Does it help firms to secretly pay for stock promoters?

Journal of Financial Stability 26 (2016) 45–61

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Journal of Financial Stability journal homepage: www.elsevier.com/locate/jfstabil

Does it help firms to secretly pay for stock promoters? Nadia Massoud a , Saif Ullah b,∗ , Barry Scholnick c a

Melbourne Business School, The University of Melbourne, 200 Leicester Street, Carlton, VIC 3053, Australia John Molson School of Business, Concordia University, 1450 Guy St., Montreal H3H 0A1, Canada c School of Business, University of Alberta, Edmonton T6G 2R6, Canada b

a r t i c l e

i n f o

Article history: Received 19 November 2015 Received in revised form 4 August 2016 Accepted 8 August 2016 Available online 20 August 2016 Keywords: Promoters Paid analysis Pump and dump

a b s t r a c t We examine deals between listed firms and promoters who have been secretly hired to increase their stock prices. This behavior by the secret promoter is illegal (and leads to prosecution) but the actions of the hiring firm are legal. We use data from these prosecutions to analyze the behavior and motivations of the hiring firms. We find that secret promotion leads to an initial increase in the price and trading volume of the firms on the date that the secret promotion started. Subsequently, however, we find that this increase in price is reversed when regulators (e.g. SEC or NASD) take action against these promoters for not disclosing their relationships with the hiring firms. We find that the main motives behind these relationships are to maximize the private benefits of the firm’s managers and owners through pumping the share prices and subsequently dumping their shareholdings. © 2016 Elsevier B.V. All rights reserved.

1. Introduction This paper examines secret deals between publically listed firms and promoters, hired by these firms. We examine cases where the promoter offers his/her expertise to raise the firm’s share price in exchange for some form of payment (e.g. a fee or a percentage of an increase in the share price). For example, on August 12, 2002, the Securities and Exchange Commission (SEC) filed a complaint against Mark Schultz for promoting at least twelve share issues between 1995 and 1998. (These included Acacia Research Corp., American Entertainment Group and American Nortel Communications among others.)1 The SEC alleged that Mr. Schultz had made inflated financial projections and predicted share price increases of 100% or more. According to the complaint, he portrayed his analysis as independent whereas in reality he was being paid by the firms for his projections. In another case (August 12, 2002) the SEC charged the publishers of an Internet newsletter, called the Future SuperStock (FSS), for promoting stocks without disclosing the financial compensa-

∗ Corresponding author. E-mail addresses: [email protected] (N. Massoud), [email protected] (S. Ullah), [email protected] (B. Scholnick). 1 These included Acacia Research Corp., American Entertainment Group, American Nortel Communications, AWG, Ltd., Eutro Group Holdings, Inc., EVRO Corp., Imagica Entertainment, Inc., Imaging Diagnostic Systems, Inc., N.U. Pizza Holding Corp., Tessa Complete Health Care, Inc., Wasatch International Corp., and WestAmerica Corp). http://dx.doi.org/10.1016/j.jfs.2016.08.002 1572-3089/© 2016 Elsevier B.V. All rights reserved.

tion that they had received from the firms they had promoted. The complaint alleged that FSS projected that their recommended stocks would double or triple in price during next three to twelve months. During the period of 1995–2006, 40 such complaints were lodged by the SEC and the National Association of Securities Dealers (NASD) for failure to disclose these relationships and the compensation they received.2 The legal response from the SEC to these events is that the promoters were charged under Section 17 (B) of the Securities Act of 1934 which states that it is unlawful for any person: “to publish. . or circulate any notice, circular, advertisement. . or communication which, though not purporting to offer a security for sale, describes such security for a consideration received or to be received, directly or indirectly, from an issuer. . without fully disclosing the receipt, whether past or prospective, of such consideration and the amount thereof.” A key implication of this Act, and one of the central motivations for this paper, is that the hiring firm is not legally liable for hiring such promoters. Only the promoter is legally responsible for divulging the relationship. Indeed, in none of the cases we examine below did the authorities act against the hiring firm, but in all of these cases the authorities acted against the secretly hired promoters.

2 However, promoters have continued to engage in this illegal activity beyond 2006. We have found several new cases of this illegal activity in litigation release section of Securities and Exchange Commission.

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An important motivation for our study are recommendations from the “Final Report of the Advisory Committee on Smaller Public Companies to the United States Securities and Exchange Commission” (April 23, 2006).3 This report argues that smaller companies are often covered by very few or even no independent analysts. This results in significant lack of information about these firms, which can lead to lower market capitalization and higher financing costs for these smaller firms. Because of this, the report recommended the continuation of the SEC’s regulations that allow smaller firms to hire analysts to cover them. Critically, however, these regulations require full disclosure about the nature of the relationship. Kirk (2011) and Billings et al. (2014) look at the value of this ‘paidfor research’ to the investors and the firm. Our study is the first to examine the case where the hired firm fails to disclose its relationship with the hiring firm. Thus, while Billings et al. (2014) comment that some analyst firms have been investigated by the Securities and Exchange Commission for not disclosing their relationship with hiring firm, our study provides actual empirical evidence on this issue. Specifically, we provide evidence on the type of firms that might hire analysts secretly, and also the motivations behind this surreptitious hiring. In this paper we use the event study methodology to examine events in which firms hire promoters to increase investor interest in securities without disclosing their association with these firms. We define this event as occurring at the date when the secretly hired promoter begins public promotion of a stock, as defined in the SEC complaints. In all cases in our study, however, the SEC and NASD subsequently took legal action against the promoters for their failure to disclose these relationships. These events thus enable us to examine situations where the market is: (1) initially unaware and then (2) subsequently becomes aware of the secret relationship between a firm and a paid promoter. Specifically, in this paper we examine the following questions: (i) how does the market initially react to recommendations by promoters? (ii) how does the market subsequently react to SEC charges with respect to these promoters? (iii) how do (and if so, which) managers of the firm benefit from such deals? (iv) how much does the promoting company benefit from the deal? (v) whether managers succeed in achieving specific corporate objectives, for example acquiring a firm or raising capital? and (vi) what type of firm is more likely to consider such deals with a promoter? This paper complements the existing literature, which examines why public firms may choose to misreport, and also to invest in creating opportunities for misreporting. For example, Beneish and Vargus (2002) show the possibility of insider trading increasing after accrual mispricing. Bebchuk and Bar-Gill (2002) examine events of misreporting in order to improve the terms upon which the company would be able to issue equity to finance new projects or stock acquisitions. Efendi et al. (2007) find that the likelihood of a misstated financial statement increases when the CEO has significant holdings of in-the-money stock options. We find that when the secretly hired promoter begins public promotion (as of the date defined by the SEC complaints) the price of the firm’s stock increases for a short period of time. In other words, secretly hiring a promoter can have a positive shortrun impact on a firm’s share price. The mean Cumulative Average Abnormal Returns (CAAR) is +11.94% for a 5 day period, including the starting date of the promotion and the four days that follow. However, we also find that when a regulator subsequently announces that a financial relationship does (or did) exist between the firm and the promoter, the returns for the firm become signif-

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http://www.sec.gov/info/smallbus/acspc/acspc-finalreport.pdf

icantly negative with a mean CAAR for the same five day window of −32.94%. A key contribution of our paper is to test various hypotheses as to why we observe such results. The current theoretical literature analyzes the benefits from hiring a promoter to insider and outsider investors. In general, there are two theories. On the one hand, it has been argued that inside and outside investors benefit equally from the promotion since promoters can boost the share price when key assumptions for strict market efficiency fail to hold. For example, Verrecchia’s (1983, [Verrecchia, 1990]1990) main focus is on how the disclosure could correct firm undervaluation by costly selective disclosure. The main argument of Merton (1987) and Trueman (1996) is that the source of any undervaluation is mainly driven by investors’ unfamiliarity with the firm. Similarly, Diamond and Verrecchia (1991) argue that disclosure helps reduce information asymmetry. Danielsson et al. (2005) contend that different fraudulent activities can take place in hedge funds. These activities include misappropriation of assets, insider trading and lack of disclosure. We document that lack of disclosure can lead to pump and dump behavior and mispricing in publicly traded firms. On the other hand, Hong and Huang (2005) analyze the private motivation of managers to increase a firm’s market liquidity and its stock price arguing that managers of small and newer firms commonly hold large blocks of shares in their own firm, which they might wish to diversify by cashing out. In so doing they use promoter firms to increase the price and trading volume of their shares. The cost of the promoter is thus paid by all investors, while the benefits mostly accrue to managers rather than to dispersed small investors. In light of these competing theories and to investigate further the questions posited above, we develop two main hypotheses: (i) the “pump and dump” hypothesis and (ii) “shareholders interest” hypothesis. The pump and dump hypothesis predicts that firms hire promoters to increase the firm’s share price and its market liquidity prior to insiders engaging in selling transactions. Thus, the pump and dump hypothesis is consistent with Hong and Huang’s (2005) theory of the divergence in the interests of inside and outside investors when hiring a promoter. On the other hand, the shareholders interest hypothesis predicts that firms hire promoters to benefit from reduced information asymmetry and increased visibility so as to help them achieve corporate objectives, such as raising capital and/or undertaking acquisitions. The shareholders interest hypothesis is therefore consistent with theories that support the view that hiring a promoter increases the firm’s visibility and removes information asymmetries which potentially benefits both inside and outside investors (see for example Verrecchia (1983, 1990), Merton (1987) and Trueman (1996)). To test these hypotheses we utilize both univariate and multivariate analysis. We conduct univariate tests to compare the change in relevant variables before and after the hiring event for our sample of SEC firms. Results from our univariate tests indicate that, following the secret hiring of a promoter, in addition to an initial positive price reaction we also find that insider holdings decrease significantly, insider selling of their shares increases significantly, and the firm’s stock market liquidity increases. These findings offer support for our pump and dump hypothesis since they indicate that insiders dumped their shares to benefit from an initial increase in the firm’s share price and market liquidity. However, our univariate results also show that the external investor visibility of the firm improves after a secret hiring, as reflected in the increase in institutional holdings and block holdings during the promotion period. We also find that a large percentage of these firms managed to achieve certain corporate objectives around the promotion period, e.g. raise capital or acquire a specific target. Thus, our univariate evidence supports both our main hypotheses.

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

Our multivariate tests are based on comparisons between treatment and control groups. Our treatment group consists of firms who have secretly hired promoters (called the “secret promoter” group). We compare this group with our main control group, which are matched firms who hired promotes, but did so publically. We call this control group the “public promoter” group. By examining the differences between the secret promoter and the public promoter groups, we can specifically examine the impact of promotion that is secret, rather than public. (As additional robustness checks, we also compare the secret promoter group with other groups of firms who do not hire promoters (either secret or public)). Results from comparing our treatment and control group also provide support for the pump and dump hypothesis. For example, when comparing the secret promoter group with the public promoter group our logit tests show that it is more likely for secret promoter firms to dump their shares to benefit from the price increase. However, we find that there are no significant differences between secret promoters and public promoters with respect to achieving specific corporate objectives. These tests thus do not provide support for the shareholders interest hypothesis. Overall, our results find support for the ‘pump and dump’ hypothesis, which is one of the main contributions of our paper. Our paper is related to, and builds on, recent work by Bushee and Miller (2012) and Solomon (2012). The goal of Bushee and Miller (2012) is to establish an understanding of the actions taken in promotion strategies, and their consequences for a firm’s visibility. In particular, they investigate whether promotion strategies are successful in impacting a firm’s following by institutional investors, analysts, and the media, as well as the stock price reaction. While they conduct interviews and surveys with investment relations professionals, they only examine promoters who disclosed their relationship with the issuer’s company. The key contribution of our paper is to examine both secret as well as publically disclosed promoters. Solomon (2012) looks at the effect of “spin” of news items of different companies, conducted by the investor relations firms hired by these companies. He finds that this ‘spin’ increases returns for the positive non-earnings announcements made by the investor relations firms. Our sample firms also experience an increase in price when the positive news is released by the promoter. However the important difference between Solomon (2012) and our paper is that promoters hired in our case do not disclose their affiliations to the hiring companies. Our paper is also related to the literature on trust as an important determinant of financial transactions. Jin et al. (2016) contend that a trust related reduction in information asymmetry can help investors. In this paper we investigate cases where investors initially trusted the unbiased nature of analysis. However, they later learn that the analysis provided to them was not unbiased. Our paper is also related to the literature that examines analysts engaging in an “earnings-guidance game”, and insiders’ selling their shares. Richardson et al. (2004) investigate cases where analysts first issue optimistic earnings forecasts and then “walk down” their estimates to a level that firms can beat at the official earnings announcements. They show that the “walk down” to beatable targets is associated with managerial incentives to sell stock after the earnings announcements. In our case, we investigate incidents in which firms secretly hire promoters and subsequently sell their shares to benefit from the increase in share prices. In both cases the analysts are secretly hired to manipulate prices. Our paper is also related to the existing literature which examines the relationship between diffusion of information about individual firms and its effect on the functioning of the financial system. More specifically Borio and Tsatsaronis (2004) state that, “First, information about any firm-be this a financial or a non-financial firm-should concern three characteristics, namely estimates of its current financial condition and profitability; esti-

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mates of its risk profile; and a measure of uncertainty surrounding those estimates” (112–113). They recognize that the third characteristic has been generally neglected by researchers. Our paper examines events that arguably enhance the uncertainty about estimates of first two characteristics. Peng et al. (2016) investigate the relationship between uncertainty about earnings and stock performance, and find that voluntary disclosure by firms can help reduce this uncertainty. The paper is organized as follows. Section 2 provides a description of our sample and the different sources of our data. Section 3 analyzes the market’s reaction to the event of secretly hiring a promoter and the subsequent prosecution of the promoter by the regulators. Section 4 discusses related theories and develops our testable hypotheses. Section 5 explains our methodologies and provides results of the tests of our hypotheses. Finally, section 6 presents our conclusion. 2. Data We collect information about promoters charged by the Securities and Exchange Commission (SEC) from the websites of the SEC (www.sec.gov) and the NASD. We include Litigation Releases from 1995 to 2006. In addition, we use FACTIVA to collect detailed information about these events. From the SEC complaints, we collect the date on which promoters started promoting these stocks as well as the date on which the SEC or NASD filed a complaint against a promoter.4 In total we are able to find data for 135 public firms. We classify these firms based on the Fama French 48 industries, and find that our sample firms are widely spread across 34 different industries. We only consider public firms that are registered with the SEC. Of the 135 firms, 105 have financial accounting data, 128 have market data and 97 that have market and financial data available. All these events occur during the period from1995 to 2002. We define promotion (or the event date) as the date that the SEC identifies as the day the promoters started promoting a stock. In the few cases where the SEC did not specify an exact date, we collect that information from Factiva. We search for the exact dates of the events using popular search engines like Google, when the information about the start of the promotion is not available in Factiva. In these cases, we take the earliest date on which the promoter began to promote the firm as the event date. The mean market value of equity for the 105 firms with financial data is $72.83 Million. The largest firm had a market value of $ 2.67 billion. In terms of the exchanges on which the firms were listed, of the 135 public firms in our study, one was on the New York Stock Exchange, 29 on Nasdaq, six on Amex, 41 on OTCBB, and 58 on the Pink Sheets. Some of these promoters had their own websites and charged subscribers for their ‘independent’ services. Some had their own TV programs on which they promoted these companies without

4 All events that deal with illegal activity suffer from selection bias, since we can only observe those events that are investigated by legal authorities. There is a voluminous literature which has looked at observed instances of financial misconduct and has provided academics, practitioners and regulators with important results (See Beasley (1996) and Erickson et al. (2006)). However, we can point out that this selection bias is less of an issue in our case. The SEC uses abnormal trading to detect illegal behavior. However, the SEC also relies on investors who can report suspicious behavior to its enforcement division. Because of the fact that internet related fraud is likely to affect a large number of investors, it is highly likely that the affected investors will report their losses to the SEC. The SEC also has a reward system for whistleblowers, which we believe might also lead to reporting of these events. Another potential concern could be that the probability of prosecution is higher for managers who sell their holdings. However, we focus on incidents where the investor relations firms are charged and not the managers of the firms hiring them secretly.

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disclosing their relationship with the firm. In other cases, spam or discussion forums were used to promote the companies. We provide information about the method of promotion used and length of promotion in Appendix A. The median length of coverage by promoters is less than a month. We use Compustat and form 10 K filings to collect annual accounting data.5 We hand collect a variety of other relevant information from firms’ online filings with the Securities and Exchange Commission. These include proxy statements (form DEF14) to collect insider ownership data and outside block holders (ownership larger than 5%), form 8 K for press releases to collect information about major events (e.g. acquisition, bankruptcy protection, firing auditor, resignation of a manager, etc.). We also use form S-8, which is filed when firms register securities (e.g. stock) that are to be offered to the firm’s employees. During the time period covered in this paper, firms defined as ‘small business’ (SB) firms could provide scaled 10-K reports to the SEC.6 While every company registered with SEC, regardless of size, has to provide financial reports, there is a scaled version of regulation S-K for SB firms. During our sample period, a firm with less than $25 million dollars in float was considered a small business (SB) firm. Different exchanges, however, can have their own disclosure requirements. To control for this, we divide our data into pink sheet and non-pink sheet firms. As we show below, pink sheet firms do not drive our results. 3. Market reaction In this section we investigate the market reaction to both the event of the secretly hired firms beginning public promotion (on the date as defined by the SEC complaint) and the subsequent charges brought by the SEC. A large literature in finance has shown that positive commentary about a firm by a reputable independent analyst could have a positive impact on the firm’s share price. Womack (1996) reports significant (2.4%) returns around buy recommendations issued by security analysts. Barber et al. (2001) find that stocks with the most favourable consensus recommendation, on average, earn annualized geometric mean returns equivalent to 18.6%. A key issue in recommendations being credible, however, is that the market needs to believe that there is an arm’s length relationship between the provider of information and the firm, i.e. it is an independent non-conflicted view. Accordingly, we expect a firm’s share price to be increased by the use of secretly paid promoters as long as the market is unaware of the secret relationship between the firm and the promoter. We also expect subsequent news, that there is indeed a relationship between the surreptitiously paid promoter and the firm, should result in a negative impact on the firm’s share price. This is because the market realizes that what it initially believed to be an arm’s length independent recommendation about the firm, is in fact a paid promotion by the firm. To test the market reaction to both the event of the secretly hired firms beginning public promotion and the subsequent charges

5 We also use Compustat and 10Q filings to collect quarterly date for these firms. The sample size for the quarterly data is lower, and is based on data availability. For example for the assets measure there are 94 firms in the quarterly data while there are 105 firms in the annual data. In general, our results are qualitatively similar for annual as well as quarterly data. 6 “Once the SEC staff declares your company’s Securities Act registration statement effective, the company becomes subject to Exchange Act reporting requirements. These rules require your company to file annual reports on Form 10-K, quarterly reports on Form 10-Q and current reports on Form 8-K with the SEC on an ongoing basis. If your company qualifies as a “smaller reporting company” or an “emerging growth company,” it will be eligible to follow scaled disclosure requirements for these reports”. From http://www.sec.gov/info/smallbus/qasbsec. htm#disclosure

brought by the SEC we employ standard event study analysis with value and equally weighted indices.7 In Table 1, Panel 1, we show that there is an increase in a firm’s abnormal return on the event of a secretly hired promoter beginning to promote its stock (day 0 is defined as the first day of promotion by the promoters). For example, for a 5 day window (0, +4) the average cumulative abnormal returns (CAAR) are 11.94% (statistically significant at the 1% level). We also run an event study using the Fama French three factor model. Our results are robust to the use of this model. For example, for event window (0, +4), the CAARs are 8.17%, significant at 0.01.8 Indeed, CAAR remains positive and significant for all windows in Table 1.9 In Table 2, Panel 1, we show that the market reacts negatively to the announcement of regulatory action against a promoter. Indeed the regulator’s intervention has a significant negative effect for event windows from (0, +4) to (0, +12) with a mean CAAR of −32.94% for the (0, +4) window (statistically significant at the 5% level). Our results are robust to the use of the Fama French three factor model. This suggests that although regulatory authorities take action against promoters and do not take action against the hiring firms, the market takes a negative view of these dealings and punishes any listed firm that is discovered to have engaged in secret relationships with promoters. These results show the reaction to the regulatory actions is much larger than initial reaction to promotion event which is consistent with empirical regularities in the finance literature that the market tends to over react to negative news (Chan, 2003). The firms in our sample have different listing venues ranging from organized exchanged such as NASDAQ to the Pink Sheets. It could be possible that these results are driven by the firms that are listed on the Pink Sheets. To rule out this possibility, we rerun the event studies for the subsample of firms that is traded on organized exchanges. There are 40 firms with available return data on CRSP for the promotion events and 36 firms for the litigation events. These results are reported in Panel 2 of Table 1 and Panel 2 of Table 2. As can be seen from the two panels, our results stay qualitatively the same. These findings confirm that our results are not driven by the smaller firms listed on Pink Sheet. 4. Theories and hypotheses development To analyze how firm managers as well as promoters benefit from secret relationships, we develop two hypotheses. The main hypotheses we test in this paper are related to those developed in the literature when the market is aware of the relationship between the firm and the promoter. The contribution of our paper is to extend these hypotheses by examining the case when the market is initially unaware of the relationship. Hong and Huang’s (2005) analysis of why managers hire investor relation specialists is based on the private motivations of managers to increase the stock price of their companies. They argue that managers of small and new firms commonly hold large blocks of shares in their own firm. Thus they might wish to diversify their own investments and use investor relations to increase the liquidity (i.e. increase price and trading volume) of their shares. Bhattacharya and Spiegel (1991) study the manipulation among asymmetrically informed risk-averse traders in financial markets.

7 We estimate the parameters for our model before the event date. We use market and individual security returns for 245 trading days ending on day −45 before the event for the estimation of parameters of different models. 8 These results are available from authors on request. 9 We exclude firms with returns considered outliers. We consider returns in excess of 999% over a two-week period unusual and outliers. We also perform all the event study tests reported in Tables 1 &2 by winsorizing the abnormal returns at 5% and 95% and we find our results remain qualitatively the same.

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Table 1 Event Study Results For Promotion Dates from 1995 to 2002. The table below presents the results of an event study testing the impact of promotion on a firm’s share price. The model is: Ri,t = ␣I + ␤i RM,t + εI,t , where Ri,t is the return on the common stock of the ith firm in our sample at time t; RM,t is the return on the value-weighted Market Index at time t, and εI,t is the error term. In Panel 1, return data for each sample firm was obtained from DATASTREAM. In Panel 2, Return data for non-Pink Sheet firms was obtained from CRSP. The market model was estimated using OLS over a 255-day period, ending 46 days before the event day. Cumulative Average Abnormal Returns (CAAR) for event day are statistically significant. Panel 1: Full Sample Days

N

Mean CAAR

Positive: Negative

Patell Z

Generalized Sign Z

(0,0) (0,+1) (0,+2) (0,+4) (0,+7) (0,+10) (0,+12)

111 111 111 111 111 111 111

7.44% 9.02% 8.78% 11.94% 6.36% 5.35% 1.14%

63:48 68:43 63:48 64:47 60:51 60:51 53:58

4.048*** 5.039*** 4.527*** 5.049*** 3.455*** 3.403*** 2.472**

3.275*** 4.238*** 3.275*** 3.467*** 2.697*** 2.697*** 1.348*

Panel 2: Event Study Results for Non-Pink Sheet Firm For Promotion Dates from 1995 to 2002 Days

N

Mean CAAR

Positive: Negative

Patell Z

Generalized Sign Z

(0,0) (0,+1) (0,+2) (0,+4) (0,+7) (0,+10) (0,+12)

40 40 40 40 40 40 40

2.07% 1.61% 1.14% 3.81% 6.88% 7.18% 6.57%

21:19 23:17 24:16 24:16 26:14 24:16 25:15

1.245 1.058 1.304* 2.244** 2.621*** 2.829*** 2.568***

0.53 1.163 1.479* 1.479* 2.112** 1.823** 1.796**

Table 2 Event Study Results For SEC Legal Action Dates from 1995 to 2002. The table below presents the results of an event study testing the impact of SEC legal action against a promoter on a hiring firm’s share price. The model is: Ri,t = ␣I + ␤i RM,t + εI,t , where Ri,t is the return on the common stock of the ith company in our sample at time t; RM,t is the return on the value-weighted Market Index at time t, and εI,t is the error term. In Panel 1, return data for each sample firm was obtained from DATASTREAM. In Panel 2, Return data for non-Pink Sheet firms was obtained from CRSP. The market model was estimated using OLS over a 255-day period, ending 46 days before the event day. Cumulative Average Abnormal Returns (CAAR) for event day are economically and statistically significant. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Panel 1: Full Sample Days

N

Mean CAAR

Positive: Negative

Patell Z

Generalized Sign Z

(0,0) (0,+1) (0,+2) (0,+4) (0,+7) (0,+10) (0,+12)

128 128 128 128 128 128 129

−4.01% −10.03% −17.10% −32.94% −37.57% −54.21% −57.35%

44:84 45:83 38:90 37:91 31:97 31:97 34:95

−0.168 −1.053 −1.221 −2.007** −2.532*** −3.184*** −2.944***

−1.306* −1.126 −2.389*** −2.569*** −3.651*** −3.651*** −3.170***

Panel 2: Event Study Results For Non-Pink Sheet Firm For Promotion Dates from 1995 to 2002 Days

N

Mean CAAR

Positive: Negative

Patell Z

Generalized Sign Z

(0,0) (0,+1) (0,+2) (0,+4) (0,+7) (0,+10) (0,+12)

36 36 36 36 36 36 36

−1.11% −3.18% −3.48% −4.57% −3.57% −6.19% −5.25%

17:19 15:21 11:25 5:31 7:29 5:31 9:27

−0.865 −1.521* −1.930** −2.362*** −1.799** −2.460*** −1.669**

−1.164 −2.350*** −2.101** −2.133** −1.318* −1.950** −2.714***

They characterize conditions under which the outsiders refuse to trade with the insiders. The implication of their study is that information asymmetry limits the ability of insiders to liquidate their holdings. These two theories show that there are benefits for shareholders to reduce information asymmetry which in turn might increases liquidity and share price. The issue here is that inside shareholders with relatively large shareholding can benefit much more from the promotion, in comparison to the marginal benefits for dispersed outside investors, while the cost is equally paid by all shareholders. According to Hong and Huang (2005) and Bhattacharya and Spiegel (1991), the benefits to large insider shareholders are much higher if they can liquidate their position at a reasonable share price. In addition to these benefits, the insiders have an information advan-

tage about the secret promoters and the quality of the information disseminated to the public. Accordingly, our first hypothesis is: H1: Pump and dump hypothesis Firm managers/owners secretly hire promoters to increase the firm’s share price and market liquidity prior to engaging in insider selling activity. The phrase “pump and dump” has been used very extensively in the financial press to describe the promotion activities of firms, followed by the selling of shares by insiders. For example, Wikipedia defines “pump and dump” as “schemes, involving the use of false or misleading statements to hype stocks, which are ‘dumped’ on the public at inflated prices.” While the pump and dump hypothesis is based on the distinction between insiders and outsiders, others argue that inside and outside investors benefit equally from the promoters since pro-

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moters can boost the share price when key assumptions for strict market efficiency fail to hold. Fishman and Hagerty (1989) argue that promoters lower the fixed cost for outside traders to obtain valuable information about the firms. Merton (1987) and Trueman (1996) argue that the source of the undervaluation is mainly driven by investors’ unfamiliarity with the firm. Accordingly, if a promoter increases the visibility of the stock it will increase the investor base and increase the price as a result of risk sharing effects. In general, these theories predict that by increasing visibility, promoters correct the market’s undervaluation and accordingly lower the cost of capital of the firm. Based on this, one could argue that the firm’s managers could benefit from this reduction in the cost of capital by taking appropriate corporate decisions to maximize the value of the firm. The most obvious corporate decisions include raising equity or debt and acquiring potential targets at a lower cost of capital. Accordingly, our second hypothesis is: H2: Shareholders Interest Hypothesis Firms secretly hire promoters to help them achieve certain corporate objectives. Promotion is done to increase visibility, which reduces information asymmetry and thus lowers the cost of capital. 5. Methodology and results To test the pump and dump and the shareholders interest hypotheses we examine both univariate as well as multivariate tests. 5.1. Univariate tests 5.1.1. Pump and dump hypothesis To test the pump and dump hypothesis we conduct four different tests. First, we investigate whether the liquidity of a firm’s shares increased after the event of secretly hiring a promoter. We consider two measures of liquidity: (i) average daily trading volume (measured in the number of shares traded) in the period from one year before to one year after the hiring event, and (ii) the average number of days in which the stock is actively traded in the market in the period from one year before to one year after the hiring event. Second, we investigate whether insider ownership decreased after the hiring event. Third, we investigate whether the event of hiring is followed by the firm filing form S-8. In general, form S-8 is filed by the firm when new securities are granted to employees, including executives, as part of their compensation, or in exchange of their maturing options or warrants. Insiders’ private benefits (profits) from exercising these options or warrants will be increased with an increase in share price. Fourth, we obtain daily insider trading data from the Thompson Financial Insider Trading data base (TFIT). TFIT reports insider trades filed with the SEC as a result of stock transactions and option exercises. There are 64 firms with available data from TFIT. We use the Richardson et al. (2004) methodology to calculate net daily trading activities. In particular, the variable Adjusted Net Shares Sold is calculated as the sum of the net daily number of shares sold by insiders. We adjust these numbers by subtracting net selling during four quarters (−5, −2). We label the variable “before” to represent the daily trading activities during window (−90 to −1 days), and “after” to represent the daily trading activities during window (0–90 days), where day 0 corresponds to the first day of the promotion event. We also calculate the adjusted net dollar selling ($), by subtracting the product of the number of shares sold and selling price from the product of the number of shares purchased and purchase price. We calculate net selling ($) during one quarter before and one quarter after the event. We adjust this variable for the net selling during four quarters (−5, −2). These variables are increasing in net sales (that is, negative numbers correspond to net acquisitions by insiders).

In Table 3 Panel 1, we find that there is a significant increase in both the trading volume and the average number of days traded for a firm’s stock, one year after the hiring event. This suggests that there is an increase in the liquidity of the stocks after the event, as suggested by Hong and Huang (2005). We obtain insider ownership from the proxy statement of the firms. We find that the change in insider ownership percentage is statistically significant in a paired sample mean tests at the 1% level. As can be seen in Table 3, 67 firms (around 64% of the total sample) filed a form S-8. These findings provide strong support for the pump and dump hypothesis. The results in Panel 3 of Table 3, comparing three insiders net selling measures (Adjusted Net Shares Sold, Adjusted Net Dollar Selling and number of days of net selling) during the quarter before and after the event, show that the insiders are net sellers after the promotion period. In particular, during the (0, 90) day window, the average sum of adjusted net dollar selling amount is $71.83 thousands which is significant at 1%. The change in the two measures of insiders’ trading activities (Adjusted Net Shares Sold, and  Number of Net Selling Days) is insignificant. This demonstrates that insiders are likely to sell shares that were owned over a longer period. In general, these results are consistent with the pump and dump hypothesis.10 In order to further illustrate these variables and these results, in Appendix B, we examine the details from a specific case study taken from our sample. We examine the case of a promoter BlueFire Reseach, which began promoting a listed firm, Orbit International Inc. on March 26, 2002. The case study illustrates graphically the net selling by insiders around the promotion date. Fig. B.1 depicts the variable Net Dollar Selling and Fig. B.2 illustrates the variable Net Shares Sold around the (−90 to +90) day window. The two figures clearly show that insiders dumped their shares after the date of the promotion. The patterns of trading by the insiders of Orbit International Inc. around the promotion event is clearly consistent with the pump and dump hypothesis, and is refelctive of the more formal econometric results in this regard, reported above. The results in Table 3 are consistent with the view that promoters help the company to achieve their short term corporate objectives. In particular, the rate of change in total debt ( total debt) is positive and significant at 1% and the change in institutional block holding interacted with the private placement dummy is positive and significant at 10%. These results confirm the success of promoters in helping the firm raise capital in the short run. The rate of change in goodwill ( goodwill) and assets ( assets), and the change in the intangible asset ratio are positive and significant at 10%. This is consistent with the view that the promoters help the firms to succeed in their acquisitions. In general, our results show that these firms also hire promoters to help them achieve certain key corporate objectives, somewhat consistent with the shareholders interest hypothesis. In summary, using univariate tests, our results support the view that these firms secretly hire promoters to achieve multiple goals including maximizing insider private benefits as well as achieving various corporate objectives. To confirm these findings, we perform multivariate tests. 5.1.2. Shareholders interest hypothesis To confirm the significance of these events we conducted univariate tests to compare the change in the relevant variables before and after the hiring event. In particular, we analyze the change of

10 There might be a concern that insiders would not be willing to dump the shares conspicuously right after the promotion starts. Such behavior might lead to suspicion of insider trading. However, the profitability of their trading around the promotion event might further lend support to pump and dump hypothesis. To confirm this intuition, we look at the 20 insider trading days before and 20 insider trading days after the event. These results are reported in Appendix B. As expected, these results are significantly stronger than the results reported in Table 3.

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51

Table 3 Liquidity, Ownership and Performance Measures Before and After the Event of Hiring Secretively a Promoter, 1995–2002. Panel 1 reports the paired sample mean before and after the event of secretly hiring a promoter for two liquidity measures: daily volume and number of trading days, stock ownership for all officers and directors (Insider Ownership), ownership of major block holders (higher than 5%) excluding officers and directors (Institutional Block Holdings), the interaction between the major block holding and dummy variable for private placement and intangible asset ratio. We report the standard error (SE) for paired sample mean difference. Panel 2 reports three accounting performance variables before and after the event as well as their rate of change. This includes total debt, goodwill and assets. In Panel 3, we report the insider’s daily net trading activities during the 20 trading days before and after the first promotion event. Insider trading data are available from the Thompson Financial insider trading data base (TFIT). TFIT reports insider trades filed with the SEC resulting from stock transactions and option exercises. There are 64 firms with available data from TFIT. We use the Richardson et al. (2004) methodology to calculate net daily trading activities. In particular, variable Adjusted Net Shares Sold is calculated as the sum of the net daily number of shares sold by insiders. Adjusted net dollar selling is calculated by subtracting the product of number of shares sold and selling price from the product of number of shares purchased and purchase price We adjust these numbers by subtracting net selling during four quarters (-5, −2). These variables are increasing in net sales (that is, negative numbers correspond to net acquisitions by insiders). We label the variable “before” to represent the daily trading activities during window (-20 to −1) and “after” to represent the daily trading activities during window (0–20) days. We winsorized the two variables at 99% and 1%. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Panel 1: Mean paired difference Variables

# of obs

Daily Volume (millions) Number of Trading Days Insider Ownership (%) Institution Holdings (%) Block Holdings (%) Block Holdings (%) x Private Placement Intangible assets ratio

109 109 91 105 90 30 98

Before the Event

After the Event

Paired T-test

Mean

Median

SD

Min

Max

Mean

Median

SD

Min

Max

Mean difference

SE

0.050 202.89 31.14 3.93 12.4 15.49 0.12

0.14 248 26.4 0.00 7.8 0.00 0.10

0.088 69.92 21.24 11.44 16.89 13.5% 20.97

0.000 18 1.41 0.00 0.00 0.0 0.00

0.520 254 100.00 68.15 82.90 69.4% 88.68

0.084 225.42 26.23 5.64 13.5 19.66 0.23

0.25 252 23.28 0.00 5.76 0.00 0.13

0.163 50.66 20.13 13.55 18.90 16.0% 77.94

0.000 39 0 0.00 0.00 0.0 0.00

0.859 254 88.5 67.37 73.70 73.7 753.75

0.034 22.49 −4.90 1.71 0.01 4.14 0.11

0.012 8.77 −7.86 0.73 1.1 2.74 0.08

*** ** *** *** * *

Panel 2: Rate of Change Variables

 Total debt  Goodwill  assets

# of observation

80 30 100

Before the Event

After the Event

Paired T-test

Mean

Median

SD

Min

Max

Mean

Median

SD

Min

Max

Mean difference

SE

22.38 11.12 74.72

0.61 3.13 3.68

59.397 18.29 296.32

0.00 0.09 0.01

467.447 82.24 2,717.71

23.73 16.62 85.03

0.645 3.7 5.87

63.167 35.94 336.22

0.00 0.00 0.00

417.613 183.67 3,025.62

3.69 1.66 9.27

1.45 1.22 7.23

*** * *

Panel 3: Adjusted Net Selling Variables

# of observation Quarter Before the Event Mean

Adjusted Net Dollar Selling ($000s) 57 Adjusted Trading Days 57 Adjusted Net Shares Sold (000s) 57

Median SD

−44.42 −3.80 −0.33 0 −0.86 −1.12

Min

Quarter After the Event Max

Mean Median SD

129.64 −256.21 119,306.25 27.22 0 1.61 −5.75 5 0.48 0 104.45 −200.00 213.75 23.88 0

Min

Paired T-test Max

Mean difference SE

184.42 −232.62 311.00 71.64 5.12 −7.75 28.25 0.81 316.48 −595.88 698.64 24.75

25.71 *** 0.64 41.08

B1. Net Selling (Dollar Amount) 12000 10000 8000 6000 Net Selling (Dollar Amount)

4000 2000 day-90 day-70 day-50 day-30 day-10 day0 day10 day30 day48 day64 day69 day70 day71 day72 day73 day76 day77 day78 day79 day80 day83 day84 day85 day86 day87 day90

0

Fig. B.1. Net Dollar Amount Selling by Insiders.

institutional block holder ownership, change of institutional block holder ownership interacted with private placement dummy, and the rate of change in total debt (long and short term debt) as a proxy of the firm success in raising capital in the periods before and after the promotion. We also use the rate of change in goodwill, change in intangible assets ratio and assets as a proxy of the firm’s success/failure in the acquisition/disposition of assets. These results are presented in Table 3. In addition to private benefit incentives, managers/owners might try to hire a promoter to help the company to achieve specific corporate objectives. To test the shareholders interest hypothesis

we create six dummy variables to capture the following corporate events: (i) raising capital either by private placement or debt, (ii) acquisition or disposition of an asset, (iii) recovering from financial distress or bankruptcy, (iv) registration with the SEC,11 (v) changing/firing of the auditor of the firm, and (vi) firing or resignation of CEO. We include the last two events, because the resignation of the CEO or the auditor of a firm are usually perceived as nega-

11

This information is collected from a variety of SEC forms filed online by the firms.

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N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

tive events by the market in that both create uncertainty about the firm’s future performance. Table 4 presents the distribution of these dummy variables. A firm might have multiple major corporate events around the event of secretly hiring a promoter. As can be seen from Table 4, during the period of hiring a promoter, firms have many significant major events: 33% of firms issued capital, 46% of the firms were involved in acquisition or a disposition of assets, 12% of the firms filed for bankruptcy protection, 15% of the firms registered with SEC, 19% of the firms changed their auditors and 5% of the firms fired/resigned their CEOs.

above, a positive number shows that insiders sold shares during that quarter. In comparison to the public promoter group, we expect firms in the secret promoter group to try to benefit from artificially inflated prices by dumping their shares during the promotion period. Thus we expect the coefficients on  insider ownership to be negative, the Dumping Dummy to be positive, form S-8 dummy to be positive, and  insider ownership x form S-8 to be negative. On the other hand, with respect to the liquidity dummy, we should not observe a significant difference between the two groups since this is one the main motives of hiring a promoter (secretly or publically) in general.

5.2. Multivariate tests In this section we use logistic regression to test the pump and dump and the shareholders interest hypotheses. A key part of methodology is to compare our treatment group of secret promoter firms, with a matched control group of public promoter firms. The public promoter firms are those who hired promoters but publicly disclosed their relationship with the promoter through their online filings to the SEC during the period of 1995–2002. In total we have 101 public promoter firms with usable accounting data, in the year prior to the event of hiring. The public promoter sample is handcollected from the online filing to the SEC by using text search (e.g. “investor relationship,” “promoters” etc.). As described above, over the period of 1995–2002, there are 105 secret promoter firms with usable accounting data. 5.2.1. Variables In this section we discuss the various proxy variables we use to test the pump and dump and shareholders interest hypotheses. We also pre-specify factors which we believe a priori should be important in determining the promoter hiring decision, and then test the impact of these factors. 5.2.1.1. Proxy variables related to testing the pump and dump hypothesis. We consider five different proxies. First, we measure insiders dumping their shares using data on changes in the insider ownership measure. The  insider ownership variable is measured as the difference between the percentage of insider ownership before and after the secret hiring event. We use the insider ownership percentage reported in the proxy statement. Second, we construct a liquidity dummy to measure the change in liquidity after the hiring period based on both the change in trading volume and trading days. The liquidity dummy variable is defined as one if both volume and trading days increased during the promotion period, otherwise it is zero. The liquidity dummy is computed based on the three month period before and after the hiring event. Third, we construct a Dumping Dummy to interact the price pumping with the share dumping. The Dumping Dummy is defined as one if insider ownership decreased and if the buy and hold return increased after the hiring event. Buy and hold returns before and after the event are computed based on daily prices during the three month period prior and after the hiring event. Fourth, to capture the increase in insider ownership prior to the promotion period we include the form S8 dummy. Form S-8 indicates that insiders acquired new shares. Insiders with increased ownership are more likely to dump their shares. Fifth, we consider the interaction between  insider ownership and form S-8 dummy. This is to control for the fact that insider ownership, in the absence of any trading by insiders, will increase when they are given additional shares by the firm (as reported in form S-8). As additional controls, we include insider ownership prior to the promotion period and buy and hold returns during the promotion period. We also include the variable adjusted net shares sold which is calculated as the sum of the net daily number of shares sold by insiders during one quarter after the event. As described

5.2.1.2. Proxy variables related to testing the shareholders interest hypothesis. Since one of the motivations to hire a promoter in general is to increase the visibility of the firm (Merton, 1987) and (Trueman, 1996), we use two proxies to measure the increased visibility of the firm during the promotion. These are the change in block holding and the change in institutional ownership after and before the promotion period. We measure these variables as the difference in the percentage ownership after and before the event,  block holding and  institutional ownership. As an additional control, we include the institutional ownership and block holdings prior to the promotion period. Similar to the univariate tests, above, our proxies for the shareholders interest hypothesis are all the major corporate events (as defined in Table 3) excluding form S-8 dummy (because it is included in the pump and dump proxy variables). In general, the motive for hiring promoters either secretly, and/or publically might be similar in terms of achieving corporate objectives or increasing visibility. Accordingly, we expect to find the coefficients raise capital dummy, acquisition dummy, bankruptcy/financial distress dummy, changing Auditor Dummy, registration with SEC dummy, firing/resigning CEO dummy,  Block Holding and  institutional ownership are insignificant. 5.2.1.3. Other control variables. We consider different control variables based on various existing theories. These variables include all the control variables that we included in our logit tests for the legal promoters matching groups. 5.2.1.3.1. Information asymmetry. The general argument in the finance literature is that the more severe the information asymmetry between a firm and its outside investors, the more likely it is for the firm to have an information discount in its stock price. We use two measures of information asymmetry; firm size and the intangible asset ratio. Firm size is measured either as (i) the natural logarithm of the book value of assets and (ii) a dummy variable that is equal to 1 if the firm’s share price is less than $15.12 We measure the intangible asset ratio as the ratio of the firm’s intangible assets divided by its total assets. Barth et al. (2001) use the intangible asset ratio as a measure of information asymmetry. Accordingly, we expect to find no significant difference between our secret promoter and the public promoter groups, because of the fact that because these groups are hiring outsiders for promotion implies that the management believes that these firms are facing information asymmetry issues. 5.2.1.3.2. The firm’s potential growth options. Baker et al. (1998) show that increased media coverage is attributable to rapidly growing companies. Accordingly, we argue that firms with more potential growth options are less likely to require promotion since the market already recognizes their growth potential. We predict a finding of no significant differences between our secret promoter

12 We also use pink sheet dummy and penny stock dummy for this. Smaller companies with lower price have higher transaction costs and face higher level of information asymmetry.

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53

Table 4 Major Corporate Events Around the Dates of Secretly Hiring a Promoter, 1995–2002. We hand collect data on major corporate events from a variety of SEC forms that are filed online by the firm. These include press releases of major events (form 8 K), general form for SEC registration (form 10–12B/10–12G), and the forms for securities to be offered to an issuer’s employees pursuant to certain plans (form S-8). ***, ** and * indicates p value of 1%, 5% and 10%, respectively.

i. ii. iii. iv. v. vi. vii.

Major Corporate Event

Number of firms

Percentage of Total (105 firms)

SE

Raise Capital Dummy Acquisition Dummy Bankruptcy/Financial Distress Dummy Registration with SEC Dummy Firing/Resigning CEO Dummy Changing Auditor Dummy Form S-8

35 48 13 16 5 20 67

33.33 45.71 12.38 15.24 4.76 19.05 63.81

0.046 0.048 0.032 0.035 0.047 0.038 0.047

and public promoter groups because both types of firms want to increase media coverage. Had the media coverage been adequate, they would not have hired outsiders to increase their visibility. We measure the potential growth of firms using two alternative measures: (i) the firm’s research and development expenditures (R&D), and (ii) Tobin’s q-ratio. We construct a dummy variable that is equal to 1 if R&D (data item XRD in Compustat) is greater than zero, and zero otherwise. We measure the Tobin’s q-ratio as the ratio of the market value of the firm’s assets divided by the book value of its assets. The market value of assets is equal to the book value of assets plus the market value of common equity (measured at year end) less the book value of the common equity. To exclude extreme outliers Tobin’s q-ratio is winsorized at the 98th percentile. We follow Masulis et al. (2007) in measuring Tobin’s Q. Chan et al. (1990) consider R &D as a measure of firms’ potential growth. 5.2.1.3.3. Borrowing. Diamond and Verrecchia (1991) show that disclosures that improve the quality of public information of the firm reduce information asymmetry and can reduce a firm’s cost of capital. One could argue that the main objective of hiring promoters is to enhance their ability to raise debt (increase leverage). We argue that there is no significant difference in accessing debt between the secret promoter and public promoter groups. Our borrowing proxy is the ratio of the sum of short and long term debt to total assets (which we label the leverage ratio). 5.2.1.3.4. Free cash flow. In the literature Jensen (1986) and Lehn and Poulsen (1989)) argue that firms with larger free cash flow are perceived to have less need for external financing.13 This result is expected to be insignificant in comparison to the legal-promoter matching group. 5.2.1.3.5. Profitability. We expect less profitable firms to be more likely to secretly hire promoters. Profitable firms are more likely to draw attention from investors. These firms are more likely to be in the news and are more likely to be followed by analysts. We use earnings per share (EPS) as a measure of profitability. 5.2.1.3.6. Market conditions and macro economic variables. We expect firms to be more likely to hire promoters during periods of poor macroeconomic conditions so as to boost their share price. We consider the impact of macroeconomic conditions on the promoter hiring decision using a comprehensive measure for macroeconomic conditions based on the U.S. Conference Board’s composite index of Leading Economic Indicators (LEI). In addition, we construct three other variables to control for economic and market conditions: (i). a hi-tech bubble dummy equal to one if the sample firm hired a promoter during the dot.com bubble period −January 1998 to February 2000– or the matching firm’s financial reporting occurred during the bubble period (zero otherwise); (ii). a hi-tech industry dummy equal to one if the event firm belonged to a hi-tech industry as defined by its SIC code; (specif-

13 Where Free Cash flow is measured by Subtracting total income taxes (TXT minus change in deferred taxes TDITC), interest expenses (XINT), Preferred and Common Dividends (DVP &DVC) from Operating income before depreciation (OIBDP).

*** *** *** *** ** *** ***

ically firms in computers and office equipment (SIC 3571–3579), electronic and electrical equipment (SIC 3600–3699), communications (SIC 4810–4899) and software and data processing services (SIC 7370–7379), and zero otherwise), and (iii). the interaction between the hi-tech industry dummy and the hi-tech bubble period dummy. We expect firms to have been less likely to hire promoters during the hi-tech bubble period, since the value of all firms stocks tended to rise in that period. Accordingly, we expect the coefficients on the hi-tech bubble dummy, the hi-tech industry dummy and their interaction dummy to have a negative effect on incentives of a firm to secretly hire a promoter. Marosi and Massoud (2008) have used these three measures to control for market conditions. 5.3. Results for the public promoter matching group Table 5 presents the coefficients for the public promoter matched logit, for the explanatory variables described above, in two panels. We use annual data using the year before the promotion event.14 In Appendix C, we report the summary statistics for all the variables we use in our annual data; we also report correlation coefficients between the different ownership variables and the adjusted net selling variables in Appendix D. We include a variety of different variables in different models, and present our results in the form of five different estimated models. We only discuss the significant results in all specifications. We provide definitions of variables used in logistical regression in Appendix E. 5.3.1. Pump and dump hypothesis Consistent with our univariate tests, we find strong support for our pump and dump hypothesis. In particular we find that;  insider ownership is negative and significant at 5% in Model 2 and at 1% in Model 3; Dumping Dummy is positive and significant at 10% in Model 4; and buy and hold returns during the promotion period is positive and significant at 1% in Model 3. Furthermore, as expected, we find the liquidity dummy and form S-8 dummy are insignificant in all specifications. These results show that it is more likely for the firms who secretly hire promoters to be involved in dumping their shares to benefit from the artificially inflated prices. We also find that adjusted net selling in the quarter after the promotion is significant, indicating that the managers of secretly promoted firms are more likely to sell shares after the start of the promotion. 5.3.2. Shareholders interest hypothesis As discussed above, the motives for the secret hiring of promoters are not very different from the public hiring of promoters, in terms of achieving corporate objectives. Our results in this regard show that, most proxy variables for major corporate events are

14 We reran our tests using quarterly data on the quarter before the event. These results are qualitatively similar to the ones obtained using annual data.

54 Table 5 Logistic Regression Comparing the Treatment Group of Secret Promoters with the Control Group of Public Promoters, 1995–2002. This table reports the coefficient score results for firms hiring promoters who hide their relationship with the hiring firm from investors. Our holdout sample consists of firms that hired promoters and publicly disclosed their relationship during the period of 1995–2002., We report annual data during the year before the promotion period. In total we have 101 firms for the control group. The dependent variable is binary and takes the value of one for firms that secretly hired promoters and zero for control firms. The standard error (SE) is corrected for firm clustering following Peterson (2006) and is reported in parentheses. Elasticity is calculated d(lnF)/d(lnx), where d is the first derivative, ln(F) is the natural logarithm of the density function, and ln(x) is the natural logarithm of the explanatory variable and is evaluated at the sample means of the explanatory variables. There are 105 firms that hired promoters without declaring their relationship over the 1995–2002 period. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Variables

Model 2

Coeff.

SE

0.26 0.02 1.6 0.02 −0.06 0.85 −0.36 – – – – – – – – – – – – – – −1.28 0.38 −0.5

0.11 0.01 0.96 0.12 0.08 0.75 0.3 – – – – – – – – – – – – – – 3.02 0.35 0.34

−1.38 196 0.06 −127.4

0.92

*** * *

Model 3

Model 4

Model 5

Elasticity

Coeff.

SE

Elasticity

Coeff.

SE

Elasticity

Coeff.

SE

Elasticity

Coeff.

SE

0.17 0.14 0.07 0 0.01 0.39 −0.09 – – – – – – – – – – – – – – −0.01 0.09 −0.07

0.14 0.03 2.57 −0.04 −0.05 1.32 −0.71 −2.82 −2.92 −3.11 −3.05 – – – 0.23 0.48 −0.85 −2.6 −1.32 −0.46 0 −1.81 0.87 −1.28

0.14 0.02 1.11 0.15 0.09 1.03 0.39 1.19 1.18 1.39 1.16 – – – 0.41 0.43 0.62 0.64 0.72 0.53 0.47 3.8 0.46 0.44

0.1 0.16 0.13 −0.01 0.01 0.64 −0.19 −0.48 −0.23 0.07 −0.03 – – – 0.04 0.11 −0.07 −0.3 −0.06 −0.06 -7.20E-04 −0.01 0.22 −0.2

0.09 0.03 1.06 −0.13 0.17 1.47 −0.92 −3.12 −2.47 −4.12 −3.16 0.63 – −0.55 0.21 0.37 −1.5 −2.69 −0.78 −0.38 −0.24 −3.82 0.33 −1.08

0.17 0.02 1.18 0.13 0.29 1.48 0.49 1.35 1.39 1.64 1.55 0.24 – 0.52 0.53 0.52 0.78 0.73 0.91 0.61 0.57 4.6 0.6 0.51

0.06 0.17 0.06 −0.03 −0.02 0.65 −0.23 −0.49 −0.19 0.09 −0.01 0.1 – −0.1 0.03 0.08 −0.09 −0.3 −0.04 −0.04 −0.07 −0.01 0.08 −0.17

0.05 0.02 1.84 −0.18 0.17 1.33 −0.78 −1.45 −1.76 – −2.02 – 1.09 −0.34 −0.14 0.06 −1.26 −2.13 −0.81 −0.38 0.08 −3.76 0.23 −1.08

0.15 0.02 1.15 0.14 0.3 1.44 0.46 1.15 1.27 – 1.25 – 0.6 0.48 0.51 0.48 0.77 0.68 0.77 0.59 0.53 4.09 0.55 0.5

0.04 0.14 0.1 −0.04 −0.02 0.62 −0.2 −0.24 −0.14 – −0.01 – 0.14 −0.07 −0.02 0.01 −0.08 −0.25 −0.04 −0.04 0.03 −0.01 0.06 −0.17

0.32 −0.01 1.58

0.19 0.02 1.58

−0.37

0.39

0.02

−0.83 −0.68 −3.37 3.17 −1.36

0.71 1.46 1.74 2.94 1.62

−0.24 −0.08 −0.24 −0.05 −0.02

−0.65 0.61

0.65 0.58

−0.06 0.11

1.47

1.06 125 0.27 −63.36

1.98

0.4 125 0.22 −67.71

1.82

0.55 0.61 1.46 1.06

0.16 −0.05 −0.09

0.26 169 0.26 −86.1505

0.84 −0.38 4.41 0.56 87 0.24 −45.31

**

* ** ** ** ***

*** *

* ***

* ** * *** ** ***

* ***

**

*

*

* *

* ***

**

Elasticity *

*

**

0.34 −0.02 0.08

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

Asset (log) Tobin-q Intangible Asset Ratio Leverage EPS Price less than $15 dummy R&D dummy Insider ownership Prior Event Block holding Prior the Event  Insider Ownership  Block Holding Buy and Hold Returns Dumping Dummy Market Liquidity Raise Capital Dummy Acquisition Dummy Bankruptcy/Fin Distress Dummy Registration with SEC Dummy Firing/Resigning CEO Dummy Changing Auditor Dummy Form S-8 Composite Leading Indicator (CLI) Hi-Tech Bubble Dummy Hi-Tech industry Dummy Adjusted Net Shares Sold Q1 Constant Number of observations Pseudo-R2 Log likelihood

Model 1

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

insignificant except for the registration with SEC dummy which is significant at 1% in all specifications. Interestingly, firms that choose to publically hire promoters are more likely to increase visibility in comparison to those that choose to secretly hire promoters. In particular the,  Block Holding is negative and significant at 1% in Model 2, at 5% in Model 3 and at 10% in Model 4 This suggests that block holders are more likely to be better informed and can differentiate between these two sets of firms, compared to smaller investors. 5.3.3. Other control variables Confirming our conjectures discussed above, there are no major differences between the secret and public promoter groups in terms of information asymmetry, growth, performance, leverage, free cash flow, and macro-economic variables and market conditions. None of these variables are significant in any specification. This implies that there are no systematic differences between secret and public promoters. These findings may also indicate that secret rather than public promotion is a choice made by the management of the firm and it is not something that has been forced upon these firms because of their firm specific characteristics. These results combined with results from the event study are consistent with the pump and dump hypothesis. It seems that the difference between legal promotion and illegal promotion is the desire of the insiders to pump and then dump the shares of their companies. 5.4. Robustness tests: propensity score matching Our main results above compare our treatment group of firms that secretly hire promoters, with our control group of firms that publically hire promoters. It is possible, however, that our results might be sensitive to the definition of the control group. For this reason, as additional robustness checks of our main results, we compare our treatment secret promoter groups with a group of firms that did not hire promoters. We use a two-step propensity score matching procedure, which is explained in more detail below. 5.4.1. Step 1 of propensity score (PS) matching Our first step of the PS test matches our treatment group of secret promoters with other firms from the Compustat database on the basis of the year of the promotion event and industry (4 digit SIC code). We restrict the matched firms to firms that were never promoted during the promotion period for the sample firms. We call this first basic matching the “general-matched” control group. In total we have 6939 firm-year observations for this control group. We compare this group with our treatment secret promoters group by running a logit tests using all our control variables in section 5.2.1.3. We use annual data from the year before the promotion event. The purpose of this step is to forecast the probability that a firm with given characteristics will secretly hire a promoter, which is needed to compute the PS in step two. As is well known, the advantage of PS matching is that it allows matching of firms over multiple dimensions. Table 6 presents the coefficients of the general-matched logit for the explanatory variables described above. We drop and introduce variables based on sample size, since some variables have missing observations. Accordingly, we present our results in the form of three different estimated models. As can be seen from Table 6, our results show that firms with high level of information asymmetry are more likely to secretly hire promoters. Specifically, log (assets) is negative and significant at 1% in all 3 models. For example, Model 1 in Table 6 shows that the log(assets) is negative and significant at the 1% level and that a 1% increase in log(assets) reduces the probability of secretly hiring a promoter by 1.21% (from its mean value), indicating that larger

55

firms are reluctant to secretly hire promoters. The coefficient on the share price less than $15 dummy is positive and significant at the 5% level in model 2 and significant at the 10% level in model 3. The intangible assets ratio is also positive and significant at the 1% level in all models. For example, a 1% increases in the intangible asset ratio increases the probability of secretly hiring a promoter by 0.15% (in model 1). This is consistent with the predictions of the pump and dump hypothesis. Our Tobin’s-q measure is insignificant in all specifications, while the R&D dummy is negative and significant at 1% in Model 3 of Table 6. Thus we find weak support for the view that firms with less growth potential and higher information asymmetry are more likely to hire promoters. We find that lower leveraged firms (that is firms with less debt) are more likely to secretly hire a promoter. These results are significant at the 10% level in model 1 and significant at the 5% level in models 2 and 3. These firms have less access to debt and have lower growth potential. Interestingly, the three market conditions dummies are all negative and significant. In model 3 the hi-tech bubble dummy is negative and significant at the 5% level and the hi-tech industry dummy is negative and significant at the 1% level. Their interaction dummy in model 2 is also negative and significant at the 1% level. The Composite Leading Indicator Index (CLI) is insignificant. These results show that the secret hiring of promoters has been more prevalent in non-bubble periods, as might be expected since the value of all firms stocks tend to rise during bubble periods. In summary, we find that smaller firms, firms with higher asymmetric information, firms with less growth potential and firms with lower leverage are more likely to secretly hire promoters in comparison to our no promoter general matched group.

5.4.2. Step 2 of PS matching For each of the firms in the general-matching group (in step one) we forecast the probability (p) that a firm with given characteristics will secretly hire a promoter. The firm’s propensity score (PS) is defined as log [(1-p)/p]. Based on each firm’s PS score, firms are matched using Leuven and Sianesi’s (2003) PS matching procedure at the nearest neighborhood within a 0.01–0.5 caliper. The predicted PS value is obtained from Model 3 in Table 6. In addition, we restrict the matched group to firms that were never promoted during that promotion period for the sample firms. Based on this procedure, we were able to find 94 matches within the same promotion year and 59 matches within the same promotion year and 2-digit SIC code. Some firms have more than one matching firms. In total we have 118 matching firms. We call this control group “nopromoter PS” group. The purpose of this step is to test whether the insiders’ choices for treatment sample is different from the control sample especially after the secret promoter hiring event. Table 7 reports the summary statistics and mean difference tests for the secret promoter sample against the matched no-promoter propensity score group. We report the summary statistics to our key variables in levels, as measured after the secret promoter hiring event and changes (difference between after and before the secret promoter hiring event). Additionally, we report the mean difference and t-test between the no-promoter PS group and secret promoter sample. In Table 8 we present the coefficient score results for the conditional (fixed effect) logit controlling for the explanatory variables described above. The fixed effect accounts for the paired matches. The dependent variable equals 1 for secret promoters and 0 for the no-promoter PS matched firms within the same promotion year and 2-digit SIC code. We present the results in four models to show the robustness of the results using different key variable. The maximum number of pairs is 59.

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N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

Table 6 Step 1 of propensity Score Matching: Logistic Regression of the Probability of Hiring Secretly a Promoter Using the General Matching Group, 1995–2002. This table reports the co-efficient score results for the firms hiring promoters who hide their relationship with the hiring firm. Our control group consists of all Compustat firms the meet 2 criteria (i) 4 digit SIC code and (ii) same year. In total we have 105 observations in the Treatment Secret Promoters group, and 6939 firm-year observations for the control group. The dependent variable is binary and takes the value of one for firms that hired secretly promoters and zero for control firms. The standard error (SE) is corrected for firm clustering following Peterson (2006) and is reported in parentheses. Elasticity is calculated d(lnF)/d(lnx), where d is the first derivative, ln(F) is the natural logarithm of the density function, and ln(x) is the natural logarithm of the explanatory variable and is evaluated at the sample means of the explanatory variables. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Variables

Model 1

Asset (log) Tobin-q Intangible Asset Ratio Leverage EPS Free Cashflow R&D dummy Share Price less than $15 dummy Composite Leading Indicator (CLI) Hi-Tech Bubble Dummy Hi-Tech industry Dummy Interaction of Bubble and Hi-Tech Dummy Constant Time dummies Number of observations Pseudo-R2 Log likelihood

Model 2

Coeff.

SE

−0.29 0.00 1.75 −0.06 −0.01 – – – – – –

0.04 0.01 0.56 0.03 0.04 – – – – – –

***

−4.15 yes 6980 0.07 −398

0.97

***

*** *

Model 3

Elasticity

Coeff.

SE

−1.21 0.01 0.15 −0.03 0.00 – – – – – –

−0.20 0.01 1.70 −0.08 0.00 – – 1.02 – – – -1.41 -5.36 yes 6980 0.10 -387

0.05 0.01 0.57 0.04 0.05 – – 0.49 – – – 0.41 1.07

–

*** *** **

**

***

Elasticity

Coeff.

SE

−0.83 0.06 0.15 −0.03 0.00 – – 0.71 – – – -0.29

−0.18 0.01 1.71 −0.10 0.00 0.02 −1.87 0.92 −4.10 −0.80 −0.93 – -2.06 No 3523 0.16 -310

0.07 0.02 0.63 0.05 0.05 0.01 0.29 0.50 3.03 0.35 0.27 – 0.70

Elasticity *** *** **

*** * ** ***

−0.68 0.04 0.16 −0.06 0.00 −0.01 −1.69 0.66 −0.06 −0.32 −0.48 –

***

Table 7 Step 2 of PS tests: Mean difference tests using the No-Promoter PS matched group. This table reports the summary statistics and mean difference tests for secret promoter sample against a no-promoter PS matched (firms that were not promoted publicly during that matched year). The sample firms are matched using Leuven and Sianesi’s (2003) PS matching procedure at the nearest neighborhood within 0.01–0.5 caliper. The predicted PS value is obtained from Model 3 in Table 6. In addition, we restrict the matched firm to firms that were never promoted during that promotion period for the sample firms. Based on this procedure, we were able to find 94 matches within the same promotion year and 59 matches within the same promotion year and 2-digit SIC code. In total we have 118 matching firm. Accordingly, some firms have more than one matched firms. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Variables

Secret Promoter dummy  Insider Ownership Insider Ownership before event Insider Ownership after event  Institution Holding Institution Holding before event Institution Holding after event  Block Holding Block Holding before event Block Holding after event Raise Capital Dummy  Block Holding x Private  Institution Holding x Private Form S-8 Dummy  Insider Ownership x S-8 dummy Acquisition Dummy Bankruptcy/Financial Distress Dummy Registration with SEC Dummy Change Auditor Dummy CEO Resignation Dummy  market value Market value before Market value after Dumping Dummy

Secret Promoter Group

No-Promoter PS Matched Group

# Obs

Mean

SD

Min

Max

# Obs

Mean

SD

Min

Max

94 81 83 92 94 94 94 80 83 91 94 80 94 94 81 94 94 94 94 94 89 94 89 94

1 −0.04 0.29 0.26 0.02 0.04 0.06 0.01 0.14 0.14 0.33 0.01 0 0.64 −0.03 0.46 0.14 0.12 0.19 0.04 6.23 78.06 99.8 0.35

0 0.13 0.19 0.21 0.08 0.11 0.14 0.17 0.17 0.19 0.47 0.09 0.02 0.48 0.1 0.5 0.35 0.32 0.4 0.2 28.6 292.05 316.28 0.48

1 −0.5 0.01 0 −0.07 0 0 −0.83 0 0 0 −0.21 −0.07 0 −0.49 0 0 0 0 0 −0.94 0.14 0.34 0

1 0.38 0.89 0.9 0.62 0.68 0.67 0.49 0.83 0.74 1 0.49 0.14 1 0.38 1 1 1 1 1 188.67 2,665.36 2,586.88 1

118 98 102 111 118 118 118 99 101 112 117 99 117 117 98 116 116 117 116 116 108 118 110 118

0 0.01 0.27 0.27 −0.01 0.06 0.04 0.3 0.12 0.39 0.22 0.01 −0.01 0.38 0 0.24 0.08 0.09 0.12 0.06 1.96 58.73 54.1 0.12

0 0.15 0.19 0.21 0.14 0.22 0.13 2.93 0.19 2.78 0.42 0.07 0.14 0.49 0.11 0.43 0.27 0.28 0.33 0.24 9.96 150.04 163.52 0.32

0 −0.35 0 0 −1.46 0 0 −0.23 0 0 0 −0.08 −1.46 0 −0.35 0 0 0 0 0 −0.97 0.05 0 0

0 0.62 0.88 0.97 0.14 2.04 0.67 29.18 0.91 29.43 1 0.42 0.04 1 0.54 1 1 1 1 1 78 1,046.36 1,291.22 1

In comparison to the no-promoter control matching group, we expect the secret promoter firms to try to benefit from the artificially inflated prices by dumping their shares during the promotion period. Accordingly, we expect the coefficients on  insider ownership to be negative, the Dumping Dummy to be positive, form S-8 dummy to be positive and  insider ownership interacted with S-8 dummy to be negative. In comparison to the no-promoter matching groups, we expect raise capital dummy, acquisition dummy, bankruptcy/financial distress dummy, changing Auditor Dummy, registration with SEC dummy,

Mean

t-test

– −0.05 0.02 −0.01 0.03 −0.02 0.02 −0.29 0.02 −0.25 0.11 0.00 0.01 0.26 −0.03 0.22 0.06 0.03 0.07 −0.02 4.27 19.33 45.7 0.23

– −2.06 0.74 0.74 2.08 −0.59 1.01 −0.88 0.48 −0.85 1.75 0.03 0.97 0.25 −1.97 3.36 1.43 0.76 0.07 −0.57 1.45 0.62 1.31 4.19

**

**

**

*** ** *** * * * * ***

and  institutional ownership to be positive and significant since it is more likely for a firm to hire a promoter around these corporate events and it is more likely for block holding and institutional ownership to increase as a result of increased visibility. 5.4.3. Pump and dump hypothesis In Table 7, the difference in mean for  Insider Ownership, and  Insider Ownership x S-8 dummy are negative and significant at the 5% level, for Form S-8 Dummy is positive and significant at 1% level, and Dumping Dummy is positive and significant at the 1% level. Sim-

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

57

Table 8 Step 2 of PS tests: Conditional (fixed-effects) Logistic Regression Using No-Promoter PS Matched Sample, 1995–2002. This table reports the coefficient score from the conditional (fixed-effects) logistic regression results for firms hiring promoters who hide their relationship with the hiring firm from investors. Our control group consists of PS matched firms that were not promoted publicly during the promotion period. The sample firms are matched using Leuven and Sianesi’s (2003) PS matching procedure at the nearest neighborhood within 0.01–0.5 caliper. The predicted PS value is obtained from Model 3 in Table 6. In addition, we restrict the matched firm to firms that were never promoted during that promotion period for the sample firms. Based on this procedure, we were able to find 59 matches within the same promotion year and 2-digit SIC code. The dependent variable equals one for firms hiring promoters who hide their relationship with the hiring firm from investors and the 0 for no-promoter PS matched firms within the same promotion year and 2-digit SIC code. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Variables

 Insider Ownership  Insider Ownership x S-8 dummy  Institution Holding  Institution Holding x Private  Block Holding  Block Holding x Private Raise Capital Dummy Acquisition Dummy Bankruptcy/Financial Distress Dummy Registration with SEC Dummy CEO Resignation Dummy Change Auditor Dummy Form S-8 Dummy Dumping Dummy Number of observations Pseudo-R2

Model 1

Model 2

Coeff.

SE

−19.12 – 7.79 – 3.36 – 2.46 2.65 2.84 −5.58 −1.29 5.83 – – 86 0.52

9.31 – 7.92 – 4.60 – 1.09 1.06 2.16 3.08 1.90 2.62 – –

**

** * * **

Model 3

Model 4

Coeff.

SE

Coeff.

SE

Coeff.

SE

– −32.25 5.13 – 8.65 – 2.48 2.86 0.29 −2.33 0.49 4.04 −0.17 – 86 0.49

– 16.17 8.30 – 4.81 – 1.20 1.19 1.53 3.18 1.81 2.44 1.08 –

– −27.16 – 38.89 – −15.90 2.25 2.63 0.76 −4.88 0.32 6.39 0.12 – 86 0.49

– 15.52 – 19.72 – 8.81 1.07 1.21 1.60 19.07 2.71 3.89 1.02 –

−22.74 – 1.64 – 6.63 – 2.34 3.23 2.24 −7.15 −1.03 5.68 −0.13 2.50 86 0.60

13.47 – 7.58 – 6.34 – 1.31 1.46 2.52 3.81 2.07 3.02 1.11 1.38

ilarly, in Table 8 the coefficients on  Insider Ownership,  Insider Ownership x S-8 dummy is negative and significant at either the 10% or 5% level. As expected the Dumping Dummy is positive and significant at 10% level in Model 4. Consistent with our previous tests, we find strong support for our pump and dump hypothesis. In summary, there is strong evidence supporting the view that the main motives for managers of the firms to secretly hire promoters is to pump and dump their shareholdings to benefit from the artificially inflated share prices. 5.4.4. Shareholders interest hypothesis Consistent with our univariate tests, we find some support for our shareholders interest hypothesis. Specifically, it is more likely for our secret promoter firms to succeed in raising capital, increase visibility, and to acquire specific targets (Acquisition Dummy) during the promotion period in comparison to the no-promoter PS matched firms. In Table 7, the mean difference for Raise Capital Dummy,  Institution Holding and Acquisition Dummy are positive and significant at either the 5% or the 1% level. Similarly, in Table 8, the coefficient score on  Institution Holding x Private is positive and significant at the 5% level in Model 3, on  Block Holding is positive and significant at 10% level in Model 2, on  Block Holding x Private is negative and significant at 10% level in Model 3, and on Raise Capital Dummy is positive and significant either at 5% or 10% level in different models. It is worth noting that the firms that tend to secretly hire promoters tend to change their auditors during the promotion period, and are more likely to be financially distressed in comparison to nopromoter propensity matched firms. In Table 7, the mean difference for the Bankruptcy/Financial Distress dummy and Change Auditor Dummy are positive and significant at the 10% level. In Table 8, the coefficient on the Change Auditor Dummy is positive and significant at either 5% or 10% level in different models. In summary, consistent with the univariate tests, our results support the view that the firm hires promoters secretly to achieve multiple goals including maximizing insider private benefits.

15 We are grateful to an anonymous referee for suggesting this test. These results are available from authors upon request.

**

* ** **

*

* ** * ** **

*

*

* ** * * *

5.5. Using CAARS around promotion and SEC action dates to test Hypotheses15 : Intuitively, if the motive of the managers of the firms is to increase the price of the underlying securities and then sell them at inflated prices, then this increase in price will be short lived around promotion dates. Importantly, if the ‘pump and dump’ hypothesis is correct and increasing visibility and improving price efficiency is not, then we should see a long term negative reaction from the market participants around the SEC action date. If visibility and improving price efficiency were the true motives of the managers, then even if the price had dropped after SEC litigation announcement, this drop in price would be transitory. The market participants would realize that the true value of firm has not changed. However, if the pump and dump hypothesis were correct, one would find that the drop in price is substantial and it will be long lasting. Our results indicate that the CAARs last for almost two weeks after the start of promotion. More importantly, we find that the drop in CAARs after the litigation announcements is substantial and it lasts for at least a quarter. This test also supports the ‘pump and dump’ hypothesis.

6. Conclusion We examine events in which firms secretly hire a promoter to promote their stocks. We develop and test various hypotheses about the effects of these activities on the prices of stocks, motives of the likely beneficiaries of these activities and the type of firms that might hire these promoters. Our event study results suggest that promotion by these promoters initially leads to an increase in the price and liquidity of the stocks of the hiring firms. However, this is a high stakes strategy. When (and if) regulators disclose that a secret relationship has existed between a promoter and a hiring firm, the price of the firm’s stock drops and these firms experience economically and statistically significant negative abnormal returns. These results suggest that market will initially react positively to the promotion by purportedly ‘independent’ analysts. However when these analysts are subsequently shown to have been secretly paid, the market reacts negatively.

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N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

In general, tests about the possible motivations of the managers of these listed firms support our pump and dump hypothesis. Our univariate tests show that, after secretly hiring a promoter the stock price increases significantly, the liquidity of the stocks increases, insider ownership decreases and institutional block holdings increase. Our logistic tests, comparing secret promoters with public promoters find that insiders of the firms that secretly hire promoters are more likely to engage in dumping shares during the promotion period to benefit from the price appreciation. This is consistent with the pump and dump hypothesis. We also find that secret promoters are less likely to succeed in increasing visibility in comparison with the public promoters matching group. In addition, we find that there are no significant differences between secret and public promoters with respect to achieving corporate objectives. Therefore, our main contribution is to show that firms secretly hire promoters when their managers want to sell their holdings. This is clearly a cause of concern for regulators, as these firms are not required to disclose hiring of these promoters. Our study also documents the motives behind a new form of stock manipulation in the market. Managers hire outside promoters to increase the share price and once the share price is artificially high, they sell their shares and exit. Investors incorrectly believe that the analysis done by these promoters is independent and purchase shares based on this information. Thus, our findings suggest that purpose of surreptitiously hiring these promoters is to manipulate the market so that insiders can sell their shares at an artificially high price. Our results are also important for academic studies that look at legal manipulation of stock price. McDonald (2013) notes that, “academics are generally skeptical about the feasibility of legal profitable manipulation” (234). In the cases we examine, managers do not face the consequences of secret promotion, yet they seem to benefit from these events. Our results also have important policy implications. Our empirical support for the pump and dump hypothesis, implies that insiders are acting in ways that are detrimental to outsiders by exploiting the legal fact that only the promoters rather, than the hiring firms, are required to divulge promotional relationships. Thus an important policy implication from our study is that the hiring firm should also be legally responsible for divulging the existence of these kinds of promotional relationships.

Acknowledgements We are also very thankful to the managing editor, Professor Iftekhar Hasan and two anonymous reviewers for their insightful and constructive comments. Massoud and Scholnick would like to acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. We appreciate the comments from conference and seminar participants at the Northern Finance Association meeting in 2008, Financial Management Association meeting in 2008, Wilfred Laurier University and Western Ontario University. Khawaja Saeed, Mark Fiumidinisi and Omer Mohammad provided excellent research assistance. All errors are ours.

Appendix A. Duration, Type of Promotion and Number of Firms This table presents the duration, type and number of firms that experienced promotion by different promoters. Even though, the majority of the promotion (74.29%) consists of one newsletter and one report. We believe that the impact might have been longer

because these reports remained accessible to investors for longer period of time. Promotion Period Type of Promotion

Number of Firms

% of Firms Cumulative Frequency of Firms

One newsletter

5

4.76%

4.76%

73

69.52%

74.29%

1

0.95%

75.24%

1

0.95%

76.19%

4 1 2 2 2 7 2 1 1 1 1 1

3.81% 0.95% 1.90% 1.90% 1.90% 6.67% 1.90% 0.95% 0.95% 0.95% 0.95% 0.95%

80.00% 80.95% 82.86% 84.76% 86.67% 93.33% 95.24% 96.19% 97.14% 98.10% 99.05% 100.00%

One report

Two reports

Two days

One month Two months Three months Four months Five months Six months Seven months Eight months Ten months Nine months Eleven months Over one year Total

Promoters used their website and paid subscribers to send reports Promoters used ‘Analysts’ Report to promote the firm Promoters sent two different ‘analyst’ reports Promotion was for more than one day. We only know the date of the first day of promotion.

105

Appendix B. Examples of Insider Trading around Promotion The main purpose of this Appendix is to provide a detailed example about a promoting firm from our sample, and to show the pattern of daily trading activities (daily sales and purchase) of insiders around the promotion event. This case concerns a promotion company BlueFire Research that was hired by Orbit International. The excerpts below are taken from the Securities and Exchange Administrative Proceedings against BlueFire Research and its CEO William P. Bartkowski. “BlueFire Partners, operating under various names, was in the investor relations business from 1959 through the period relevant to this Order. . ..In 1999 Bartkowski and BlueFire Partners’ co-owners incorporated BlueFire Research, a wholly-owned subsidiary of BlueFire Partners, to prepare research reports on BlueFire Partners’ clients. . .. In February 2000, BlueFire Research registered with the Commission as an investment adviser. In BlueFire Research’s press releases and in its research reports, it held itself out to the investing public as an SEC-registered investment adviser. . ..Bartkowski knew that First Call had a policy against disseminating research that had been paid for by the featured companies. By issuing research reports through BlueFire Research, the Respondents created the impression that BlueFire Research reports were being issued by an entity that was not being compensated for the reports, and that the reports complied with First Call’s policy against disseminating paid-for research. . . . In actuality there was no difference between BlueFire Partners and BlueFire Research. . ..client payments to BlueFire Partners were deposited into BlueFire Partners’ sole bank account; and this account was used to pay salaries of the BlueFire Research report authors. BlueFire Research was not an operating entity and it had no employees. . ..From 2000 through August 2003, BlueFire Research provided research coverage on approximately 36 public companies, all but one of which were clients of BlueFire Partners. The research reports were mostly highly favourable buy

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61

59

B2: Net Number of Shares Sold 2500 2000 1500 1000

Number of Shares Sold

500

day90

day86

day87

day85

day84

day80

day83

day79

day78

day77

day76

day73

day71

day72

day70

day69

day48

day64

day30

day0

day10

day-10

day-30

day-50

day-70

day-90

0

Fig. B.2. Net Dollar Amount Selling by Insiders.

recommendations. . ..BlueFire Partners’ clients paid it for research coverage. Most of the written service agreements between BlueFire Partners and its clients specifically show that the clients paid BlueFire Partners to have BlueFire Research prepare research reports on them and disseminate the research reports through First Call. . ..From 2000 until September 2003, BlueFire Research issued approximately 250 research reports (or research updates) on approximately 36 public companies. The research reports did not disclose that the featured companies had paid for the research.” (italics added) The insider trading of Orbit International around the BlueFire Research promotion Event On March 26, 2002, BlueFire Reseach announced its coverage of Orbit International Inc. with an “ATTRACTIVE” rating. The full report was available to clients. In Figs. B.1 and B.2, we report the insider’s daily net trading activities of Orbit Internationl during 90 trading days before and after the event (March 26, 2002). Insider trading data are available from the Thompson Financial insider trading data base (TFIT). TFIT reports insider trades filed with the SEC resulting from stock transactions and option exercises. We use

Variable

the Richardson et al. (2004) methodology to calculate net daily buying/selling. In particular, Net Shares Sold (dollar amount), reported in Fig. B.1, is calculated as the net daily number of shares sold by insiders multiplied by the price during (-90, 90) day event window. The variable Net Shares Sold is reported in Fig. B.2. These two variables are increasing in net sales (that is, negative numbers correspond to net acquisitions by insiders). As can be seen from Figs. B.1 and B.2. insiders tended sell (dump) their shares after the start of the promotion period and after the price increase. These pattern of insider trading around the promotion events are consistent with our pump and dump hypothesis. Appendix C. Summary Statistics for Secret Promoter and Public Promoter This table presents the summary statistics for different variables for the sample and the matched group. We have winsorized Tobin’s Q at the 90% level. The high level of Tobin’s Q for some firms is possibly a result of very high stock prices during the tech bubble. For Goodwill, Research & Development and Intangible Ratio, these statistics are based on non-zero values.

Sample

Tobin’s Q Assets Sale Market Value Goodwill Leverage Research and Development Expenditure Intangible Asset Ratio Earnings per Share

Matched Group

N

Mean

SD

Min

Max

N

Mean

SD

Min

Max

102 105 102 102 30 100 52 93 103

11.73 71.40 37.44 72.83 3.24 0.44 2.82 0.12 −0.44

15.46 292.28 142.35 280.86 11.00 0.67 16.00 0.22 2.22

0.49 0.00 0.00 0.14 0.09 0.00 0.004 0.001 −21.00

43.92 2,717.71 1,278.38 2,665.36 82.24 3.61 144.48 0.89 2.72

97 101 99 98 22 95 46 85 101

12.41 16.00 14.22 50.00 0.57 0.52 1.25 0.07 −0.70

14.40 56.03 52.91 175.67 1.90 1.70 4.71 0.17 3.41

0.08 0.01 0.00 4.31 0.03 0.00 0.003 0.01 −31.14

43.92 410.49 491.55 1,711.93 12.23 14.58 37.00 0.95 4.89

Appendix D. Correlation Matrices for Ownership Variables and Adjusted Net Trading Matched and Sample Firms This table presents correlation co-efficients for ownership variables and adjusted net trading variables. Adjusted net trading variables are calculated by subtracting (-455, −91) values from (90, −1) and (0, +90) variables. ***, ** and * indicates p value of 1%, 5% and 10%, respectively. Panel 1: Matched Firms

Inside ownership After Institution holding before

Inside Ownership before

Inside ownership After

0.72*** −0.28***

−0.27***

Institution holding before

Institution holding after

 Insider Ownership

 Institution Holding

Adjusted Net Dollar Selling (-90, −1)

Adjusted Net Selling Days (-90, −1)

Adjusted Net Selling Shares (-90, −1)

Adjusted Net Dollar Selling (0, +90)

Adjusted Net Selling Days (0, +90)

60

N. Massoud et al. / Journal of Financial Stability 26 (2016) 45–61 Institution holding after  Insider Ownership  Institution Holding Adjusted Dollar Net Selling (-90, −1) Adjusted Net Selling Days (-90, −1) Adjusted Net Selling Shares(-90, −1) Adjusted Dollar Net Selling (0, +90) Adjusted Net Selling Days (0, +90) Adjusted Net Selling Shares (0, +90) Panel 2: Sample Firms Inside ownership After Institution holding before Institution holding after  Insider Ownership  Institution Holding Adjusted Dollar Net Selling (-90, −1) Adjusted Net Selling Days (-90, −1) Adjusted Net Shares Sold(-90, −1) Adjusted Dollar Net Selling (0, +90) Adjusted Net Selling Days (0, +90) Adjusted Net Shares Sold (0, +90)

−0.24**

−0.46***

0.68***

−0.47*** 0.02 0.14

0.28*** −0.24** −0.03

0.04 −0.36*** −0.12

−0.24** 0.44*** −0.18

−0.35*** −0.26*

−0.05

0.31**

0.34**

0.1

0.05

0

−0.05

0.21

0.15

−0.11

0.09

0.09

−0.23

−0.08

0.58***

0.07

0.03

−0.03

0.16

−0.06

−0.09

−0.25

0.12

0.08

0.18

0.28*

0.23

0.15

0.16

−0.09

0.01

0.43***

0.44***

0.37***

0.21

0.21

0.14

0.24

−0.05

−0.13

−0.33**

0.15

0.1

0.29*

0.83***

0.75*** −0.21**

−0.24**

−0.17

−0.25***

0.6***

−0.43*** 0.04 −0.16

0.28*** −0.01 −0.16

−0.02 −0.35*** 0.12

−0.08 0.54*** 0.04

−0.07 −0.16

−0.06

0.26*

0.2

−0.2

−0.23*

−0.09

−0.12

−0.13

−0.2

−0.22*

−0.02

0.07

−0.13

0.14

0.68***

−0.21

−0.24*

−0.36***

0.07

0.27**

−0.1

0.21

0.27**

−0.22*

0.23*

0.29**

0.19

0.01

−0.16

−0.08

−0.35***

−0.09

0.29**

−0.26**

−0.02

−0.14

−0.35***

0.09

0.21

−0.22

0.03

0.26**

−0.22

0.23*

0.65***

0.2

0.19

Appendix E. Variables used in the Logistic Regression Variables used in the Logistic Regression Name of Variable

Description

Tobin’s q

Tobin’s q was measured as the Market Value of Assets divided by the book value of the assets. The market value of assets is equal to the book value of the assets plus the market value of the common equity (measured at year end) less the sum of the book value of the common equity and balance sheet deferred taxes. Tobin’s q was measured for the fiscal year prior to the event date Masulis et al. (2007) The logarithm of the firm’s asset value. Compustat variable AT (for annual data) & ATQ (for quarterly data) The ratio of the firm’s intangible assets (XRD) to total assets. Total debt (long and short term debt) divided by total Assets. Free Cash flow divided by book value of Common Equity. Where Free Cash flow is measured by Subtracting total income taxes (TXT minus change in deferred taxes TXDITC interest expenses (XINT), Preferred and Common Dividends (DVP & DVC) from Operating income before depreciation (OIBDP). All Compustat items were collected for fiscal year prior to the event date Lehn and Poulsen (1989) Capital expenditure (CAPX) divided by sales (SALE) as measure of growth of a firm. Both Capital Expenditure and Sales were collected for fiscal year prior to the event date. The data is obtained from form 13f filings by the financial institutions. The data is collected from Proxy Statements. A dummy equal to one if the sample firm was subject to false news attack during the bubble period −January 1998 to February 2000— or the matching firm financial reporting occurred during the bubble period (zero otherwise). Equal to one if the firm belongs to a hi-tech industry, i.e. computers and office equipment (SIC 3571–3579), electronic and electrical equipment (SIC 3600–3699), communications (SIC 4810–4899) and software and data processing services (SIC 7370–7379), otherwise zero. The interaction term between the hi-tech bubble dummy and the hi-Tech industry dummy.

Log(Assets) Intangible Asset Ratio Leverage Free Cashflow Ratio

Capital Expenditure Ratio Financial Institution Holdings Dummy Insider Ownership Hi-Tech Bubble Dummy Hi-Tech industry Dummy

Hi-Tech Bubble x Hi-Tech Industry Dummy

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