1. Introduction There is still a lot of controversy on the forecasting ability of analysts. On one hand, if markets are efficient in its semi-strong (or even strong) form, in the sense of Fama (1970), there would be no ground for the existence of research departments as it would be impossible to implement a profitable strategy on the basis of the publicly available information. Yet, some authors claim that evidence of analysts’ forecasting ability in itself should not be interpreted as a violation of market efficiency if one cannot implement that strategy effectively. In other words, finding that research analysts play an important role in disseminating information may be consistent with market efficiency; only evidence of effective trading strategies on the basis of public information, such as research reports or analysts’ recommendations, should be accepted as contradictory evidence. Recent research (see Warmers, 2000, as an example) suggests that the performance of active management is not superior to a passive strategy due to trading costs.
Our research can thus also inform on the value created in active management done on the basis of stock picking skills. On the other hand, in the last few years, there is growing suspicion on the information value of analysts’ recommendations (particularly for sell side-analysts) motivated by anecdotal evidence on lack of independence of research departments due to pressures by other investment bank departments such as brokerage or M&A (Mergers & Acquisitions).
The Term Paper on Research Analyst
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The main purpose of this study is to evaluate the effectiveness of several trading strategies built on the basis of the recommendations produced by a team of research analysts in a Portuguese investment bank. Analysts identify undervalued assets for which they issue a buy recommendation. These investment recommendations are regularly published. All recommendations refer to stocks traded in markets for which the investment bank offers brokerage services.
Analysts also issue recommendations on other kind of securities, but we exclude them from our sample. We design simple trading strategies on the basis of these recommendations. Stocks are bought at the time of the recommendation disclosure, held for a certain period and then sold. We create different strategies for an array of holding periods and analyze the results over time and for subgroups of stocks. The rest of the paper is organized as follows. Section 2 provides a brief literature review on analysts’ recommendations.
Section 3 presents the methodology, explains the different recommendation-based trading strategies and describes our data. Section 4 presents our main results and section 5 concludes. 2. Literature Review The literature on analysts’ recommendations has focused on three different questions: (1) the type of analysis used by financial analysts to evaluate stocks; (2) the stock price reaction to the analysts’ recommendations (3) the value of analysts’ recommendations as effective tools for stock selection from an investor perspective. Our paper addresses this last question. In a competitive and rational world, investors will only follow analysts’ recommendations if the expected benefits are greater than the cost of advice, in other words, when analysts’ recommendations are expected to have (informational) value.
Financial theory tells us that the most economically rational benefits extracted from an investment recommendation are the positive excess returns following recommendations. Moreover, analysts’ recommendations have (informational) value if the analysts have superior or inside information on the financial asset, and / or if the advice service is cost-free. Even if our central approach is from an investor point of view perspective, our evidence could also shed some light on the impact of this public information (published investment recommendations) on prices and therefore inform about semi-strong or strong market efficiency in the sense of Fama (1970).
The Essay on Stock Investment
STOCK INVESTMENT Having an imaginary million dollars, with the purpose of investment, makes it quite impossible to come up with the right decision of how to maintain a high level of financial security, as the money is not real. Therefore, the considerations of security will not be here quite as important as they are in the field of real stock trade. Nevertheless, there are a certain guide lines, ...
However, the implications of our study in terms of the impact on stock prices and on market efficiency are limited because these recommendations are disseminated to a small number of investors. 2. 1 Evidence on Analysts’ Recommendations The debate over the value of analysts’ recommendations to stock selection is not settled and the existing empirical evidence that supports the hypothesis of real superior returns from investing on the basis of analysts’ recommendations is not consensual.
The seminal article on investment recommendations was written by Alfred Cowles III (1933) who studied investment recommendations of 16 financial services companies, 25 financial periodicals and The Wall Street Journal editors. Cowles showed that recommended stocks had, on average, a negative performance when compared against a market benchmark, and concluded that investment recommendations didn’t add (informational) value. Until Womack (1996), there was little evidence on whether analysts’ recommendations would yield abnormal returns. In spite of some findings suggesting that recommended stocks had positive excess returns, there were criticisms of sample bias or imprecise data. Womack (1996) looks at stock prices’ daily reactions to changes in the 14 biggest U. S.
brokerage house analysts’ recommendations and finds statistically significant positive excess returns from investments on recommended stocks. However, these excess returns show strong mean reversion in the six months following the announcement. Their main focus is to determine the impact of changes in analysts’ recommendations on stock prices (and evaluate semi-strong form of market efficiency) rather than to assess the usefulness of these recommendations from an investor’s perspective. Yet, Jaffe and Mahoney (1999) conclude that common stock recommendations made by investment newsletters do not outperform appropriate benchmarks (control firms).
Moreover, there is no evidence of performance persistence, when performance is measured by abnormal returns. Barber, Leh avy, Mc Nichols and Trueman (2001) show that buying (selling short) stocks with the most (least) favourable consensus recommendations, together with daily portfolio re balancing and a timely response to recommendation changes, would allow a monthly abnormal return of 0.
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75 percent. The data used in their paper included over 360 000 investment recommendations from 269 brokerage houses and 4340 analysts, from 1985 to 1996. The authors conclude that these results reflect market semi-strong inefficiency, and exclude the data-snooping or pricing model weakness hypotheses. However, this trading strategy inherently assumed high trading levels, so when transaction costs were considered, the abnormal returns were no longer statistically different from zero. Even though, analysts’ recommendations provide value, because, ceteris paribus, investors would be better off investing in the most favoured stocks rather than in the least favoured stocks. In a more recent article, these same authors re-evaluate the returns of analysts’ recommendations using a new sample for the 1996-2001 period.
Barber et al. (2003) confirm their previous findings for the period of 1996 to 1999, i. e. , the most favoured stocks had a better performance. Yet, in the years 2000 and 2001 (distinguished by rising doubts on the independence of some analysts’ recommendations), these most favoured stocks turn out to have a negative performance. In sum the literature suggests that, on average, financial analysts show forecasting ability skills but there remain many doubts on the profitability of recommendation-based strategies.
3. Methodology and Data We test the forecasting ability of a research department in a Portuguese investment bank and we examine the profitability of strategies based on stock recommendations designed by these analysts. If we find that these recommendations are valuable for stock selection, the results will be consistent with analyst forecasting ability and inconsistent with market efficiency. Otherwise, our results will support one of two views: either analysts have no forecasting skills or they do but stock prices immediately adjust when recommendations become public. Womack (1996) uses the traditional event study methodology to measure stock price reaction to changes in analysts’ recommendations and to draw implications in terms of market efficiency. Barber et al.
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(2001, 2003) focus their study on the design of investment portfolios based on analysts’ recommended stocks. Barber et al. (2001 and 2003) use stocks whose rating is periodically reviewed, so their portfolios have to be reviewed or rebalanced every time when those stocks’ recommendations are changed. We cannot replicate their methodology on our sample, as the stock recommendations are randomly published depending on trading opportunities detected by the investment bank’ financial analysts, and as such we wouldn’t be able to review our portfolio periodically. Instead, we analyze the recommendations as individual investments and use an event study methodology that is similar to Desai, Liang and Singh (2000), based on buy-and-hold returns.
We compute the abnormal performance of each recommended stock for different holding-periods. The profitability of these recommendation-based strategies is then assessed looking at the simple average abnormal returns. These averages can be interpreted as the payoff to a stock picking recommendation-based strategy for a particular holding period. Desai et al. (2000) compare the performance of the recommended stock’s performance with the performance of a matching company to control for size and industry effects. In our event study, we use a simple market-model where the normal returns are given by market returns of relevant stock indexes.
Our stock recommendations comprise Portuguese and non-Portuguese (other European countries and American) stocks. We first look at the entire sample of stocks. The analysis is then repeated for sub-samples. We use different benchmarks for the aggregate and for the different sub-samples of recommendations analysed. 3.
1 Methodology We analyse buy-and-hold returns for each recommended stock: (3. 1) where: Ri, T Buy-and-hold return for stock i, for T days rit Daily total return (with dividends) for stock i, on day t We assume that returns are jointly normal, and identically and independently distributed through time (IID).
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Methodology: The paper uses historical returns from 1871-2004 to assess the President's personal accounts proposal. It does 91 different simulations for a worker born in 1990 assuming that he or she experiences the actual returns from 1871-1914, 1872-1915, 1873-1916, all the way through 1961-2004. This sample has an average real stock market return of 6. 8% annually, slightly above the 6. 5% ...
Without assuming normality, all results would be asymptotic. We analyse the performance of recommendation-based strategies for different holding-periods (in calendar time), so we can evaluate the returns obtained by investors with different profiles (short-term, medium-term and long term-oriented investors).
Alternatively, the different holding period averages show how a strategy performs over time. Each holding-period starts in the day after the event day.
We thus assume that the publication day is day 0 and that each investment could only start after that day. In other words, we buy the stock at event day closing price. We examine six holding-periods of 3, 10, 25, 125, 250 and 500 days. The excess return of stock i, Hpart, for holding-period T, is given by: (3. 2) where: Rm, T Buy-and-hold return for the passive benchmark for T days For its liquidity and representativeness, we choose the MSCI World as the general benchmark, the PSI 20 for Portuguese stocks, the DJ Eurostoxx 50 for other non-Portuguese European stocks, the S&P 500 for American stocks and the Nasdaq 100 for TMT stocks. By analyzing abnormal returns computed as excess returns to the relevant benchmark we evaluate if a trading strategy outperforms an alternative passive trading strategy.
So, for each holding-period, we calculate the abnormal average return of the recommended stocks, AART, using a simple arithmetic average: (3. 3) where: n Number of active recommendations for T days We test the null hypothesis that there are no abnormal returns associated with analysts’ recommendations. We calculate the statistical significance of AART using a t-statistic computed as: (3. 4) where: SET Estimated standard error of AART To compute the SET we need to specify the kind of relationship between the abnormal returns of the recommended stocks. The assumption of independent abnormal returns may be reasonable in many cases (Brown and Warner, 1985).
However, it may exist stock cross-section return interdependence that can create inter dependencies in the abnormal returns (AART) series.
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Marketing Strategy for Wal-Mart Mission Statement The mission of this paper is to define the best management strategy for Wal-Mart Corporation. In order for us to come up with recommendation of how to increase Wal-Marts commercial effectiveness, we will have to analyze different aspects of companys operations. In its turn, this will require an understanding of what defines companys commercial ...
If in our sample this cross-section returns interdependence effect is real, it can influence our inference results because our time period of analysis is short (three and half years).
To account for this possibility we compute SET allowing for cross-section dependency in abnormal returns, using an approach similar to Desai et al. (2000).
We compute the correlation coefficient of daily abnormal returns, rij, for 751 days (from day -250 to day +500, in event time), for all pairs of i and j of recommended stocks. Covin for any period T is given by rij si sj, where si is the standard deviation of abnormal returns for stock i over time period T. For an equally weighted portfolio of n stocks, the dependence-adjusted standard error of portfolio abnormal returns SET is given by: (3.
5) where: VART Estimated cross-sectional variance for the average abnormal returns for T daysCOVij Estimated covariance between RiT and Rit While it is generally accepted that there are temporal dependencies on returns volatility, our inference results should not be severely affected by the assumption of IID returns because we use (cumulative buy and hold returns) cross-sectional (instead of time-series) variances to estimate the variance parameter (VART) in equation 3. 5. Yet the covariance estimates (COV) use times series estimates of σ i (standard deviation of T-period returns for stock i) and are computed using daily estimates of σ id and assuming time series independence over time (σ iT = √ T σ i).
As such, our inference results could be biased.
Assuming that there is mean reversion in returns, the true variance σ T for an horizon T should be lower than σ iT. As a consequence the SET estimates above are presumably too large and we are rejecting the null too often. To compare the several strategies for different holding-periods, we compute annualized abnormal returns, HPART (a. p. ): (3.
6) 3. 2 Data Table 3. 1 shows the number of analysts’ recommendations by geographical region and by year of publication. It is notorious the increase in the number of published recommendations (although in 2003 we only have 4 months of data) and in the number of stocks covered by this research team. Table 3. 1 – Sample of Recommendations – Table of Frequencies The table presents the “buy” recommendations issued by a Portuguese investment bank’s research team over the period from 1999 to 2003.
Year Portuguese Other European American Total Sample Nr. Firms 1999 1 0 0 1 12000 11 2 7 20 172001 14 7 16 37 292002 13 30 23 66 412003 2 9 4 15 14 Total 41 48 50 139 66 The recommendations were published in the investment bank’s website therefore avoiding ex-post selection bias in the sense that, the investment bank could publicize only the short list of stock recommendations that indeed proved to be valuable. There is thus no ambiguity on the recommended stocks or on the publication date. Of a total of 194 recommendations published by the investment bank, between December 1999 and May 2003, we kept 139 for our sample. We removed the analysts’ recommendations on Warrants, Coba trackers (QQQ) and ADR’s.
We have also removed any recommendation on a stock that had already been recommended in less than a month and whose acquisition price was higher than the previous recommendation acquisition price. Stock prices data are from Datastream. 4. Results We evaluate our trading strategies by comparing the total returns of each strategy with the total returns of a comparable benchmark. In fact, we are comparing our trading strategies against a passive strategy. Table 4.
1 summarises the results for the entire sample of recommendations. Average abnormal returns are all negative and often significant: on average an investor who follows a recommendation-based trading strategy, buying the recommended stock and selling it later, obtains negative abnormal returns, regardless of the holding period. For holding-periods greater than a month, the investor gets annualized abnormal losses between 7. 51 percent and 16. 52 percent, significant at the 1 percent level. For the 3, 10-day and 1-month holding-periods, we observe shorter negative abnormal returns of around 1 percent.
The percentage of recommendations that result in positive abnormal returns is small, almost always below 50 percent, and it notices a decrease as the holding period increases. Table 4. 1 – Recommendation-based Strategies Performance – Total Sample The table presents the buy-and-hold portfolio annualized abnormal returns (HPAR) and the t-statistics for the recommendation-based strategies over several holding periods. The last column shows the percentage of profitable recommendations. Days Nr. of Recommendations HPART t-statistic HPART (a.
p. ) Positive (%) 0 139 0, 97 % 3, 50 242, 50 % 57, 55 %1 – 3 139 -0, 82 % -1, 84 -68, 33 % 43, 89 %1 – 10 139 -0, 78 % -1, 07 -19, 50 % 49, 64 %1 – 25 136 -0, 89 % -0, 75 -8, 90 % 50, 74 %1 – 125 114 -6, 96 % -2, 65 -13, 92 % 41, 23 %1 – 250 76 -16, 52 % -5, 04 -16, 52 % 27, 63 %1 – 500 31 -15, 01 % -3, 06 -7, 51 % 32, 26 % Statistically significant at the 10% level. Statistically significant at the 1% level. Analysts’ recommendations seems to affect stock prices but only on day 0: the 139 recommendations earned average positive excess returns of 0. 97 percent (57. 55 percent of the strategies yielded positive excess returns) on the event day, the day the recommendation is disseminated.
These results are similar to Dims on and Marsh (1984) and are consistent with market efficiency. Yet the negative significant performance associated to these strategies is puzzling. We should stress again that it is erroneous to jump to implications of these results in terms of market efficiency relative to this particular information set, because as mentioned above these recommendations are disseminated to few investors: Further, we do not control for any other information made public around the event day that may in fact be truly informative. 4.
1 Analysis by Stock Market We now divide the entire sample in three sub-samples, Portuguese, Other non-Portuguese European stocks and American stocks, and analyse each one separately. If this team of Portuguese analysts has some kind of comparative home advantage in selecting Portuguese stocks rather than foreign stocks, then we may observe a different performance between the samples. Table 4. 2 shows the results for this specific analysis. Table 4.
2 – Recommendation-based Strategies Performance – Portuguese stocks The table presents the buy-and-hold portfolio annualized abnormal returns (HPAR) the t-statistics for the Portuguese stocks-based strategies over several holding periods. The last column shows the percentage of profitable recommendations. Days Nr. Of Recommendations HPART t-statistic HPART (a. p. ) Positive (%) 0 41 0, 39 % 0, 89 97, 50 % 58, 54 %1 – 3 41 -0, 30 % -0, 63 -25, 00 % 58, 54 %1 – 10 41 0, 48 % 0, 61 12, 00 % 63, 41 %1 – 25 41 1, 89 % 1, 32 18, 90 % 63, 41 %1 – 125 38 7, 00 % 2, 20 14, 00 % 65, 79 %1 – 250 26 7, 46 % 1, 65 7, 46 % 69, 23 %1 – 500 19 8, 00 % 1, 53 4, 00 % 78, 95 % Statistically significant at the 10% level.
Statistically significant at the 5% level. If we only look at the Portuguese stock recommendations, the outlook for the recommendation-based strategies substantially improves: the buy-and-hold abnormal returns are positive for almost all the holding-periods (the exception is the 3-day holding period that yields negative, although not statistically significant, returns).
For the medium and long-term investment horizons, strategies built on the basis of Portuguese most favoured stocks, obtain annualized abnormal returns between 4 percent and 14 percent (the 6-month and 1 year holding-period returns are statistically significant at the 5 and 10 percent level, respectively).
The percentage of analysts’ recommendations that result in positive abnormal returns is higher than 50 percent for any holding period, and increases to 78.
95 percent for the 2-year holding period. The positive performance of Portuguese stocks’ recommendations strategies may be explained by some kind of comparative “Home Advantage.” Bj erring, Lakonishok and Verma elen (1983) suggest that a Canadian brokerage house may have had comparative advantages in obtaining information about local companies. Coal and Moskowitz (1999) refer that because local researchers can talk to employees, managers, and suppliers of the local firms, and may obtain important information from the local media, and have close personal ties with local executives, they may have an information advantage. The existence of this “Home Advantage” for the particular case of Portuguese analysts and Portuguese stocks could arise not only because of the closeness of information but also because the number of Portuguese stocks is small. Alternatively, the positive returns of Portuguese stocks’ recommendations may also be related to the relative inefficiency of the Portuguese stock market and to slower dissemination of information. Tables 4.
3 and 4. 4 show the results for the non-Portuguese stocks subsample’s. Trading strategies based on the recommendations for Other European and American stocks have negative abnormal returns (Tables 4. 3 and 4. 4, respectively).
The 1-year and 2-years holding-periods investments on Other European stocks yield very large negative annual abnormal returns, and none of the recommended stocks earns positive excess returns with a 2-year holding-period trading strategy.
For shorter holding-periods, we observe mixed and statistically insignificant results. Trading strategies for American stocks also have negative abnormal returns for every holding period, being the medium and long horizon strategies the most significant. Again, it seems that the hypothetical recommendation’s value is rapidly eroded on the publication day. Surprisingly, given that this should be a non-event, there is a significant positive impact on stock prices for American stocks when recommendations are published. Table 4. 3 – Recommendation-based Strategies Performance – Other non-Portuguese European Stocks The table presents the buy-and-hold portfolio annualized abnormal returns (HPAR) and the t-statistics for the Other European stocks-based strategies over several holding periods.
The last column shows the percentage of profitable recommendations. Days Nr. of Recommendations HPART t-statistic HPART (a. p. ) Positive (%) 0 47 0, 37 % 1, 07 92, 50 % 59, 57 %1 – 3 47 -0, 57 % -0, 90 -47, 50 % 42, 55 %1 – 10 47 0, 38 % 0, 35 9, 50 % 53, 19 %1 – 25 44 0, 09 % 0, 05 0, 90 % 50, 00 %1 – 125 34 0, 24 % 0, 04 0, 48 % 52, 94 %1 – 250 16 -17, 73 % -1, 72 -17, 73 % 43, 75 %1 – 500 4 -30, 58 % -4, 13 -15, 29 % 0, 00 % Statistically significant at the 10% level. Statistically significant at the 1% level.
Table 4. 4 – Recommendation-based Strategies Performance – American Stocks (denominated in USD) The table presents the buy-and-hold portfolio annualized abnormal returns (HPAR) and the the t-statistics for the American stocks-based strategies over several holding periods. The last column shows the percentage of profitable recommendations. Days Nr. of Recommendations HPART t-statistic HPART (a. p.
) Positive (%) 0 51 1, 98 % 3, 51 495, 00 % 66, 67 %1 – 3 51 -0, 82 % -0, 95 -68, 33 % 47, 06 %1 – 10 51 -1, 59 % -1, 18 -39, 75 % 43, 14 %1 – 25 51 -1, 66 % -0, 75 -16, 60 % 50, 98 %1 – 125 42 -16, 36 % -3, 98 -32, 72 % 30, 95 %1 – 250 34 -26, 82 % -5, 06 -26, 82 % 20, 59 %1 – 500 8 -36, 13 % -4, 49 -18, 07 % 12, 50 % Statistically significant at the 1% level. Looking at the European investors’ perspective, any currency gains / losses (valuation / devaluation of the USD against the EUR) should be taken into account. We ignore the USD brokerage commissions received by the investment bank paid by investors to buy / sell foreign currency (EUR 10 per quarter).
The modified abnormal return, MHPART, is now written as: MHPART = HPART +/- Currency Gain/Loss (4.
1) The USD/EUR performance was positive in 1999 and 2000, virtually flat in 2001 and negative in 2002 and 2003. As the number of recommendations increased in the most recent years, and because we assume that one invest the same amount in each recommendation (a simple arithmetic average), the more recent years have more weight in the average abnormal returns’ calculation. As the dollar devaluated in the most recent years, the (euro) abnormal returns in Table 4. 5 are even more negative.
The best trading strategy assumed a 2-years horizon, although it still gets negative annualized abnormal returns of 21. 58 percent. Table 4. 5 – Recommendation-based Strategies Performance – American stocks (denominated in EUR) The table presents the buy-and-hold portfolio annualized abnormal returns in Euros (HPAR) and t-statistics for the American stocks-based strategies over several holding periods. The last column shows the percentage of profitable recommendations.
Days Nr. of Recommendations HPART t-statistic HPART (a. p. ) Positive (%) 0 51 1, 95 % 3, 28 487, 50 % 64, 71 %1 – 3 51 -0, 81 % -0, 92 -67, 50 % 50, 98 %1 – 10 51 -1, 67 % -1, 25 -41, 75 % 41, 18 %1 – 25 51 -2, 80 % -1, 25 -28, 00 % 45, 10 %1 – 125 42 -20, 60 % -4, 75 -41, 20 % 26, 19 %1 – 250 34 -38, 19 % -7, 10 -38, 19 % 11, 76 %1 – 500 8 -43, 15 % -5, 58 -21, 58 % 0, 00 % Statistically significant at the 1% level. We also test whether the differences in the sub-samples are statistically significant. We use a simple F-statistic where the null hypothesis is the equality of average abnormal returns for the different sub-samples.
We confirm that the performance of the three sub-samples is statistically different at the 5% level (except for the 10-day and 25-day holding periods), so we reiterate the better performance of Portuguese stocks’ recommendations. Additionally, we observe that the performance of the Other European stocks recommendations is better than the performance of the American stocks recommendations (although the difference is poorly significant).
This could be the case that geographic proximity between a company and an analyst promotes a better understanding of the company underlying prospects. 4. 2 Analysis by Year of Publication The three and a half years sample period includes a 3-year bear market (one of the longest bear markets in the history of world stock markets).
We checked the recommendation-based strategies performance over that period. Appendix D shows the results for each year in the sample period. The six recommendations published until April 2000, a short period of time marked by the highest peaks of the speculative stock market bubble, had, on average, a negative performance regardless of the holding-period. Even short-term horizons (25-days holding-periods or shorter), that could have permitted investors to lock their gains before the beginning of the bear market, show negative abnormal returns. Results are also negative for the years 2000 and 2002: trading strategies show mostly negative abnormal returns. Yet, the results of years 2001 and 2003 are mixed and insignificant.
Again, there are positive and significant excess returns in event day. The performance of our trading strategies is mostly negative along the sample period. Our results are similar to Barber et al. (2003), who found that.
