1. A Direct Test of Private Information on Analysts’ Recommendations: Examination of Profits among Institutions and Individuals on Brokerages Recommendations A Direct Test of Private Information on Analysts’ Recommendations: Examination of Profits among Institutions and Individuals on Brokerages Recommendations Yu-Jane LiuK.C. John Wei Vivian W. Tai 戴維芯 Vivian W. Tai Department of Banking and Finance National Chi Nan University NTU International Conference on Finance Taipei, Taiwan 2006.12.14

2. Information Value of Analysts’ Recommendations  The information value of analysts’ researches is mostly recognized by cumulative abnormal returns.(e.g. Elton, Gruber,and Grossman, 1986; Womack, 1996; Barber et al, 2001)  Behavioral Finance emphasize the differences among investors. (e.g. Barber, Lee, Liu and Odean, 2005)  Unfortunately, little researches focus on the information value of analysts’ research for types of investors, especially individual investors.

3. Trading behaviors of institutions and/or individual investors on analysts’ information  Chen and Cheng (2002): types of institutions  Iskoz (2003): institutions’ trading on underwriter’s and non-underwriters’ recommendations  Malmendier and Shanthikumar (2003): big traders tend to account distortion of affiliated analysts and small investors follow recommendations naively.  Lee, Liu, and Tai (2004): actual demands of five types of investors  The studies mentioned above have not further verified the exact reason for different trading behaviors between institutions and individuals.

4. Why do institutions and individuals react differently to analysts’ recommendations?  Institutions’ long-term relationship with brokerages »Goldstein, Irvine, and Kandel (2004): commission revenue »Lim (2001): Information assess channel »Lin and McNichols (1998), Michaely and Womack (1999), Malmendier and Shanthikumar (2003): underwriting business »Kim, Lin and Slovin (1997), Lee, Liu, and Tai (2004): Indirect evidences of analysts’ information leakage.  Institutions’ superior ability than individuals »Lee, Liu, and Tai (2004) »Malmendier and Shanthikumar (2003, 2005)

5. Contributions  This paper provide a direct measure, actual trading profit, to measure who do really benefit from the information value of brokerages, which help us to clarify the impact of different trading behaviors between institutions and individual investors.  We classify type of investors into customers and non- customers to directly test the existence of private information provided by recommending brokerages, which help us to clarify the exact reason of different reactions between institutions and individuals.  Shed further light on information value of brokerages recommendations and the relationship between brokerages and their customers.

6. Three Issues 1) Who does really benefit from analysts’ recommendations? Individuals vs Institutions? 2) Do customers of recommending brokerages benefit from the private information provided by brokerages? Customers? Institutional customers? 3) Is the information value of institutions caused by clients’ relationships with brokerage houses or their superior selectivity? Profit differences among customers in mutual funds, corporations and individual investors are larger in customers than those in non-customers.

7. Data Sources  Intra-day trading data from TSEC  Recommendation data collected reports from brokerages by Central News Agency.  Sample period: June 1996~Dec. 1999  Excluding recommendations which experience IPO, de-list, moving from OTC to TSE, covered less than 5 times, and brokerages which recommend less than 10 times. »Number of recommendations: 56,655 »Event number of recommended day-stock: 33,919 »Number of recommending brokerages: 46

8. Empirical Measurements  Customers definitions: »Investors who have been submitted orders from recommending brokerages are defined as customers.  Actual Trading Profit: »Revenue at each selling time deduct mapped cost and transaction costs. »Mapped cost calculations: FIFO and Averaged methods »Excluding profit of short sale Short Sale restriction of institutions in Taiwan Hedge instruments and derivatives market in not active Optimal Rule of individuals investors Limits to open margin account Percentage of Short Sale is small The largest percentage of sell recommendation is “Downgrade” »Profits of type of investors: equal weighted and value weighted  Recommendation characteristics: »Rating: keywords mapping following Chang (2003) »Consensus score: Averaging the rating »Type: Initiate, Continuous, Upgrade, and Downgrade

9. ISSUE 1: Information Value of Recommendations ISSUE 1: Information Value of Recommendations -Table 2. Profits of Different Type of Investors >0 Largest Smallest Largest Smallest Largest Smallest Largest Smallest

10. Adjusted trading profit t= -15 t= +15 t=0 Buy (Cost) Sell (Revenue) Buy (Cost) Sell (Revenue) Actual trading profit t= -15 t= +15 t=0 Buy (Adjusted Cost) Sell (Revenue) Buy (Cost) Sell (Revenue) Buy: Profit of Previous holding Buy:Profit of buy and sell during event period Buy (Cost) Sell (Paper Gain/Loss) Buy: Profit of buy and hold Sell (Revenue) Buy (Fictitious Cost) Sell: Profit from avoiding loss

11. ISSUE 1: Information Value of Recommendations ISSUE 1: Information Value of Recommendations -Table 3. Adjusted Profits of Different Type of Investors >0

12. ISSUE 2&3: Private Information Value ISSUE 2&3: Private Information Value -Table 6. Profits of Customers and Non-customers 5.69 8.51 2.92 4.45

13. ISSUE 2&3: Private Information Value ISSUE 2&3: Private Information Value -Table 7 & 8. Adjusted Profits of Customers and Non-customers

14. Regression Analysis  To examine the relation between actual trading profits of investors and customer relationships, we ran profit regressions as follows, where, i is the type of investors, bis brokerages, including recommending brokerages, and non-brokerages. kis the k th event for k=1, …, K.

15. Table 9. Profit Regression Analysis in buy recommendations

16. Table 10. Profit Regression Analysis in sell recommendations

17. Conclusions  We document the information value of recommendations by looking at actual trading profits. We find: »All investors get positive benefits and significantly profit from brokerages’ buy recommendations. »Professional institutions earn more profits than retail investors during the event periods, which supports that professional investors have superior selectivity and long-term relationship with brokerages.  To test the private information from brokerage houses, we separate types of investors into customer and non-customer based and find: »Private information made their domestic institutional clients wealthier through buy recommendations. »Controlling superior abilities of institutions, institutional customers earn more than institutional non-customers, which support domestic institutions get benefit from their deep relationship with brokerages. »Our finding both supports institutions’ superior ability hypothesis and customer relationship hypothesis.