta as a determinant of ETF trading activity. The results suggest that ETF investors chase ‘hot’ ETFs as ETF flow increases after a good performance, high beta, and high share turnover. In line with the existing evidence on the low-risk anomaly, the results of this study show that while the relation between beta and ETF flow is increasing and inverse U-shaped, the relation between beta and ETF trading is decreasing and U-shaped. These findings indicate that it is more important for investors to avoid low beta than to achieve high beta. Intriguingly, the results also show that relative performance and attention are important drivers of investors’ allocation decisions in passive investment products. Since ETFs are passive investment products, this evidence does not support the view that investors generally chase fund returns because of managerial skill. The remainder of the study is organized as follows: Section 2 reviews the relevant literature and the hypothesis of this study. Section 3 discusses describes the methodology and data used. Section 4 presents the results and the discussion, and Section 5 concludes. 2. Background and hypothesis Several studies have investigated asset flow-performance relation in mutual funds. In general, these studies present evidence for return chasing behavior, which is indicated by a positive return–flow relationship. Sirri and Tufano (1998), for example, examine the flow-performance relationship across mutual funds using a monthly dataset and present evidence that investors invest more in well-performing funds. For daily fund flows, Edelen and Warner (2001) present evidence that the relationship is positive. Consistent with these two studies, Friesen and Sapp (2007) study the timing ability of mutual fund investors using monthly returns during the period 1991–2004 and present evidence that mutual fund investors exhibit return chasing behavior. After considering the above-mentioned evidence for positive asset-flow performance relation in mutual funds, it is reasonable to suppose that the relation between the performance of an ETF and its subsequent fund flow is positive.1 Results from earlier studies also lend support to this expectation, as the explicit evidence of

1 If aggregate ETF funds flows are considered, the fund flow-performance relation appears to be negative at the aggregate fund level (cf. Rakowski and Wang, 2009; Warther, 1995).

Clifford et al. (2014) on ETF flow suggests that ETF flow increases following a higher return. These findings are fair indications that ETF investors chase returns similar to mutual fund investors and that the asset flow-performance relation is also positive in ETFs. Considering also the evidence for a positive relation between prior returns and stock trading volume in Karpoff (1987), Schwert (1989), and Gallant, Rossi, and Tauchen (1992), it appears that investors both trade more and invest more if the investment performs well. The past trading volume of an ETF should be related to the trading behavior of its investors and fund flows alike. It has already been shown by Lee and Swaminathan (2000) that past trading volume can be an important explanatory variable of trading strategies used by investors trading the stock. There are at least two reasons why an increasing share turnover of an ETF should increase investors’ subsequent activity in the ETF in terms of ETF flow and ETF trading. First, a higher share turnover may intrinsically attract more investors and short-term traders because it increases liquidity. Ben-David et al. (2014) note that liquidity should attract shortterm investors because of lower transaction costs, as the theory of Amihud and Mendelson (1986) posits that the short-term trading of more liquid securities is less costly. Second, Barber and Odean (2008) present evidence that investor attention to a stock, measured as high abnormal trading volume by the authors, attracts buying pressure from investors. Consequently, sector ETFs with high share turnover should attract investors and trading, which is a consideration in line with the evidence presented by Chordia, Huh, and Subrahmanyam (2008) that more visible stocks are traded more, as visible stocks are more likely to gain attract investors’ attention. Since industry-wide categorization should influence the investment decisions of retail investors, as shown by Jame and Tong (2014), relative measures of performance should likewise be determinants of ETF activity.2 In support of the relevance of simple benchmarking and the use of beta, Barber, Huang, and Odean (2014) find that the CAPM-based alpha explains fund flows better than the Fama-French three-factor alpha or the Carhart four-factor alpha. Baker et al. (2011) argue that low-beta stocks outperform highbeta stocks due to investors’ irrational preference for high volatility, which implies that the demand for sector ETFs increases with beta. They also argue that benchmarking of institutional investors discourages them from exploiting this low-risk anomaly, as they would risk them losing against their benchmarks by investing in low beta securities. As an explanation for the low-risk anomaly, Frazzani and Pedersen (2014) argue that high-beta stocks have low alphas as a result of investors bidding-up high-beta assets because of the investors’ margin constraints.3 With high relevance to sector ETFs, Baker, Bradley, and Taliaferro (2014) investigate the low-risk anomaly by decomposing it into micro and macro effects, and find that industry betas determine a significant fraction of the outperformance of low-beta stocks. As the finding suggests that macro-effects drive the returns of the anomaly, the study implies that investors predictably increase their market exposure by adjusting their exposure to different sectors. Therefore, the demand for sector ETFs in particular should increase with beta. Christoffersson and Simutin (2014) present evidence on the relation between beta and security demand that is even

2 It should be noted that relating return chasing behavior in sector ETFs to only behavioral biases is not unproblematic since return chasing behavior in investing in passively managed sector ETFs can also be a rational active strategy for investors given the evidence for the profitability of industry momentum strategies (e.g. Moskowitz and Grinblatt, 1999). 3 In a broader overview of the explanations for the low-volatility anomaly, Blitz et al. (2014) relate it to the assumptions of the CAPM from different aspects and also consider leverage constraints and short selling restrictions as reasons for the existence of the anomaly.

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more related investor activity. They show that fund managers substitute low-beta stocks for high-beta stocks due to benchmarking pressure increasing the demand for high-beta stocks. The evidence for investors’ higher demand for high-beta stocks due to benchmarking concerns (e.g. Christoffersson & Simutin, 2014) and margin constraints (e.g. Frazzani & Pedersen, 2014) leads to the research hypothesis of this study concerning ETF flow.

Hong and Sraer (2014) relate the low-risk anomaly to the common macro-factor of firms’ cash flows, but also give a reason to consider the beta – ETF trading relation. They present evidence that high-beta stocks are more prone to speculative overpricing than low-beta stocks because optimistic investors can use highbeta stocks to increase their exposure to the macro-factor. Further, they point out that this overpricing should lead to a higher share turnover of high-beta stocks as arbitrageurs would short them. This evidence is considered as motivation to account for a possible relation between beta and trading activity, but without a hypothesis since the literature does not provide explicit guidance for addressing the relation. 3. Data and methods The sector ETFs included in the analysis of this study are the following: Materials Select Sector SPDR (XLB), Energy Select Sector SPDR (XLE), Financials Select Sector SPDR (XLF), Industrial Select Sector SPDR (XLI), Technology Select Sector SPDR (XLK), Consumer Staples Select Sector SPDR (XLP), Utilities Select Sector SPDR (XLU), Health Care Select Sector SPDR (XLV), and Consumer Discretionary Select Sector SPDR (XLY). Although a broader sample of sector ETFs would be available, choosing to include these particular nine sector ETFs offers several advantages. First, the estimated betas for sector ETFs may be affected more by liquidity and trading volume effects (e.g. Carpenter & Upton, 1981; Vijh, 1994) when extending the sample, and including less traded and not well-established sector ETFs in the sample. Second, the nine sector ETFs together have a long record of historical price information and are benchmarks that have been followed by investors, which makes their comparison with one another meaningful. Some more specialized sector ETFs involve more idiosyncratic risk and are marketed as portfolio diversifiers placing them on the back burner in managing portfolio beta. Third, the chosen sector ETFs are traded under the same sponsor and their trading mechanisms are the same, which thus controls for various ETF characteristics. These advantageous features of a small but uniform sample of ETFs are noted in several studies (e.g. Ackert & Tian, 2008; Tse, 2015). Thus, the use of small sample of ETFs is not uncommon. For example, Charupat and Miu (2011) use a sample of eight leveraged ETFs to measure their pricing and performance. The data on the nine different sector ETFs were accessed from the website of State Street Advisors for SPDR ETF at https://www. spdrs.com/index.seam and Yahoo Finance at http://finance.yahoo. com/. The former data source is used for collecting the data on net asset values, shares outstanding, and total net asset value. The latter data source is used for collecting the data on share trading volumes and market prices. The prices and values are given in US dollars and the returns are calculated using dividend adjusted closing prices. The daily net fund flows, FLOW, are calculated using the total net asset value, TNA, as follows: FLOWt =

TNAt − (NAVt /NAVt−1 )TNAt−1 TNAt−1

Table 1 Definitions of variables. Definition Fund variables Flow Asset FlowEvent Return Pricing

H: Sector ETFs with high-beta attract ETF flow

(1)

In addition to the data on the sample ETFs, this study uses the data on several components of the Cleveland Financial Stress Index (CSFI) to assess the impact of financial stress on investor activity

3

Turnover RRel TurnoverRel Abs(RRel) Fund risk variables Return risk Beta RelRisk FlowRisk PricingRisk

Net asset flow Total net asset value (TNA) Flow event dummy variable: one if the outflow of an ETF exceeds 10% of its TNA value ETF return Price-to-NAV difference: price-to-NAV ratio over a value of one Share turnover calculated as the ratio of daily trading volume and end of the day shares outstanding Return of an ETF over the return of the SPDR S&P500 ETF Sector ETF turnover over the SPDR S&P500 ETF turnover The absolute value of RRel 21 days rolling standard deviation of Return 21 days rolling beta using the SPDR S&P500 ETF as the market benchmark 21 days rolling standard deviation of RRel 21 days rolling standard deviation of Flow 21 days rolling standard deviation of Pricing

Financial stress variables BankBondS Contributions of bank bond spread to the CFSI Contributions of commercial paper minus T-Bill spread ComTbill to the CFSI DollarC Contributions of weighted dollar crashes to the CFSI Contributions of the bid and ask spread on 3-Month US LiquidityS Treasuries to the CFSI index Contributions of interbank liquidity spread to the CFSI InterBankS StocksC Contributions of stock market crashes to the CFSI This table presents the definitions of the variables used in this study.

in ETFs. The components are downloaded from the website of the Federal Reserve Bank of St. Louis at http://research.stlouisfed.org/ . The selected components represent financial stress in different markets and are specified in Table 1, where the definitions for the variables used in this study are presented. The sample period of this study is from May 31, 2006 to March 6, 2012 and includes 1453 daily observations. The sample period is representative of different market states, as it includes the Financial Crisis of 2008 and the ensuing recovery period. Although the start of the sample period is chosen due to the availability of data on ETF asset flows, the market for the ETFs prior to the start of the sample period was relatively immature, not being representative of the state of the market after the Financial Crisis. The use of daily data in this study differs from that in Clifford et al. (2014), who use monthly data. The daily data are utilized in this study because it is at the end of the day when ETFs are created and redeemed to meet the daily pricing pressure, thus making daily data particularly interesting for ETFs. The first empirical model of this study, which is used to examine what factors explain subsequent ETF activity, is the following: Activityi,t = Fundi,t−1 + Riski,t−1 + FinStressi,t−1 + FundSectors + εi,t, (2) where the dependent variable Activity is a measure of net fund flow or turnover for fund i; Fund includes the fund variables;4 Risk includes the fund risk variables; FinStress includes the financial stress variables; and FundSectors includes eight dummy variables for different ETFs. The variables of fund characteristics, fund risk, and financial stress are defined in Table 1. Beta is calculated using

4

For total net asset variables, the log of the variable is used.

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4 Table 2 Descriptive statistics. Flow Panel A. Fund variables 0.00 Mean Median 0.00 Maximum 0.63 −0.40 Minimum 0.04 Std. Dev. 1.57 Skewness 29.14 Kurtosis N 13,059

Panel B. Fund risk variables Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis N

Return

Pricing

Asset

Turnover

RRel

TurnoverRel

Abs(RRel)

0.00 0.00 0.16 −0.17 0.02 0.15 14.35 13,059

0.00 0.00 0.05 −0.03 0.00 1.97 54.28 13,059

3170 m$ 2450 m$ 13100 m$ 369 m$ 2060 m$ 1.17 3.82 13,059

0.15 0.10 2.29 0.00 0.16 3.42 24.96 13,059

0.00 0.00 0.13 −0.11 0.01 0.40 18.57 13,059

−0.17 −0.16 1.57 −1.17 0.18 0.13 9.18 13,050

0.01 0.00 0.13 0.00 0.01 4.17 35.03 13,059

ReturnRisk

Beta

RRelRisk

FlowRisk

PricingRisk

0.01 0.01 0.09 0.00 0.01 2.65 12.41 12,879

0.94 0.93 3.38 −0.16 0.37 0.76 5.60 12,879

0.01 0.01 0.05 0.00 0.01 2.99 15.11 12,919

0.03 0.03 0.25 0.00 0.03 2.42 11.50 12,919

0.00 0.00 0.02 0.00 0.00 3.38 17.76 12,919

BankS Panel C. Financial stress variables Mean 0.00 0.00 Median 0.10 Maximum −0.10 Minimum 0.01 Std. Dev. −0.05 Skewness Kurtosis 38.94 11,826 N XLB Panel D. Sector ETF betas 1.16 Mean 1.18 Median 1.91 Maximum Minimum 0.30 0.24 Std. Dev. Skewness −0.66 4.10 Kurtosis 1431 N

ComMTbill

DollarC

LiquidityS

InterBanks

StocksC

0.00 0.00 1.14 −0.76 0.08 3.53 60.74 11,826

0.00 0.00 2.00 −0.46 0.09 9.97 221.13 11,826

0.00 0.00 0.05 −0.04 0.01 1.24 21.95 11,826

0.00 0.00 0.78 −0.50 0.08 1.83 24.07 11,826

0.00 0.00 0.81 −0.40 0.07 3.12 39.16 11,826

XLE

XLF

XLI

XLK

XLP

XLU

XLV

XLY

1.13 1.18 2.43 −0.09 0.30 −1.05 6.16 1431

1.40 1.30 3.38 0.71 0.43 1.45 5.52 1431

1.03 1.06 1.79 0.49 0.18 −0.12 3.86 1431

0.94 0.91 1.73 0.53 0.17 1.61 6.83 1431

0.52 0.53 0.93 0.17 0.12 0.01 3.50 1431

0.59 0.59 1.43 −0.16 0.24 0.01 3.92 1431

0.64 0.65 1.25 0.15 0.15 −0.10 3.89 1431

1.01 1.00 1.73 0.52 0.17 0.55 4.11 1431

This table presents the descriptive statistics of the variables used in this study. See Table 1 for the definitions of the variables. N refers to the number of observations. The sample period is from May 31, 2006 to March 6, 2012.

the covariance of the returns of a sector ETF and the S&P500 index returns. Earlier research on ETFs suggests that the pricing of an ETF can have a non-linear relation with factors affecting the pricing (e.g. Ackert & Tian, 2008). The model also includes a squared value of beta so that a non-linear relation between market risk and investor activity can be analyzed. Considering possible nonlinearity between the market risk and investor activity is motivated by the evidence of Hong and Sraer (2014), showing that the slope of the security market line (SML) is piecewise constant due to investors’ preference for high-beta assets. Therefore, the marginal impact of a higher beta on investor activity in an ETF may not be constant at different levels of beta. The financial stress variables in Eq. (2) control for changes in the common level of financial stress. In addition, the inclusion of financial stress variables in the analysis makes it possible to assess how fund-specific common indicators of market-wide financial stress drive ETF activity. Variables in the model other than Fund are selected using the stepwise selection method with a p-value of 0.1 used as a forward selection criterion. The first model does not account for the possibility that cyclical and defensive sector ETFs may have different determinants of investor activity. Therefore, it would also be important to test the

determinants of high and low-beta investment in separate subsamples, which is also motivated by the evidence for different investor behavior in leveraged ETFs, which have higher market exposure, and regular ETFs. For example, Charupat and Miu (2011) present evidence that leveraged ETFs are traded by retail investors with short holding periods, indicating that sector ETFs which offer more aggressive market exposure may be traded with shorter holding periods. Also, Jiang and Yan (2012) present evidence that investors trade levered and regular ETFs differently. Thus, the analysis of the first empirical model, presented as Eq. (2), is performed separately on a sample of sector ETFs with a beta of more than 1.2, and another sample of sector ETFs with a beta of less than 0.8. The beta used as the selection criterion is a 21-day rolling beta, and is not used as an independent variable in the analyses of the two samples. As the beta is time varying and can change over time, the samples are reformed daily and the classification of defensive and cyclical sector ETFs is not static. An interesting aspect of this division of sector ETFs into the two groups is that while sector ETFs with high beta share the characteristic of high market exposure with leveraged ETPs, sector ETFs with low beta do not have similar counterparts. Table 2 presents the summary statistics of the variables used in this study. Based on the maximum and minimum values of the

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0.63 0.68 0.23 0.25 0.18 0.38 −0.15 0.07 0.09 0.53 0.55 0.23 0.47 0.41 0.12 0.15 −0.17 −0.14 0.14 0.15 0.01 0.01 0.00 0.00 0.00 0.02 0.02 0.52 0.01 −0.03 0.07 −0.01 −0.03 0.01 0.00 −0.05 0.19 0.00 −0.01 −0.02 0.01 0.02 −0.04 −0.01 0.01 0.02 0.03 0.25 0.45 0.41 0.30 0.02 0.44 0.22 0.01 0.01 0.00 0.00 0.02 −0.03 −0.02 0.07 0.00 0.00 −0.01 0.00 0.13 0.00 0.05 0.07 0.09 0.37 0.06 0.12 0.40 0.47 0.59 0.00 −0.03 −0.01 0.01 −0.07 0.01 −0.01 0.05 −0.08 0.00 −0.01 −0.01 −0.02 0.02 0.11 0.01 −0.01 0.00 0.00 0.00 −0.01 0.06 −0.04 0.08 0.17 0.11 0.10 0.01 −0.06 0.00 0.01 0.00 0.00 −0.01 0.00 0.04 0.02 −0.06 0.01 −0.03 −0.02 −0.03 −0.03 0.18 −0.01 0.07 0.02 −0.01 −0.07 0.10 0.00 0.00 −0.02 0.00 0.00 0.16 0.13 0.11 −0.04 0.46 0.04 −0.03 0.01 0.02 0.00 0.01 0.00 0.00 0.02 0.02 −0.03 0.00 −0.02 −0.02 −0.03

InterBankS DollarC ComTbill BankS

This table presents the descriptive statistics of the variables used in this study.

5 This suggests that the smaller dataset selected for this study appears to have the advantage of containing fewer outliers than some other available datasets used in earlier studies such as the data used by Clifford et al. (2014).

Table 3 Correlation statistics.

Table 4 presents the ordinary least squares (OLS) estimates of Eq. (2), with ETF flow and ETF trading as the dependent variables. Considering the hypothesis of this study that sector ETFs that have high beta attract ETF flow, the coefficient value for Beta from the analysis of ETF flow is positive and statistically significant. The result for the relation between beta and ETF flow is interesting because the coefficients for Beta and Beta2 together show that the relation is non-linear and concave. This evidence is in line with the previously documented finding by Baker et al. (2014) that industry betas determine a significant part of the returns to anomaly, as well as corroborating the work of Baker et al. (2014), Frazzani and Pedersen (2014), and Hong and Sraer (2014) that high-beta securities attract new investments, which may explain the low-risk anomaly. The finding of the concave relation means that the marginal impact of a higher beta is weaker at higher levels of the beta. Thus, it supports investors’ leverage constraints (see Blitz et al., 2014;

LiquidityS

4.1. Analysis of the determinants of investor activity in sector ETFs

0.04 0.00 0.05 0.11 0.13 0.02 0.00 −0.01 0.01 −0.01 0.01 −0.05 0.08 −0.11 0.08 0.08 0.03 0.00

StocksC

4. Results

ComTbill DollarC InterBankS LiquidityS StocksC Abs (RRel) Flow Beta Pricing Return RRel Asset Turnover TurnoverRel FlowRisk PricingRisk Return Risk RRelRisk

Abs(RRel)

Flow

Beta

Pricing

Return

RRel

Asset

Turnover

TurnoverRel

FlowRisk

PricingRisk

Return Risk

fund variables presented in Panel A, it can be stated that these variables do not contain any unreasonable outliers necessitating the variables to be winsorized. However, some of the variables had very high and low values occurring during the Financial Crisis of 2008.5 For example, the maximum turnover is 229%, meaning that the shares of a sector ETF were traded twice as many times as the number of its outstanding shares, and the maximum value of Pricing is 0.05, meaning that the maximum premium of a sector ETF was 5%. Except for Beta, which gets theoretically reasonless values of less than 0 and more than 2, the variables in Panel B get reasonable values. The financial stress variables in Panel C have fewer observations than the other variables in Panels A and B, since there are missing observations for some dates. Lastly, the summary statistics in Panel D show the distributions of beta for the sector ETFs. Although some sector ETFs can be perceived as more cyclical or defensive, as the variation of the mean values in the panel also suggests, the statistics in Panel D show that all of the sector ETFs show a marked variation. For example, each ETF obtains a minimum value for a beta of less than one, 0.93. The only ETF which does not obtain a maximum value of more than one is XLU for Consumer Staples Select Sector, but seven out of the nine sector ETFs obtain maximum values of more than 1.7. The variables presented in Table 2 illustrate the activity in general of investors in ETFs. The average share turnover is 15% and the 21-day rolling standard deviation of ETF flows is 3%. Both these values indicate that investors show modest activity in sector ETF products. Table 3 presents the correlation statistics for the variables. For ETF trading, these statistics show that Turnover as well as TurnoverRel have a high correlation with FlowRisk. This result implies that heavily traded ETFs have high volatility of their net fund flows. Interestingly, the relation between Flow and Turnover is only −0.03, which means that the statistical dependence between ETF flow and ETF trading is weak. Moreover, the correlation between PricingRisk and LiquidityS has a moderate correlation, 0.17, showing that the volatility of ETF pricing error is related to market liquidity. For investors and financial regulators, this finding suggests that mispricing of ETFs is more likely to occur in times of low market liquidity.

5

0.80

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6 Table 4 Determinants of investors activity in ETFs. ETF flow

C Flow Return Pricing Turnover log (Asset) TurnoverRel RRel Abs (RRel) Fund risk (stepwise) Beta Beta2 ReturnRisk RRelRisk FlowRisk

ETF trading (Turnover)

Coef.

t-Stat.

Coef.

t-Stat.

0.097*** −0.021** 0.384*** −0.299** 0.048*** −0.005** 0.015*** 0.210** −0.069

4.76 −2.31 14.98 −2.05 14.06 −5.31 5.35 4.54 −1.08

0.003 −0.048*** −0.643*** 0.583** 0.712*** 0.001 −0.005 −0.134 0.340***

0.06 −2.69 −12.87 2.05 97.42 0.74 −0.97 −1.49 2.87

0.009* −0.005**

1.89 −2.48

−0.026*** 0.012*** 0.692***

−3.18 3.86 7.23

−0.216**

−2.29 0.622***

16.86

0.024**

2.22

−0.049*** −0.020** Yes 0.77 11,646

−5.73 −2.34

Financial stress (stepwise) −0.016*** StocksC 0.133** LiquidityS DollarC InterBanks Yes Sector dummies 0.07 Adj. R2 11,646 N

−2.96 1.97

This table presents the parameter estimates of the ordinary least squares (OLS) analysis of investor activity in sector ETFs. The t-statistics are presented in italics. The analysis model is the following: Activityi,t = Fundi,t−1 + Riski,t−1 + FinStressi,t−1 + FundSectors + εi,t, where the dependent variable Activity is a measure of the ETF flow or ETF trading for fund i; Fund includes the fund variables; Risk includes the variables of fund risk; FinStress includes the market stress variables; and FundSectors includes eight dummy variables for different ETFs. The standard errors are White (1980) heteroskedasticity robust. Variables other than the fund variables are selected using the stepwise selection method. Thus, the number of always included variables is 17, and the number of variables for the stepwise selection method is 12. For the stepwise selection of the variables, a p-value of 0.1 is used as a forward selection criterion. See Table 1 for more specific definitions of the variables. * Statistical significance at the 10% level. ** Statistical significance at the 5% level. *** Statistical significance at the 1% level.

Frazzani & Pedersen, 2014) rather than speculative reasons (see Hong & Sraer, 2014) as the reason for the low-risk anomaly. The intuition is that the marginal impact is weak where the speculators should be most active. The explanation for this finding could be the supply of leveraged investment products, which decreases the value of sector ETFs with high beta as a distinct investment opportunity. Therefore, finding support for the hypothesis at lower levels of beta in particular is consistent with the availability of leveraged ETPs as alternative high-beta investments. However, this possibility is not persuasive in light of the study by Box et al. (2016), whose results do not show strong evidence for the impact of ETF leverage on ETF flow. Thus, another explanation for finding the concave relation between beta and ETF flow is that it is more important for investors to avoid low beta than to achieve high beta, which results in a weaker impact of beta at its higher levels. This feature may be a result of the benchmarking pressure of investment managers considered in Baker et al. (2011), and in Christoffersson and Simutin (2014). Their benchmarking pressure behavior could be asymmetric in nature in line with the adaptive markets hypothesis (AMH) by Lo (2004), which stresses the importance of survival. Investors may be relatively more concerned with avoiding underperforming the market return than outperforming it, which increases investors’ focus on low-beta investments. The relation between beta and ETF trading, in turn, is negative and convex, implying that extremely defensive ETFs tend to

be traded more frequently, but the marginal impact is relatively weaker at higher levels of beta. Considering the evidence of Nadler and Schmidt (2015) for the impact of macroeconomic information on ETF trading volume, the negative impact of beta on ETF trading adds to the evidence that ETF characteristics are also important. The result for beta and ETF trading is evidence in contrast to what can be expected according to Hong and Sraer (2014), that overpricing should lead to a higher share turnover of high-beta stocks as arbitrageurs would short them. Hameed and Ting (2000) provide one possible explanation for this finding. They find that the returns to a contrarian portfolio strategy increase with the level of trading activity which implies that high share turnover could be associated with overreaction. Thus, if low-beta stocks are strongly undervalued, they could be more actively traded than high-beta stocks. It is also noteworthy that ETFs diversify private information away (see Chelley-Steeley & Park, 2010), and that investors can use them for tactical adjustments. Thus, investors may use ETFs for different purposes than stocks which may explain this finding. The convex beta – ETF trading relation is interesting as it complements the finding of the concave beta – ETF flow relation in that low-beta sector ETFs are more important and traded more than high-beta sector ETFs. Considering also other determinants of ETF flow than beta, it can be seen from the results in Table 4 that ETF flow has a positive association with Flow, Return and Turnover, as can be expected based on the previous literature (e.g. Clifford et al., 2014; Box et al., 2016). In addition, ETF flow has a negative and statistically significant association with Pricing and Asset. However, as a more novel finding, the results also suggest that ETFs with high benchmarkadjusted share turnover attract investor activity with respect to ETF flow. The coefficient for TurnoverRel is positive and statistically significant at the 1% level suggesting that ETF flow increases after a higher benchmark-adjusted share turnover. The coefficient value, 0.015, for the variable implies that a 10-percentage-point higher share turnover relative to the SPDR S&P500 ETF increases ETF flow by 0.15%. As TurnoverRel could be considered a measure of abnormal trading activity, this finding is consistent with the evidence reported by Barber and Odean (2008) for attention-driven buying activity by investors. The results in Table 4 suggest that ETFs with high benchmarkadjusted return attract investor activity because, on average, the statistically significant coefficient for RRel from the analysis of ETF flow suggests that a 1% return results in a 0.210% fund inflow. Thus, the results in Table 4 reveal additional evidence in support of Clifford et al. (2014) in that relative performance matters for return chasing investors. This finding is in line with the evidence presented by Jame and Tong (2014) that industry-wide categorizations matter in investment decision making. It also adds to the evidence presented by Barber et al. (2014) by revealing that, although investors respond to category returns, they do not mistake them for managerial skill since sector ETFs are passively managed products. Barber et al. (2014) present evidence suggesting that investors may confuse strong category performance with manager fund skill. The results in Table 4 are convincing evidence that investors invest more money in ‘hot’ sector ETFs with high benchmarkadjusted share turnover and performance. These findings are critical with respect to the commonly held belief that investors exhibit return chasing behavior because they are seeking skilled fund managers. While Baily, Kumar, and Ng (2011) have already presented evidence that the return chasing behavior of mutual fund investors is related to behavioral biases, the present study corroborates their evidence by showing that investors chase passive ETFs without any possibility of their having stock picking skill. Regarding the role of the financial stress and fund risk variables as determinants of ETF flow, RRelRisk is the only fund risk variable

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in addition to Beta that has a statistically significant impact on ETF flow. The impact is negative suggesting that ETF flow decreases with the volatility of fund relative returns. The coefficient for StocksC is statistically significant and negative, and the coefficient for LiquidityS is statistically significant and positive, suggesting that the ETF flow of sector ETFs decreases after stock market crashes, but increases with market illiquidity. This finding for the relation between ETF flow and financial stress is puzzling because it may not be convincing to expect investors to pour money into sector ETFs at times of high market illiquidity, but it may be possible that the variable is weakly related to stock market liquidity specifically. In support of this possibility, the correlations between LiquidityS and other risk-related variables in Table 2 are relatively low, for example, the correlation between LiquidityS and Return Risk is 0.11. The results for ETF trading in Table 4, however, are not in line with the results for ETF flow. To begin with, the two coefficient values for Returns also highlight a notable contrast between the two ETF activities; high returns attract more ETF flow, meaning that investors do increase their net long overnight positions, while poor returns work as a catalyst for ETF trading activity. Thus, the results in Table 4 reveal that the behavior of ETF flow and ETF trading is markedly different with respect to prior returns. It is possible that the documented positive volume – volatility relation (e.g. Chen & Daigler, 2008; Jones, Kaul, & Lipson, 1994) and the asymmetric volatility phenomenon (e.g. Dennis, Mayhew, & Stivers, 2006) give rise to this different behavior of ETF flow and ETF trading; volatility increases after poor returns with higher trading volume, and investors redeem shares as a result of higher risk. In addition, the coefficient value for RRel is negative and not statistically significant in the analysis of ETF trading as it was statistically significant for ETF flow. The coefficient for Pricing is also positive for ETF trading which it was not for ETF flow. Thus, the results yield different findings in relation to the previously documented positive relation between prior stock return and trading activity (see Gallant et al., 1992; Karpoff, 1987; Schwert, 1989). With regard to the other significant results in Table 4 concerning ETF trading, the results for Abs(RRel) imply that sector ETFs that have returns diverging from the market portfolio attract more trading activity. This characteristic of ETF trading is also clearly different in comparison to the characteristics of ETF flow for which the relation is insignificant. The positive statistically significant coefficient for ReturnRisk in the analysis of ETF trading is also notable, as it differs from the negative coefficient for Beta. This contradiction between total and systematic risk as drivers of ETF trading is interesting, and implies that volatility is the more important driver of ETF trading. Overall, it is evident that the two types of investor activity examined in ETFs, ETF flow and ETF trading, are partly driven by different ETF characteristics. For the relation between ETF trading and financial stress variables, it can be seen in Table 4 that the components of the Cleveland Financial Stress Index (CSFI), which are particularly important in explaining ETF trading, are the components for the stock market, the foreign exchange market, and the interbank market. The first appears to be a catalyst for trading, while the other two are dampeners of trading. The result for the interbank market stress variable, InterBanks, is interesting as the variable work as an indicator funding liquidity. More specifically, the negative and statistically significant coefficient for the variable is striking because weaker funding liquidity could force arbitrageurs to reduce trading activity, as the result suggests. Consistent with the theory of mutually reinforcing market and funding liquidity by Brunnermeier and Pedersen (2008), this finding suggests that funding liquidity has an impact on market liquidity in ETFs. Regarding the aspect of financial stability, the finding also suggests that ETF liquidity is sensitive to the funding liquidity of the financial intermediaries providing it.

7

Table 5 Analysis of cyclical and defensive sector ETFs. Variable

Cyclical sector ETFs

Defensive sector ETFs

Coef.

t-Stat.

Coef.

t-Stat.

4.11 2.01 6.64 0.35 6.86 −4.15 3.61 −0.06 −1.80

0.040** 0.002 0.135*** −0.201 −0.008 −0.002** 0.002 0.150*** 0.097*

2.20 0.13 4.73 −1.53 −1.44 −2.22 1.01 3.84 1.75

0.076* No 0.01 4609

2.77

Panel B. Analysis of ETF trading (Turnover) −0.391*** −5.06 C Flow −0.078* −1.85 *** −1.046 −5.11 Return Pricing 0.729 0.82 0.705*** 38.31 Turnover 0.018*** 5.22 log (Asset) 0.001 0.07 TurnoverRel −0.076 −0.20 RRel Abs (RRel) 1.013*** 3.46

−0.058 −0.071** −0.190*** −0.029 0.761*** 0.003 0.018*** 0.130* −0.168

−1.52 −2.45 −3.31 −0.11 68.95 1.41 3.57 1.67 −1.35

Fund risk (stepwise) ReturnRisk RRelRisk FlowRisk PricingRisk

0.538*** 1.655*** 0.646*** −1.902***

2.71 6.30 12.01 −3.20

0.035*** −0.039***

3.27 −5.02

Panel A. Analysis of ETF flow 0.133*** C 0.037** Flow Return 0.553*** Pricing 0.128 Turnover 0.053*** −0.006*** log (Asset) 0.032*** TurnoverRel −0.009 RRel Abs (RRel) −0.214* Fund risk (stepwise) RRelRisk FlowRisk Sector dummies Adj. R2 N

−0.374** −0.066* No 0.12 2698

1.492*** 0.893***

Financial stress (stepwise) StocksC −0.104*** DollarC InterBanks −0.054* No Sector dummies 0.72 Adj. R2 N 2430

−2.44 −1.77

4.00 10.03

−3.19 −1.85

No 0.78 4145

This table presents the parameter estimates of the ordinary least squares (OLS) analysis of investor activity in cyclical and defensive sector ETFs. A sector ETF is defined as a cyclical (defensive) ETF; its lagged beta is more (less) than 1.2 (0.8). The t-statistics are presented in italics. The analysis model is the following: Activityi,t = Fundi,t−1 + Riski,t−1 + FinStressi,t−1 + εi,t, where the dependent variable Activity is a measure of the ETF flow and i; Fund includes the fund variables for fund i; Risk includes the variables of fund risk, excluding fund beta, for fund i and MarketStress includes the market stress variables for fund i. The standard errors are White (1980) heteroskedasticity robust. Variables other than the fund variables are selected using the stepwise selection method. Thus the number of always included variables is nine and the number of variables for the stepwise selection method is ten. For the stepwise selection of the variables, a p-value of 0.1/0.1 is used as a forward selection criterion. See Table 1 for more specific definitions of the variables. * Statistical significance at the 10% level. ** Statistical significance at the 5% level. *** Statistical significance at the 1% level.

4.2. Further analysis The results in Table 5 are designated for comparing the determinants of investor activity in cyclical and defensive sector ETFs.6 This comparison relates to Jiang and Yan (2012), who report evidence that investors trade differently regular and leveraged ETFs, which

6 The sector dummies are not used as control variables since they would have been biased toward high-beta or low-beta sectors.

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have intrinsically different betas. Based on the adjusted R-squares, the most striking result in Table 5 is that the ETF trading of defensive sector ETFs can be better predicted than that of cyclical sector ETFs, whereas ETF flows are less predictable in defensive sector ETFs. In relation to Hong and Sraer (2014), this predictability could originate from the tendency of speculative investors to invest more in cyclical ETFs. In addition, the result in Table 5 that the ETF flow of defensive sector ETFs is affected neither by prior share turnover nor by prior benchmark-adjusted share turnover supports this view. In Table 5, the results for Turnover and Return are, overall, similar to the results in Table 4, but Table 5 also shows at least two results that are exclusive and notable with regard to cyclical and defensive sector ETFs. First, the coefficient for Flow is positive and statistically significant in the analysis of cyclical sector ETFs. This result implies that ETF flow is persistent in cyclical sector ETFs. Second, Turnover and TurnoverRel do not have a statistically significant impact on ETF flow for defensive ETFs, indicating that their investment flows are not attention driven, and investors in defensive sector ETFs do not seem to chase ‘hot’ ETFs as they appear to do otherwise. These results imply that investor activity in cyclical and defensive sector ETFs are driven by different fund characteristics, which is a novel finding in relation to the extant literature (e.g. Clifford et al., 2014). The results for the fund risk variables show that the ETF flow of cyclical sector ETFs decreases with RRelRisk and FlowRisk, while the ETF trading of these ETFs increases with them. For defensive sector ETFs, ETF trading increases with FlowRisk, while ETF flow increases with ReturnRisk, RRelRisk, and FlowRisk. These results for investor activity in cyclical and defensive sector ETFs show that ETF flow in cyclical sector ETFs is the only analysis in which ETF activity decreases with fund risk variables. This finding implies that defensive sector ETFs can be considered as a defensive investment prone to investors fleeing from the investment when their risks increase. One more interesting characteristic of defensive sector ETFs is that stock market crashes increase investors’ trading in them, as implied by the positive and statistically significant coefficient for StocksC. Overall, these results continue showing evidence that investor behavior is different in cyclical and defensive stocks. As several studies such as Ben-David et al. (2014), Da and Shive (2013), and Staer (2014) have raised concerns over the consequences of ETFs for financial stability, we test further whether an ETF beta is a significant factor of large ETF outflows using an ETF outflow event as the dependent variable in the following regression specification: Outflowi,t = Fundi,t−1 + Riski,t−1 + FinStressi,t−1 + FundSectors + εi,t,

high ETF-related risk. The finding implies that the determinants of infrequent investment decisions such as reallocation decisions to investment funds differ from those of trading activity. This study also adds to the evidence presented by Barber et al. (2014) by revealing that, although investors respond to category returns, they may not respond to category returns confused with chasing managerial skills. However, relating this behavior to behavioral biases only is not unproblematic, since return chasing behavior in investing in passively managed sector ETFs can also be a rational active strategy for investors given the evidence of the profitability of industry momentum strategies (e.g. Moskowitz & Grinblatt, 1999). This study further shows that beta has a vital role in explaining investor activity in ETFs, and the impact of beta appears to be differential for ETF flow and ETF trading. The relation between beta and ETF flow (ETF trading) is increasing (decreasing) and inverse Ushaped (U-shaped). This finding is in line with previous evidence that low-beta assets offer higher risk-adjusted returns because of low investor demand due to leverage and funding constraints (e.g. Frazzani & Pedersen, 2014). As these results suggest that low-beta ETFs may play such a key role in explaining investor behavior, this study shows a notable feature of investor behavior with respect to the documented low-risk anomaly (Baker et al., 2011; Baker et al., 2014; Frazzani & Pedersen, 2014; Hong & Sraer, 2014). It is also interesting that the increasing and inverse U-shaped relation between beta and ETF flow in sector ETFs is consistent with the existence of leveraged ETFs, which can potentially outplace investors’ need to invest in traditional high-beta investments. For investment product developers, this finding is interesting as it relates to the unfolding of the popularity of leveraged ETPs. Alternatively, the increasing and inverse U-shaped relation may be observable because it is more important for investors to avoid low beta than to achieve high beta, which results in a weaker impact of beta at its higher levels. This finding is intriguing, as it suggests that the benchmarking pressure, which is one potential explanation for the low-risk anomaly (see Baker et al., 2011; Christoffersson & Simutin, 2014) has an asymmetric impact on trading of low-beta and high-beta investments. For future studies, this evidence suggests that it would be interesting to devote more attention to the role of the importance of investor survival, as the AMH proclaims, together with benchmarking pressure in explaining the low-risk anomaly.

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where the dependent variable Outflow is a dummy variable of whether a fund i has a daily outflow of more than 10% of its net asset value, otherwise the model is similar to Eq. (2). Regarding ETF risk, the untabulated results in Eq. (3) suggest that sector ETFs that experience large outflow already have high pricing error volatility, high fund flow volatility, and low return volatility, but beta appears to be relatively insignificant variables of large ETF flows. Thus, although beta is an important determinant of ETF flow, it does not seem to contribute to large ETF outflows, which seem to be more related to ETF specific risk. 5. Conclusion By using daily data on SPDR sector ETFs and considering both ETF flow and ETF trading, this study extends the evidence on the determinants of investor activity in ETFs. The multivariate analysis suggests that the two activities are also partly driven by different characteristics: ETF flow is driven by strong relative performance and gathered investor attention, whereas ETF trading is driven by

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Please cite this article in press as: Peltomäki, J. Beta as a determinant of investor activity in sector exchange-traded funds. The Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.06.006