The market reaction on stock recommendations provided in the Dutch television show Business Class

Transcription

1 The market reaction on stock recommendations provided in the Dutch television show Business Class Master Thesis Author: Jasper Box BSc. (421190) Supervisor: dr. P. C. de Goeij Study program: Master Finance Tilburg School of Economics and Management Department of Finance August 22, 2013

2 Abstract This study investigates the market reaction on stock recommendations provided in the Dutch television show Business Class between September 20, 2009 and March 17, I find a shortterm attention effect and confirm the retail attention hypothesis of Barber and Odean (2008). There are large and positive abnormal trading volumes in the week following (strong) buy recommendations. The cross-sectional results indicate that the abnormal trading volumes following a recommendation are larger for small stocks and stocks to which more time is devoted during the broadcast. I find positive abnormal returns for the weekend of recommendation indicating that the abnormal volumes are mainly driven by buy orders. Furthermore, there is evidence that recommendations provided by analyst Geert Schaaij lead to larger abnormal volumes than recommendations of professional asset managers. Finally, abnormal volumes are already present in the two-week period preceding (strong) buy recommendations and more pronounced for small stocks. The cumulative average abnormal return from the week prior to recommendation is also positive. Together, these results provide some evidence of front-running. 2

3 Contents 1. Introduction Literature Survey & Hypothesis Development Literature Survey Hypothesis Development Data & Methodology Data Methodology Calendar-time portfolios Event study of trading volumes Event study of returns Cross-sectional analysis Empirical Findings Descriptive statistics Calendar-time portfolios Event study of trading volumes Event study of returns Cross-sectional analysis Conclusion References Appendix

4 1. Introduction The influence of media attention on asset prices has become an increasingly popular subject amongst financial economists. Huberman and Regev (2002) find that even though no genuinely new information was presented, a front-page article in the New York Times edition of Sunday May 3, 1998 about a potential development of a new cancer-drug caused the stock price of EntreMed to rise from the closing price of $12 on Friday to an opening price of $85 on Monday. This spike was not in line with the efficient market hypothesis (Fama, 1970) since there was no new information presented that could lead to a change in fundamental value. The headline of an article in Het Financieele Dagblad on January 29, 2011 was: 'Particulier volgt blind de adviezen in Business Class'. 1 When translated to English: Retail investors blindly follow recommendations provided in Business Class. If this is true, it is questionable whether the recommendations contain value-relevant information or that these retail investors trade based on noise. The market reaction on stock recommendations provided in the Dutch television show Business Class is therefore the subject of this research. I study recommendations provided between September 20, 2009 and March 17, Previous research has also focused on stock recommendations provided in television shows. However, my research is unique in several ways. To my knowledge, I am the first to examine Dutch television shows, making this research particularly interesting. The Netherlands is also a small country where all television shows are broadcasted nationwide at the same local time. Furthermore, Business Class is recorded and broadcasted during the weekend. Hence, no immediate market reaction is visible since markets are closed. Finally, the analysts serve their clients with recommendations or by managing part of their wealth, potentially making their recommendations on Business Class not entirely independent. Engelberg, Sasseville and Williams (2012) find no evidence of long-term outperformance of recommendations provided by Jim Cramer in the U.S. television show Mad Money. In fact, they argue that there is an attention effect that leads to overnight returns, which reverse over the following months. The effect is most pronounced for small, illiquid stocks that are hard to arbitrage. They provide evidence for the retail attention hypothesis of Barber and Odean (2008) 1 The news article is presented as Article 1 in the Appendix. 4

5 that individual investors are buyers of attention-grabbing stocks, due to the problems these investors have with picking stocks from the broad universe of investment possibilities. My findings are in line with the results of Engelberg et al. (2012). First, I use the calendar-time portfolio approach, or Jensen alpha approach to find evidence of long-term outperformance of stocks recommended as a (strong) buy. I find none, even after omitting transaction costs and including the first weekend return, the analysts do not seem to have any stock-picking ability. The second part of my analysis is an event study of abnormal trading volumes. It is particularly striking that there are abnormal volumes present surrounding the recommendations despite the lack of long-term value in the recommendations. The average abnormal volume of the first day and week following a recommendation is respectively 36% and 103% for strong buy recommendations. If analysts inform their clients before the show is broadcasted, this insider information should be noticeable in the trading volumes of the preceding week. I find significant abnormal volumes in the weeks prior to recommendation, which may suggest that there are indeed pre-announcement leaks. Third, I conduct an event study of returns to get inference on whether abnormal volumes are mainly driven by buy orders or sell orders. A positive abnormal return suggests that orders during that day are mainly buy driven. The cumulative abnormal returns are positive and significant for the week preceding and following (strong) buy recommendations thereby providing evidence for front-running and a short-term announcement effect. The positive average abnormal return and hence the mispricing is most severe for the weekend of recommendation. The final part of my research is a cross-sectional regression on the abnormal volumes of the event study. The short-term announcement effect is more pronounced for small stocks and stocks on which more time is spent during the broadcast. The results provide evidence for the retail attention hypothesis of Barber and Odean (2008) and thereby also for the previously mentioned news article, which states that retail investors blindly follow recommendations provided in Business Class. The cross-sectional results also indicate that the market reaction on the Monday following the recommendation is stronger if the recommendation is provided by analyst Geert Schaaij. Finally, the pre-announcement abnormal volumes are larger for smaller stocks providing some additional evidence for front-running. 5

6 2. Literature Survey & Hypothesis Development 2.1 Literature Survey News items can provide valuable information about the intrinsic value of a stock. For instance, an announcement that a firm changes its dividend policy is a valid reason for a change in the firm s stock price considering the dividend discount model. On the other hand, the information of a news item or recommendation can be stale, not valuable or not well interpreted by the ones who make use of the news item. This might lead to an overreaction or underreaction in the stock price. In that case there is only a short-term effect on the stock price caused by attention or misinterpretation of information by investors, while over time the stock price reverts back to its intrinsic value. Another way to measure this effect of attention is by looking at trading volumes since trading volumes have proven to be a proxy for attention. Barber and Odean (2008) investigate the rationale of individual- and institutional investors with respect to buying stocks. They find that individual investors buy stocks that caught their attention or stocks they feel comfortable with. Hence, individual investors tend to buy the attentiongrabbing stocks. This is due to the fact that information of thousands of stocks is available while individual investors only have limited time and capacity to process all information. As proxy for attention they use abnormal trading volumes, previous day s returns and news coverage. They find that the attention-grabbing effect is not present for selling stocks since individual investors tend to sell stocks they already have in their portfolio, which is only a small subset of all stocks. The attention-grabbing effect is not present for institutional investors who devote more time to searching for stocks and often sell short as well. In addition, they find that individual investors are not able to achieve superior returns with attention-based buying. The findings of Barber and Odean (2008) imply that individual investors do not act fully rational since they do not solely trade on relevant information. Instead, there is some behavioral aspect in individual decision making. Consequently, investors may underreact to important information due to limited attention. Hirshleifer, Lim, and Teoh (2009) document that when many firms announce their earnings on the same day, the market reaction is weaker and post-announcement drift is stronger. They argue that this is due to multiple firms competing for the attention of investors. On the other hand, investors can overreact to unimportant information due to excessive 6

7 attention (e.g. Huberman and Regev, 2002). Tetlock (2011) provides another example by investigating the effect of attention for stocks with respect to repeat news stories. His results are consistent with the idea that investors on an individual basis overreact to stale news, causing temporary movements in stock prices of companies. Seasholes and Wu (2007) find stocks hitting upper price limits to be attention-grabbing. The Shanghai Stock Exchange imposes daily limits on stock price movements. If the stock price passes the upper price limit, trading may continue but the transaction price cannot exceed the limit. They document that the buy-sell imbalances of individual investors are positive for those stocks and even larger when fewer stocks hit upper limits on the same day. Consequently, rational investors are able to systematically generate profits at the expense of those individual investors. Engelberg and Parsons (2011) document that individual investors are more prone to trade after an earnings announcement of an S&P 500 Index firm, if the announcement is discussed in the local newspaper of the investor. The previously mentioned papers about overreaction are evidence for the price pressure hypothesis, following Barber and Loeffler (1993). With respect to my topic, the price pressure hypothesis states that the abnormal announcement returns on a stock recommendation are caused by an over-reaction from uninformed investors. Regardless of the information content in the recommendation, these investors go out to buy or sell the stock and consequently cause price pressure. Da, Engelberg and Gao (2010) find that stocks that experience higher search frequency in Google will achieve higher returns in the following two weeks and an eventual reversal within the next year. They provide evidence for price pressure effects from individual investors as argued in Barber and Odean (2008). On the other hand, the information hypothesis argues that investors solely trade on the informational value of the stock recommendations. This theory suggests that there is only a change in the stock price noticeable if the stock recommendation provides value related information that was previously not known. Following analyst recommendations has proven to be profitable even though we would expect analysts to use public information and therefore not provide significantly new information that can lead to a change in stock price. Bjerring, Lakonishok and Vermaelen (1983) argue that following the recommendations of a Canadian brokerage house leads to significantly positive abnormal returns even after controlling for transaction costs. Womack (1996) finds that major U.S. brokerage firms have stock-picking ability. Barber, Lehavy, McNichols and Trueman (2001) 7

8 document that investors can make a profit of 75 basis points per month following the consensus analyst recommendations when rebalancing daily and reacting on time with respect to changes in recommendations. However, the positive abnormal returns evaporate after correcting for transaction costs. Jegadeesh and Kim (2006) investigate analyst recommendations in G7 countries. Stock price reactions to recommendations revisions are significant on the day of recommendation and the next day in all countries, except Italy. There is a positive drift for upgrades and negative drift for downgrades in the consecutive two to six months indicating that the analyst revisions provide valuable information. The value of the recommendations is the largest in the U.S. where the price reactions and price drift are strongest. Their standardized volumes are significantly different from one during the three-day window surrounding the recommendations except for Italy. There are several media that try to provide their audience with analyst recommendations. It is questionable whether these analysts are able to generate positive abnormal returns as well. Jaffe and Mahoney (1999) and Metrick (1999) investigate the stock-picking ability of investments newsletters collected by the Hulbert Financial Digest. Both do not find evidence for short-term or long-term outperformance of investment newsletters. Mathur and Waheed (1995) investigate the effect of securities that were positively recommended in the column Inside Wall Street in the magazine Business Week. The information about these securities is secondary information since the securities were discussed by analysts before or have been subject of rumors. Positive significant excess returns are found for the securities on the day preceding publication, the day of publication and the two days afterwards. Investors could have made a profit by holding the securities one, three or six months preceding the publication date. However, if they had continued holding these securities after the publication date, they would have experienced negative excess returns. This indicates that the dissemination of secondary information is a signal of subsequent underperformance. Earlier, Sant and Zaman (1996) already found evidence of price pressure for the recommendations in the Inside Wall Street column. Several authors looked at the Heard on the Street column in the Wall Street Journal. Liu, Smith and Syed (1990) find significant abnormal returns and higher trading volumes on the day of publication and to a less extent the two days before, for both buy and sell recommendations. Beneish (1991) argues that the column contains some valuable information by indicating that the 8

9 column is not often a source of secondary dissemination. Bauman, Datta and Iskandar-Datta (1995) confirm this by documenting that for a six or twelve months holding period the buy recommendations outperform a market portfolio and the sell recommendations underperform the market portfolio. Sarkar and Jordan (2000) examine articles in regional publications of the Wall Street Journal and find a statistically significant impact on stock prices for the day of publication. For years, the Wall Street Journal has also published a comparison between stock picks of portfolio managers and random selections out of all stocks traded on the New York, American or NASDAQ exchange by Wall Street Journal staffers throwing darts. Metcalf and Malkiel (1994) examine this contest and find that while excess returns from portfolio managers are higher, they cannot systematically beat the market. Controlling for risks, the experts perform no better than the darts. They do find a very strong announcement effect on the recommendations of the portfolio managers caused by attention. The Wall Street Journal s monthly Dartboard Column has also been subject of investigation since it was believed to be a potential source of ideas for investors. Most evidence of this column suggests that there are short-term effects for the recommendations. Barber and Loeffler (1993) document an average positive abnormal return of four percent and a doubling of the average trading volume for the two days following a recommendation. The positive abnormal return reverses partially in approximately twenty-five trading days. The authors conclude that the positive announcement returns are driven by naive buying pressure from uninformed investors as well as the information content in recommendations. Albert and Smaby (1997) use a post-event estimation period and find a positive market response on the publication date as well. However, they do not find a significant pattern of reversal suggesting that there is an information effect instead of a price pressure effect. Liang (1999) documents a significant two-day announcement effect, which reverts within fifteen days. The abnormal returns and trading volumes around the announcement date are mainly caused by noise trading from uninformed investors providing evidence for the price pressure hypothesis. Greene and Smart (1999) also find a short-term price pressure effect and substantial rise in trading volume. Lidén (2006) investigates initiated and changed recommendations in popular Swedish newspapers, thereby providing international evidence. In the long run, buy recommendations seem to mislead investors whereas sell recommendations seem to direct investors in the right 9

10 way. He argues that this is probably due to the fact that positive news from a company s management is more intricate to interpret. Schuster (2003) describes two German papers in this field of research. Kladobra and von der Lippe (2002) investigate stock recommendations in leading German business magazines. They document that stocks are more likely to decline in value after being recommended than to rise in value. Dorfleitner and Klein (2002) note that the forecasts in the German investment magazine Börse online have no informational value about future price trends. Kerl and Walter (2007a) focus on the German Personal Finance Magazine and find long-term informational value in sell recommendations of journalists. They document that buy recommendations mainly contain valuable information if it is a recommendation for a value stock that experienced a positive performance prior to publication. Kerl and Walter (2007b) argue that both the price-pressure and information hypothesis can be confirmed by investigating buy recommendations in the Personal Finance Magazine. They document that is mainly caused by high abnormal returns for small stocks and value stocks. In addition, they find a large increase in trading volume on the day of publication. Hirschey, Richardson and Scholz (2000) investigate recommendations provided during the U.S. online nightly performance recap of The Motley Fool s Rule Breaker Portfolio. They find that small-cap buy recommendations lead to significant abnormal returns. In addition, there is an increase in trading volume noticeable suggesting that the recommendations influence the market. A drawback however, is that their final dataset consists of only 38 recommendations. Dewally (2003) looks at recommendations on two online investment newsgroup sites and finds no stock market reaction. In the U.S., there have been television programs that provided recommendations about buying or selling stocks. Desai and Jain (1995) examine buy recommendations by money managers on the show Barron s during the Annual Roundtable item for the period 1968 to They find a positive market reaction for buys between the recommendation day and publication day, which is a period of about fourteen days. However, this positive return does not persist after the publication day. In general, research reports stronger market reactions for buy recommendations than for sell recommendations. Pari (1987) investigates the recommendations in the television program Wall $treet Week with Louis Rukeyser for the time period. The author documents abnormal returns for the first trading day following a recommendation providing 10

11 evidence for the price pressure hypothesis. In line with Pari (1987), Beltz and Jennings (1997) document short-term positive price and volume changes for stocks that are recommended as a buy between 1990 and In addition, they note differences in performance between recommendations from different individuals. Ferreira and Smith (2003) observe 351 stock recommendations between December 27, 1996 and December 26, They find a positive, significant abnormal return on the first trading day following the recommendation, which reverts in the four days hereafter. However, the authors find some long term effects as well, indicating that the recommendations of the panel provide valuable information next to price pressure. Another well-known television program in this area of research is Mad Money, which is presented by former hedge fund manager Jim Cramer. Neumann and Kenny (2007) looked at a dataset of 171 recommendations ranging from July 26, 2005 to September 9, They find positive abnormal returns and an increase in trading volumes surrounding buy recommendations in this television show. The negative abnormal returns and increase in trading volumes surrounding sell recommendations are smaller and less significant. This could be due to the fact that only a relatively small group of investors owns the stock and is consequently spurred to sell the stock. They also find that professional investors with enough capital available might be able to profit from buy recommendations by following a contrarian strategy. Keasler and McNeil (2010) looked at 7,807 Mad Money recommendations from December 2005 through December They report significantly positive (negative) announcement returns for buy (sell) recommendations. In addition, they document an increase in trading volumes for buy recommendations. The effect is strongest for buy recommendations of small stocks. They argue that the change in returns and increase in trading volume is primarily due to overreaction of uninformed investors and that the recommendations do not contain new valuable information for the market. This is supported by the fact that the announcement returns are completely reversed within twenty-five trading days following the recommendations. In addition, bid-ask spreads temporary decline and there are no long-term positive abnormal returns. Investors should therefore be cautious with trading following the investment advice. Lim and Rosario (2010) argue that Jim Cramer especially picks stocks that have outperformed over a period preceding the recommendation. They investigated 10,589 recommendations between June 28, 2005 and December 22, Looking at abnormal trading volumes and next- 11

12 day returns, they document that the market impact is larger for buys, smaller stocks and noncaller picks. They find limited evidence of stock-picking ability for a six month horizon, especially for small-cap stocks. In contrast, Keasler and McNeil (2010) and Engelberg et al. (2012) find no evidence of stock-picking ability. Engelberg et al. (2012) investigated a sample of Mad Money recommendations ranging from July 28, 2005 to February 9, They use a direct measure of attention namely proprietary viewership data, in contrast with other papers that use proxies such as trading volume. To increase the likelihood and impact of an attention shock, they only examine first-time buy recommendations leading to a final number of 826 stock recommendations. They find a large increase in stock prices during the hours that the show is broadcasted and the recommendations are provided. In addition, greater viewership leads to a stronger market reaction looking at trading volumes. Their results are therefore in line with the attention hypothesis of Odean (1999) and the retail attention hypothesis of Barber and Odean (2008). They underpin their findings by indicating that there is no other meaningful news for the stock at that moment. The abnormal returns tend to reverse over the months after the recommendation. Again, the effect is strongest for small (and illiquid) stocks, which can be explained by several reasons documented by Barber et al. (2001). First of all, there is less public information available for small stocks leading to a larger price reaction on individual analyst upgrades or downgrades (Womack, 1996). Second, arbitrage is harder for small stocks due to higher volatility (Shleifer and Visny, 1997) and relatively high transaction costs (Pontiff, 1996). Finally, analyst recommendations might have less influence on large firms since they are a larger part of all available investment opportunities. Engelberg et al. (2012) only find a weak price effect for sell recommendations providing evidence for the hypothesis of Barber and Odean (2008) that attention is more important for buy recommendations than sell recommendations. This could be due to the fact that it is hard for individual investors to take a short position in stocks that were recommended as a sell. Furthermore, it is not possible to make a profit by forming a portfolio preceding the broadcasts and holding the stocks for 50, 100, 150, 200 or 250 days. It indicates that the initial change in return is due to mispricing. In their cross-sectional analysis, they find the mispricing to be more severe when the attention for the stock is larger. Their findings are consistent with the findings of 12

13 Barber and Odean (2008) and other papers that media endorsements of specific stocks (especially via television) might lead to short-term non-rational mispricing. Gerke (2000) mentions a case where the recommendations of an analyst in the German television program 3sat Börse could generate an excess return of nine percent even before the show was broadcasted. If there is indeed a short-term price effect, some analysts or journalists might be tempted to act upon the information in their upcoming recommendations. The knowledge of future price movements caused by analyst recommendations can provide an opportunity for front-running. Hence, the investors aware of the upcoming recommendation might take a position already before the show airs and liquidate the position on time. A possible effect of front-running can be investigated by looking at abnormal stock returns and abnormal trading volumes before the show airs. An interesting case occurred in the U.S. in the 1980s involving a co-author (R. Foster Winans) of the Wall Street Journal column Heard on the Street. He passed the information of the column to stock brokers of the investment company Kidder Peabody before publishing it and shared the illegal profits with them afterwards. Winans and his contacts were sentenced for fraud when the scandal was exposed. Syed, Liu and Smith (1989) investigate this column and document that before publication; the insider information could generate an excess return of 6.25 percent. 2.2 Hypothesis Development My research focuses on the recommendations provided in the Dutch television show Business Class. To my knowledge, I am the first to examine Dutch televisions shows, making this research particularly interesting. The Netherlands is also a small country where all television shows are broadcasted nationwide (and at the same local time) in contrast with Mad Money. I solely investigate recommendations on Dutch stocks. Furthermore, Business Class is recorded and broadcasted during the weekend. Hence, no immediate market reaction is visible since markets are closed. This is in contrast with Mad Money, which is broadcasted daily when markets are open. There are four analysts who visit Business Class on a regular basis to provide stock recommendations, in contrast with Mad Money where Cramer is the only one providing recommendations. One of them, Geert Schaaij, works as independent analyst and the others, 13

14 Martine Hafkamp, Edwin Wierda and Han Vermeulen, as professional asset managers. The analysts have to pay thousands of euros to visit the show, making the recommendations perhaps not completely independent. Therefore, the topic of investigation for my Master thesis is: The market reaction on stock recommendations provided in the Dutch television show Business Class I investigate this market reaction by means of seven hypotheses. First, it is questionable whether the analyst recommendations provide value for investors in the long run and hence whether it is meaningful to watch the television show for the stock recommendations. Keasler and McNeil (2010) and Engelberg et al. (2012) did not find a long term outperformance of stocks that were recommended on Mad Money. Hypothesis 1: There is no long-term outperformance of stocks that are recommended in Business Class Second, I investigate whether there is already a market reaction before the show is actually broadcasted, which might suggest that there is some front-running. Hirschey, Richardson and Scholz (2000) find a significant stock price increase for small-cap buy recommendations on the day preceding the broadcast of The Motley Fool. They suggest that this would also be true in the presence of preannouncement leaks or front-running. Some investigations on Mad Money describe abnormal returns and volumes preceding a recommendation. Neumann and Kenny (2007) find small upward movements in the week before buy recommendations are made at Mad Money, and a small downward trend in the week preceding sell recommendations. Lim and Rosario (2010) document a small outperformance of recommendations prior to the pick date. However, they argue that Cramer uses a positive-feedback trading strategy, which implies that Cramer buys stocks that have outperformed. Hence, they do not argue that there is front-running. Keasler and McNeil (2010) document significant abnormal trading volumes ten days before the broadcast, which might be evidence of front-running, and also ten days after the broadcast, which is a short-term announcement effect that might be caused by attention. By watching Business Class, I notice this intonation that the recommendations that the analysts provide, are in line with what they advise their clients. Therefore, it would be strange if the 14

15 recommendations that are provided in the show are very different from the advice for their own clients. This might be considered as insider trading if they give the same recommendations to their clients before the show is broadcasted. Analyst Geert Schaaij also advises his clients by means of his magazine Beursgenoten, newsletters, text messages and online analyses. This might give him an incentive to provide recommendations, which are in favor of his advice for clients. Geert Schaaij has been accused of insider trading when working as director for asset manager International Assets. The case was settled and he claims to be independent now. That is also why he and other analysts mention whether they have positions in a stock they recommend. To investigate whether there is increased trading surrounding the recommendations, I can examine abnormal returns by means of an event study in line with Engelberg et al (2012). However, if a stock price rises during the morning, but decreases to its original level during the afternoon, no change in price is noticeable when looking at the opening and closing price. A possible solution would be to investigate intraday data, which are however not available for my dataset. Therefore, trading volumes might be a better measure for an abnormal market reaction than stock returns since trading volumes record each buy and sell. This pattern is confirmed in the accounting literature by Morse (1981) and Bamber (1986, 1987). They document that after earnings announcements prices adjust quickly, but abnormal volumes remain present for several days. However, trading volumes do not indicate whether an increased market reaction is due to sell order or buy orders. Therefore, I will conduct an event study of both trading volumes and returns. I will mainly focus on (strong) buy recommendations since it is hard for individual investors to take short positions in stocks recommended as a sell. In contrast with Mad Money where callers can bring up stocks during the show, all stocks of Business Class are chosen preceding the broadcast. Therefore, front-running and perhaps insider trading may be more pronounced than for Mad Money. To investigate whether there is already information leaked before the show is broadcasted, I hypothesize further: Hypothesis 2: There are significant and positive abnormal trading volumes before the (strong) buy recommendations are provided in Business Class 15

16 Hypothesis 3: There are significant and positive abnormal returns before the (strong) buy recommendations are provided in Business Class If there is indeed an effect of front-running, we would expect the abnormal returns to reverse to negative values during the week following a recommendation. Negative abnormal returns (corrected for the market return) most likely indicate that there are more sell orders than buy orders as also argued by Pastor and Stambaugh (2001): On a day when a stock's price does not change but the market goes up, it seems reasonable to identify the stock s order flow on that day as more likely initiated by sellers than buyers. If there are indeed negative abnormal returns, it may provide evidence that investors who bought the stock based on insider trading, sell the stock after the recommendation is provided on Dutch television. I also investigate whether there is a short-term announcement effect by looking at abnormal trading volumes and abnormal returns following recommendations. In contrast with the negative returns due to front-running, an announcement effect may lead to positive abnormal returns following a recommendation. Therefore, I expect that there is first a short-term announcement effect with positive abnormal returns and afterwards negative abnormal returns due to front-running. For Mad Money, Neumann and Kenny (2007), Keasler and McNeil (2010) and Lim and Rosario (2010) provide evidence for short-term price effects and abnormal volumes. Hypothesis 4: There are significant and positive short-term abnormal trading volumes following (strong) buy recommendations provided in Business Class Hypothesis 5: There are significant and positive short-term abnormal returns following (strong) buy recommendations provided in Business Class Hypothesis 6: There are significant and negative abnormal returns in the end of the first week following (strong) buy recommendations provided in Business Class Furthermore, I investigate whether possible abnormal trading volumes are caused by attention in line with the retail attention hypothesis of Barber and Odean (2008). Engelberg et al. (2012) find a short-term announcement effect for Mad Money and argue that it is caused by attention. News articles indicate that professional investors talk about a Schaaij-effect for stocks that analyst Geert Schaaij recommends during Business Class. There are multiple examples where his 16

17 recommendations (mainly for small stocks) lead to substantial changes in stock prices during the next Monday morning, even though no new genuinely new information about the stocks was available. 2 It is an indication that the increase in stock price is caused by attention. In addition, his text messages and newsflashes have led to a sudden increase in price for small stocks. On November 17, 2010, Geert Schaaij recommended Ordina in his newsflash. During that afternoon, the stock increased 7.5 percent in value compared to an increase of 1.2 percent for the AMX. Analysts argued that the increase was caused by the newsflash of Geert Schaaij. The same happened with a recommendation that Edwin Wierda made in Business Class on March 9, During the following Monday, the stock increased seven percent in value and the trading volume was about four times higher than average. 3 Again, I focus on (strong) buy recommendations since it is hard for individual investors to take short positions. Therefore, I expect a limited market reaction for stocks that are recommended as a sell. Hypothesis 7: The significant and positive short-term abnormal trading volumes following (strong) buy recommendations provided in Business Class are caused by attention. 2 Particulier volgt blind de adviezen in Business Class in Het Financieele Dagblad on January 29, 2011 and Dwaze Beleggers in Het Financieele Dagblad on October 30, The articles are presented as Article 1 and Article 2 in the Appendix. 3 Het Wierda-effect in Het Financieele Dagblad on March 9, The article is presented as Article 3 in the Appendix. 17

18 3. Data & Methodology 3.1 Data Business Class is broadcasted on the Dutch television channel RTL 7 on Sundays at a.m. and repeated around midnight on the same day. The recordings take place on the preceding Saturday. The business show is hosted by Harry Mens and lasts approximately 96 minutes of which ten minutes are devoted to stock recommendations. The conversation with an analyst often starts with a short review of the current economic situation. Subsequently, the analyst discusses, inter alia, stocks and sometimes recommends those as a strong sell or buy. Unlike Lim and Rosario (2010) who also investigate the effect of the analysts opinions on the stocks that callers bring up during Mad Money, my investigation solely includes recommendations on stocks that were chosen preceding the broadcast. My dataset consists of all the recommendations of Dutch stocks between September 20, 2009 and March 17, I watched each broadcast and decided for each stock whether the analyst gives a verdict on it and whether this is a strong buy, buy, sell or strong sell recommendation. For instance, a strong buy recommendation is one where the analyst states that this is definitely a good stock to invest in right now and a buy recommendation is one where the analyst states this is an undervalued stock but there are still some dangers ahead in the upcoming months. Furthermore, I document the total number of recommendations provided during each broadcast and the time that is spent on each recommendation. This leads to a number of 499 recommendations. I eliminated all recommendations of stocks that had other relevant news during the weekend or Monday morning based on LexisNexis, and stocks that were already recommended in the past six weeks. I downloaded the opening price (PO) and closing price (P) of each stock, the AEX, the AMX and AScX from Thomson Reuters Datastream to determine daily returns. In addition, I downloaded trading volumes, the market capitalization and market-to-book equity of each stock from this database. I divided one by the market-to-book equity to obtain the book-to-market equity. Of the remaining 207 recommendations, seventeen were made on days for which I lack viewership data from RTL Nederland, and Thomson Reuters Datastream lacked market capitalization, market-tobook equity, trading volumes and/or prices for thirteen recommendations. In addition, I deleted 18

19 two recommendations from Jerry Langelaar since those were the only remaining recommendations provided by him. My final sample consists of the remaining 177 recommendations between September 20, 2009 and March 17, Recommendations were strong buy, 39 were buy, 14 were sell and 13 were strong sell recommendations. I investigate the recommendations that I perceive as buy and strong buy. I investigate them separately since the results might be more pronounced for the strong buy recommendations. As stated above, there is only a limited amount of strong sell and sell recommendations, which makes the results from these sell recommendations less reliable. There are not enough recommendations to assume a normal distribution of abnormal volumes for event studies. The small amount of observations leads to fat tails of the distribution and thereby rejecting the null hypothesis too often. In addition, Engelberg et al. (2012) find no significant postrecommendation trend in sell-recommendation returns, perhaps because it is harder for retail investors to take short positions in sell recommendations. For sake of completeness however, I perform several tests for the sell and strong sell recommendations as well and include the results in the Appendix. To determine the risk free rate, I extracted the yearly return on a one-month Dutch government bond from Thomson Reuters Datastream and transformed it to a daily rate using 312 trading days per year. I use the risk free rate of Friday for the weekend as well since there is no risk free rate available for the weekend. I found the historical composition of the AEX 4, AMX 5 and AScX 6 on the website of Euronext. 3.2 Methodology My empirical research can be divided into four sections. I examine long-term returns by forming calendar-time portfolios, conduct event studies on trading volumes and returns surrounding the dates of recommendation and perform a cross-sectional regression on abnormal trading volumes Calendar-time portfolios The point of departure for my analysis is to determine whether there is value-relevant

20 information in the stock recommendations provided in Business Class. I do this by forming calendar-time portfolios going long the (strong) buy recommendations in line with Keasler and McNeil (2010) and Engelberg et al. (2012). In case the recommendations contain informational value, I expect long-term outperformance of the recommended stocks. As said before, Business Class is broadcasted during the weekend when markets are closed. Therefore, I measure weekend returns to incorporate the change in stock price that takes place during the weekend. If I would only include returns based on closing prices, I would not be able to distinguish the weekend return from the Monday return. Consequently, each week consists of six trading days. I will consider portfolios that hold the recommended stocks for 48, 96, 144, 192 and 240 days through June 30, 2013, which corresponds to 8, 16, 24, 32 and 40 weeks. The weekend return for each stock is calculated in the following way: Weekend return = LN (PO monday ) LN (P friday ) where PO is the opening price and P the closing price of a stock The return on Monday is calculated in the following way: Monday return = LN (P monday ) LN (PO monday ) where PO is the opening price and P the closing price of a stock The returns for Tuesday, Wednesday, Thursday and Friday for each stock are calculated in the following way: Daily return = LN(P t ) LN (P t-1 ) where P t is the closing price of day t The returns of the stocks are equally weighted to calculate the portfolio return on a daily basis. The excess portfolio return is calculated by subtracting the risk free rate. In case there are no holdings at any moment in time, I use the risk free rate as portfolio return, thereby causing the excess portfolio return to be zero. It is important to incorporate transaction costs in my analysis as well. Based on the rates that Binck Bank charges its customers for transactions, I charge 1% transaction costs for each stock that is bought. 7 Recommended stocks are incorporated in the portfolio on the Monday following the recommendation since this is the first moment that

21 individual investors (without insider knowledge) are able to trade based on the recommendation. However investors might be too late to profit from the recommendations if they trade on Monday and the transaction costs might also have a diminishing effect on the long-term outperformance. Therefore, I also conduct an analysis where stocks are bought on the Friday preceding the recommendation, thereby incorporating the first weekend return as well. I perform this latter analysis without incorporating transaction costs. The Dutch stock market includes three indices named the AEX, AMX and AScX. I summed up the market capitalizations of all stocks in each index on a daily basis. Next, I allocate weights to each index based on those total market capitalizations and use them to determine the market return for the entire Dutch stock market. I excluded the companies for which no market capitalization was available at all. In case the market capitalization of a stock was not available for a specific year, I used the market capitalization of the preceding year. In addition, I used the market capitalization of the Fridays to determine the weekend market weights. First, the equally weighted portfolio returns are regressed on the excess market return, in line with Engelberg et al. (2012). In case the recommendations contain new valuable information, we would expect positive abnormal returns from such a portfolio over a long horizon, which can be investigated by looking at the alphas of the regressions. Significant positive alphas are an indication of outperformance in the long run and a good method to test hypothesis 1. Therefore, I use the following model: ( ) The previous model is based on the Capital Asset Pricing Model, which is the most famous and parsimonious model to explain stock returns. There have been several investigations that found contradictions with the CAPM (Banz, 1981, Basu, 1983, Rosenberg, Reid, and Lanstein, 1985, Bhandari, 1988). These contradictions were the inspiration for Fama and French (1992) to investigate other variables that might explain stock returns such as size, book-to-market equity, leverage and earnings/price ratios. They found that size and book-to-market equity are the best variables to explain stock returns for their time period. Consequently, Fama and French (1993) developed the Fama-French three-factor model to explain stock returns, which includes two new factors named SMB and HML, next to a market risk premium. SMB (small minus big) is the 21

22 difference in return between a portfolio of small stocks and a portfolio of large stocks, which mimics the risk factor size. HML (high minus low) is the difference in return between a high book-to-market equity portfolio and low book-to-market equity portfolio, which is a premium for value stocks over growth stocks. I also regress the excess returns from the equally weighted portfolio on the Fama-French factors for robustness. In line with Fama and French (1993), I divide all AEX, AMX and AScX stocks in two groups based on the market capitalization of each stock compared to the median on June 30 of each year. Next, I subtract the average returns of large stocks from the average returns of small stocks to generate the SMB factor on a daily basis. The portfolios are reformed on June 30 each year. Similarly, I divide the all stocks of the AEX, AMX and AScX into three groups based on their book-to-market equity on June 30 of each year. I create groups for the 30 percent stocks with lowest book-to-market equity, the 30 percent stocks with highest book-to-market equity and the 40 percent of stocks that lie in between. Next, I subtract the average return from the group with the lowest book-to-market equity from the average return of the group with the highest book-to-market equity, to obtain the HML factor on a daily basis. Again, I look at the alphas to measure outperformance of the recommendations in the long run. I use the following model, which is based on the Fama-French three-factor model: ( ) Event study of trading volumes Next, I perform an event study of trading volumes to investigate whether there are abnormal trading volumes surrounding the (strong) buy recommendations. Therefore, I investigate the two weeks before and after the recommendations leading to a (-10,+10) event window. It is important to note that Business Class is broadcasted during the weekend, in contrast with Jim Cramer s Mad Money. Hence, there are no trading volumes available on the day that the show is broadcasted. The trading volumes of the Monday following the recommendation are the first measure of market reaction, which I label as day 1. Consequently, the (-10,+10) event window is a four-week period with no trading volumes for day 0. 22

23 Neumann and Kenny (2007) investigate Mad Money recommendations using log-transformed relative volumes as depicted in Campbell and Wasley (1996). Therefore, I decided to use the same method. The results in Ajinkya and Jain (1989) stress the importance of using a logtransformed measure of volume due to skewness of prediction errors of raw trading volumes. First, I add the small constant of to avoid taking the logarithm of zero in the case of zero trading volume, in line with Campbell and Wasley (1996) and Cready and Ramanan (1991). Next, the log-transformed relative volume is calculated in the following way: where n i,t is the number of shares traded for company i on day t and S i,t is the number of shares outstanding for firm i on each day t The number of shares outstanding during 2013 is not available for all stocks. Hence, I use the number of shares outstanding per December 31, 2012 for those. A market model is used as benchmark with an estimation window of (-180,-11) in line with Neumann and Kenny (2007). Jain and Joh (1988) find differences in average trading volumes across different hours of the day and days of the week. Choria, Roll and Subrahmanyam (2001) document a significant decrease in trading activity and liquidity on Friday and increase in trading activity and liquidity on Tuesday. Therefore, I expand the market model benchmark with dummies for Tuesday, Wednesday, Thursday and Friday to capture differences in trading volumes for different days from the week. I perform the following ordinary least squares regressions for the estimation window, to estimate the alpha, beta and deltas for each stock recommendation: The market volume is measured by investigating the stocks that are included in the index (AEX, AMX or AScX) in which the recommended stock is noted. If a Dutch company is not listed on the AEX, AMX or AScX, I will use the AScX as benchmark. The market volume for each index is measured in the following way: where N is the number of securities in the corresponding market index 23