The market impact of stock recommendations: The case of Business Class

Master Thesis Author: Jeroen Vereijken ANR: 558498 Supervisor: dr. P. C. de Goeij Second Reader: Dr. B. van Groezen Study Program: Master Finance Tilburg School of Economics and Management Department of Finance Date: 19 September 2016

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Abstract

I study the market impact of stock recommendations issued in the Dutch television show Business Class. The abnormal weekend return is 0.86% for strong buy recommendations and -0.38% for strong sell recommendations in the weekend of the broadcast. Thus, there is a short term announcement effect around the issuing of the stock recommendations in Business Class. There are also statistically significant abnormal trading volumes for all types of recommendations in the weekend of the broadcast. The impact on both trading volumes and returns does not reverse in the two weeks after publishing. There are already abnormal trading volumes and returns in the week before the show, implying front running. The impact is larger for smaller companies, stocks that are listed on the AScX and stocks with an higher idiosyncratic volatility. Also when an analyst talks longer about the recommendation the impact is larger, but the impact of the Duration is economically negligible. There is not any long term value in investing in strong buy or buy recommendations. However, shorting strong sell recommendations can create positive excess returns. Because there is not any long term value, this study supports the attention grabbing hypothesis. Wierda and Schaaij create the biggest short term announcement effect in terms of returns and attention. However, only portfolios based on the recommendations of Wierda create positive excess returns.

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Table of Content 1. Introduction ...... 4 2. Literature review ...... 6 3. Hypothesis development ...... 19 4. Data and Methodology ...... 23 4.1. Data ...... 23 4.2. Methodology ...... 25 4.2.1. Event Study for Abnormal Returns ...... 25 4.2.2. Event study for Abnormal Trading Volumes ...... 27 4.2.3. Cross sectional analysis ...... 30 4.2.4. Long term performance ...... 32 5. Empirical Results ...... 34 5.1. Descriptive statistics ...... 34 5.2. Event study returns ...... 36 5.3. Event study trading volumes ...... 41 5.4. Cross sectional analysis ...... 45 5.4.1. Robustness ...... 49 5.5. Long term performance ...... 54 5.5.1. Robustness ...... 58 5.6. Results for different analysts ...... 59 6. Conclusion ...... 64 7. References ...... 67 8. Appendix ...... 74

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1. Introduction I analyse the stock recommendations published in the Dutch television show Business Class. Stock recommendations are an example of investment advice. The interest rates on savings have been dropping in the recent years. The interest rates on savings were not above 0.80% in the Netherlands on 16th July 2016 according to the ‘Financiele Dagblad’.1 Some weeks later, the same newspaper mentioned that the first German bank decreased the interest rates below zero percent for savings above the €100.000.2 The decrease of these interest rates have made saving money less interesting for the average person. This makes it more interesting for these persons to search for other ways to invest their money in. Therefore investment advice is getting more important.

A possible consideration is to invest the money in stocks. Stock recommendations are a way to get investment advice. Stock recommendations have been a popular topic in research. Barber et al. (2001) find a return of 75 basis points per month after controlling for the size, value and momentum effect. High trading levels are needed to reach these gains. Stickel (1995) finds an overnight abnormal return of 1.16% for buy recommendations and -1.28% for sell recommendations. This shows that stock recommendations can create abnormal returns around the publication.

Some studies investigates also the stock recommendations issued in television shows, because it is possible to measure how many investors are viewing these recommendations. This makes it possible to evaluate the impact of attention for the recommendation. A popular television show in research is Mad Money with Jim Carter. Neumann and Kenny (2007) find a market-adjusted abnormal return of 0.59% (-0.20%) for buy (sell) recommendations on the announcement day. They find also significant abnormal trading volumes around the publication of stock recommendations. Engelberg et al. (2012) find an abnormal overnight return of more than 3% after a recommendation in Mad Money. However, they do not find any positive long term trading strategies when an investor would invest in stock recommended by Carter. They find also that when more wealthy viewers are watching the show, the impact is larger, suggesting that attention is creating a larger impact.

I investigate the stock recommendations of Business Class from 5/9/2004 until 24/4/2016. This study differs from other studies, because Business Class is a Dutch television show. There is only one study which focuses on Dutch stock recommendations. Wijmenga (1990) shows that there are positive abnormal returns for recommendations published in three written media sources in the first week after publishing. The effect reverses itself over time and therefore it is not profitable to invest for the long term in these recommended stocks. This study differs from the study of Wijmenga (1990), because I focus on stock recommendations which are published in a television show. A difference is that it is possible to measure how many people are watching a television show. Also Wijmenga (1990) does not

1 http://fd.nl/beurs/1160679/nergens-meer-dan-0-80-aan-spaarrente 2 http://fd.nl/ondernemen/1163286/boeterente-privaat-banktegoed-vanaf-100-000 4 use a cross sectional analysis, but this study does use this methodology to evaluate the effect of attention on stock recommendations.

Finally, this study differs from other studies which investigates stock recommendations published in television shows, because I investigate stock recommendations published in a Dutch television show. Most of the related studies are orientated on the United States. The Netherlands is smaller in comparison than the United States, which means that the recommended companies are also smaller in general. When the recommendations creates attention for the stock, a smaller amount of trading is needed to see an impact. Another difference in comparison with other television shows is that Business Class is broadcasted on Sunday. The stock market is closed in the weekend and therefore trading is impossible in the weekend. So, a weekend return is cleaner to investigate the impact, because investors can not react on each other. Therefore the impact is caused by the recommendation.

I find an abnormal weekend return of 0.86% for strong buy recommendations and -0.38% in the case of strong sell recommendations. There are also positive abnormal trading volumes after all type of recommendations. The abnormal trading volume is 42.34% for strong buy recommendations and 62.25% for strong sell recommendations on the Monday after the show. There is also clear front running in the week before the show when looking to abnormal trading volumes and returns. The abnormal values do not reverse in the two weeks after show.

The abnormal returns and trading volumes can be explained by some measures of attention. The duration of the recommendation is positive related with the abnormal returns and trading volumes. However, the impact is economically negligible. Smaller companies are creating larger abnormal returns and abnormal trading volumes than larger companies. Also an higher idiosyncratic volatility creates larger abnormal returns confirming the findings of Engelberg et al. (2012). When a stock diverges more from its fundamental value, it is harder for arbitrageurs to profit from these abnormal returns and therefore the impact is bigger.

I test if there is any long term value in investing in the recommended stocks by forming equally weighted calendar time portfolio. The portfolios does not create any long term excess returns when the portfolios is tested with the CAPM. The alpha’s are negative, suggesting that investors lose money when they invest in the recommended stocks for the long term. Even after testing the three factor model by Fama and French (1993) and the Carhart four factor model Carhart (1997) the alpha stays negative in the case of strong buy and buy recommendations. When an investor shorts the strong sell recommendations, the alpha is positive in all the three models. However, it can be difficult to go short in all these stocks for an individual investor.

Finally, I investigated whether there is a difference between the performance of the different analysts. The results show that Vermeulen and Hafkamp are recommending stocks which are already getting the

5 attention of the investor in the two weeks before the show. Wierda and Schaaij are creating the biggest attention and abnormal returns around the broadcast. I also created calendar time portfolios which only buys stocks recommended by one analyst. The alpha is only positive for portfolios based on the recommendations of Wierda. However, these alphas are not statistically significant.

The study has the following structure: first, I present the literature review wherein the existing studies on this topic is discussed. I discuss the hypotheses of this study in the hypothesis development part. The data section explains the data and how I gathered the data. The methodology to evaluate the stock recommendations is explained in the methodology section. The empirical results of these methodology is explained in the results section. Finally, I make conclusions and recommendations for further research in the conclusion section.

2. Literature review A fundamental discussion in finance is about the reaction of investors on new information. Information is a factor for investors to evaluate whether a stock price on a certain moment is right according to the fundamental value of the stock. For example, when a company announces to take- over another company, investors can use this information to make a choice to buy or sell the stock. Fama (1970) shows that in an efficient market all available information is incorporated in the stock price. This hypothesis is named the strong market efficiency hypothesis. When new relevant information appears, this should have a direct observable effect on the asset price according to Fama (1970). Fama (1970) describes also the semi-strong market efficiency hypothesis and the weak market efficiency hypothesis. The semi strong hypothesis assumes that the asset price only incorporates past information and recently published information, in contrast to the strong efficiency which incorporates all information. The weak version assumes that only historical prices have an effect on the asset price, which assumes that only past information have an effect on the asset price today.

Most of the announced news is objective, like dividend announcements and annual reports. However, also stock recommendations can be seen as a news event. A professional analyst uses inside knowledge and expertise to form an opinion about the stock price. Normally, the analyst has more expertise and knowledge about a company than an individual investor. Therefore, issuing a stock recommendation can be seen as revealing information which the investor does not have access to or does not understand. Consequently, a stock recommendation is also a news event. These news events are more intuitive, because it is announced by a third party, which has an opinion about a stock. The investor does not have to agree with the opinion about the stock, which could make this kind of information less valuable.

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Information can only be valuable for an investor, when the investor actually observes the information and the stock. In the standard models, it is often assumed that investors choose their portfolio from all the stocks in the world. However, Merton (1987) models the fact that investors only consider to invest in assets of which they are aware. Generally, investors only consider a subset of all assets. In the literature this is known as familiarity. Odean (1999) proposes that investors manage the problem of choosing between all stock which are available by only seeking information of stocks which already caught their attention. Huberman (2001) shows by looking to the shareholders of Regional Bell Operating Company that most shareholders are living close by the company in which they invest. This implies that investors are more likely to invest in assets which they already know. Therefore it is important for a company that investors are aware of the company and what it does. Bamber, Barron, and Stober (1997) show that on days of earnings announcements the trading volumes on that day increases. This shows that the created attention by the announcement triggers trading. Miller (1977) finds that an increase in visibility for a firm rises the stock price in the upcoming period. Ultimately, an investor does only consider an asset to invest in when the investor is already aware of the asset or a specific news event causes attention for that asset.

As the previous studies show, investors limited attention that investors only to invest in a subset of assets. Barber and Odean (2008) show that due to bounded rationality and cognitive limits to the human brain investors do not consider all assets. The human brain can not process the information of all assets in the world. Therefore, it is important that a company gets the attention from an investor. Stock recommendations creates attention for the stock. Barber and Odean (2008) show that individual investors mainly invest in attention grabbing stocks. Further, when there are a lot of choices, and the costs of evaluating the possibilities, attention plays a bigger role. The stock market is an example of market where investors can choose between many assets and where information is costly. So attention is an important factor in choosing which assets to invest in.

The fact that attention plays an important role in investing, can cause an overreaction or under reaction effect in the asset price. Several studies show an under reaction effect after the releasing of information. Hirshleifer and Teoh (2011) created a model, in which some investors neglect information about the firms future earnings when the company announces an earnings surprise. This causes an under reaction of the stock price. DellaVigna and Pollet (2009) show that the market’s reaction on an earnings announcement is more complete on regular week days than on a Friday, due to the upcoming weekend. The upcoming weekend is seen as reason for causing distraction by DellaVigna and Pollet (2009). Hirshleifer, Lim, and Teoh (2009) use days with numerous earnings announcements as proxy for inattention. They find that the reaction of the market is weaker on the days with more earnings announcements. Loh (2010) shows by using prior turnover as measure for inattention that firms with already a low turnover have a lower reaction when there is a stock

7 recommendation for that stock. Those stocks do also have a larger post-recommendation drift. This shows that that inattention leads to an under reaction when there is a stock recommendation.

In contrast, alter studies show an overreaction effect. Huberman and Regev (2001) use the case of cancer drug company EntreMed. The stock price of Entremed almost tripled after a front-page article in the New York Times, however the news was already released five months earlier in the journal Nature. The New York Times article made the news more visible for most investors and when they observed the news, they started buying the stock. Investors overreacted on the releasing of the information in the New York Times, because normally the price would change after the first time it was published according to the market efficiency hypothesis by Fama (1970). Tetlock (2011) investigates the reaction of investors on repeated news events. He concludes that individual investors overreact to stale information about public companies. When news is repeated, it attracts more attention of more investors. Busse and Green (2002) show the effect of mentioning a stock in Midday Call on CNBC TV. Trading volumes of the specific stock are abnormal big after mentioning the stock in the Midday Call. The created attention for the stock leads to more trading.

The previous section showed that attention is important for stocks to be traded. In research, a lot of effort is put in how to measure attention. Barber and Odean (2008) proxy attention with three variables: news events, unusual trading volume and extreme returns. Generally, events which are attention worthy are also news worthy. So, when the event could create attention for an investor, it is often also worthy enough to be ‘news’. However, the source of the news can also be important. The previous mentioned example of Huberman and Regev (2001) show that some news bringers are creating more attention than other media sources. The news sources which are more visible and popular will create more attention, because more people will notice the news event. The second variable which Barber and Odean (2008) use as proxy, is unusual trading volume. When investors have a lot of attention for a specific stock, it could lead to a unusual trading volume. When investors are trading more of a stock, it proxies that investors have a lot of attention for that stock at that moment. Gervais, Kaniel and Mingelgrin (2001) use also trading volume to proxy attention. They find that stocks which traded excessively are followed by extreme excess returns in the next month. Finally, Barber and Odean (2008) use extreme returns as proxy for attention. When the price of a stock changes substantially, the probability that the reason of this change also attracts the attention of the investor is big. A fourth proxy for attention is used by Grullon, Kanatas, and Weston (2004). They measure attention with the advertising expenses of a company. Their conclusion is that the visibility of a firm is higher when a firm has higher advertising expenses. Loh (2010) use prior turnover as proxy for attention. When the stock has a lower prior turnover, it could suggest that the firm does not attract the attention of the investors.

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The previous mentioned proxies for attention can measure attention, however those variables can also explain other factors. Therefore, they are not perfect proxies to measure attention. Engelberg et al. (2012) look to the effect of stock recommendations in the television show Mad Money. In a television show, attention can be measured as the amount of viewers. The viewers of the show, are paying attention to the stock recommendations. Thus, the recommendations in a television show could be a more precise way to measure exactly how many investors are paying attention to the recommendation.

Stock recommendations can have a positive effect for an investor. As mentioned above, stock recommendations can create attention for a stock and attention is important for an investor. The created attention can lead to abnormal returns in the short term, which is discussed later. In the relation between financial institutions and investors there can arise a conflict of interest. Financial corporations have a differentiated way of doing business. Mostly, issuing stock recommendations is not their only way of doing business. They are also helping non-financial companies in getting capital (IPO, SEO) and consulting in mergers and acquisitions for example. Other companies are only giving financial advice and thereby issuing stock recommendations. These companies focus more on investing.

A possible conflict of interest can arise when the financial institution recommends a stock of a firm at the same moment when they are consulting for that firm by for example a merger. This could increase the profits for the financial institution. Normally, this is not allowed. However, this topic has been a popular topic in research. Michaely and Womack (1999) found that financial corporations which operates in both types of business have the incentive to recommend the stock in which it also consults for an IPO. The financial corporation also recommends the stock of firm that undergoes an IPO when the quality of that stock is low. This shows that there is a possible conflict of interest effect. Mokoaleli-Mokoteli, Taffler and Agarwal (2009) show by looking to stock recommendations that analysts are getting incentives for a conflict of interest when issuing first time buy recommendations, due to an optimism bias and a representativeness bias. Agrawel and Chen (2008) find that investment banks indeed give positive recommendations to stocks with a possible conflict of interest. Although, they find also that the market already notices these possible conflicts. Trading volumes and prices show that investors do not trade much in stocks whereby the recommendation could be affected by a conflict of interest. Therefore, Agrawel and Chen (2008) conclude that investment banks can not manipulate investors with positive buy recommendations. Barber, Lehavy, and Trueman (2007) find that recommendation upgrades by investment banks, which also have brokerage business, underperform recommendation upgrades of non-IB brokerages and independent research firms.

In opposition of the possible conflict of interest, reputation is also important for financial institutions and financial analysts. The reputation of the analysts is a factor for investors, because investors have to trust the opinion of the analysts. When they lose the trust of the investor, their stock

9 recommendations will not be used by the investors and that type of business will be useless. Fang and Yasuda (2014) show that financial analysts with better reputation also have better investment value for the investors. Stickel (1995) shows also that analysts with a better reputation have a larger market impact. Loh and Stulz (2011) show also that star analysts –analysts with a better reputation- causes bigger excess returns. The fact that the brokerage houses want a good reputation can help avoiding a conflict of interest, because the brokerage house can think that reputation is more important. So a possible conflict of interest and the reputation of the analyst have a balancing effect. An investor should however know that there could be a conflict of interest.

There are opposing hypotheses in the literature, which try to explain the abnormal returns around the publication of stock recommendations. Krauss and Stoll (1972) and Scholes (1972) developed the information hypothesis and the price pressure hypothesis. The information hypothesis states that the recommendation reveals valuable and relevant information and therefore the abnormal return is a fundamental change in the asset. In this case, the change in the asset price due to the abnormal returns around the publication day will not reverse, because the asset has changed fundamentally due to new information about the stock. The price pressure hypothesis states that the recommendation creates temporary pressure for naïve investors to buy the recommended stocks. The bought stocks by the naïve investors create the abnormal returns on the day of publication and the days thereafter. Because the price pressure effect is not a fundamental change of the asset, the price will reverse itself in a short time after the news event. This could also be seen as the overreaction effect, which was discussed earlier. The way to test both hypotheses is to look if the asset price reverse itself after some time. When this does not happen, the information hypothesis is supported and otherwise the price pressure hypothesis is supported. Also the in Barber and Odean (2008) findings can be seen as a third hypothesis which try to explain the abnormal returns around the publication of a recommendation. The hypothesis is called the attention-grabbing hypothesis. The hypotheses assumes that investors are buying the stocks which gets the attention of the investors. It assumes also that naïve investors’ behaviour affects the market. So investors are only starting to trade the stock due to the created attention by the recommendations. Because that the trading happens due to attention and naïve trading, the effect should also reverse after some time.

Often, the second-hand dissemination of the stock recommendation is analysed. Normally, there should not be an effect by the second-hand dissemination of information, because Fama (1970) states that when information is revealed it should immediately change the asset price if the market is efficient. The second-hand dissemination of information should then not have any effect, because the fundamental change of the asset price did already happen by the first dissemination of information. Therefore, more studies should expect to find evidence supporting the price pressure hypothesis or

10 attention grabbing hypothesis after a stock recommendation, because the fundamental change in asset price did already happen.

Eventually, the importance of stock recommendations lies in the returns an investor can gain by investing in the recommended stocks. Returns of recommended stocks have been a popular research topic. Alfred Cowles (1933) wrote the first study about the returns after the recommendation of stocks. He found that recommended stocks performed less than the average stock. Financial corporations invest money in human capital and information to give the best possible stock recommendations, these efforts do however not lead to better stock recommendations according to Cowles (1933).

More recent literature finds however positive abnormal returns after stock recommendations. Womack (1996) concentrated on changes in stock recommendations. When the focus lies on changes in recommendations, the method makes sure that it measures attention. This is the case, because for example when a specific stock has a recommendation to buy for more than a year, the recommendation will not get much attention. However, a specific noted change of a recommendation will attract the attention of an investor. Ultimately, Womack (1996) concludes that added-to-buy (sell) recommendation changes are followed by positive (negative) abnormal returns. On average added to buy recommendations increase five percent in several months and added to sell recommendations decrease on average 11 percent in the same time period. Stickel (1995) uses an event study approach to determine whether stock recommendations have market impact. Buy recommendations create positive abnormal returns of 1.16% and sell recommendations create negative abnormal returns of - 1.28%. Analyst with better reputation have a bigger impact on the stock price. Also larger brokerage houses have bigger impact than smaller brokerage houses. At last, smaller companies have a bigger impact. Barber et al. (2001) investigate stock recommendations in the 1986-1996 period. They find a return of 75 basis points per month after controlling for the size, value and momentum effect. However, high trading levels are required to reach these abnormal returns and due to transaction costs the potential gains completely disappear. Bjerring, Lakonishok and Vermaelen (1983) look to the recommendations of a Canadian brokerage house. They find positive returns for event time portfolios and calendar time portfolios. The overall abnormal return for the total recommended list is 14.9% annually. After controlling for transaction costs, they find an annual abnormal return of 9.3% annually. Ho and Harris (1998) find positive abnormal returns around brokerage stock rating reports, whereby recommendation upgrades have a smaller effect on the stock price than downgrades. Chang and Chan (2008) are looking to recommendation changes and find that market-adjusted stock returns are associated with the direction of stock recommendation revisions. The evidence also suggests that downgrades contains more valuable information than upgrades, which is in line with Ho and Harris (1998).

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Jegadeesh and Kim (2006) examine stock recommendations in the G7 countries. They find that frequencies of sell and strong sell recommendations are way less common than buy and strong buy recommendation in these countries. For all the G7 countries, they find a significant price reaction op the publication day and the day thereafter, except for Italy. The largest price reaction can be found in the United States, followed by Japan. Moshirian, Ng and Wu (2009) investigated stock recommendations in emerging markets. They find that recommendations create positive (negative) abnormal buy and hold returns for buy (sell) recommendations. The post event drift continues for several weeks. The price reaction of stocks in the emerging markets is way more expansive than in G7 countries due to a larger risk premium.

Some studies also focusses on recommendations which are posted on the internet. Dewally (2003) evaluates stock recommendations of two internet newsgroups. Using an event-study approach he does not find any positive cumulative abnormal returns over the next 5, 10 or 20 days. Almost 60% percent of the recommended stocks had extreme performance before the recommendation. This implies that the extreme abnormal returns before the stock recommendation can not persist over time. Therefore these recommendations are underperforming after the recommendation. The stocks which created the largest abnormal returns after the publication day, were the stocks with the lowest abnormal returns before publication. Hirschey, Richardson and Scholz (2000a) investigated stock recommendations, which are published in the nightly performance recap of The Motley Fool's Rule Breaker Portfolio. They find positive abnormal returns of almost four percent, which are larger than recommendation which are published in written media or on television. The abnormal trading volumes which are found suggest also that investors are following the Rule Breaker Portfolio. Hirschey, Richardson and Scholz (2000b) used the same database to evaluate The Motley Fool's Rule Breaker Portfolio. They find an average abnormal return of 1.62% on the announcement day for buy recommendations and a 2.4% abnormal return in the announcement period [-1,1]. Sell recommendations cause a -1.49% abnormal return on the announcement day and a -3.33% abnormal return in the announcement period. This suggest that activity on the internet about stocks also grabs the attention of the investors and can create abnormal returns. Sabherwal et al. (2011) show that internet message boards can be used to manipulate investors in trading stock for which is not any new fundamental news. These manipulated trading causes a two day jump in the stock price.

There are also a lot of studies which focus on stock recommendations which are presented in other media. Jaffe and Mahony (1999) investigated the stock recommendations in newsletters. The Hulbert Financial Digest follows a lot of newsletters and they collect the stock recommendations in these. They find an average monthly abnormal return of 3.1 basis points, which is not statically significant.

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So, the recommended stocks do not outperform the benchmark. The result indicates that it is not profitable to invest in the recommended stocks which are presented in newsletters.

There are also some studies which focuses on stock recommendations published in columns. Palmon, Sudit and Yezegel (2009) analyse columns which are published in Business Week, Forbes and Fortune during the period 2000-2003. Evidence shows that previous returns shown in other research are sample specific and not representative for stock recommendations in general. The market impact is larger for recommendations which contain rumours to mergers and acquisitions and references to the management. They do not find any long term abnormal returns. The Wall Street Journal published different columns in which the newspaper presents stock recommendations. Sarkar and Jordan (2000) find a positive abnormal return on the publication day of stock recommendations in regional publications of the Wall Street Journal. The effect is larger for companies which are recommended in a column whereby that stock is the only stock which gets a recommendation.

Sarkar and Jordan (2000) investigated different columns in different regional publications, although there are also several authors which looked to a specific column of the Wall Street Journal. The heard- on-the street column – HOTS - is an example of such a column. Davies and Canes (1978) found statistically significant abnormal returns after the stock recommendations in the column. Liu et al. (1990) concluded that the stock recommendations in the column are followed by positive abnormal returns on the publication day. However, they also find a positive and statistically significant abnormal returns and abnormal trading volumes on the two days preceding the publication of the recommendations. Beneish (1991) finds positive abnormal returns on the publication day and the two days preceding the publication of the column. He concludes also that the column contains valuable information, which does not need to be a secondary dissemination of information. Bauman et al. (1995) found also a positive (negative) abnormal return for buy (sell) recommendations. Although, they also looked to the long term abnormal returns. The buy (sell) recommendations made a cumulative negative (positive) abnormal return on the 8th day after the publication. This suggests that due to transaction costs it is not profitable to invest in the recommended stocks.

Another column in the Wall Street Journal which is a popular research topic is the Dartboard column. Barber and Loeffler (1993) conclude that the average abnormal return is 4% for the two days following the stock recommendation. However the effect is reversed within 25 trading days. They find also double normal trading volumes in the two days following the stock recommendations. Liang (1999) finds a two day abnormal return of 3.52% after the publication day. The effect is reversed within 15 days after publication. He concludes that the abnormal returns are caused by the price pressure hypothesis. Mainly naïve investors are buying the recommended stocks. Albert and Smaby (1996) use an event study approach whereby they use a post-event estimation period. They also find

13 positive abnormal returns for the two days after the publication date. However they do not find a reversing effect. Their results suggest that there is an information effect instead of a price pressure effect. Pruit et al. (2000) find also an abnormal return of more than 3% on the publication day. In the Dartboard column, four professional analysts are picking each one stock, which they think will gain most price increase in the upcoming months. These gains are compared to four stocks which are chosen randomly by throwing darts. The idea behind this column is to look whether expert analysts can outperform randomly chosen stocks. Metcalf and Malkiel (1994) document that the excess returns of the stocks picked by the analysts are creating bigger excess returns than the randomly chosen stocks. Although, they can not systematically beat the market, because after controlling for risk, the stocks of the analysts do not outperform the dart stocks. Atkins and Sundali (1997) show that the picked stocks by analysts are outperforming the randomly chosen stocks, however the results are not statistically significant. Although the results are not significant, they conclude that the experts can outperform the market.

The Inside Wall Street Column of Business Week has also been a popular topic in research. The column reports stock recommendations which are quoted by an analyst. Sant and Zaman (1996) conclude that the positive stock recommendations of stocks which are followed by less than 20 analysts are creating positive abnormal returns on the publication day. However, when more than 20 analysts follows the stock, then the effect disappears. They find also that the size adjusted six month performance of the recommendations is negative for stocks which are followed by less than 20 analysts. This offsets the abnormal returns on the publication day. Mathur and Waheed (1995) find also positive abnormal returns on the day prior to the publication day, the publication day itself and the two days after the publication date. They also find long term positive excess returns prior the publication date. However, the long term periods after the publication date generate negative excess returns, which could indicate that secondary information – like stock recommendations – are of less value for long term investors.

The previous mentioned papers were all stock recommendations which were published in the United States. There is also some research for written stock recommendations in other countries. Lidén (2007) investigated Swedish stock recommendations. He collected 2,282 stock recommendations from six Swedish newspapers and magazines. He finds a 0.79% (-1.5%) abnormal return on the publication day for buy (sell) recommendations. The effect for buy recommendations was reversed almost fully in 20 days supporting the price pressure effect. The prices of sell recommended stocks continued decreasing after the publication day suggesting the information hypothesis is working for sell recommendations. Lidén (2006) uses the same six printed media to look whether it is profitable to invest in recommendation changes. Following the buy recommendation changes will not earn more than the benchmark for an investor. However, when an investor shorts the sell recommendations, it can earn

14 positive abnormal returns. He concludes that buy recommendations are misleading investors, where sell recommendations are leading investors. The misleading effect of buy recommendations is due to the fact of over optimism about future prospects of the firm.

Kerl and Walter (2007) analysed buy recommendations by German Personal Finance Magazines. They find an abnormal five day return of 2.58% after the publication day. Their conclusion is that both the price pressure effect and the information effect is creating the abnormal returns. For small and glamour stocks the prices will reverse over time and for value stocks the stock recommendations will have a more permanent change of the price. Kerl and Walter (2009) use the same printed media to evaluate the long term performance of stock recommendations in Germany. They find strong evidence for sell recommendations to create investment value in the long term. Also value stocks and stocks with a positive prior performance can create positive investment value when there is a buy recommendation for that stock. Kladobra and von der Lippe (2001) examined 1,647 stock recommendations published in leading German business magazines and they find that stocks that fall in returns after being recommended outnumber by far the recommended stocks which increase in returns after publication.

Schlumpf, Schmid and Zimmermann (2008) evaluate 1,460 stock recommendations published in the Finanz und Wirtschaft (FuW), Switzerland’s largest financial newspaper. This paper is authentic in the way that it analyses the same stock recommendations twice. The stock recommendations are first published to a limited clientele and after some time they are published in the FuW for all the readers. They find for both publication days positive (negative) abnormal returns for buy (sell) recommendations. However, the effect of the first publication does not reverse, supporting the information hypothesis. The effect of the second publication does reverse over time, which supports the price pressure hypothesis. So, all the relevant price change of the asset is already incorporated in the stock price on the first publication day.

The Turkish column Investor Ali in the Moneymatik is also used to evaluate stock recommendations. Muradoğlu and Yazici (2002) find that small investors can not make any profit from the recommendation in Investor Ali. Although, if it is possible to front-run the recommendations five days, it is possible to earn more than 5% per week in excess of the index return. Kiymaz (2002) looks to the market rumours/gossips in the Ekonomik. He finds that there are none abnormal returns in comparison with the Istanbul Exchange Index. Although, he finds also that there are some abnormal returns before the recommendation. Finally, there are some countries with only a limited studies about stock recommendations. Menéndez- Requejo (2005) looks to stock recommendations in the Spanish column The Indiscrete published in Cinco Dıas. In the period 1997-1999, she shows that the market reacts before the publication of the

15 stock recommendations. The cumulative abnormal returns are 1.13% for buy recommendations and - 2% for sell recommendations. Wijmenga (1990) is the only study which analyses Dutch stock recommendations. Using three written media sources, he finds that there are abnormal returns for the first week after publication. However, after the first week the prices reverse to the old level. Therefore there is no long term investment value for the Dutch stock recommendations. Cervellati, Ferretti and Pattitoni (2014) investigated Italian stock recommendations. They find asymmetric price and volume reactions on the publication day supporting the attention-grabbing hypothesis. Because sell recommendation are creating a null effect for both the returns and trading volumes. Kumar et al. (2009) shows that there are positive abnormal returns for stock recommendations in India. Although, sell recommendations do not show any significant abnormal returns. Chan and Fong (1996) are looking to stock recommendations in Hong Kong. They find that there is new information in the second hand dissemination of stock recommendations for buy recommendations. The abnormal returns which are created after the publication of the buy recommendation in the newspaper will have a fundamental change of the stock price. However, this does not happen for sell recommendations. The information value of these recommendations are already incorporated in the first hand dissemination.

The previous mentioned papers analysed the price reaction of recommended stocks, which were published in written media. The following studies discusses the stock recommendations in television shows. The most famous television show in research is Mad Money with Jim Cramer. Engelberg, Sasseville and Williams (2012) analyse 391 recommendations and find a positive abnormal overnight return of over 3%. The largest abnormal returns are found among the smallest companies, with an overnight return of 6.7%. There are also other effects which makes the impact bigger: stocks that have performed relatively poorly over the previous twelve months, stocks recommended on days when relatively few recommendations are issued, and stocks recommended during the discussion segment. Within a few days the prices are reversing to levels before the announcement. Engelberg et al. (2012) find evidence supporting the theory of Shleifer and Vishny (1997). The model predicts that it is harder for arbitrageurs to profit from mispricing when there is an extreme diverse of the stock price from its fundamental value. Engelberg et al. (2012) measure the diverge from the stock price with the idiosyncratic volatility of the recommended stock. They find a positive relationship between the idiosyncratic volatility and the overnight abnormal returns. They find also supporting evidence by looking to the cost of going short for the arbitrageur. When the cost is higher to short a stock, the abnormal return is also larger supporting the theory of Shleifer and Vishny (1997). Finally, Engelberg et al. (2012) do not find any profitable trading strategies. There are abnormal high levels of short selling the recommended stocks after the show. These investors are profiting of the reversing effect after a few days. The show is normally not live, Engelberg et al. (2012) do not find any significant effects on the moment of taping. The study differs from other papers which discusses stock recommendations in that they can measure attention with the viewership. As expected, they find

16 an relationship between attention –viewership – and abnormal returns. The relationship is stronger when more wealthy people are among the viewers.

There are also other papers which investigates the stock recommendations in Mad Money. Neumann and Kenny (2007) analyse 171 recommendations in the period 7/26/2005- 9/16/2005. They find positive abnormal returns for buy recommendations on the publication day and the day after, however the stock price is steadily declining after the first day. Sell recommendations create negative abnormal returns on the publication day and the day thereafter. The effects for buy recommendations are stronger than those of sell recommendations. Also higher trading volumes are found after a recommendation by Jim Cramer. The average investor can however not profit from these abnormal returns. Neumann and Kenny (2007) show also that a portfolio of going short in the recommendations can create positive abnormal returns for an one-month period. Although these returns are only reachable for professional investors with enough capital to invest.

Bolster, Trahan and Venkateswaran (2012) examined 1,592 buy recommendations and 700 sell recommendations which are recommended in Mad Money. The time period which they examine is 28 July 2005 through 31 December 2008. Using an event study approach, they find positive (negative) abnormal returns for buy (sell) recommendations. Consistent with the price pressure effect the abnormal returns of buy recommendations reverse within a month. The returns stay at a significant level for sell recommendations. They also find a cumulative return for a dollar weight portfolio of - 22.90%, although the S&P500 earned -26.81% in the same period. A cross sectional analysis shows that the returns of recommended stocks are driven by beta exposure, smaller stocks, growth-oriented stocks, and momentum effects. Finally, they show that analysts which are following the same stocks are adjusting their recommendations in the same way as Cramer. However, the other way works also, Cramer is also adjusting his recommendations when the other analysts are changing their recommendations.

Hobbs et al. (2012) investigated 1,234 buy recommendations in the period February 2006 until December 2006. They find a positive abnormal return in the days after the publication day. The abnormal returns reverse after these short term price effect. They find also a positive relation between going short and a buy recommendation for that stock, even after controlling for factors which normally explain going short. They do not find these results for sell recommendations. Lim and Rosario (2010) find that Cramer’s stock picking style is consistent with the positive-feedback trading strategy. This strategy involves picking the stocks which have positive prior performance. The six-month return provides some evidence that Cramer has some stock picking ability. The long term returns of recommended stocks have the proper sign and are significant. However they are only 1% above or under the benchmark. Cramer is especially accurate in stock picking small stocks. Small stocks are

17 normally less known by investors and have a lower trading volume than large stocks. Created trading by the stock recommendation creates therefore a larger effect for small stocks. His impact is larger for buy recommendations than for sells. Keasler and McNeil (2010) find also positive (negative) abnormal returns for buy (sell) recommendations. There are also abnormal trading volumes after the announcement of the recommendations. Especially, small stocks are causing abnormal returns. Because there is no evidence of long term outperformance of recommended stocks, the authors suggest that the price pressure hypothesis causes the short term abnormal returns. Therefore it is not profitable to invest in the recommended stocks in the long term.

Also other television shows have been used for research. Ferreira and Smith (2003) investigated the television show: Wall Street Week with Louis Rukeyser. Using an even-study approach in 1997, they determine that the abnormal return on the publication day is 0.65%. Using industry and size matching, they determine that the increase of the recommended stocks is larger than the increase of the matched sample. The results suggest that the recommendations in Wall Street Week with Louis Rukeyser contains relevant information for investors. Earlier, Pari (1987) found an excess return of 0.66 percent on the publication day for a sample of 1983-84. However, within two weeks the recommended stocks underperform the benchmark with almost 1%. Griffin et al. (1995) find an abnormal return of 1.1 percent in a dataset from 1972 until 1990. Beltz and Jennings (1997) use a sample of 801 recommendations during 1990 until 1992. They conclude that negative recommendations contain valuable information, because after the recommendation the stock price drops and the effect does not reverse over time. Most of the investors ignores this news, because the abnormal trading volumes of sell recommendations is relative low. Examining the portfolios of the panellists in the show, Beltz and Jennings (1997) show that the price after the recommendations does ‘quite well’, suggesting that they bring some relevant information.

Desai and Jain (1995) investigated recommendations made by experts at Barron's Annual Roundtable. The buy recommendations create a positive abnormal return of 1.91 percent in the time between the recommendation date and the publication date. After the publication date, the stock does not create any abnormal returns. Therefore it is not profitable to invest in buy recommendations. The findings for sell recommendations are stronger and suggest that there is some investment value in selling those stocks. Although, the sample of sell recommendations is small, causing the outcomes less reliable. Gerke (2000) investigates the German show 3sat Börse which was weekly broadcasted on 3sat. He found that stock recommendation can cause ‘extreme price movements’. Cumulated returns amount 24% on average. He also describes an case whereby a journalist could earn nine percent by trading before the recommendation. This could suggest that analysts could manipulate the viewers and trade before they broadcast the recommendation to make large profits.

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Stock recommendations can create positive returns as previous studies have shown, but the question remains whether stock recommendations from one analyst can have different value than those of another analyst. Li (2005) looks to persistence in performance for stock recommendations. He shows that analysts with above median risk adjusted performance persistence outperform below median risk adjusted performance analysts. Underlying reason is that above median performance analysts uses more information to create the recommendations. Li (2005) finds also that investors underreact to recommendations of past winners, which could create an investment strategy to follow the top analysts of previous periods.

3. Hypothesis development Engelberg et al. (2012) evaluated the stock recommendations which are published in Mad Money. I perform an out of sample investigation to test whether stock recommendations can create investment value for a retail investor. The stock recommendations of the Dutch television show Business Class are used. The most important difference between the existing literature and this thesis is, that almost every paper investigates recommendations from the United States. Next, the Dutch stock market is much smaller with less assets to choose from. Merton (1987) showed that investors only invest in assets of which they are aware. Teslar and Werner (1995) show that there exists an home bias, whereby investors are biased to invest in domestic assets. So Dutch investors would be biased to invest in Dutch assets. Because of the small size of the Dutch market , stock recommendations on television will reach a higher percentage of domestic viewers than in the United States. Thus, the recommendations presented in Business Class could considered to be more valuable for investors.

Engelberg et al. (2012) find positive abnormal overnight returns for stock recommendations in Mad Money. Neumann and Kenny (2007), Bolster, Trahan and Venkateswaran (2012) and Keasler and McNeil (2010) are confirming these findings. These papers also find negative abnormal returns for sell recommendations. In the popular press attention is paid to Business Class. As an example, the stock prices of AMG, Kendrion and Imtech increased after a recommendation by Geert Schaaij in 2014 in Business Class3. Therefore, the first hypothesis is:

Hypothesis 1: Buy (sell) recommendations in Business Class create positive (negative) short term abnormal returns on the publication day

Measuring the impact of a recommendation using daily abnormal returns has one disadvantage. During an entire day, prices could increase and decrease or vice versa with a zero total effect on daily returns. Although, the stock recommendation could have created value for some first traders or investors which already had the stock in their portfolio. When this is not the case, it did at least create

3 https://www.beleggersbelangen.nl/2014/12/08/goedkoop-kunstje-van-beleggingsexperts/ 19 attention for that stock. Beaver (1968) documents that the change in prices reflects the beliefs of the average investor and the change in trading volume shows the sum of the reactions of all the investors. Trading volumes are often used as measure for attention (Barber and Odean (2008) and Gervais, Kaniel, and Mingelgrin (2001)). Neumann and Kenny (2007), Engelberg et al. (2012) and Keasler and McNeil (2010), do all find positive abnormal trading volumes after the publication of the stock recommendations in Mad Money. I test this for Business Class using the following hypothesis:

Hypothesis 2: Recommendations in Business Class create positive short term abnormal trading volumes on the publication day

The information hypothesis states that new revealed information creates a long term price effect, because the information reveals fundamental changes in the asset. Therefore, the information hypothesis states that the short term price effect will not reverse itself in the long term. The price pressure and attention grabbing hypothesis states that the revealed information is not valuable. Only naïve investors are trading after the news event. The impact of the created short term effects will reverse itself after sometimes according to these two hypotheses.

Engelberg et al. (2012), Neumann and Kenny (2007), Bolster, Trahan and Venkateswaran (2012) and Keasler and McNeil (2010) all are testing these hypotheses. They find a reversing effect after some days for buy recommendations. This is supporting the price pressure hypothesis, whereby naïve investors are trading after the stock recommendation. Bolster and al. (2012) also find that a dollar- weighted portfolio of recommended stocks outperforms the S&P500 in the same time period. However, the excess returns are not positive and statistically significant. Thus, these papers conclude that there is not any value in investing in stocks recommended in Mad Money. I test this for Business Class using the following hypothesis:

Hypothesis 3: There is not any long term value in investing in the stocks which are recommended in Business Class

A conflict of interest can cause several issues for the relationship between the analyst and the investor. Business Class tries to persuade the viewer to think it is objective and informative. However, the professionals and companies which presents itself in the show pay a lot of money to be in the show. Therefore, Business Class is essentially a way to promote the companies of the analysts. This gives rise to a conflict of interest between the investment bank and the investor which follows the recommended stocks.

Another issue in the releasing of the stock recommendations of analyst Geert Schaaij is that he might reveal his recommendations to his clients before the show by presenting them in his magazine for clients. His magazine is normally published on Wednesday and Business Class is broadcasted on Sunday. This implies that clients who receive his Beursgenoten magazine could profit in the days

20 between Wednesday and Sunday. The other analysts in the show mention often that they already have a position for their clients in the stocks they recommend. The fact that they already have a position in the stock before the show, can lead to a pre-event drift in abnormal returns and abnormal trading volumes. Geert Schaaij has another possible conflict of interest with the viewers of Business Class. He started his own investment fund in December 20154. It could be interesting for him to recommend stocks in which he invested with his fund.

Neumann and Kenny (2007) do find abnormal returns preceding the airing of Mad Money. Keasler and McNeil (2010) find positive significant abnormal trading volumes in the ten days before the show. These results suggest insider trading before Mad Money, which is possible due to the fact that the show is not live. The described factors of Business Class can also be seen as a form of insider trading. So the next hypothesis is as follows:

Hypothesis 4: There is a pre-event drift in abnormal returns and abnormal trading volumes in the days before the show.

Previous research has shown that the created abnormal returns are not the same for every type of company. Stickel (1995) shows that smaller firms have larger abnormal returns than large firms. These findings are confirmed by Engelberg et al. (2012), which find that the abnormal returns are biggest for firms in the smallest quintile. Lim and Rosario (2010) show that Cramer is especially good in recommending small stocks. Finally, Keasler and McNeil (2010) conclude that firms with the smallest market capitalization creates the largest abnormal returns.

Kerl and Walter (2007) find that value stock have larger positive abnormal returns, which also persist longer over time. Fama (1993) shows that normally value stock outperform growth stocks. This leads me to test the following hypothesis:

Hypothesis 5: Small and value firmsl create bigger positive (negative) abnormal returns and positive trading volumes than large and growth firms.

A large part of the existing literature does find evidence for the price pressure hypothesis. However, attention for stocks is also important. Only when an investor is aware of the stock, he will consider the stock as investment option. Odean (1999) proposes that investors only seek information about stocks which already caught their attention. Barber and Odean (2008) show that investors only buy attention- grabbing stocks. Attention can therefore be the factor which explains the abnormal returns and abnormal trading volumes, which are created by the stock recommendations. Engelberg et al. (2012) do find a statistically significant relationship between viewership and the abnormal overnight returns, suggesting that attention leads to trading in the stock. In this thesis, I investigate whether the stock

4 http://www.quotenet.nl/Nieuws/Geert-Schaaij-is-6-miljoen-rijker-en-CEO-van-zijn-eigen-beursfonds-170089 21 recommendation of Business Class support the attention-grabbing hypothesis of Barber and Odean (2008), whereby naïve investors are trading the recommended stocks because of the attention the recommendation creates. I use viewership as proxy for attention following Engelberg et al. (2012). In addition, the number of recommendations and the duration of the recommendation are used as proxies for attention. This leads to the following hypotheses:

Hypothesis 6: Attention is correlated with the positive (negative) short term abnormal returns for buy (sell) recommendations

Hypothesis 7: Attention is correlated with the abnormal trading volumes

Arbitrageurs try to profit from mispricing on the stock market. Shleifer and Vishny (1997) argue that arbitrage can become ineffective when asset prices diverge far from fundamental values. Especially stocks with high idiosyncratic volatility are risky for arbitrageurs. Idiosyncratic risk can not be hedged away and arbitrageurs are normally not diversified. The findings of Shleifer and Vishny (1997) suggest that stocks with high idiosyncratic risk may be overpriced, which makes it risky for the arbitrageur to go short in the stock. Therefore, these stocks are less valuable for arbitrageurs. Engelberg et al. (2012) test this theory by looking at abnormal returns. They hypothesize that when there is more idiosyncratic risk for a stock, the abnormal return is higher. Because arbitrageurs will not correct the mispricing due to the extra risk. I test this hypothesis in the same way as Engelberg et al. (2012):

Hypothesis 8: Stocks with higher idiosyncratic risk will make arbitrage ineffective

There is a clear distinction between the type of analysts and their trading strategies in Business Class. Martine Hafkamp for example is always searching for ‘the best player in each sector’. Therefore, she often invests in the United States. I therefore expect that the impact of her recommendations are lower, because a Dutch analyst prefers to invest in Dutch assets. Dutch media speak often about the ‘Geert Schaaij effect’, whereby they find a direct market impact after the recommendations of Geert Schaaij. He did recommend Arseus to his clients in the end of 2008. The stock increased annually 25% to 30% for six years5. Another example is the recommendation of Wessanen. The stock price of Wessanen increased almost 9% on the first day after the recommendation of Geert Schaaij in Business Class6.

Geert Schaaij and Edwin Wierda mainly focus on Dutch stocks and therefore, I expect that both of these analysts have a bigger market impact than the analyst who recommends more foreign stocks. In the Financiele Dagblad of 9 March 2012, Jan Kooiman writes about the Wierda effect after his recommendation of Ordina. The stock price increased almost seven percent on the day after the

5 http://www.iex.nl/Column/126661/Arseus-en-het-Geert-Schaaij-effect.aspx 6 http://www.quotenet.nl/Nieuws/Koers-Wessanen-de-lucht-in-door-Geert-Schaaij-31015 22 recommendation7. Reputation is also a factor for the difference between the analysts. A better reputation for one of the analysts can cause that investors will follow that analyst before the other analysts, because investors think that they can profit more of the recommendation of that analyst. The difference between these three analysts and also the others are clear in the type of companies they recommend and the reputation they have. Therefore, I test the following hypothesis:

Hypothesis 9: The abnormal returns and abnormal trading volumes are different for each analyst.

The abnormal returns and abnormal trading volumes are looking to the short term. Reputation plays a role in the short term effects of an recommendation. Long term performance evaluates whether the analysts predicted any growth of the stock price which persists over time. Due to difference in skill, knowledge and budget, I expect a difference in the long term performance between the analysts. The previous mentioned articles about the ‘Geert Schaaij effect’ and the ‘Wierda effect’ can improve the reputation of these analysts. Therefore, I expect that the long term value of the recommendations of Wierda and Schaaij are more valuable. Therefore I test the following hypothesis:

Hypothesis 10: It is more profitable to follow the recommendations of Wierda and Schaaij than those of Vermeulen and Hafkamp

4. Data and Methodology 4.1. Data The stock recommendations which are used in this study are presented in Business Class. Business Class is a Dutch television show, which is broadcasted on RTL 7 on each Sunday morning at 11 am. The show is repeated around midnight at Sunday. Business Class is recorded on the Saturday before the broadcast. The show lasts about 1h30min including commercial breaks. There is a variety of topics which are discussed in a broadcast of Business Class, for example the economy, companies and even sometimes politics. Almost every show, the host Harry Mens has a conversation with a financial analyst who presents his/her opinion about certain stocks.

The conversation wherein the analysts reveal their opinions about the stocks lasts about ten minutes on average. The conversation usually starts with a talk about the general economic state. Often this talk is about the change in oil price or change in interest rates. Thereafter the analyst discusses several news events about companies, stocks and other assets. The data that I, with the help of others, collected consists of interpretations these conversations between Harry Mens and the financial analysts. The recommendations that are provided are classified in four categories: strong buy, buy, sell or strong sell. However, these classifications are interpretations by myself and the other persons which worked

7 https://www.wierdavermogensbeheer.nl/mediadepot/61f9b9c869/Artikel-FD-maart-2012.pdf 23 on the dataset, this could mean that there are some different interpretations between the persons. I did my best to minimize these differences, by watching old broadcasts and compare those with the interpretations of the other persons.

The dataset contains 1,146 recommendations over the period 5/9/2004 until 24/4/2016. I deleted the recommendations with an reprise of 5 or smaller, which means that the same stock is already recommended in the 5 weeks before the recommendation. The duration of each recommendation was not measured for all the observations. So, I deleted the 156 observations which were missing the duration variable. The broadcast of 25/12/2009 was deleted because it was broadcasted on a Friday. Finally, I deleted the recommendations of Bert van Arkel, Jerry Langelaar and Etienne Platte, because they did not attend many shows. The dataset consists therefore of the recommendations by Edwin Wierda of Weirda en Partners, Geert Schaaij of Beursgenoten, Han Vermeulen of Aberfeld and Martine Hafkamp of Fintessa. Finally, the dataset contains 681 recommendations of which are 385 strong buy recommendations, 152 buy recommendations, 66 sell recommendations and 78 strong sell recommendations.

I downloaded the opening and closing prices of the recommended stocks and all the stocks in the three Dutch indices (AEX, AMX and AScX) and the indices itself from Thomson Reuters Datastream. I downloaded also trading volumes, market capitalization and the market-to-book. I converted the market-to-book values into book-to-market values with the following formula:

1 푏푡푚푣푖,푡 = , 푚푡푏푣푖,푡

Where btmv is book-to-market value for stock i on date t and mtbv is market-to-book value for stock i on date t. I obtained viewership data for each broadcast and the rerun (Sunday nigt) from RTL. For the risk-free rate, I use the returns of an one month Dutch government bond from Datastream, which I transformed into daily risk-free rates. There are 312 trading days in a year when I create weekend returns (6*52=312). For a weekend risk free rate, I used the same risk free rate as on Friday. I obtained the composition of the AEX, AMX and AScX from Euronext.

Finally, I evaluated whether there was any news about the recommended stock in the weekend of the broadcast. I searched in LexisNexis if there was any news during the weekend and Monday morning. For those recommendations which were made in a longer weekend due to closed stock markets, I adjusted the period for which I searched for news around days on which the stock market was closed due to holidays. For example, when a recommendation is made on the Sunday of Easter, I looked if there was any news from Friday morning (closed due to Good Friday) until Tuesday morning.

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4.2. Methodology The methodology of this thesis contains three parts: an event study, a cross sectional regression and calendar time portfolios. For those three methods, I need the returns of the recommended stocks. After downloading the opening and closing prices of the assets, the first step is to calculate the daily returns of the assets. Normally, the daily returns are calculated as follows:

(1) 푟푖,푡 = 퐿푁(푃푖,푡) − 퐿푁(푃푖,푡−1), where 푃푖,푡 is the closing price of stock i on day t and 푟푖,푡 is the daily return for firm i on day t. There is one complication in using this formula to calculate the daily returns. Business Class is broadcasted on Sunday morning and the stock market is closed in the weekend. In order to measure the impact of a recommendation, I therefore create a weekend return:

(2) 푟푖,푊푒푒푘푒푛푑 = 퐿푁(푂푃푖,푀표푛푑푎푦) − 퐿푁(푃푖,퐹푟푖푑푎푦), where 푂푃푖,푀표푛푑푎푦 is the opening price of stock i on Monday and 푃푖,퐹푟푖푑푎푦 is the closing price of stock i on Friday.

(3) 푟푖,푀표푛푑푎푦 = 퐿푁(푃푖,푀표푛푑푎푦) − 퐿푁(푂푃푖,푀표푛푑푎푦), where 푂푃푖,푀표푛푑푎푦 is the opening price of stock i on Monday and 푃푖,푀표푛푑푎푦 is the closing price of stock i on Monday. On the other days the normal formula is used. I also account for days the stock market is closed. The mentioned formulas are adjusted for each closed trading day. For example on Easter, I use (2), however then I used the opening price on Tuesday and the closing price on Thursday. The day after the weekend, (3) is used however then for Tuesday.

4.2.1. Event Study for Abnormal Returns The created attention by the stock recommendations could lead to higher returns. To evaluate whether there are abnormal returns in the days around the show an event study methodology is used. An event study is useful to look if those returns are different from a normal situation after an specific event. Abnormal returns are calculated in the following way:

퐴푅푖,푡 = 푅푖,푡 − 푁푅푖,푡,

where 퐴푅푖,푡 is the abnormal return for stock i at time t, 푅푖,푡 is the return of that stock at time t and

푁푅푖,푡 is the normal return for stock i at time t. To calculate those abnormal returns, I need the normal returns. The market model is used to calculate the normal return:

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̂ 푁푅푖,푡 = 훼̂푖 + 훽푖 × 푅푚,푡, where the 푅푚,푡 is the market return at time t, which are proxied by the return of the index in which the stock belongs at the moment of the recommendation. When a stock is not listed on the AEX, AMX or

AScX, I used the AScX as benchmark. The 훼̂푖 and 훽̂푖 for stock i are estimated in the estimation period using the following regression model:

푅푖,푡 = 훼푖 + 훽푖 × 푅푚,푡 + 휀푖,푡, where 푅푖,푡 is the return of stock i at time t, 푅푚,푡 is the market return at time t and 휀푖,푡 is the error term for stock i at time t. I use an estimation period of an half year. Using the fact that I have only a six days trading week, which results in (52x6=312) 312 trading days per year. An half year estimation period is 156 days. Finally, the estimation window is [-40,-196]. The estimation window is also reasonable considering other studies, for example Neumann and Kenny (2007) use an 170 day estimation window. The forty days before the event seems a long period, but it is needed for the idiosyncratic volatility variable which I create in the cross sectional analysis.

An abnormal return of one recommendation does not have much meaning, therefore I look to the average abnormal return (AAR) and the cumulative average abnormal return (CAAR). I calculate the AAR for the event window [-12,12], which covers the two weeks before the show and the two weeks after the show. The average abnormal returns are calculated as follows:

1 퐴퐴푅 = ∑푁 퐴푅 , 푡 푁 푖=1 푖,푡 where 퐴퐴푅푡, is the average abnormal return at time t, N is the number of recommendations and 퐴푅푖,푡 is the abnormal return of stock i at time t. To test whether the average abnormal return is significant, I use the following test statistic:

퐴퐴푅푡 푇푆1 = √푁 푎 ~푁(0,1), 푠푡

1 where 푠푎 = √ ∑푁 (퐴푅 − 퐴퐴푅 )2, 푡 푁−1 푖=푡 푖,푡 푡

where N is the number of observations, 퐴푅푖,푡 is the abnormal return for stock i at time t and 퐴퐴푅푡 is the average abnormal return at time t.

I test also the abnormal returns over more than one day. Therefore the cumulative average abnormal return is used. It is calculated as follows:

퐶퐴푅 = ∑푡2 퐴푅 , 푖 푡=푡1 푖,푡

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where 퐶퐴푅푖 is the cumulative abnormal return for stock i and 퐴푅푖,푡 is the abnormal return of stock i at time t. I calculate the 퐶퐴푅푖 for different event windows: [-6,-1], [0,1], [0,6], [-3,-1] and [-3,3]. The [0,1] window measures the effect due to the broadcast itself, because it is possible to start trading after the show on Monday. So the effect could also happen on Monday. I took the [0,6] window to test if there are also significantly abnormal returns in the week after the show. The other three event windows test the hypothesis that there is an drift before the show. Therefore I picked the period of [- 6,-1] to look after the whole week before the show. The [-3,-1] event window is also taken to investigate any front running. In principle this is not possible for the outsider however in the case of Geert Schaaij it is known that he publishes the stock recommendations which he makes on the Sunday already on the Wednesday before in “Beursgenoten”. The cumulative abnormal return is not useful as measure, because it reveals only the CAR for one company. To evaluate the whole dataset, I use the cumulative average abnormal return, which is calculated as follow:

1 퐶퐴퐴푅 = ∑푁 퐶퐴푅 , 푁 푖=1 푖

where 퐶퐴퐴푅푖 stands for cumulative average abnormal return at time i. To test if the CAAR is statistically significant, the following test statistic is used:

퐶퐴퐴푅 푇푆2 = √푁 푐 ~푁(0,1), 푠푡

1 where 푠푐 = √ ∑푁 (퐶퐴푅 − 퐶퐴퐴푅)2, 푡 푁−1 푖=푡 푖 where N is the number of recommendations, 퐶퐴푅푖 is the cumulative abnormal return for stock i and 퐶퐴퐴푅 is the cumulative average abnormal return.

4.2.2. Event study for Abnormal Trading Volumes The literature review indicated that the attention created by the stock recommendations will not always lead to abnormal returns, as it is possible that price movements due to the announcements are not reflected in the daily returns. When an investor already has the stock in the portfolio, the investor can realize their profits after the created abnormal returns by the stock recommendation. When this happens on the same day as the stock recommendation there are not any significant and positive (negative) abnormal returns for buy (sell) recommendations. Therefore I consider abnormal trading volumes, because when stock recommendations create attention, it can lead to trading. It does not matter whether the investors are buying or selling the stocks, because the stock recommendation can still have created attention. Therefore I also look to abnormal trading volumes which are caused by the

27 stock recommendations. Engelberg et al. (2012) and Neumann and Kenny (2007) are also investigating trading volumes for this reason. Neumann and Kenny (2007) calculate the abnormal trading volume following the method of Campbell and Wasley (1996) as will I. Ajinkya and Jain (1989) show that trading volumes are not normal distributed. A log transformation of the trading volumes will create trading volumes, which are approximately normally distributed. Therefore, I use log-transformed trading volumes in this study. Next, I explain the method to calculate abnormal trading volumes suggested by Campbell and Wasley (1996). The first step is to calculate the trading volume variable which is used to calculate the abnormal trading volumes:

(푛푖,푡×100) 푉푖,푡 = 퐿푁 ( + 0.000255), 푆푖,푡 where 푛푖,푡 is the number of shares traded for firm i on day t, and 푆푖,푡 is the shares outstanding for firm i on day t. The 0.000255 constant is added, because it is impossible to take the log of zero. Some small companies can have trading values of zero at some days. To calculate market adjusted abnormal trading volumes an market trading volume is also needed. Campbell and Wasley (1996) calculate market trading volumes as follows: 푁 1 푉̅ = ∑ 푉 , 푚,푡 푁 푖,푡 푖=푡 where N is the number of stocks in the market index and 푉푖,푡 is the trading volume for stock i at time t. I created a market volume for each of the three Dutch indices (AEX, AMX, AScX). When a recommended stock is not listed on one of the three indices, I used the AScX as market index. The market model is used to measure the normal trading volume. Jain and Toh (1988) show that trading volumes differ significantly on different hours on a day. They show also that trading volumes differ on trading days. Monday is normally the day on which trading volumes is the lowest. On Tuesday and Wednesday the trading volumes will start increasing and thereafter the trading volumes will decrease on Thursday and Friday. Lakonishok and Maberly (1990) shows also that trading volumes are the lowest on Monday. Therefore, I create dummies for each day in the week to capture this day-in-the-week effect. The market volume for each stock is the market in which the stock is placed at that moment of time. If the stock is not in any of the three indices, I use the AScX as benchmark. The estimation window for the trading volumes is also [-40,-196]. However, because there are not any trading volumes for the weekend, this estimation has a longer duration in weeks then the estimation window of the return event study. The following regression model is used to estimate the normal volumes:

푉푖,푡 = 훼푖 + 훽푖푉푚,푡 + 휃1,푖퐷푚표푛푑푎푦 + 휃2,푖퐷푇푢푒푠푑푎푦 + 휃3,푖퐷푇ℎ푢푟푠푑푎푦 + 휃4,푖퐷퐹푟푖푑푎푦 + 휀푖,푡,

Using these estimation results, I can calculate the abnormal trading volume in the following way:

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퐴푉푖,푡 = 푉푖,푡 − (훼̂푖 + 훽̂푖푉푚,푡 + 휃̂1,푖퐷푚표푛푑푎푦 + 휃̂2,푖퐷푇푢푒푠푑푎푦 + 휃̂3,푖퐷푇ℎ푢푟푠푑푎푦 + 휃̂4,푖퐷퐹푟푖푑푎푦)

The abnormal trading volumes per company are not useful, therefore I use the average abnormal trading volume and the cumulative average abnormal trading volume. The average abnormal trading volume is calculated as follow: 1 퐴퐴푉 = ∑푁 퐴푉 , 푡 푁 푖=푡 푖,푡

Where 퐴퐴푉푡 is the average abnormal trading volume at time t and 퐴푉푖,푡 is the abnormal trading volume for stock i at time t and N is the number of recommendations. I calculate the average abnormal trading volume for the [-10,10] event window, which considers the two weeks before and after the show. To test whether the AAV is different for zero at a specific event date, the following test statistic is used:

퐴퐴푉푡 푇푆3 = √푁 푣 ~푁(0,1), 푠푡

1 Where 푠푣 = √ ∑푁 (퐴푉 − 퐴퐴푉 )2, 푡 푁−1 푖=푡 푖,푡 푡

where 퐴퐴푉푡 is the average abnormal trading volume at time t, 퐴푉푖,푡 is the abnormal trading volume for stock i at time t and N the number of observations. I also analyse the cumulative average trading volumes. The first step is to calculate the cumulative trading volumes:

퐶퐴푉 = ∑푡2 퐴푉 , 푖 푡=푡1 푖,푡 where 퐶퐴푉푖 is the cumulative abnormal trading volume at time i and 퐴푉푖,푡 is the abnormal trading volume for stock i at time t. The cumulative average abnormal trading volume is then calculated as follow:

1 퐶퐴퐴푉 = ∑푁 퐶퐴푉 , 푁 푖=1 푖

where 퐶퐴퐴푉 is the cumulative average abnormal trading volume and 퐶퐴푉푖 is the cumulative abnormal trading volume for stock i. I calculate the CAAV for the [-5,-1], [-3,-1] [0,4] and [-3,2] window. The [- 5,-1] and [-3,-1] window captures the week before and some days before the show and is used to evaluate whether there is any front running before the broadcast of Business Class. The Monday is event day zero in this case, because there are not any trading volumes in the weekend. The [0,4] event window is used to evaluate whether the impact on trading values stays after a week of the show. It does not add any value to calculate the [0,1] event window, as in the return event study, because there is not a weekend trading volume. The next test statistic is used to test if the CAAV is statistically significant different from zero:

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퐶퐴퐴푉 푇푆4 = √푁 푐푣 ~푁(0,1) 푠푡

1 ,where 푠푐푣 = √ ∑푁 (퐶퐴푉 − 퐶퐴퐴푉)2, 푡 푁−1 푖=푡 푖 where N is the number of recommendations, 퐶퐴푉푖 is the cumulative abnormal trading volume for stock i and 퐶퐴퐴푉 is the cumulative average abnormal trading volume.

4.2.3. Cross sectional analysis After the event studies, a cross sectional analysis is conducted to determine which variables explain abnormal trading volumes and abnormal returns. I test the following periods: a week before the event, the event day itself and the week after the event. So the abnormal returns are tested for [-6,-1], [0], [1,6] and I test for returns also [0,1]. I test [0,1] also because it is the event date itself and the Monday thereafter. Investors can normally start trading on the Monday after the show, so when investors are buying the recommended stock on the Monday after the show, it can also create abnormal returns. For the abnormal trading volumes the following event periods are tested: [-5,-1], [0], [0,4]. There are not any abnormal trading volumes in the weekend, because the stock market is closed in the weekend. Therefore I can only measure the abnormal trading volumes on the Monday. So the zero event day is Monday in this case. Therefore it does not have any value to evaluate periods like [0,1] for trading volumes. Ultimately, the following regression is performed:

푟 푟 (퐶)퐴푅(푖,푡) = 훼 + 훽푗 푋푖,푗 + 휀푡

푣 푣 (퐶)퐴푉(푖,푡) = 훼 + 훽푗 푋푖,푗 + 휀푡

A cross sectional analysis is conducted to investigate whether the created attention by the recommendations creates abnormal returns or abnormal trading volumes. Attention is measured with the following variables:

 Viewership – The logarithm of viewership (x1000) of each broadcast. The viewership measure is the sum of the amount of viewers of the first broadcast and its rerun.  Number of total recommendations – The number of total recommendations is the number of recommendations which are made during one broadcast, which is including bonds and other assets. When there are more recommendations during a show, the total attention is divided over more recommendations, which could lead to less attention for one stock and therefore a smaller effect.  Number of Dutch recommendations – The number of Dutch recommendations is the number of recommendations of Dutch assets. This measure is included, because this study only looks to the effects on the Dutch stocks. The show is also in Dutch, so I assume only Dutch

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investors are watching the show. For those investors it is more likely that they invest in Dutch assets. Teslar and Werner (1995) shows that there is an home bias wherein investors are biased to invest in domestic assets. Dutch stock recommendations can so be more valuable for the Dutch investors which watch Business Class.  Duration – The duration of the stock recommendation measured in seconds.

Not only attention can create abnormal returns or trading volumes, but also other effect can cause the abnormal returns or trading volumes. Therefore I include the following control variables:

 IDIOVOL – Engelberg et al. (2012) tested the theory of Shleifer and Vishny (1997) that higher idiosyncratic risk makes arbitrage ineffective. Engelberg et al. (2012) create a variable which measures the idiosyncratic volatility. The IDIOVOL measure of Engelberg et al. (2012) is calculated as the standard deviation of the abnormal returns in the [-35,-5] window. I changed the window to [-37,-7], because I also test the CAAR and CAAV in the week before the show. If it is more difficult to arbitrage when the idiosyncratic risk is high, I expect IDIOVOL to have a positive impact on the CAR and CAV.  Dummy for Hafkamp – The dummy takes the value of 1 when the recommendation is done by Martine Hafkamp and 0 when the recommendations is done by another analyst.  Dummy for Schaaij – The dummy takes the value of 1 when the recommendation is done by Geert Schaaij and 0 when the recommendations is done by another analyst.  Dummy for Vermeulen – The dummy takes the value of 1 when the recommendation is done by Han Vermeulen and 0 when the recommendations is done by another analyst.  Dummy for Wierda – The dummy takes the value of 1 when the recommendation is done by Edwin Wierda and 0 when the recommendations is done by another analyst.  Dummy for AEX – The dummy takes the value of 1 when the stock is listed on the AEX on the moment of the recommendation and otherwise 0.  Dummy for AMX – The dummy takes the value of 1 when the stock is listed on the AMX on the moment of the recommendation and otherwise 0.  Dummy for AScX – The dummy takes the value of 1 when the stock is listed on the AScX on the moment of the recommendation and otherwise 0.  Market capitalization – The logarithm of the market capitalization of the recommended stock measured on the day of the recommendation.  Book-to-price-value – The book-to-price value of the recommended stock measured on the date of the recommendation.  News Dummy – The dummy takes the value of 1 when there is other important news about the recommended stock in the weekend of the recommendation. The news articles are found on

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LexisNexis. The variable takes only the value of 1 when there is news during the weekend and Monday morning.

In the event periods before the broadcast, I exclude the attention measures, because they are not known at that moment and can not influence the abnormal returns or trading volumes. I also conduct robustness tests. Therefore, I created interaction variables. Each previous named variable is multiplied with the news dummy to create these interaction variables.

4.2.4. Long term performance To determine if there is any long term performance after a stock recommendations, I create calendar time portfolios. I create separate portfolios for each type of recommendation: strong buy recommendations, buy recommendations, sell recommendations and strong sell recommendations. When the portfolios are based on sell or strong sell recommendations, the investor would have to short the stocks. In reality, it is difficult to short for a retail investor.

The portfolios, which I consider have a duration of 4 weeks, 3 months, 6 months and a year. Because I created weekend returns, a week has 6 trading days. So these portfolio durations corresponds to 24, 78, 156 and 312 trading days. The stocks are bought on the Monday morning after the show. The portfolios are equally weighted, because the market capitalizations of the Dutch stocks can differ a lot. I corrected the returns for transaction costs when the stocks are bought and sold. I followed Binck bank which has transaction costs of at least €10,- based on a portfolio starting with €1.000,-, which corresponds with a percentage of 1%.8

To evaluate whether there is long term outperformance of these portfolios, the CAPM model is estimated. A market return is needed as benchmark to evaluate excess returns. In this case, the return of an index (AEX, AMX, AScX) can not be used as market return, because each portfolio consists stocks from all those indices. Therefore I create a market return by summing the market capitalizations of the companies in each index on a daily basis. Each index gets a weight based on these summed market capitalizations. Using these weights, I can calculate a market return using the returns of these indices. The market return is calculated as follow:

푅푚,푡 = 푤퐴퐸푋,푡 × 푅퐴퐸푋,푡 + 푤퐴푀푋,푡 × 푅퐴푀푋,푡 + 푤퐴푆푐푋,푡 × 푅퐴푆푐푋,푡,

Where 푤푖,푡 is the weight for index i at time t and 푅푖,푡 is the return of index i at time t. I exclude a stock, if the market capitalizations or stock prices are not available on Datastream. Datastream does not provide the market capitalizations for 2016, so I assume that these market capitalizations are the same as on 31/12/2015. I also assume that the weekend market capitalizations are the same as on Friday.

8 https://www.binck.nl/zelf-beleggen/tarievenoverzicht 32

As mentioned before the performance evaluation is done using the CAPM, when there are excess returns, there will be long term outperformance. The following regression equitation is estimated to test the CAPM:

(푅푝 − 푅푓)푡 = 훼 + 훽푝(푅푚 − 푅푓)푡 + 휀푡, where 푅푝 is the return of a portfolio, 푅푓 is the risk free rate and 푅푚 is the market return. Jensen (1968) describes that alpha can be used as measure to evaluate performance over time. The portfolio outperforms the market when the alpha is positive and statistically significant. In this study, Jensen’s alpha is used to evaluate the excess returns.

In addition, in line with Engelberg et al. (2012) I use the three factor Fama French model that shows rejections of the CAPM (Fama and French (1993)). First, SMB explains the premium of small stocks over big stocks and is the difference between a portfolio of the small stocks and big stocks. Second, HML is a factor which explains the premium of value stocks over growth stocks and is the difference between a portfolio of value stocks and growth stocks. Fama and French (1993) found that value stocks outperform value stocks in the long term. I created those factors myself by creating portfolios of all the stocks which are listed on the AEX, AMX and AScX. These portfolios are re-evaluated at the 1th of July of each year.

The Fama-French three factor regression model is:

(푅푝 − 푅푓)푡 = 훼 + 훽푝(푅푝 − 푅푓)푡 + ℎ푝퐻푀퐿푡 + 푠푝푆푀퐵푡 + 휀푡

In this regression there is also long term performance when the alpha is positive and statistically significant following Jensen (1968).

Finally, Carhart (1997) extended the model with a fourth factor based on the findings Jegadeesh and Titman (1993). Jegadeesh and Titman (1993) shows that investors can profit by buying stocks which performed well in the past and selling stocks which performed poorly in the past. This factor is called momentum. I created the momentum factor myself by making portfolios of the average performance of the last six months for all the stocks in the AEX, AMX and AScX. These portfolios are revaluated each month.

The Carhart four factor (Carhart (1997)) regression model is:

(푅푝 − 푅푓)푡 = 훼 + 훽푝(푅푝 − 푅푓)푡 + ℎ푝퐻푀퐿푡 + 푠푝푆푀퐵푡 + 푚푝푀표푚 + 휀푡

This model outperforms the market also when the alpha is positive and statistically significant.

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5. Empirical Results

5.1. Descriptive statistics Table 1 presents the descriptive statistics for some variables, while Figure 1 shows the corresponding histograms. There are many more buy recommendations than sell recommendations. Geert Schaaij issued 349 stocks, Edwin Wierda issued 156 stocks, Han Vermeulen issued 69 stocks and Martine Hafkamp issued 107 stocks.

On average, there were 126,842 viewers for each show. However, there were also some broadcasts which did not have many viewers. There was a show where only 8,000 people were watching. 126,842 viewers seems to be not very much, but the Netherlands is also a small country. There lives 324,4 million people in the United States9, where there lives only 17.04 million people in the Netherlands10.

The number of Dutch recommendations is on average 5.163. However the average of the total number of recommendations is 6.22, which implies that most recommendations were for Dutch assets. Shows for which only issued foreign recommendations are not included in the dataset. For example, Martine Hafkamp specializes her company in “choosing the best asset in each class”. For her, it does not care in which country she invests. So, she recommends more foreign companies than the other three analysts.

The average duration of each recommendations is 54.01 seconds. However there are also some recommendations with a duration of a few seconds. The maximum duration is more than four minutes.

The average book-to-price value is 0.715 with an standerd deviation of 0.705. The average log market size is 14.844. In real numbers the average market capitalization is € 13,125,980. The maximum market capitalization is 175 million.

9 http://www.census.gov/popclock/ 10 https://www.cbs.nl/nl-nl/visualisaties/bevolkingsteller 34

Figure 1: Histogram of the variables

Type of recommendations shows how many recommendations are classified as strong buy, buy, sell or strong sell. Recommendation by analyst shows the amount of recommendations per analyst. Total Dutch recommendations is the number of recommended Dutch assets. The total number includes also foreign recommendations. Viewership is the number of viewers who watched the broadcast wherein the stock was recommended. Duration is the time in seconds that the analyst was talking about the recommendation. The size variable is natural logarithm of the market capitalization of the recommended company at the time of the recommendation. The book-to-price value shows the ratio between the book value of the company and the market value of the company at the time of the recommendation

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Table 1: Descriptive statistics Viewership is the number of viewers who watched the broadcast wherein the stock was recommended. Duration is the time in seconds that the analyst was talking about the recommended stock. Total Dutch recommendations is the number of recommended Dutch assets. The total number of recommendations takes also foreign assets in count. The size variable is natural logarithm of the market capitalization of the recommended company at the time of the recommendation. The book-to- price value shows the ratio between the book value of the company and the market value of the company at the time of the recommendation

Mean Median Min Max Std. Dev Viewership x(1000) 126.842 123.500 8 283.632 43.158 Duration (in sec) 54.010 53 1 243 30.567 Total Dutch recommendations in show 5.163 5 1 12 2.202 Total number of recommendations in show 6.220 6 1 13 2.223 Log size (market capitalization) 14.803 14.612 9.141 18.981 1.906 Book-to-price value 0.753 0.592 -3.448 9.091 0.754

5.2. Event study returns An event study of returns is used to evaluate whether the stock recommendations in Business Class create abnormal returns. Figure 2 shows the graphs of the cumulative average abnormal returns for the strong buy and strong sell recommendations.

Figure 2: Cumulative average abnormal returns The figure shows the cumulative average abnormal returns of all type of recommendations. The left graph shows the CAAR of strong buy and buy recommendations, where the right graph shows the CAAR of the strong sell and sell recommendations. The CAAR is shown for the [-12,12] event window. The results consist of 360 strong buy recommendations and 149 buy recommendations,62 sell recommendations and 70 strong sell recommendations.

The strong buy graph shows an upward drift before the broadcast. The CAAR almost peaks on the day of the broadcast, and only increases a bit afterwards. This implies that the strong buy recommendations makes almost a CAAR of 2.5% in the 25 days period around the show and the effect does not disappear in the two weeks after the show. A relative large part is due to the drift before the show. The effect for the buy recommendations is smaller. However, in the days before the show, one can see an upward trend, similar to the strong buy recommendations. After the show the CAAR is still increasing for almost a week. After that it starts decreasing a bit, but the CAAR stays different from zero, so the effect does not disappear over time.

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The graph of the strong sell recommendations starts increasing, to a CAAR of 1%, but five days before the show the CAAR of the strong sell recommendations starts decreasing. The two days before the show it recovers from the fall and stays almost on the same level for the rest of the event window. The biggest decrease happened already before the show for strong sell recommendations.

In the case of sell recommendations, ten days before the show the CAAR increases to more than two percent, however two days later it starts decreasing. The slope of the line is going down until the fifth day after the show and stays thereafter on the same level. This means that the effect does not disappear in the two weeks after the show. But the effect does also not happen in the weekend of the show of the first trading day after the show. The biggest fall did already happen before the show.

Table 2 presents the results of the event study for strong buy and buy recommendations. The abnormal weekend return on the day of the show is statistically significant at the 1% level for strong buy recommendations. Strong buy recommendations create positive average abnormal return of 0.86% on the day of the broadcast. The 0.86% is also economically significant. Keeping this return for each day for 312 trading days would create an annual return of more than 200%. The return on the Monday is also positive and statistically significant at the 1% level for the strong buy recommendations. The Monday is the first day investors can buy the recommended stocks after the broadcast. Engelberg et al. (2012) find an abnormal overnight return of more than 3%, in the case of Business Class, it is less Neumann and Kenny (2007) found an 0.59% abnormal return on the event day for Mad Money, which is comparable to the findings in Table 2. The table shows also the upwards drift before the show. Especially on the Thursday before the show, the results are significant at the 1%, but the AAR is also 0.42%, which is also economically significant. Engelberg et al. (2012) find scant evidence of an upwards drift before the show. Neumann and Kenny (2007) find an upwards drift in the week before the show for recommendations by Jim Cramer.

The CAAR is statistically significant at the 1% level for all the four event windows. The abnormal returns are positive and statistically significant in the periods before the show. The [-3,-1] event window is and statistically significant at the 1% level. The CAAR is 1.12% in the week before the show. So there is a clear up drift in the week before the show, which could indicate insider trading. The AAR’s become sometimes negative in the week after the show, but the CAAR for the [0,6] event window is 1.30% and statistically significant, which means the effect stays positive after a week and it does not disappear.

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Table 2: Event Study of returns for strong buy and buy recommendations The table presents the results of an event study based on returns of strong buy and buy recommendations. The normal returns are calculated by using the market model. The used estimation window is [-196,-40]. The average abnormal return is calculated for the [-12,12] period. Thereby also the cumulative average abnormal is calculated for the [0,1], [-6,-1], [0,6], [- 3,-1] and [-3,3] event period. The results consist of 360 strong buy recommendations and 149 buy recommendations. The table shows also the t-value of the abnormal returns. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period Strong buy t-value Buy t-value (0,1) 1.15%*** 6.58 0.33%* 1.71 (-6,-1) 1.12%*** 4.08 1.01%* 1.78 (0,6) 1.39%*** 4.98 1.02%** 2.21 (-3,-1) 0.80%*** 3.49 0.37% 0.92 (-3,3) 2.00%*** 6.27 0.91%* 1.66

-12 0.11% ** 2.30 -0.03% -0.29 -11 -0.16% -1.45 -0.09% -0.56 -10 0.09% 0.97 0.28% 1.56 -9 0.11% 0.89 0.03% 0.25 -8 -0.10% -0.96 -0.24% -1.23 -7 0.11% 1.10 -0.09% -0.65 -6 0.13%** 2.49 0.44% 1.36 -5 0.19%** 1.97 -0.03% -0.24 -4 0.00% -0.01 0.23% 1.01 -3 0.06% 0.65 0.23% 1.10 -2 0.42%*** 2.96 -0.22% -0.76 -1 0.32%** 2.29 0.37%** 2.15 0 0.86%*** 6.48 0.33%*** 4.07 1 0.28%*** 2.85 0.00% 0.00 2 -0.01% -0.09 0.02% 0.15 3 0.06% 0.65 0.19% 1.35 4 0.00% 0.04 0.09% 0.63 5 0.12% 1.03 0.25% 1.20 6 0.07% 1.46 0.15%* 1.86 7 -0.29%*** -3.59 -0.08% -0.65 8 -0.15% -1.60 -0.16% -1.09 9 -0.07% -0.79 -0.01% -0.08 10 -0.06% -0.61 -0.14% -1.12 11 0.04% 0.43 -0.05% -0.51 12 0.22% 1.48 0.11% 1.29

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Buy recommendations create also positive and statistically significant abnormal returns on the day of the broadcast. However, the effect is only 0.33%. In contrast to the strong buy recommendations the Monday is not statistically significant and only 0.00%. The buy recommendations have also an upward drift before the show, however the AAR is only statistically significant on the Friday. The Friday creates an bigger AAR than the weekend in which the show is broadcasted. When looking to the CAAR’s, only the [0,6] event window is positive and statistically significant at the 5% level. So the effect does not disappear after a week of the recommendation. The CAAR is 1.01% in the week before the show and is also statistically significant at the 10% level, suggesting that there is an upward drift before the show.

Table 3 presents the results for the event study for sell and strong sell recommendations. The sell recommendations do only create statistically significant returns for the 6th day after the broadcast. The average abnormal return on the day of the broadcast is positive for sell recommendations. So when the recommendation is negative about the stock, the price is still increasing. The created attention by the recommendation could also trigger the investors to buy the stock. The AAR is however negative for the days before and after the show. The CAAR’s are negative in all the cases, but those are not statistically significant. However, the CAAR of -3.52% in the week before the show is economically significant. This is more than -150% on an annual basis. The cumulative average abnormal return is - 1.56% in the week after the show, suggesting that the effect does not disappear in the week after the show.

Strong sell recommendations create a -0.38% average abnormal return on the day of the broadcast. On the Monday after the show – the first trading day after the show – the return is also negative but not statistically significant. Neumann and Kenny (2007) find an abnormal return of -0.20% after sell recommendations for Mad Money, which is a comparable result. They find also an -0.55% abnormal return in the [0,1] event window. The CAAR is 0.58% in the [0,1] event window. It implies that strong sell recommendations create a short term decrease for the recommended stocks. However in the week after the show the effect almost disappeared. The [0,6] CAAR is 0.05% and thus positive. This is not statistically significant, but it suggests that the effect is disappearing for a small part in the week after the show.

The AAR is sometimes negative and statistically significant on the days before the show. On the Thursday before the show there is even a –0.85% average abnormal return for the strong sell recommendations. The -4 event day has also a -0.82% AAR. The [-6,-1] and [-3,-1] period are both negative but not statistically significant. These results suggest an downward drift in the days before the show, which could suggest insider trading.

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Table 3: Event Study of returns for sell and strong sell recommendations The table presents the results of the event study based on returns of sell and strong sell recommendations. The normal returns are calculated by using the market model. The used estimation window is [-196,-40]. The average abnormal return is calculated for the [-12,12] period. Thereby also the cumulative average abnormal is calculated for the [0,1], [-6,-1], [0,6], [- 3,-1] and [-3,3] event period. The results consist of 62 sell recommendations and 70 strong sell recommendations. The table shows also the t-value of the abnormal returns. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period Sell t-value Strong Sell t-value (0,1) -0.40% -0.82 -0.58%** -2.37 (-6,-1) -3.52% -1.56 -1.40% -1.35 (0,6) -1.56% -0.90 0.05% 0.08 (-3,-1) -1.08% -1.46 -0.75% -0.78 (-3,3) -1.79% -1.34 -1.30% -1.23

-12 0.56% 0.81 0.10% 0.93 -11 -0.01% -0.03 0.50%** 2.04 -10 0.06% 0.11 0.09% 0.26 -9 -0.16% -0.42 0.34% 1.25 -8 2.87% 0.84 -0.28% -0.71 -7 -0.16% -0.78 -0.26% -1.11 -6 -1.62% -1.26 0.27% 1.17 -5 -0.48% -0.50 -0.10% -0.41 -4 -0.35% -0.49 -0.82%** -2.48 -3 -0.18% -0.61 -0.29% -0.83 -2 -0.31% -0.94 -0.85%** -2.17 -1 -0.58% -0.81 0.39% 0.54 0 0.22% 1.12 -0.38%*** -2.67 1 -0.62% -0.96 -0.21% -1.01 2 -0.63% -1.21 0.28% 0.70 3 0.32% 0.82 -0.25% -0.87 4 -0.89% -0.82 0.54%* 1.80 5 -0.19% -0.90 -0.17% -0.91 6 0.23%* 1.81 0.23%** 2.01 7 0.06% 0.16 -0.16% -0.69 8 -0.05% -0.25 -0.05% -0.15 9 0.09% 0.43 -0.73%** -2.52 10 -0.03% -0.13 0.06% 0.18 11 0.10% 0.55 -0.11% -0.34 12 0.03% 0.25 0.06% 0.26

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5.3. Event study trading volumes This part of the results describes the results of the event study based on the trading volumes. The previous section described the abnormal returns. As mentioned before, Beaver (1968) found that returns describe the effect of the average investor, where trading volumes describe the effect of the sum of all the investors. So, it does not matter whether the stock is bought or sold after the recommendation but just traded. There is attention for the stock when it is traded abnormally often. Figure 3 shows the graphs of the cumulative average abnormal trading volumes in the [-10,10] event window.

The graphs all have a similar pattern. The line goes slowly upwards until the end of the event window. At that moment, the line is getting more flat. The graph for strong buy recommendations is increasing more around event day zero. The CAAV after a strong sell recommendation has the biggest growth, which could mean that these recommendations create the most attention for a stock. In an event window of 21 trading days the cumulative average abnormal trading volume is almost 800%. After another type of recommendation the CAAV grows also close to the 400%.

Figure 3: Cumulative average abnormal trading volumes The figure shows the cumulative average abnormal trading volumes for strong buy and buy recommendations in the left graph and sell and strong sell recommendations in the right graph. and strong sell recommendations. The graphs are based on 340 strong buy recommendations, 141 buy recommendations, 59 sell recommendations and 66 strong sell recommendations. The CAAV is shown for the [-10,10] event window. The 0 event day is in this case the Monday due to the fact that there are not any trading volumes in the weekend.

Table 4 presents the results of the event study of trading volumes for strong buy and buy recommendations. The average abnormal trading volumes after a strong buy recommendation are all statistically significant at a 1% or 5% level. The AAV is also positive on each event day. On the Monday after the broadcast, the average abnormal trading volume is the biggest with 42.34%. Neumann and Kenny (2007) find an abnormal trading volume of 15.72% on the event day and 27.78% on the day after Mad Money. This is less than the findings for Business Class, but stocks on the Dutch market are normally less traded than those on the American stock market. The AAV’s are closer to zero when it goes to the end of the event window. The AAV‘s are also statistically significant and positive in the days before the show, suggesting that there is already attention for the stock before the

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Table 4: Event Study of trading volumes for strong buy and buy recommendations The table presents the results of the event study of trading volumes for strong buy and buy recommendations. The normal returns are calculated by using the market model. The used estimation window is [-196,-40]. The average abnormal return is calculated for the [-10,10] period. Thereby also the cumulative average abnormal trading volume is calculated for the [-5,-1], [0,4], [-3,-1] and [-3,3] event period. The results consist of 340 strong buy recommendations and 141 buy recommendations. The zero event date is the Monday, because there are not any trading volumes in the weekend. The table presents also the t- value of the abnormal returns. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period Strong buy t-value Buy t-value [0,4] 118.60%*** 7.48 67.55%*** 3.82 [-5,-1] 94.65%*** 6.79 110.69%*** 6.09 [-3,-1] 65.56%*** 7.13 75.28%*** 6.22 [-3,2] 151.73%*** 8.47 126.27%*** 5.85

-10 6.85% ** 2.33 0.73% 0.19 -9 8.69%** 2.47 7.54%* 1.88 -8 14.91%*** 4.21 -1.40% -0.35 -7 11.03%*** 3.30 3.28% 0.76 -6 11.66%*** 3.54 4.90% 1.24 -5 14.54%*** 4.60 12.59%** 2.53 -4 14.55%*** 4.39 22.82%*** 4.50 -3 19.80%*** 5.56 23.45%*** 4.41 -2 22.61%*** 6.54 24.34%*** 4.84 -1 23.15%*** 6.18 27.49%*** 5.39 0 42.34%*** 10.42 28.40%*** 5.72 1 24.56%*** 6.28 13.56%*** 3.06 2 19.28%*** 5.53 9.03%** 2.14 3 14.84%*** 4.57 9.12%* 1.88 4 17.59%*** 4.73 7.44%* 1.92 5 10.86%** 2.45 8.45%* 1.94 6 10.60%*** 3.13 14.09%*** 3.09 7 14.72%*** 3.67 11.37%*** 2.60 8 13.74%*** 4.02 0.31% 0.07 9 10.00%*** 3.42 2.25% 0.58 10 15.53%*** 3.46 2.09% 0.47

42 recommendations. Neumann and Kenny (2007) do not find any positive abnormal trading volumes in the days before Mad Money. When looking to the CAAV, all the four event windows create positive and statistically significant cumulative average abnormal trading volumes around a strong buy recommendation. In the week after the broadcast the increase is bigger than in the week before the broadcast (118.60% over 94.65%).

Buy recommendations create in general only positive and statistically significant average abnormal trading volumes in the event days -5 until day +7. The AAV is 28.40% on the Monday after the show, which is the biggest average abnormal trading volume in the event window. The AAV’s are bigger in the days before the broadcast than after the broadcast. This implies that there is already trading and attention for the recommended stock before the broadcast. This could insinuate that there is some insider trading. The cumulative average abnormal trading volumes are positive and statistically significant for the four event windows. The CAAV is bigger in the week before the show in comparison with the week after the show (110.69% against 67.55%). So, the largest effect is already before the show.

Table 5 presents the results of the event study of trading volumes for sell and strong sell recommendations. The AAV is positive and statistically significant for most of the days in the [-10,10] event window around sell recommendations. On the -7 event date, the AAV is relative small in comparison with the other event days, but 11.03% AAV on one day, is still a noticeable growth for one day. The average abnormal trading volume is decreasing in the last days of the event window. On the Monday after the show, the AAV is 18.04%, which is statistically significant at the 5% level. 18.04% is a noticeable effect, but when compared to the days around the broadcast the other days have a bigger average abnormal trading volume. The AAV’s are larger in the days before the show, than one the days after the show. Especially, the AAV is 49.36% on the -4 event date. When looking to the CAAV’s, the statistic is bigger in the week before the show in comparison with the week after show. The cumulative average abnormal trading volume is in the week before the show 179.09%. The CAAV is still positive and statistically significant in the week after the show.

In the case of strong sell recommendations, the average abnormal trading volume is 62.25% on the Monday after the broadcast. The AAV is statistically significant at the 1% level. The 62.25% is economically also a noticeable effect, because a 62% increase in one day is on yearly basis a lot. However, the AAV is bigger in the days before the broadcast. The AAV’s before the show are statistically significant. The average abnormal trading volume is also above the 60% on the Friday before the show. At the end of the event period, the AAV is decreasing in value. The cumulative average abnormal trading volume for the [-3,2] event period is 311.40%. So in these 6 days, the total trading volumes is more than 311% than normal. Also in the week after the broadcast the CAAV is

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Table 5: Event Study of trading volumes for sell and strong sell recommendations The table presents the results of the event study of trading volumes for sell and strong sell recommendations. The normal returns are calculated by using the market model. The used estimation window is [-196,-40]. The average abnormal return is calculated for the [-10,10] period. Thereby also the cumulative average abnormal trading volume is calculated for the [-5,-1], [0,4], [-3,-1] and [-3,3] event period. The results consist of 59 sell recommendations and 66 strong sell recommendations. The zero event date is the Monday, because there are not any trading volumes in the weekend. The table presents also the t- value of the abnormal returns. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period Sell t-value Strong sell t-value [0,4] 103.59%*** 3.38 221.45%*** 3.22 [-5,-1] 179.09%*** 4.28 234.38%*** 3.54 [-3,-1] 93.93%*** 4.04 159.50%*** 4.29

[-3,2] 156.75%*** 3.75 311.40%*** 3.83

-10 13.02% ** 1.93 27.05% * 1.83

-9 31.08%*** 4.23 30.53%* 1.88 -8 16.59%** 2.44 38.47%*** 2.65

-7 11.03% 1.51 29.33%** 2.11 -6 18.70%** 2.30 24.11%** 2.03

-5 35.80%*** 2.85 37.86%** 2.25 -4 49.36%*** 4.37 37.02%** 2.35

-3 25.36%*** 2.61 41.51%*** 3.00 -2 30.44%*** 3.20 56.82%*** 4.20 -1 38.13%*** 3.88 61.17%*** 4.22

0 18.04%** 2.01 62.25%*** 3.54 1 21.00%*** 2.64 42.84%*** 2.92

2 23.79%*** 3.28 46.82%*** 3.00 3 20.01%*** 2.68 36.34%*** 2.94

4 20.76%*** 2.99 33.21%** 2.33 5 23.06%*** 3.10 31.87%** 2.52

6 23.94%*** 3.39 26.46%** 2.38 7 24.03%*** 4.09 28.45%*** 2.73

8 10.75% 1.55 20.71%* 1.74 9 9.80%* 1.81 29.36%*** 2.79

10 20.47%*** 3.02 28.25%** 2.20

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221.45%, which means that after the recommendation investors are trading a lot of the recommended stock. These results show that the strong sell recommendations are creating the biggest effect in trading and thereby in attention.

5.4. Cross sectional analysis In this section of the thesis, I investigate whether the abnormal trading volumes and abnormal returns are correlated with the created attention by the stock recommendations in Business Class. Therefore a cross-sectional analysis is used on both abnormal returns and trading volumes. In this section the results for the strong buy and strong sell recommendations are shown. The results for the buy and sell recommendations can be found in the Appendix.

Table 6 presents the estimation results of the cross sectional analysis for strong buy recommendations. There are some variables statistically significant when looking to the abnormal return on the Sunday. The attention measures viewership and duration are statistically significant and positive. When more investors are watching the show, the abnormal return on the Sunday will be larger. Also when the financial analyst is talking for a longer time about a specific stock the abnormal return will be bigger. However, the coefficient of the duration factor is rounded only 0.000, thus the effect is economically negligible.

There are some control variables statistically significant. The size is statistically significant at the 1% level. The coefficient for log size is -0.003. So the abnormal return on day 0 is lower for bigger companies. An one percent increase in size leads to an higher abnormal return of 0.003%, which seems negligible. However, the size of a company can also increase more than one percent, which increases the effect. The IDIOVOL coefficient is also positive and statistically significant at the 1% level. So stocks with higher idiosyncratic risk have larger abnormal returns, following the theory of Shleifer and Vishny (1997). It is harder for arbitrageurs to profit from mispricing when the idiosyncratic volatility is higher. Also the AMX is negative and statistically significant at the 5% level, which implies that the abnormal return on the Sunday is lower for companies that are listed in the AMX. Also the news variable is statistically significant and positive. Thus, when there is news about the company, there is more attention which leads to an higher impact.

The CAR is used as dependent variable in three cases. I excluded the attention parameters in the week before the show, because these are not known at that moment. In the days and week after the show the attention parameters do not have any statistically significant effect on the cumulative abnormal return. However, the direction of the viewership and duration factors are the expected. When there are more viewers or more time for one recommendation the effect is bigger. Looking to the number of recommendations, which are not statistically significant, the total number of recommendations is negative, when there are more recommendations the AR, or CAAR is lower. This is expected,

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Table 6: Cross Sectional Analysis for strong buy recommendations The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,- 1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.027 0.011 0.035 0.017 1.512 2.688*** 10.345*** (-0.65) (0.43) (1.04) (0.29) (0.70) (3.51) (3.19) Viewership 0.006* 0.005 0.009 0.004 -0.554 (1.77) (1.15) (1.20) (0.04) (-1.39)

Duration 0.000** 0.000 0.000 0.004*** 0.013** (2.33) (1.56) (1.38) (3.32) (2.57)

Total recommendations -0.001 - 0.001 -0.002 0.002 0.005 (-1.03) (-0.69) (-0.80) (0.08) (0.04)

Dutch recommendations 0.001 0.001 0.002 -0.007 0.042 (0.65) (0.71) (0.89) (-0.22) (0.32) book-to-price 0.002 -0.000 0.003 0.009** -0.195 -0.019 -0.090 (0.55) (-0.00) (1.43) (2.35) (-1.02) (-0.40) (-0.45) log size 0.001 - 0.003*** - 0.004*** -0.005** -0.126 - 0.174*** -0.540*** (0.25) (-2.72) (-3.10) (-2.07) (-0.98) (-5.34) (-3.91) IDIOVOL 1.651*** 0.808*** 1.058*** 2.002*** 94.254*** 5.691 52.125*** (4.78) (5.62) (5.55) (5.83) (5.37) (1.31) (2.83) AMX 0.008 - 0.008** -0.010** -0.017** -0.343 -0.13 - 1.184*** (0.94) (-2.25) (-2.17) (-2.09) (-0.81) (-1.23) (-2.65) AScX 0.002 0.000 -0.007 -0.018 1.161** 0.437*** 1.205* (0.15) (0.04) (-1.12) (-1.58) (1.98) (2.96) (1.92) Wierda 0.001 0.002 0.000 -0.010 -0.347 -0.095 - 0.369 (0.10) (0.63) (0.03) (-1.51) (-1.05) (-1.10) (-1.01) Vermeulen -0.001 -0.002 0.002 -0.005 0.476 -0.143 -0.052 (-0.12) (-0.37) (0.30) (-0.44) (0.94) (-1.07) (-0.09) Hafkamp -0.003 0.002 -0.001 -0.005 0.110 -0.072 -0.035 (-0.33) (0.52) (-0.14) (-0.56) (0.29) (-0.64) (-0.07) News 0.007 0.004* 0.003 0.002 0.294 0.126* 0.370 (1.22) (1.68) (0.81) (0.33) (1.00) (1.69) (1.17)

R-Squared 9.48% 23% 22% 19.81% 17.70% 39.28% 29.09% Observations 342 337 337 337 328 323 323

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Table 7: Cross Sectional Analysis for strong sell recommendations The table presents the results of the regression analysis with different dependent variables for the strong sell recommendations. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,- 1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.342* - 0.024 -0.108* 0.045 10.767 4.895 23.944** (-1.82) (-0.68) (-1.94) (0.34) (1.26) (1.63) (2.17) Viewership 0.003 0.005 0.024 -0.207 - 1.633 (0.57) (0.68) (1.29) (-0.50) (-1.08)

Duration 0.000* 0.000 0.000 0.008* 0.030 (1.81) (0.90) (1.02) (1.66) (1.62)

Total recommendations -0.001 -0.004 -0.000 -0.165 - 0.721 (-0.50) (-1.48) (-0.02) (-1.28) (-1.53)

Dutch recommendations 0.001 0.004 -0.004 0.123 0.296 (0.51) (1.30) (-0.62) (0.81) (0.54) book-to-price -0.030** - 0.001 0.003 0.027*** -2.464*** -0.554*** -2.155*** (-2.06) (-0.55) (1.03) (3.46) (-3.76) (-3.08) (-3.26) log size 0.021* 0.000 0.005** -0.011* -0.621 -0.250* - 0.897* (1.94) (0.25) (2.00) (-1.80) (-1.26) (-1.85) (-1.82) IDIOVOL -0.428 0.124 0.681*** 1.182*** 150.356*** 26.715** 132.483*** (-0.52) (1.02) (3.57) (2.59) (3.99) (2.57) (3.47) AMX 0.078** -0.004 0.003 -0.026 - 1.409 -0.210 - 1.158 (2.19) (-0.67) (0.34) (-1.32) (-0.85) (-0.46) (-0.69) AScX 0.054 - 0.006 -0.002 -0.011 - 0.637 0.677 1.981 (1.09) (-0.80) (-0.15) (-0.38) (-0.27) (1.02) (0.82) Wierda -0.012 - 0.003 -0.010 0.005 0.520 - 0.068 - 1.666 (-0.38) (-0.55) (-1.26) (0.28) (0.36) (-0.16) (-1.04) Vermeulen -0.004 - 0.002 -0.004 0.013 0.236 0.017 -0.728 (-0.11) (-0.35) (-0.60) (0.71) (0.15) (0.04) (-0.47) Hafkamp 0.052 0.011* 0.013 0.023 2.182 0.951* 2.245 (1.34) (1.66) (1.23) (0.94) (1.22) (1.72) (1.11) News 0.014 - 0.006* - 0.015*** - 0.044*** 0.023 0.214 0.220 (0.62) (-1.67) (-2.84) (-3.38) (0.02) (0.72) (0.20)

R-Squared 21.52% 18.25% 33.07% 44.93% 57.48% 60.01% 64.72% Observations 67 66 66 66 64 63 63

47 because more recommendations will lead to less attention for one specific stock. The book-to-price value is positive and statistically significant in the week after the show, implying that value companies have a higher announcement effect. The size variable is also negative and statistically significant after the show, meaning that smaller companies are creating a larger effect. This is reasonable, because larger companies are already known by the investors. Attention is created easier for smaller companies. The IDIOVOL coefficient is positive and statistically significant for all the three event windows, supporting the theory of Shleifer and Vishny (1997). The effect is not statistically significant for the different analysts, which means that there is not any different abnormal returns for each of the analysts.

When looking to the abnormal trading volumes, there are different results in comparison with the abnormal returns as dependent variable. The duration variable is still positive and statistically significant. The coefficient is bigger in the case of the trading volumes, suggesting that duration has a bigger impact on trading volumes. Viewership is however negative in the week after the broadcast, which is not following the expectation. A difference between trading volumes and returns is the AScX variable, which is statistically significant and positive. When the stock is listed on the AScX, there is more trading around the broadcast than when the stock is listed on another index. This is also following the expectation because AScX companies are less known normally by investors, therefore a recommendation can easily create attention for that stock. The IDIOVOL coefficient is 92.254 in the week before the show and 52.125 in the week after the show, which implies that an higher idiosyncratic volatility leads to more trading.

The same cross sectional analysis is done for strong sell recommendations. The estimation results of the cross sectional analysis is presented in table 7. First, the results of the abnormal returns are discussed. When looking to the attention measures, only the duration measure is once statistically significant. The direction of the duration measure is always positive, which is following the expectation. The viewership variable is also always positive. In case of the CAR[0,6] the factor is even 0.024, but not statistically significant. The results for the amount of recommendations are different for the different dependent variables. Normally, the expectation is that when there are more recommendations, the attention for each recommendation is less. But some factors are still positive in the results, especially, for the amount of Dutch recommendations.

The control variables are also not often statistically significant. The size and book-to-price value are statistically significant in the week before the show and in the week after the show. Bigger companies create a bigger CAAR in the week before the show, which is not expected. After the show, the smaller companies are creating a bigger CAAR. The IDIOVOL coefficient is positive and statistically significant in the [0,1] and [0,6] event window. In the week before the show, the companies listed in the AMX has also a positive factor. Finally, the news factor is negative for the strong sell

48 recommendations, suggesting that when there is news about the recommended stock the impact is smaller.

Finally, duration is also positive when looking to the abnormal trading volumes. In contradiction to the expectation, the viewership variable is negative, which means that when there are more people watching the show, the stock is less traded for strong sell recommendations. The logarithm of size is also negative for trading volumes. So bigger companies have a smaller abnormal trading volume around the broadcast around a sell recommendation. Hafkamp creates significantly more abnormal trading volume on the Monday after the show.

The results for the buy and sell recommendations can be found in Table 15 and 16 in the Appendix. The abnormal trading volumes are bigger for companies which are listed on the AScX in the case of buy recommendations. The duration coefficient is positive but only statistically significant when the dependent variable is based on trading volumes. The rest of the results can be compared with the results of the strong buy recommendations. However, sell recommendations do have some noticeable results. The coefficient for Wierda is positive and statistically significant for CAR[-6,-1] and CAR[0,1]. This means that sell recommendations done by Wierda creates bigger abnormal returns. Vermeulen has a positive and statistically significant coefficient on the Sunday. The Duration variable is negative in all the cases in contradiction to most of the results. The only statistically significant attention measure is the coefficient of the Dutch recommendations. The coefficient is negative, suggesting that when there are more recommendations for Dutch assets, the abnormal return is lower in the case of CAR[0,1].

5.4.1. Robustness In some of the cases, the news variable was statistically significant, meaning that a part of the abnormal returns or trading volumes are explained by the news about a recommended stock. Next, I perform a robustness test to investigate the impact of the news on the dependent variables. Figure 4 shows the cumulative abnormal return of all the strong buy recommendations, only recommendations for which was news in the weekend and the recommendations for which was not any news.

Figure 4 shows that strong buy recommendations with news create bigger cumulative abnormal return than the strong buy recommendations without news. The graphs have the same pattern, but the only news recommendations impact is bigger.

To test if there is a difference, I performed also a cross sectional analysis of the strong buy recommendations for which was none news. Table 8 presents the estimation results of that cross sectional analysis. The coefficients of the attention parameters does not change much. The t-test value of these coefficients are also almost the same. Removing the news variable adjusts only the

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Figure 4: Cumulative abnormal return for strong buy recommendations with or without news The figure presents the cumulative abnormal return for all the strong buy recommendations, only recommendations for which was news in the weekend and the recommendations for which was not any news in the [-12,12] event window. There are in total 360 strong buy recommendations of which are 241 strong buy recommendations without news and 119 with news. The abnormal returns are calculated using the market model.

estimation results of the IDIOVOL variable. IDIOVOL was positive and statistically significant at the 1% level when all recommendations are considered. However, the coefficients are less significant after removing the recommendations for which was news in the weekend. The IDIOVOL coefficient became even negative on the Sunday. The t-score is also smaller.

I included the same cross sectional analysis without the recommendations which had news in the weekend for buy, sell and strong sell recommendations. The results are presented in Table 17, 18 and 19, which can be found in the Appendix. In general, the analysis without news does not change much in the coefficients. The coefficients for some of the analysts are becoming statistically significant for sell and strong sell recommendations. Also the IDIOVOL, size and book-to-market value are less statistically significant, but the reason could be that there are to less observations for sell and strong sell recommendations when the observations with news are removed.

Finally, I created interaction variables to evaluate whether news has more effect on a specific variable. I created those interaction variables for each of the variables which were included in the previous analysis. The focus lies only by strong buy recommendations, because those have the most observations. Table 9 presents the results of the cross sectional analysis wherein the interaction variables are included. The news coefficient is statistically significant and negative for abnormal returns, suggesting that news decreases the abnormal returns. The news coefficient was positive in table 6. So adding the interaction variables causes that the news coefficient becomes negative. The duration*news variable is statistically significant and negative in the cases of abnormal trading volumes. Without news, a longer duration would lead to an higher abnormal trading volume, but when there is also news about the recommended stock, the duration effect would be less. The interaction

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Table 8: Cross Sectional Analysis for strong buy recommendations without news The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations. Only the recommendations without news in the weekend of the broadcast are included in the regression analysis. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book- to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant 0.009 0.036** 0.058** 0.067 3.590 3.044*** 12.101*** (0.22) (2.36) (2.21) (1.00) (1.26) (3.08) (2.78) Viewership 0.005*** 0.005 0.011 0.043 -0.535 (2.60) (1.50) (1.30) (0.36) (-1.01) Duration 0.000** 0.000* 0.000 0.007*** 0.023*** (2.43) (1.75) (0.25) (4.05) (3.15) Total recommendations -0.000 -0.000 -0.001 0.025 0.111 (-0.11) (-0.03) (-0.37) (0.67) (0.68) Dutch -0.000 -0.000 0.001 -0.019 0.025 recommendations (-0.64) (-0.11) (0.42) (-0.47) (0.14) book-to-price -0.001 0.002* 0.004** 0.013*** -0.308 -0.048 -0.148 (-0.34) (1.66) (2.10) (2.82) (-1.18) (-0.75) (-0.52) log size -0.001 - 0.003*** -0.005*** -0.008*** -0.239 - 0.215*** -0.708*** (-0.28) (-5.26) (-4.50) (-2.63) (-1.39) (-5.02) (-3.76) IDIOVOL 0.914*** -0.156* 0.062 0.740* 81.978*** -0.475 28.742 (2.68) (-1.78) (0.42) (1.96) (3.65) (-0.09) (1.18) AMX -0.000 - 0.005** -0.005 - 0.019*** -0.605 - 0.239* - 1.577*** (-0.02) (-2.12) (-1.48) (-2.09) (-1.11) (-1.77) (-2.65) AScX -0.004 0.003 -0.004 - 0.021* 0.925 0.295 0.714 (-0.37) (1.03) (-0.77) (-1.68) (1.26) (1.63) (0.90) Wierda -0.002 -0.001 -0.002 -0.014* -0.530 - 0.085 -0.154 (-0.34) (-0.44) (-0.49) (-1.74) (-1.21) (-0.75) (-0.31) Vermeulen 0.009 - 0.005* -0.004 -0.009 0.491 - 0.150 0.422 (0.81) (-1.78) (-0.79) (-0.68) (0.68) (-0.78) (0.50) Hafkamp -0.002 -0.003 -0.004 -0.010 0.208 - 0.122 0.075 (-0.24) (-1.16) (-1.14) (-1.06) (0.43) (-0.88) (0.12)

R-Squared 4.38% 38.81% 26.79% 17.11% 18.07% 44.82% 32.60% Observations 231 228 228 228 223 220 220

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Table 9: Robustness test for strong buy recommendations

The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,- 1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. There are also interaction variables created with the news variable for each previous mentioned variable. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant 0.009 0.036 0.058 0.067 3.590 3.044*** 12.101*** (0.19) (1.47) (1.61) (0.96) (1.33) (3.30) (3.07) Viewership 0.005 0.005 0.011 0.043 -0.535 (1.62) (1.09) (1.24) (0.38) (-1.12)

Viewership*news 0.001 -0.002 - 0.005 -0.128 -0.097 (0.19) (-0.23) (-0.34) (-0.63) (-0.11)

Duration 0.000 0.000 0.000 0.007*** 0.023*** (1.52) (1.27) (0.24) (4.34) (3.49)

Duration*news -0.000 - 0.000 0.000 -0.008*** -0.027*** (-0.17) (-0.75) (0.67) (-3.16) (-2.60)

Total recommendations -0.000 - 0.000 - 0.001 0.025 0.111 (-0.07) (-0.02) (-0.36) (0.72) (0.75)

Total recom.*news -0.003* -0.003 -0.003 -0.076 -0.281 (-1.86) (-1.15) (-0.59) (-1.28) (-1.10)

Dutch recommendations -0.000 -0.000 0.001 -0.019 0.025 (-0.40) (-0.08) (0.40) (-0.51) (0.15)

Dutch recom.*news 0.004** 0.003 0.003 0.052 0.091 (2.06) (1.35) (0.65) (0.82) (0.34) book-to-price -0.001 0.002 0.004 0.013*** -0.308 - 0.048 -0.148 (-0.29) (1.04) (1.52) (2.69) (-1.25) (-0.80) (-0.58) book-to-price*news 0.008 - 0.004 -0.001 -0.009 0.244 0.062 0.099 (1.10) (-1.31) (-0.14) (-1.14) (0.61) (0.64) (0.24) log size -0.001 - 0.003*** - 0.005*** - 0.008** -0.239 - 0.215*** -0.708*** (-0.24) (-3.29) (-3.27) (-2.51) (-1.48) (-5.39) (-4.16) log size*news 0.009* 0.007*** 0.007** 0.014** 0.425 0.175** 0.695** (1.71) (3.48) (2.45) (2.52) (1.53) (2.49) (2.32) IDIOVOL 0.914** - 0.156 0.062 0. 740* 81.978*** -0.475 28.742 (2.28) (-1.11) (0.30) (1.87) (3.87) (-0.09) (1.31) IDIOVOL*news 2.971*** 3.321*** 3.44*** 4.395*** 52.829 26.297*** 103.953** (4.00) (12.72) (8.98) (5.96) (1.36) (2.78) (2.57)

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Table 9: Robustness test for strong buy recommendations Continued

AMX 0.000 - 0.005 -0.005 - 0.019** -0.605 - 0.239* -1.577*** (-0.02) (-1.33) (-1.08) (-2.00) (-1.17) (-1.90) (-2.93)

AMX*news 0.038** 0.006 0.001 0.029 1.139 0.521** 1.955** (2.15) (0.98) (0.13) (1.59) (1.23) (2.24) (1.97) AScX -0.004 0.003 -0.004 -0.021 0.925 0.29 5* 0.714 (-0.31) (0.64) (-0.56) (-1.60) (1.34) (1.75) (0.99) AScX*news 0.026 0.008 0.003 0.033 0.109 0.317 0.888 (0.92) (0.77) (0.18) (1.08) (0.07) (0.80) (0.53) Wierda -0.002 - 0.001 -0.002 -0.014* -0.53 - 0.085 -0.154 (-0.29) (-0.27) (-0.35) (-1.66) (-1.29) (-0.81) (-0.34) Wierda*news 0.008 0.007 0.004 0.011 0.712 0.123 0.013 (0.59) (1.47) (0.57) (0.78) (0.99) (0.66) (0.02) Vermeulen 0.009 - 0.005 -0.004 -0.009 0.491 - 0.150 0.422 (0.69) (-1.11) (-0.58) (-0.64) (0.72) (-0.83) (0.55) Vermeulen*news -0.026 0.003 0.007 0.001 0.054 0.065 -0.829 (-1.33) (0.40) (0.68) (0.03) (0.05) (0.24) (-0.73) Hafkamp -0.002 - 0.003 -0.004 - 0.010 0.208 - 0.122 0.075 (-0.20) (-0.73) (-0.83) (-1.01) (0.46) (-0.94) (0.13) Hafkamp*news -0.014 0.007 0.003 0.008 -0.437 0.182 -0.192 (-0.86) (0.99) (0.32) (0.39) (-0.51) (0.72) (-0.18) -0.189** - 0.150*** -0.14** - 0.256** -7.614 - 1.967 -9.131 News (-2.10) (-3.31) (-2.10) (-2.00) (-1.60) (-1.19) (-1.29)

R-Squared 16.89% 51.04% 39.08% 28.85% 18.07% 44.01% 33.44% Observations 342 337 337 337 328 323 323

variable is also statistically significant for size when the dependent variable is based on abnormal returns. However, the interaction variable is positive. So when there is news about the recommended stock, the larger companies have larger abnormal returns in comparison to smaller companies.

The IDIOVOL variable is positive and statistically significant in all the cases except on the event dates. The coefficients are just below 1. The interaction coefficient is also positive but, those factors are at least 2.97. So when there is news about the recommended stock, an higher idiosyncratic volatility has a bigger impact on the abnormal returns and the abnormal volumes. The interaction coefficient with the AMX is also positive and statistically significant, suggesting that when there is news and the stock is listed on the AMX the abnormal returns and abnormal volumes would be bigger.

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5.5. Long term performance The previous section showed that there is a short term effect around the stock recommendations in Business Class. In this section, calendar time portfolios are used to study if there is any long term value in investing the recommended stocks in Business Class. The created portfolios have a duration of 24, 78, 156 and 312 trading days. The portfolios are created from 1/11/2004 until 1/6/2016. Figure 5 presents the performance of a portfolio of an investor which starts with €1,000 and invests only in one type of recommendation and keeps that stock in his portfolio for 312 trading days. Stocks which get a buy or strong buy recommendation are bought. The investor shorts the stock after a sell or strong sell recommendation. The stocks are bought or sold on the Monday, because that is the first trading day on which the investor can buy the stock after Business Class. When the investor is trading these stocks, the investor has to pay transaction costs. Therefore I also corrected for the trading costs when the stocks are bought and sold.

Figure 5: Performance of a portfolio based on 312 trading days The figure presents the performance of a calendar time portfolio which buys only one type of recommendations. The stocks are bought or sold on the Monday after the broadcast, which is the first day investors can buy or sell the stocks after the broadcast. The stocks are kept for 312 trading days. The portfolios are created from 1/11/2004 until 1/6/2015. The market returns is calculated as a value weighted index of the AEX, AMX and AScX. The left graph shows the performance of strong buy and buy recommendations. The right graph shows the performance of the sell and strong sell recommendations.

When an investor invests all his money in the market, the investor has €1,072.81 at the end of the period. A portfolio only consisting of stocks with a strong buy recommendation results in a final value of €618.91. A portfolio for buy recommendations results in €237.46 at the end of the period. Both of the portfolios ended with less money than when the investor would invest all his money in the market. The portfolios did well before the financial crisis in 2007 and 2008 as the graph shows. The portfolios followed the market and made positive excess returns. After the crisis, the market return was recovering, but the portfolios which consists of buy and strong buy recommendations did not recover from the crisis. They stayed almost at the same level as at the end of 2008.

The portfolios consisting of sell recommended stocks results in a final value of €660.80 and a portfolio based on strong sell recommended stocks results in €1,820.38 at the end of the period. So it

54 was profitable to short the strong sell recommendations. The portfolio grew more than 800 euros in the twelve years of the analysis and it also outperforms the portfolio which invests in the market. The portfolio based on sell recommendations ends lower than the start amount, so the market also outperforms this portfolio. The portfolio based on sell recommendations is underperforming from the start of the analysis.

Table 10 presents the estimation results of the CAPM, three factor model and four factor model for calendar time portfolios based on strong buy and buy recommended stocks. The CAPM creates a statistically significant alpha for portfolios based on strong buy recommendations, when the portfolio is based on 24 and 78 trading days. A daily excess return of -0.057% implies an annual excess return of less than -17%. However, for example the 312 trading days portfolio create an annual excess return of -3%. Overall, a long term investment strategy of buying strong buy recommendations is not profitable. Engelberg et al. (2012) find also negative intercepts for portfolios which buy the positive recommended stocks in Mad Money. The estimated betas of the portfolios are close to one, implying that the systematic risk is lower than then the market.

The results do not change much in case of the three factor Fama French model and four factor Carhart model. The alphas are still negative in all the four cases. The SMB and HML factors are positive and statistically significant, following the findings of Fama and French (1993). Small companies are outperforming big companies and value companies are outperforming growth companies. Finally, the momentum factor is negative, which is against the expectation. A negative coefficient implies that stocks which performed well in the past are underperforming in comparison with companies which did poorly in the past.

Table 10 presents also the results for portfolios based on buy recommended stocks. The results for buy recommendations are similar to the results in the case of strong buy recommendations. The alphas are negative for all the four maturities. These results do also not change much in the case of the three factor Fama-French model and the four factor Carhart model. So, it is also not a profitable trading strategy to invest in buy recommendations for the long term.

Table 11 presents the estimation results of portfolios for sell and strong sell recommendations. For portfolios based on strong sell recommendations, the alpha is positive and statistically significant at the 1% level for portfolios for 24, 78 and 156 trading days. The annual excess return is more than 20% in those three cases. The beta is negative in the case of strong sell recommendations, which is following the expectation. Because by creating portfolios based on strong sell recommendations, the investor shorts the recommended stocks. When the SMB and HML factors are added, the model does not change much. The SMB and HML factors are statistically significant and negative, which is also due to the fact that the investor is going short in the recommended stocks. In the four factor model the momentum factor is positive and statistically significant, which is not the right direction. Because the

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Table 10: The CAPM, three factor model and four factor model for strong buy and buy recommendations The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on buy and strong buy recommendations. The portfolios are created from 1/11/2004 until 1/6/2016. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods are 24, 78, 156 and 312 trading days. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Strong Buy 24 78 156 312 Buy 24 78 156 312 a. CAPM Intercept -0.057*** -0.030*** -0.016 -0.010 -0.038** -0.013 -0.019 - 0.032* (-3.14) (-2.25) (-1.48) (-1.10) (-1.98) (-0.68) (-1.10) (-1.86) Rm-rf 0.585*** 0.878*** 0.899*** 0.939*** 0.391*** 0.662*** 0.862*** 1.040*** (38.82) (77.84) (99.35) (123.90) (24.58) (41.52) (58.66) (71.99)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 29.69% 62.93% 73.44% 81.14% 14.47% 32.57% 49.09% 59.22% b. Three factor model Intercept -0.060*** -0.026** -0.012 -0.005 -0.041** -0.012 -0.016 -0.017 (-3.33) (-1.97) (-1.12) (-0.62) (-2.14) (-0.65) (-0.90) (-1.07) Rm-rf 0.568*** 0.877*** 0.895*** 0.937*** 0.372*** 0.653*** 0.857*** 1.061*** (36.88) (77.02) (99.93) (126.86) (22.92) (40.08) (57.54) (75.63) SMB -0.021 0.211*** 0.241*** 0.249*** -0.003 0.100*** 0.225*** 0.565*** (-0.69) (9.24) (13.41) (16.77) (-0.11) (3.04) (7.51) (20.05) HML 0.109*** 0.085*** 0.115*** 0.111*** 0.124*** 0.100*** 0.122*** 0.074*** (5.44) (5.74) (9.88) (11.56) (5.88) (4.74) (6.32) (4.09)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 30.29% 64.04% 75.23% 82.98% 15.31% 33.12% 50.33% 63.41% c. Four factor model Intercept -0.056*** -0.022* -0.009 -0.003 -0.040** -0.012 - 0.014 -0.008 (-3.12) (-1.68) (-0.86) (-0.30) (-2.11) (-0.61) (-0.82) (-0.51) Rm-rf 0.549*** 0.858*** 0.882*** 0.923*** 0.369*** 0.649*** 0.849*** 1.016*** (34.66) (73.39) (95.77) (121.93) (21.99) (38.58) (55.28) (71.69) SMB -0.049 0.182*** 0.221*** 0.228*** -0.009 0.094*** 0.214*** 0.496*** (-1.57) (7.86) (12.09) (15.18) (-0.26) (2.81) (7.01) (17.64) HML 0.101*** 0.076*** 0.109*** 0.105*** 0.122*** 0.098*** 0.119*** 0.055*** (5.04) (5.18) (9.38) (10.97) (5.79) (4.64) (6.14) (3.06) MOM -0.082*** -0.085*** -0.060*** - 0.061*** -0.015 -0.017 - 0.032* - 0.203*** (-4.62) (-6.52) (-5.79) (-7.17) (-0.80) (-0.93) (-1.85) (-12.84)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 30.71% 64.47% 75.46% 83.22% 15.32% 33.14% 50.37% 65.02%

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Table 11: The CAPM, three factor model and four factor model for strong sell and sell recommendations The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on sell and strong sell recommendations. The portfolios are created from 1/11/2004 until 1/6/2016. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods are 24, 78, 156 and 312 trading days. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Strong Sell 24 78 156 312 Sell 24 78 156 312 CAPM Intercept 0.076*** 0.125*** 0.101*** 0.036* 0.052*** 0.030 0.002 0.002 (3.96) (4.59) (3.38) (1.64) (2.70) (1.30) (0.08) (0.09) Rm-rf -0.285*** - 0.652*** -0.921*** -0.941*** -0.174*** - 0.405*** -0.538*** - 0.716*** (-17.74) (-28.55) (-36.65) (-51.14) (-10.77) (-21.21) (-31.48) (-44.80)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 8.11% 18.60% 27.34% 42.29% 3.15% 11.19% 21.74% 36.00% Three factor model Intercept 0.074*** 0.118*** 0.092*** 0.027 0.049** 0.027 0.000 0.002 (3.87) (4.39) (3.12) (1.27) (2.54) (1.19) (0.01) (0.12) Rm-rf -0.285*** - 0.644*** -0.913*** -0.931*** -0.182*** - 0.414*** -0.542*** - 0.711*** (-17.32) (-27.97) (-36.26) (-51.64) (-11.00) (-21.18) (-30.95) (43.37) SMB -0.086*** -0.412*** -0.545*** -0.549*** -0.087*** -0.050 -0.043 - 0.015 (-2.61) (-8.91) (-10.77) (-15.14) (-2.61) (-1.28) (-1.22) (-0.44) HML -0.033 - 0.207*** -0.254*** -0.273*** 0.020 0.044 0.010 -0.043** (-1.56) (-6.94) (-7.80) (-11.71) (0.95) (1.72) (0.01) (-2.04)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 8.33% 21.20% 30.53% 47.30% 3.37% 11.31% 21.78% 36.07% Four factor model Intercept 0.075*** 0.114*** 0.081*** 0.021 0.045** 0.022 -0.003 - 0.000 (3.93) (4.24) (2.78) (0.98) (2.35) (0.97) (-0.15) (-0.01) Rm-rf -0.292*** -0.624*** -0.862*** -0.901*** -0.164*** - 0.389*** -0.527*** - 0.698*** (-17.16) (-26.26) (-33.44) (-48.68) (-9.61) (-19.35) (-29.15) (-41.31) SMB -0.096*** -0.381*** -0.467*** -0.503*** -0.059* -0.012 -0.019 0.005 (-2.86) (-8.08) (-9.12) (-13.69) (-1.74) (-0.31) (-0.53) (0.14) HML -0.036* - 0.198*** -0.232*** -0.260*** 0.028 0.054** 0.017 -0.038* (-1.69) (-6.62) (-7.14) (-11.17) (1.32) (2.15) (0.75) (-1.78) MOM -0.029 0.092*** 0.229*** 0.134*** 0.082*** 0.111*** 0.070*** 0.056*** (-1.54) (3.48) (7.95) (6.49) (4.29) (4.93) (3.47) (2.96)

Obs 3571 3571 3571 3571 3571 3571 3571 3571 R-squared 8.39% 21.47% 31.74% 47.92% 3.86% 11.91% 22.04% 36.23%

57 investor goes short in the recommended stocks, the expectation is that the momentum factor would be negative.

The alphas are also positive for sell recommendations, however these are less than the alphas for strong sell recommendations. The results do also not change much in the case of the three factor Fama French model and four factor Carhart model. Figure 5 showed that the final value of a portfolio of sell recommendations is €660.80, which is less than the €1000 start amount. So, the positive alphas are not in line with the findings of Figure 5.

5.5.1. Robustness Figure 5 showed that portfolios created from buy and strong buy recommendations fall a lot in the period 2008-2009 due to the financial crisis. Therefore I performed the CAPM, three factor model and four factor model in the period 1/1/2010 until 1/6/2016, to look if the portfolios were performing better after the financial crisis. The results are presented in table 20 and 21, which can be found in the Appendix. The alpha does not change significantly for strong buy recommendations. The alpha is still negative and statistically significant for the 24 trading days portfolio. The alpha is even more negative in the case of 24 trading days. The beta is larger in this model than in the previous model.

The alpha does not change much when the three factor Fama French and four factor Carhart model are estimated. The SMB and HML factors are still positive and statistically significant in general. However, the effect is decreasing. The SMB factor is for example not statistically significant for portfolios with a duration of 24 trading days. The momentum factor is still negative and statistically significant. So the momentum factor is still not according to the expectation. In general, the R-squared increases when the model is only looking to the period after the crisis.

The same is happening for portfolios based on buy recommendations. The alpha’s are still negative and in some cases statistically significant. The beta is also increasing and the SMB and HML factors are still positive and statistically significant. The R-squared is also increasing in most of the cases.

The alphas are still positive and statistically significant for portfolios based on sell and strong sell recommendations. The alphas are becoming less positive in the case of strong sell recommendations. The alphas for portfolios consisting of sell recommendations are creating bigger and statistically significant excess returns when only looking to the period after the financial crisis. The betas are also becoming less close to 1. There are not any significant changes in the SMB, HML or momentum factor.

In general, the crisis does not have any effect on the long term performance of the recommended stocks. It would not lead to any positive excess returns when an investor only invests in buy and strong buy recommendations after the financial crisis. This was also the case in the results over the whole period.

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5.6. Results for different analysts In this section of the results, I compare the recommendations of the analysts. First, I look whether the AR[0], CAR[0,1] and AV[0], differ by analyst. Thereafter, I focus on the strong buy recommendations, because those have the most observations. Some analysts do have only eight observations for sell or strong sell recommendations for example.

Table 12 presents the AAR[0], CAAR[0,1] and AAV[0] for each analyst. Geert Schaaij is creating the biggest AAV[0] for strong buy recommendations, suggesting that Geert Schaaij is creating more attention than the other analysts after strong buy recommendations. The AAR[0] and CAAR[0,1] are similar for Edwin Wierda and Geert Schaaij after a strong buy recommendation, which is more than the abnormal returns than those of Vermeulen and Hafkamp. The CAAR[0,1] for Hafkamp is the only result which is not statistically significant in the case of strong buy recommendations. Edwin Wierda create the biggest AAR[0] and AAV[0] after a buy recommendation, suggesting that the effect is the biggest for Wierda. The AAV[0] is not statistically significant for Martine Hafkamp, suggesting that she does not create enough attention for the stock after a buy recommendation. None of the CAAR[0,1]’s are statistically significant after a buy recommendation. However, the cumulative average abnormal returns are positive following the expectation.

There are not any statistically significant after a sell recommendation. Schaaij creates a negative CAAR[0,1] after a sell recommendation. The other results based on returns are all positive, suggesting that a sell recommendation creates positive abnormal returns. The average abnormal trading volume is positive in all the cases. Vermeulen creates the biggest AAV on the Monday after the show.

Table 12: Abnormal returns and trading volumes by analyst The table presents the AAR[0], CAAR[0,1] and AAV[0] per analyst. The results are shown for strong buy, buy, sell and strong sell recommendations. The market model is used to calculate normal returns and trading volumes. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

a. Strong Buy b. Buy Wierda Schaaij Vermeulen Hafkamp Wierda Schaaij Vermeulen Hafkamp AAR[0] 1.07%*** 1.08%*** 0.41%*** 0.19%*** 0.48%** 0.36%*** 0.07% 0.23%** (2.58) (6.14) (3.08) (2.82) (1.92) (3.32) (0.52) (2.17) CAAR[0,1] 1.39%*** 1.38%*** 1.04%*** 0.19% 0.29% 0.33% 0.56% 0.21% (2.68) (5.94) (3.07) (1.25) (0.78) (1.02) (1.12) (0.79) AAV[0] 34.18%*** 56.49%*** 36.39%*** 13.60%*** 40.23%*** 29.99%*** 18.89%** 14.90% (5.02) (8.47) (3.17) (2.62) (3.09) (4.08) (2.48) (1.63) c. Sell d. Strong Sell AAR[0] 0.18% 0.21% 0.45% 0.18% -0.27% - 0.45%** - 0.49% 0.17% (0.87) (0.50) (1.80) (1.37) (-0.66) (-2.40) (-1.39) (0.77) CAAR[0,1] 0.30% -1.31% 0.47% 0.33% -0.90% - 0.44% - 1.06%* - 0.31% (1.23) (-1.26) (0.59) (1.36) (-1.27) (-1.44) (-1.75) (-0.31) AAV[0] 9.64% 16.63% 49.39% 15.31% 26.86% 68.28%** 57.95%* 86.43%*** (0.71) (1.15) (1.43) (1.36) (1.32) (2.55) (1.95) (2.72)

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This implies that Vermeulen creates the most attention after a sell recommendation. Hafkamp creates the biggest AAV[0] after a strong sell recommendation. The abnormal returns are not statistically significant after the strong sell recommendations for her. Schaaij creates the biggest abnormal return for strong sell recommendations on the Sunday. Vermeulen creates the CAAR[0,1], which is most negative after a strong sell recommendation.

Hereafter, the focus lies on strong buy recommendations, because these have the most observations. Figure 6 shows the cumulative average abnormal returns for each analyst in the [-12,12] window and the cumulative average abnormal trading volume in the [-10,10] window.

Figure 6: The cumulative average abnormal return and trading volume for each analyst The left figure presents the cumulative average abnormal return for each analyst in the [-12,12] event window. The normal returns are calculated using the market model. The right figure presents the cumulative average abnormal trading volume for each analyst in the [10,10] window.

There is a clear increase in the CAAR for each analyst around the event day. The CAAR for Hafkamp decreases after the recommendation, suggesting that the created abnormal returns disappears after some time. Vermeulen and Hafkamp do have the biggest drift upwards before the show. An upwards drift before the event could suggest insider trading. However, the CAAR starts increasing on the -12 event day in the case of Vermeulen and Hafkamp. This is also circumstantial evidence that Vermeulen and Hafkamp are recommending stocks that already got the attention of the investors in the two weeks before the show. The upwards drift before the show starts only a few days before the show for Schaaij and Vermeulen. This could be circumstantial evidence that those analysts are recommending stocks, which did not get any attention before the recommendation.

The CAAR of Edwin Wierda and Vermeulen peaks on the event day, but the largest increase for Vermeulen already happened. Note that the CAAR of Vermeulen is only based on 32 recommendations, which is a small sample. The CAAR of Geert Schaaij is still increasing after Business Class and becomes at an higher point than the line of Wierda. The CAAR of Schaaij is the biggest after two weeks.

The right figure of Figure 6 presents the cumulative average abnormal trading volume for each analyst. The graphs of Wierda, Vermeulen and Hafkamp show the same growth over time. Where it

60 seems that Wierda has a bigger growth on the Monday after the show. Schaaij has a bigger CAAV in the period before and after the show, suggesting that stocks which he recommends were already traded a lot in the time before the recommendation.

Next, I created per analyst calendar time portfolios. Figure 7 in the appendix shows the performance over time for each of the portfolios based on 312 trading days. Only the portfolio of following the strong buy recommendations of Edwin Wierda results in a positive return in comparison with investing in the market. The portfolio which follows the strong buy recommendations of Geert Schaaij results in the lowest final value. Despite the fact that Geert Schaaij has probably the highest reputation of all these analysts. Investing in the recommendations of Hafkamp and Vermeulen leads to performance close to the market. The portfolio of Hafkamp perfoms a little better than the market and the portfolio of Vermeulen a bit less than the market.

The portfolios are evaluated with the CAPM, three factor Fama French model and the four factor Carhart model. Table 13 presents the results of these evaluations. Only the portfolios for Wierda are creating positive excess returns, but the alpha is not statistically significant. 0.001% per day is only an excess annual return of 0.312%, which is economically not much. Vermeulen, Schaaij and Hafkamp are creating negative excess returns, but these are also not statistically significant. The betas are low or close to zero for all three types of evaluations. The SMB factor is negative for Edwin Wierda, suggesting that bigger companies are outperforming smaller companies. The momentum factor is negative against the expectation for all the four analysts.

I performed the same analysis with the other holding periods (24, 78 and 156 trading days). The results can be found in table 22, 23 and 24 in the Appendix. The alpha has also become negative for Wierda in the case of 24 and 78 trading days. The excess returns of Geert Schaaij is negative and statistically significant for all the three holding periods. The alpha of -0.03 in the case of 156 trading days, means an annual return of more than minus 9%. The intercepts are also statistically and economically significant for Vermeulen and Hafkamp in the case of 24 trading days.

Finally, I perform a cross sectional analysis for the AR[0], CAAR[0,1] and AV[0] for each analyst. Table 14 presents the results of the cross sectional analysis with AR[0] as dependent variable. Viewership and duration are positive in all of the cases except for the viewership of Vermeulen. Both of those variables are statistically significant in the case of Geert Schaaij, suggesting that created attention increases the abnormal return on the day of the show. The AScX coefficient of Geert Schaaij is also positive and statistically significant. So when a company is listed on the AScX and Geert Schaaij recommends that stock, the abnormal return is bigger on the Sunday. The viewership effect is however bigger for Edwin Wierda.. The SMB factor for portfolios based on Edwin Wierda

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Table 13: The CAPM, three factor model and four factor model for strong buy recommendations by analyst for portfolios with an holding period of 312 days The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on strong buy recommendations by each analyst. The portfolios are created in the time period in which the analyst came regularly to Business Class. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods is 312 trading days. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

312 days Wierda Schaaij Vermeulen Hafkamp

CAPM Intercept 0.001 -0.010 -0.020 - 0.013 (0.07) (-1.01) (-0.75) (-1.04)

Rm-rf 1.089*** 0.937*** 1.002*** 0.955*** (54.30) (111.44) (37.61) (80.42)

Obs 2029 3571 1341 2053 R-squared 59.26% 77.68% 51.37% 75.92% Three factor model 0.005 -0.005 -0.011 - 0.009 Intercept (0.26) (-0.57) (-0.43) (-0.76) Rm-rf 1.087*** 0.932*** 1.028*** 0.962*** (52.86) (114.19) (37.19) (78.57)

SMB 0.167*** 0.273*** 0.226*** 0.122*** (4.42) (16.67) (4.39) (5.48)

HML 0.120*** 0.137*** -0.033 0.039*** (5.58) (13.01) (-1.04) (3.07)

Obs 2029 3571 1341 2053 R-squared 60.10% 80.00% 52.21% 76.32% Four factor model Intercept 0.015 -0.003 -0.010 - 0.008 (0.75) (-0.27) (-0.38) (-0.61) Rm-rf 1.053*** 0.918*** 1.023*** 0.956*** (50.45) (109.58) (36.23) (76.54)

SMB 0.107*** 0.252*** 0.221*** 0.112*** (2.81) (15.17) (4.25) (4.92)

HML 0.110*** 0.131*** -0.034 0.038*** (5.18) (12.46) (-1.07) (2.96) MOM -0.141*** -0.062 -0.019 - 0.026 (-7.39) (-0.27) (-0.77) (-0.61)

Obs 2029 3571 1341 2053 R-squared 61.15% 80.24% 52.23% 76.38%

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Table 14: Cross Sectional Analysis for strong buy recommendations by each analyst The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations by each analyst.. The dependent variables is the abnormal return on event day 0. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] window. AMX and AScX are dummies which are one when the company is listed on one of the indices. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

AR[0] Wierda Schaaij Vermeulen Hafkamp

Constant -0.113 0.013 0.085* 0.002 (-1.19) (0.65) (1.85) (0.10)

Viewership 0.032** 0.008*** -0.012* 0.000 (2.27) (3.13) (-1.64) (0.15)

Duration 0.000 0.000** 0.000 0.000 (1.13) (2.48) (0.26) (1.11) Total recommendations -0.005 -0.000 -0.000 - 0.000 (-1.35) (-0.25) (-0.35) (-0.94) Dutch recommendations 0.005 -0.000 -0.000 0.000 (1.14) (-0.48) (-0.09) (0.06) book-to-price -0.003 0.002 0.013*** -0.000 (-0.59) (1.54) (3.34) (-0.01) log size -0.003 - 0.003*** -0.002 - 0.000 (-0.95) (-3.42) (-1.27) (-0.22) IDIOVOL 1.721*** -0.189 - 0.088 0.186 (5.08) (-1.39) (-0.26) (0.94) AMX -0.021* -0.003 -0.004 0.002 (-1.72) (-0.82) (-1.10) (1.00) AScX -0.014 0.007* 0.002 - 0.003 (-0.73) (1.70) (0.42) (-0.68) News 0.010 0.001 0.003 0.000 (1.14) (0.33) (0.88) (0.09)

R-Squared 39.19% 37.34% 80.54% 16.98% Observations 80 174 24 59

63 was negative, but the logsize factor in these regression is negative, suggesting that smaller companies create a bigger abnormal return. The AMX coefficient is negative and statistically significant for Wierda. Stocks listed on the AMX are smaller than those listed on the AEX, so the expectation was that the AMX coefficient would be positive.

Table 25 in the appendix presents the results of the cross sectional analysis with the CAAR[0,1] as dependent variable. The viewership and duration factors are not statistically significant for one of the analysts. In the cases of Geert Schaaij and Edwin Wierda the attention measures are positive, suggesting that the attention measures are creating positive cumulative average abnormal returns. Hafkamp does have a negative coefficient for the total recommendations variable, suggesting that more recommendations lead to a smaller CAAR[0,1]. The log size factor is negative and statistically significant for Schaaij. Wierda and Hafkamp do however also have an negative coefficient for the log size variable. The book-to-price value is positive and statistically significant for Schaaij and Vermeulen, suggesting that value companies are causing bigger cumulative average abnormal returns.

Finally, Table 26 in the appendix presents the results of the cross sectional analysis for AV[0]. The duration factor is positive and statistically significant for Schaaij and Vermeulen. The effect is the biggest for Vermeulen. The viewership factor is relative big and negative for Vermeulen, suggesting that more viewers lead to less abnormal trading. The AScX factor is positive and statistically significant for Geert Schaaij, suggesting that companies listed in the AScX have a bigger effect on trading volume than those listed on the other indices. Also the news coefficient is positive and statistically significant for Schaaij. Thus, news in the same weekend as the recommendation leads to an higher abnormal trading volume on the Monday after the show.

In general, the abnormal returns peaks at the highest point for Geert Schaaij around the event, meaning that recommendations issued by Geert Schaaij create short term value. However in the long term, portfolios based on Geert Schaaij recommendations are performing the least. Portfolios based on the strong buy recommendations of Edwin Wierda lead to the biggest return in the long term.

6. Conclusion Stock recommendations in Business Class create short term abnormal returns around the publication. There are statistically significant abnormal returns after strong buy, buy and strong sell recommendations in the weekend of the broadcast. The abnormal weekend return is 0.86% for strong buy recommendations, 0.33% for buy recommendations and -0.38% for strong sell recommendations. There is also an upwards drift in the abnormal returns before the show in these cases. The effect does not disappear in the week after the show, since the cumulative average abnormal return is positive after strong buy and buy recommendations for the [0,6] event window. The CAAR[0,6] is 0.05% for

64 strong sell recommendations, so instead of negative strong sell recommendations create slightly positive abnormal returns.

There are positive abnormal trading volumes after all recommendations. The abnormal trading volume is the biggest on the Monday after Business Class for strong buy, buy and strong sell recommendations. The abnormal trading volume is 42.34% for strong buy recommendations and 62.25% for strong sell recommendations. The abnormal trading volumes are already positive in the week before the show, which implies that the stocks are on average already traded more in the days before the show. The cumulative abnormal trading volume is positive in the week after the show for all recommendations. So the attention which is created by the recommendation triggers trading.

In the cross sectional analysis, I find that the attention parameter Duration often has a positive relationship with the abnormal trading volumes and abnormal returns. So when the analysts are talking longer about the stock, the abnormal return and volume is bigger. So, attention is triggering trading and returns. However, in the case of returns the coefficient is only 0.000, which is economically negligible. Generally, smaller firms have a larger recommendation impact. The AScX variable is often also positive and statistically significant, supporting the statement that smaller companies are creating a bigger impact than large companies. The book-to-price value coefficient does differ over the different kind of recommendations. Value companies are creating bigger abnormal returns but smaller abnormal trading volumes for strong buy recommendations. So there is not a clear difference in the performance for growth or value companies.

In general, idiosyncratic volatility has a positive and statistically significant relationship with the abnormal returns and abnormal trading volumes around the announcement of the recommendations. This is evidence for the theory that it is more difficult for arbitrageurs to profit from divergence of fundamental values when the stock has more idiosyncratic volatility.

I corrected also for other news about the recommended stock in the weekend of Business Class. The stocks with news created larger abnormal returns around the publication, but when looking to the results of a cross sectional analysis of only stocks without news, they do not differ significantly from the results of all the recommendations. For example, smaller companies are still creating larger abnormal returns.

To evaluate the long term performance of the recommended stocks, I created equally weighted calendar time portfolios. These portfolios create negative excess returns when considering holding periods of 24, 78, 156 and 312 trading days. The alphas are negative in the case of the CAPM, three factor Fama French model and four factor Carhart model for strong buy, buy and sell recommendations. This means that there is not any long term value to invest in these stocks. The alpha

65 is positive when an investor goes short in strong sell recommendations. However, it can be impossible for an individual investor to go short in all of these recommendations.

So when looking to the abnormal returns, in general it seems that the effect does not disappear in the two weeks after the show. This would support the information hypothesis, which means that the stock recommendations reveals valuable and relevant information. But the calendar time portfolio reveals that the buying or selling the stocks would not lead to any value for the investor in the long term. Therefore, the results in the thesis support the attention-grabbing hypothesis of Barber and Odean (2008). The abnormal trading volume also shows that the recommended stocks are traded more around the announcement. The cross sectional analysis showed that some attention parameters - e.g. duration - , which also shows that attention is a factor which triggers the trading.

Only Wierda creates positive excess returns in the long term. This insinuates that investors are buying the stocks that he recommends due to the valuable information he reveals, but the same analysis shows that the stocks of Schaaij are mostly traded in the period surrounding the show. The analysis per analyst shows also that Vermeulen and Hafkamp are recommending stocks which already had abnormal returns in the two weeks before the show. This is circumstantial evidence that they recommend stocks which already got the attention of the investor. The cross sectional analysis per analyst showed that the attention parameters had the biggest effect for Schaaij, followed by Wierda.

There are some considerations for further research. First, I tested the arbitrage theory of Shleifer and Vishny in the same way as Engelberg et al. (2012) by adding the idiosyncratic volatility in the cross sectional analysis. However, Engelberg et al. (2012) investigated also to the cost of going short for arbitrageurs. Arbitrageurs are not going to profit from the mispricing after a recommendation when the cost of going short is too high. Engelberg et al. (2012) measure the cost of going short by the difference of between the federal funds rate and the rebate rate. The rebate rate is the interest paid on the amount of money that the short seller posted on his account to borrow the stocks. However, in those analysis the equity lending data is needed and it is not in the scope of this study to obtain this data.

Another possibility for further research is to extend more in the analysis between the analysts. For example, it is known that Geert Schaaij has his community ‘Beursgenoten’, there in reveals he his recommendations already in the Wednesday before the show. It is possible to create an investment strategy of the recommendations of Schaaij which buys the stocks on the Wednesday. However, I would need the recommendations in ‘Beursgenoten’ to optimally perform this kind of trading strategy. Because an investor can only buy the stocks which are in ’’Beursgenoten’. The data of these recommendations are not freely available. Bolster, Trahan and Venkateswaran (2012) use a style analysis to investigate which type of stocks Jim Carter recommends in Mad Money. This can also be

66 done for the analysts in Business Class. It can be interesting to compare the value of the recommended stocks and the corresponding style of recommending for each analyst.

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8. Appendix Table 15: Cross Sectional Analysis for buy recommendations The table presents the results of the regression analysis with different dependent variables for the buy recommendations. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.075 0.046*** 0.081** 0.084 -5.031* 0.187 -0.387 (-0.87) (2.63) (2.30) (1.07) (-1.96) (0.19) (-0.10) Viewership 0.001 -0.001 - 0.005 0.014 -0.525 (0.63) (-0.17) (-0.62) (0.14) (-1.33) Duration 0.000 0.000 - 0.000 0.003* 0.010* (-0.39) (-0.13) (-0.79) (1.92) (1.75) Total recommendations -0.000 - 0.001 - 0.001 -0.001 0.029 (-0.61) (-0.36) (-0.18) (-0.04) (0.22) Dutch recommendations 0.000 - 0.001 -0.001 -0.019 0.029 (-0.07) (-0.85) (-0.44) (-0.47) (0.19) book-to-price -0.005 0.002** 0.002 0.008** -0.220 0.016 0.005 (-0.81) (2.57) (1.34) (2.00) (-1.18) (0.32) (0.03) log size 0.004 - 0.003*** -0.004*** -0.003 0.303** -0.003 0.127 (0.80) (-3.95) (-2.75) (-1.03) (2.04) (-0.07) (0.81) IDIOVOL 1.034* -0.008 -0.262 1.110*** 56.099** -7.350 2.581 (1.76) (-0.10) (-1.63) (3.11) (2.48) (-1.22) (0.11) AMX 0.023 - 0.008*** -0.007 - 0.022** 0.987* 0.158 0.659 (1.29) (-3.34) (-1.32) (-1.99) (1.89) (1.12) (1.23) AScX 0.004 -0.004 - 0.007 -0.017 3.049*** 1.086*** 4.117*** (0.14) (-1.01) (-0.87) (-0.96) (3.70) (4.82) (4.78) Wierda -0.006 0.002 -0.001 0.011 0.058 - 0.052 0.083 (-0.40) (0.71) (-0.17) (1.13) (0.13) (-0.44) (0.18) Vermeulen -0.013 -0.003 0.007 -0.010 0.234 0.082 -0.147 (-0.67) (-1.05) (1.17) (-0.77) (0.43) (0.50) (-0.24) Hafkamp -0.003 0.001 -0.001 0.012 -0.286 -0.128 -0.123 (-0.17) (0.53) (-0.12) (0.95) (-0.59) (-0.84) (-0.21) News 0.017 -0.001 0.001 -0.006 0.590 - 0.005 0.285 (1.34) (-0.44) (0.24) (-0.79) (1.62) (-0.05) (0.76)

R-Squared 5.40% 25.81% 13.63% 19.49% 15.01% 35.30% 28.67% Observations 145 141 141 141 138 134 134

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Table 16: Cross Sectional Analysis for sell recommendations The table presents the results of the regression analysis with different dependent variables for the sell recommendations.. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.451** - 0.033* -0.002 0.085 16.13** 4.329** 7.132 (-2.02) (-1.77) (-0.03) (0.57) (2.45) (2.18) (0.94) Viewership 0.001 0.008 0.002 -0.166 - 0.773 (0.23) (1.31) (0.13) (-0.72) (-0.88) Duration -0.000 -0.000 -0.000 -0.006 - 0.019 (-0.24) (-1.10) (-0.42) (-1.50) (-1.28) Total recommendations 0.001** 0.003 0.007 0.033 0.104 (2.05) (1.30) (1.35) (0.46) (0.39) Dutch recommendations -0.001 -0.004* -0.003 -0.134 - 0.345 (-0.94) (-1.67) (-0.40) (-1.52) (-1.03) book-to-price 0.053 - 0.001 - 0.017** 0.008 -0.230 -0.052 0.400 (1.31) (-0.50) (-2.46) (0.38) (-0.20) (-0.20) (0.40) log size 0.023* 0.002* -0.001 -0.007 -0.976** -0.171* -0.059 (1.70) (1.67) (-0.49) (-1.04) (-2.41) (-1.83) (-0.17) IDIOVOL -2.807*** 0.304*** -0.700*** - 2.694*** -11.844 -1.601 8.262 (-9.41) (16.19) (-13.32) (18.05) (-1.38) (-0.82) (1.10) AMX 0.123*** -0.000 0.005 0.018 -2.563* -0.602* -0.475 (2.76) (-0.02) (0.64) (0.76) (-1.96) (-1.90) (-0.39) AScX 0.191*** -0.001 0.018* 0.033 -1.026 -0.013 1.455 (2.88) (-0.15) (1.69) (1.08) (-0.50) (-0.03) (0.86) Wierda 0.084*** 0.003 0.010* 0.020 0.598 -0.156 - 0.630 (2.67) (1.19) (1.65) (1.21) (0.65) (-0.70) (-0.74) Vermeulen 0.063 0.007** 0.011 -0.007 1.91 0.070 0.579 (1.32) (2.45) (1.32) (-0.30) (1.39) (0.23) (0.49) Hafkamp 0.014 0.001 0.001 0.017 2.356 -0.176 -0.479 (0.29) (0.40) (0.07) (0.62) (1.51) (-0.47) (-0.33) News 0.011 - 0.001 0.005 0.012 1.603* 0.500** 0.676 (0.33) (-0.64) (0.89) (0.79) (1.67) (2.42) (0.85)

R-Squared 70.94% 88.62% 85.29% 90.61% 25.12% 36.16% 19.14% Observations 61 60 60 60 59 58 58

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Table 17: Cross Sectional Analysis for buy recommendations without news The table presents the results of the regression analysis with different dependent variables for the buy recommendations. Only the recommendations without news in the weekend of the broadcast are included in the regression analysis. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.113 0.044** 0.055 0.059 - 4.952 0.419 -5.088 (-1.27) (2.50) (1.21) (0.54) (-1.44) (0.33) (-1.08) Viewership -0.000 -0.003 - 0.006 -0.002 -0.201 (-0.16) (-0.62) (-0.62) (-0.01) (-0.45) Duration 0.000 0.000 - 0.000 0.005** 0.018** (0.50) (1.06) (-0.22) (2.49) (2.45) Total recommendations -0.001 -0.001 0.001 0.051 0.290* (-1.07) (-0.45) (0.27) (1.07) (1.66) Dutch 0.001 0.000 - 0.002 -0.064 -0.247 recommendations (1.55) (0.07) (-0.38) (-1.28) (-1.35) book-to-price 0.013 0.001 0.000 0.016** -0.137 0.010 -0.075 (1.28) (0.66) (0.12) (1.97) (-0.36) (0.11) (-0.21) log size 0.006 - 0.003*** -0.002 - 0.002 0.315 - 0.015 0.312 (1.05) (-3.46) (-1.12) (-0.47) (1.55) (-0.28) (1.56) IDIOVOL 0.662 0.039 -0.233 1.003** 51.441** -10.441 1.434 (1.31) (0.59) (-1.36) (2.48) (1.97) (-1.58) (0.06) AMX 0.022 - 0.009*** -0.006 - 0.023 0.549 0.046 0.571 (1.21) (-3.96) (-0.97) (-1.58) (0.82) (0.27) (0.91) AScX 0.034 - 0.002 -0.003 - 0.014 2.734*** 0.921*** 4.069*** (1.26) (-0.60) (-0.33) (-0.65) (2.70) (3.48) (4.81) Wierda 0.005 - 0.000 - 0.004 0.004 - 0.060 - 0.144 -0.026 (0.35) (-0.40) (-0.68) (0.33) (-0.11) (-0.97) (-0.05) Vermeulen -0.034 - 0.005 -0.003 - 0.028 0.062 - 0.133 -0.52 (-1.43) (-1.20) (-0.33) (-1.20) (0.07) (-0.48) (-0.51) Hafkamp 0.006 0.002 - 0.001 0.004 - 0.431 - 0.287 -0.552 (0.38) (-0.72) (-0.11) (0.24) (-0.71) (-1.54) (-0.81)

R-Squared 10.11% 38.98% 10.94% 23.01% 14.81% 43.58% 35.19% Observations 87 85 85 85 84 82 82

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Table 18: Cross Sectional Analysis for sell recommendations without news The table presents the results of the regression analysis with different dependent variables for the sell recommendations. Only the recommendations without news in the weekend of the broadcast are included in the regression analysis. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book-to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant 0.174 - 0.031 0.043 0.076 -5.089 - 6.198** -27.866* (1.42) (-1.05) (0.56) (0.33) (-0.62) (-2.00) (-1.72) Viewership 0.007 -0.005 -0.053 -0.157 0.002 (1.37) (-0.39) (-1.40) (-0.28) (0.00) Duration -0.000 -0.000 -0.000 0.001 -0.002 (-0.28) (-0.69) (-1.11) (0.30) (-0.08) Total recommendations -0.003** -0.004 -0.008 0.035 -0.004 (-2.07) (-1.18) (-0.75) (0.25) (-0.01) Dutch 0.003** 0.003 0.020* -0.047 0.106 recommendations (2.18) (0.73) (1.70) (-0.30) (0.13) book-to-price 0.008 0.003 -0.005 0.072** 4.357*** 1.180*** 5.132** (0.32) (0.79) (-0.51) (2.46) (2.93) (3.05) (2.55) log size -0.012* - 0.000 -0.000 0.005 0.060 0.379** 1.59* (-1.71) (-0.32) (-0.11) (0.53) (0.12) (2.23) (1.80) IDIOVOL -0.428 0.440** 0.348 - 0.178 68.416 0.145 -36.618 (-0.38) (2.36) (0.71) (-0.12) (0.89) (0.01) (-0.32) AMX -0.009 - 0.010** -0.010 0.017 -0.086 0.667 3.086 (-0.43) (-2.26) (-0.81) (0.47) (-0.06) (1.11) (0.99) AScX 0.014 - 0.009* -0.009 0.042 -0.768 0.710 4.351 (0.49) (-1.88) (-0.71) (1.10) (-0.37) (1.16) (1.36) Wierda 0.028* 0.003 0.001 0.036 3.503*** 0.578* 1.907 (1.70) (0.77) (0.15) (1.35) (3.47) (1.65) (1.04) Vermeulen -0.037 0.015** 0.011 0.027 -0.116 0.431 -1.17 (-1.01) (2.50) (0.68) (0.59) (-0.05) (0.71) (-0.37) Hafkamp -0.036* 0.013*** 0.012 0.022 2.842** -0.285 -0.848 (-1.89) (2.65) (0.91) (0.56) (2.04) (-0.46) (-0.26)

R-Squared 50.38% 56.89% 39.63% 62.62% 63.43% 62.53% 47.45% Observations 26 26 26 26 24 24 24

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Table 19: Cross Sectional Analysis for strong sell recommendations without news The table presents the results of the regression analysis with different dependent variables for the strong sell recommendations. Only the recommendations without news in the weekend of the broadcast are included in the regression analysis. The dependent variables are the abnormal return on event day 0, the cumulative abnormal return for [-6,-1], [0,1] and [0,6]. Also the abnormal volume on day 0 is an dependent variable. Note that this is the Monday, because there are not any trading volumes in the weekend. Also the cumulative abnormal volume on [-5,-1] and [0,4] are dependent variables. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book- to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] event window. AMX and AScX are dummies which are one when the company is listed on one of the indices. Wierda, Vermeulen and Hafkamp are dummies which take the value of one when the recommendation is done by that analyst. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Event period CAR[-6,-1] AR[0] CAR [0,1] CAR [0,6] CAV [-5,-1] AV[0] CAV[0,4]

Constant -0.095 - 0.037 -0.114** 0.106 13.822 0.448 15.405 (-0.38) (-0.84) (-2.26) (0.45) (0.86) (0.09) (1.02) Viewership -0.002 -0.006 -0.015 -0.066 -2.683 (-0.28) (-0.94) (-0.47) (-0.09) (-1.26) Duration -0.000 -0.000 -0.001 0.004 -0.02 (-0.55) (-0.61) (-1.44) (0.32) (-0.56) Total recommendations 0.001 -0.003 -0.016 -0.048 -0.205 (0.28) (-1.07) (-1.38) (-0.19) (-0.28) Dutch -0.001 0.002 0.004 0.056 -0.601 recommendations (-0.29) (0.88) (0.31) (0.20) (-0.72) book-to-price 0.000 - 0. 003 0.004 0.026 -2.159 - 0.169 -0.040 (0.00) (-0.43) (0.63) (0.81) (-1.10) (-0.24) (-0.02) log size 0.007 0.002 0.009*** -0.003 -0.602 - 0.020 0.098 (0.48) (0.89) (2.79) (-0.23) (-0.68) (-0.06) (0.10) IDIOVOL 0.087 0.232 1.364** 5.961** -213.864 - 25.764 - 18.167 (0.04) (0.46) (2.37) (2.21) (-1.53) (-0.43) (-0.10) AMX 0.013 0.008 0.005 0.020 0.443 0.707 3.090 (0.34) (1.13) (0.66) (0.51) (0.18) (0.77) (1.16) AScX -0.008 0.003 0.008 0.069 -1.208 1.217 6.286 (-0.16) (0.29) (0.71) (1.33) (-0.35) (0.92) (1.62) Wierda -0.041 0.006 -0.013* 0.032 2.146 0.363 0.387 (-1.32) (1.01) (-1.77) (0.95) (1.05) (0.48) (0.18) Vermeulen -0.039 0.009 0.002 0.055* 0.445 0.571 2.699 (-1.26) (1.38) (0.24) (1.65) (0.20) (0.65) (1.05) Hafkamp -0.187*** 0.01 1 0.051*** -0.032 2.766 0.543 -1.751 (-3.27) (1.16) (4.61) (-0.62) (0.75) (0.48) (-0.53)

R-Squared 43.66% 30.52% 67.60% 68.99% 26.46% 24.80% 39.59% Observations 30 29 29 29 29 28 28

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Table 20: The CAPM, three factor model and four factor model for strong buy and buy recommendations The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on buy and strong buy recommendations. The portfolios are created from 1/1/2010 until 1/6/2016. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods are 24, 78, 156 and 312 trading days. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Strong Buy 24 78 156 312 Buy 24 78 156 312 a. CAPM Intercept -0.069*** -0.025 - 0.013 - 0.014 -0.036 -0.014 - 0.032** -0.037* (-3.16) (-1.61) (-1.09) (-1.34) (-1.29) (-0.67) (-2.03) (-1.88) Rm-rf 0.810*** 1.040*** 1.006*** 1.022*** 0.719*** 1.027*** 1.094*** 1.056*** (38.44) (70.89) (88.58) (102.40) (26.75) (50.86) (71.31) (56.52)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 42.79% 71.78% 79.88% 84.14% 26.58% 56.70% 72.01% 61.78% b. Three factor model Intercept -0.070*** -0.020 - 0.009 - 0.010 -0.035 -0.011 - 0.029* -0.020 (-3.19) (-1.35) (-0.78) (-0.97) (-1.25) (-0.51) (-1.85) (-1.08) Rm-rf 0.798*** 1.041*** 1.006*** 1.024*** 0.704*** 1.021*** 1.09*** 1.117*** (36.59) (69.42) (87.55) (101.75) (25.38) (49.29) (69.41) (60.14) SMB 0.020 0.159*** 0.153*** 0.152*** 0.093*** 0.155*** 0.134*** 0.428*** (0.51) (5.82) (7.27) (8.28) (4.97) (4.10) (4.68) (12.61) HML 0.076*** 0.102*** 0.102*** 0.090*** 0.142 0.137*** 0.108*** -0.036* (3.37) (6.57) (8.59) (8.65) (-1.25) (6.40) (6.65) (-1.89)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 43.12% 72.66% 80.90% 85.04% 27.51% 57.74% 72.78% 65.00% c. Four factor model Intercept -0.063*** -0.013 - 0.004 - 0.004 -0.034 -0.010 -0.025 - 0.005 (-2.90) (-0.85) (-0.35) (-0.42) (-1.20) (-0.49) (-1.58) (-0.27) Rm-rf 0.777*** 1.016*** 0.990*** 1.006*** 0.699*** 1.019*** 1.077*** 1.068*** (34.90) (66.98) (84.81) (99.05) (24.58) (47.97) (67.08) (58.10) SMB -0.016 0.116*** 0.125*** 0.121*** 0.085 0.152*** 0.111*** 0.343*** (-0.39) (4.21) (5.88) (6.53) (1.63) (3.93) (3.79) (10.24) HML 0.070*** 0.095*** 0.098*** 0.085*** 0.141*** 0.137*** 0.104*** -0.050*** (3.12) (6.19) (8.27) (8.27) (4.91) (6.36) (6.43) (-2.69) MOM -0.086*** - 0.102*** -0.066*** -0.075*** -0.020 - 0.007 - 0.056*** - 0.202*** (-4.24) (-7.40) (-6.18) (-8.08) (-0.77) (-0.37) (-3.81) (-12.10)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 43.63% 73.39% 81.26% 85.52% 27.53% 57.74% 72.98% 67.41%

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Table 21: The CAPM, three factor model and four factor model for strong sell and sell recommendations The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on sell and strong sell recommendations. The portfolios are created from 1/1/2010 until 1/6/2016. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods are 24, 78, 156 and 312 trading days. The t- value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

Strong Sell 24 78 156 312 Sell 24 78 156 312 CAPM Intercept 0.045** 0.083*** 0.053* 0.012 0.092*** 0.094*** 0.050* 0.049*** (2.17) (2.86) (1.79) (0.63) (2.80) (2.69) (1.96) (2.85) Rm-rf -0.234*** - 0.643*** - 0.839*** -0.826*** -0.323*** - 0.766*** -0.921*** - 0.905*** (-11.88) (-23.11) (-29.77) (-45.15) (-10.24) (-22.74) (-37.33) (-55.29)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 6.66% 21.28% 30.97% 50.78% 5.04% 20.75% 41.36% 60.74% Three factor model Intercept 0.044** 0.078*** 0.047 0.007 0.085*** 0.085** 0.043* 0.048*** (2.15) (2.70) (1.61) (0.38) (2.59) (2.44) (1.69) (2.83) Rm-rf -0.237*** - 0.646*** - 0.846*** -0.836*** -0.351*** - 0.802*** -0.945*** - 0.899*** (-11.58) (-22.47) (-29.04) (-44.22) (-10.73) (-23.04) (-37.08) (-53.01) SMB -0.001 - 0.160*** - 0.178*** -0.152*** -0.173 - 0.215*** -0.186*** -0.029 (-0.03) (-3.05) (-3.35) (-4.41) (-2.90) (-3.39) (-4.00) (-0.93) HML 0.015 -0.089*** - 0.08*** -0.053*** 0.030 0.049 0.006 -0.052*** (0.71) (-2.97) (-2.64) (-2.71) (0.89) (1.36) (0.23) (-2.96)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 6.69% 21.87% 31.49% 51.34% 5.55% 21.39% 41.87% 60.91% Four factor model Intercept 0.044** 0.075*** 0.037 0.002 0.075** 0.072** 0.034 0.041** (2.15) (2.60) (1.26) (0.11) (2.28) (2.07) (1.32) (2.44) Rm-rf -0.238*** - 0.637*** - 0.812*** - 0.819*** -0.317*** - 0.760* ** -0.915*** - 0.877*** (-11.31) (-21.60) (-27.36) (-42.43) (-9.50) (-21.43) (-35.23) (-50.84) SMB -0.002 - 0.144*** -0.120** - 0.124*** -0.115* - 0.142** - 0.133*** 0.010 (-0.04) (-2.69) (-2.21) (-3.52) (-1.89) (-2.20) (-2.82) (0.31) HML 0.015 -0.086*** -0.070** - 0.048** 0.040 0.061* 0.015 -0.046*** (0.70) (-2.88) (-2.34) (-2.48) (1.18) (1.70) (0.56) (-2.62) MOM -0.001 0.038 0.140*** 0.068*** 0.139*** 0.175*** 0.126*** 0.091*** (-0.05) (1.43) (5.17) (3.87) (4.59) (5.42) (5.34) (5.80)

Obs 1978 1978 1978 1978 1978 1978 1978 1978 R-squared 6.69% 21.95% 32.41% 51.71% 6.55% 22.54% 42.70% 61.57%

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Figure 7: Long term portfolios by investing in strong buy recommendations of each analyst The figure presents the returns of investing €1000,- in strong buy recommendations of each of the analysts. Each panel shows a part of the total time period, because some of the analysts are not in the whole period attending in Business Class.

A.

B.

C.

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Table 22: The CAPM, three factor model and four factor model for strong buy recommendations by analyst for portfolios with an holding period of 24 days The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on strong buy recommendations by each analyst. The portfolios are created in the time period in which the analyst came regularly to Business Class. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods is 24 trading days. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

24 days Wierda Schaaij Vermeulen Hafkamp

CAPM Intercept -0.032 - 0.054*** -0.036* -0.041** (-1.16) (-2.99) (-1.71) (-2.02)

Rm-rf 0.541*** 0.469*** 0.270*** 0.441*** (20.79) (30.95) (13.14) (22.83)

Obs 2029 3571 1341 2053 R-squared 17.57% 11.43% 20.26% Three factor model -0.033 (- -0.055*** - 0.032 (- -0.039* Intercept 1.20) (-3.06) 1.55) (-1.90) 0.526*** 0.458*** 0.276*** 0.442*** Rm-rf (19.49) (29.57) (12.83) (22.50) 0.013 0.018 0.086** 0.092** SMB (0.27) (0.58) (2.14) (2.52)

0.087*** 0.076*** 0.006 0.052** HML (3.11) (3.78) (0.23) (2.47)

Obs 2029 3571 1341 2053 R-squared 17.97% 21.47% 11.74% 20.66% Four factor model -0.027 - 0.052*** -0.033 - 0.039* Intercept (-1.00) (-2.88) (-1.59) (-1.94) 0.508*** 0.442*** 0.279*** 0.444*** Rm-rf (18.39) (27.70) (12.71) (21.71)

-0.019 - 0.006 0.090** 0.097** SMB (-0.37) (-0.20) (2.22) (2.59)

0.082*** 0.069*** 0.006 0.052** HML (2.92) (3.42) (0.26) (2.50) -0.075*** - 0.071*** 0.015 0.012 MOM (-2.96) (-3.99) (0.75) (0.61)

Obs 2029 3571 1341 2053 R-squared 18.32% 21.82% 11.77% 20.68%

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Table 23: The CAPM, three factor model and four factor model for strong buy recommendations by analyst for portfolios with an holding period of 78 days The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on strong buy recommendations by each analyst. The portfolios are created in the time period in which the analyst came regularly to Business Class. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods is 78 trading days. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

78 days Wierda Schaaij Vermeulen Hafkamp CAPM

Intercept -0.024 - 0.033** -0.041 - 0.013 (-0.81) (-1.99) (-1.63) (-0.63)

Rm-rf 0.984** 0.798*** 0.645*** 0.821*** (34.20) (57.79) (25.68) (43.05)

Obs 2029 3571 1341 2053 R-squared 36.59% 48.34% 32.94% 47.47% Three factor model

Intercept -0.023 - 0.029* -0.031 -0.005 (-0.78) (-1.79) (1.23) (-0.23)

Rm-rf 0.972*** 0.794*** 0.666*** 0.848*** (32.60) (56.75) (25.64) (43.23) SMB 0.079 0.215*** 0.282*** 0.241*** (1.45) (7.66) (5.82) (6.73) HML 0.117*** 0.110*** 0.007 0.012 (3.77) (6.06) (0.25) (0.58)

Obs 2029 3571 1341 2053

R-squared 37.04% 49.59% 34.70% 48.62% Four factor model Intercept -0.013 - 0.024 - 0.037 - 0.005 (-0.44) (-1.48) (-1.48) (-0.26) Rm-rf 0.937*** 0.769*** 0.688*** 0.849*** (30.86) (53.56) (26.10) (42.41)

SMB 0.018 0.176*** 0.308*** 0.244*** (0.32) (6.20) (6.35) (6.69)

HML 0.107*** 0.099*** 0.012 0.012 (3.46) (5.46) (0.42) (0.60) MOM -0.145*** - 0.114*** 0.098*** 0.008 (-5.21) (-7.09) (4.15) (-0.46)

Obs 2029 3571 1341 2053 R-squared 37.88% 50.29% 35.53% 48.63%

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Table 24: The CAPM, three factor model and four factor model for strong buy recommendations by analyst for portfolios with an holding period of 156 days The table presents the results of the CAPM, three factor model and four factor model calendar time portfolios based on strong buy recommendations by each analyst. The portfolios are created in the time period in which the analyst came regularly to Business Class. The rm-rf variable is the market return minus the risk free rate. The SMB factor is variable which controls for the outperformance of small companies. The HML factor controls for the outperformance of value companies over growth firms. The MOM factor controls for the outperformance of stocks which performed well in the past. The holding periods is 156 trading days. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

156 days Wierda Schaaij Vermeulen Hafkamp CAPM Intercept 0.022 - 0.030** -0.032 - 0.007 (0.87) (-2.11) (-1.39) (-0.44) Rm-rf 1.073*** 0.920*** 0.866*** 0.900*** (43.95) (76.63) (37.77) (60.93)

Obs 2029 3571 1341 2053 R-squared 48.79% 62.20% 51.58% 64.42% Three factor model Intercept 0.026 - 0.027** -0.024 - 0.000 (1.01) (-1.97) (-1.03) (-0.02) Rm-rf 1.069*** 0.907*** 0.885*** 0.922*** (42.40) (75.73) (37.25) (60.79) SMB 0.156*** 0.232*** 0.219*** 0.192*** (3.39) (9.62) (4.95) (6.95) HML 0.125*** 0.172*** -0.008 0.004 (4.78) (11.08) (-0.29) (0.28)

Obs 2029 3571 1341 2053 R-squared 49.51% 64.20% 52.52% 65.25% Four factor model Intercept 0.036 - 0.024* -0.025 - 0.001 (1.45) (-1.72) (-1.07) (-0.07) Rm-rf 1.032*** 0.890*** 0.889*** 0.924*** (40.35) (72.23) (36.59) (59.65) SMB 0.091* 0.205*** 0.224*** 0.196*** (1.96) (8.38) (5.00) (6.95) HML 0.115*** 0.164*** -0.007 0.005 (4.41) (10.59) (-0.26) (0.31) MOM -0.153*** - 0.079*** 0.015 0.010 (-6.53) (-5.73) (0.71) (0.70)

Obs 2029 3571 1341 2053 R-squared 50.56% 64.52% 52.54% 65.26%

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Table 25: Cross Sectional Analysis for strong buy recommendations by each analyst The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations by each analyst.. The dependent variables is the cumulative abnormal return in the [0,1] event window. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book- to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] window. AMX and AScX are dummies which are one when the company is listed on one of the indices. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

CAAR[0,1] Wierda Schaaij Vermeulen Hafkamp

-0.078 0.063* 0.006 0.050 Constant (-0.62) (1.93) (0.05) (1.13) 0.027 0.005 - 0.006 - 0.003 Viewership (1.45) (1.27) (-0.34) (-0.68) 0.000 0.000 - 0.000 0.000 Duration (0.60) (1.44) (-0.16) (0.78) -0.001 -0.001 0.003 - 0.003*** Total recommendations (-0.13) (-0.41) (1.01) (-2.92) 0.003 -0.000 - 0.004 0.003*** Dutch recommendations (0.55) (-0.02) (-0.94) (2.15) -0.004 0.007*** 0.019** 0.000 book-to-price (-0.49) (3.44) (2.11) (0.09) -0.005 - 0.005*** 0.001 - 0.002 log size (-1.03) (-3.82) (0.15) (-1.19) 1.862*** 0.123 0.628 0.696* IDIOVOL (4.12) (0.57) (0.80) (1.69) -0.020 - 0.008* 0.009 - 0.008 AMX (-1.27) (-1.67) (1.13) (-1.53) -0.022 -0.004 0.010 - 0.009 AScX (-0.84) (-0.65) (0.87) (-1.01) 0.007 0.000 0.002 - 0.000 News (0.58) (0.06) (0.21) (-0.01)

R -Squared 28.91% 28.14% 71.24% 26.92% Observations 80 174 24 59

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Table 26: Cross Sectional Analysis for strong buy recommendations by each analyst The table presents the results of the regression analysis with different dependent variables for the strong buy recommendations by each analyst.. The dependent variables is the abnormal trading volume on the Monday after the show. Viewership is a natural logarithm of the amount of viewers for the first broadcast and the rebroadcast. Duration is the time in sec which the analyst uses to talk about the recommendation. Total recommendations are the total number of domestic and foreign recommendations. Dutch recommendations is the amount of recommended Dutch assets. Book-to-price is the book- to-price value of the stock at the moment of the recommendation. The log size is a natural logarithm of the market capitalization of the company at the time of the recommendation. IDIOVOL is the standard deviation of the abnormal returns in the [-37,-7] window. AMX and AScX are dummies which are one when the company is listed on one of the indices. News is a dummy which takes the value of one when there was relevant news in the weekend of the recommendation for that company. The t-value is shown between brackets. The *, **, *** presents significance levels of 1%, 5% and 10%.

AV[0] Wierda Schaaij Vermeulen Hafkamp

0.753 3.107** 6.493 0.483 Constant (0.52) (2.51) (1.63) (0.31) 0.177 0.070 - 1.139* 0.002 Viewership (0.80) (0.46) (-1.71) (0.02) 0.001 0.007*** 0.012*** -0.001 Duration (0.31) (3.36) (3.19) (-0.41) -0.072 0.023 0.026 - 0.011 Total recommendations (-1.27) (0.45) (0.22) (-0.31) 0.058 -0.022 - 0.105 - 0.024 Dutch recommendations (0.94) (-0.41) (-0.78) (-0.52) -0.102 - 0.007 0.187 0.004 book-to-price (-1.22) (-0.11) (0.62) (0.03) -0.095* - 0.230*** -0.056 - 0.029 log size (-1.71) (-4.18) (-0.48) (-0.43) 17.490*** -6.447 - 17.746 25.137* IDIOVOL (3.38) (-0.81) (-0.65) (1.74) -0.140 - 0.069 - 0.201 - 0.040 AMX (-0.76) (-0.37) (-0.73) (-0.23) 0.358 0.400* 0.625 - 0.394 AScX (1.16) (1.69) (1.49) (-1.32) 0.199 0.108** 0.094 0.112 News (1.48) (2.51) (0.38) (0.81)

R -Squared 35.97% 46.41% 81.12% 17.51% Observations 78 164 23 58

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