Abstract
We argue that financial analysts can be viewed as participants of two tournaments (the “All-Star” tournament and the intrafirm tournament) and examine whether analysts are incentivized by the tournament compensation structure. Using data from 1991 to 2007, we find that interim losers are more likely to increase the boldness of their forecasts in the remainder of the tournament period than interim winners. This finding survives several robustness checks and is more pronounced when the interim assessment date is closer to the end of the tournament period, when analysts are inexperienced, and when the market activity is high. In addition, we show that interim losers’ changes in boldness are less informative than interim winners’. Collectively, our findings suggest that viewing financial analysts as participants of tournaments provides a useful framework for understanding analysts’ behavior.
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Notes
The web link is http://www.bloomberg.com/apps/news?pid=newsarchive&sid=aUHCJ33.KB.Q&refer=us.Groysberg et al. (2011) implicitly acknowledged this practice in their paper: “Collectively, the evidence indicates that during periods of high market activity and correspondingly high trading commissions and corporate finance fees, a bank’s bonus pool expands.”
Analysts’ career concerns do not affect our prediction. To illustrate it, assume that the lowest ranked analyst will be fired. The analyst with the lowest interim ranking has strong incentives to issue bold forecasts, because, if she herds in the remainder of the period, her ranking likely remains the lowest, while, if she issues a bold forecast, there is a chance that her ranking may improve and she may avoid being fired.
We do not claim that forecast accuracy is the most important criterion in evaluating analysts’ performance in the tournaments. Our conclusions are valid as long as forecast accuracy is positively related to the likelihood of winning the tournaments.
In our reasoning above, we assume that there is no strategic interaction between interim winners and interim losers. (Talylor 2003), however, shows in a game theoretical setting that interim winners may attempt to lock in their current positions by mimicking the strategy of the losers. There are two reasons why this is unlikely to be true for the tournament among financial analysts. First, by definition, the interim loser’s private information is observable to her but not to the interim winner. Given the different information set, it is not possible for the interim winner to follow the interim loser’s strategy to retain her lead. Second, strategic interactions are likely to occur if there are only a small number of individuals interacting (Mas-Colell et al. 1995). Only in those settings can the players involved consider the actions of the other players. However, the number of participants is likely to be numerous in analysts’ tournaments, reducing the possibility of strategic interactions.
The intuitions behind the hypothesis are as follows. The tournament-like compensation structure provides financial analysts with strong incentives to win the tournament. In order to win, interim losers need to improve their relative performance by a great margin to make up for their prior deficit. They are unlikely to herd with other analysts in the remainder of the tournament period because herding does not differentiate them from their competitors and thereby does not help to improve their relative rankings. On the contrary, they are likely to issue bold forecasts to distinguish their performance in an effort to catch up with interim winners. As for interim winners, they are already in advantageous positions and are less inclined to issue bold forecasts to “rock the boat.”
Financial analysts who are interim winners are likely to have different characteristics from those who are interim losers. For example, interim winners may have more experience or have superior predictive abilities than interim losers. However, it does not affect the validity of our analysis because we are interested in changes in the forecast boldness at the mid-year. To the extent that analysts’ characteristics stay the same during the entire year, our variable of interest shall be unrelated to analyst characteristics.
The majority of listed firms in the U.S. have December 31 as their fiscal year-end and typically announce annual earnings in the first quarter of the next calendar year, shortly before money managers receive the Institutional Investor All-star surveys. These earnings announcements help money managers to evaluate the performance of analysts in forecasting prior year earnings and cast their votes according to analysts’ performance.
We assume that analysts’ performance in the first half-year and in the second half-year are equally important for winning the tournament. To the best of our knowledge, there is no evidence suggesting otherwise.
An analog is that the performance at the recruiting seminar is much more important for rookies than for seasoned scholars.
Hong et al. (2000) find that inexperienced analysts are less likely to deviate from consensus forecasts than experienced analysts. Our finding does not contradict theirs since our focus is on the association between interim performance ranking and changes in boldness.
Requiring at least three or five analysts following for each firm-quarter observation does not change our results.
Our results remain unchanged if we include all one-quarter-ahead forecasts and define boldness using the proportion of bold forecasts among all forecasts made by the analyst.
We obtain similar results if we use the boldness measure in Hong et al. (2000).
Analyst interim performance ranking is not empirically observable, and we need to estimate it. Measurement errors bias against finding significant results.
An alternative ranking measure is to rank all analysts from 1 to n, with n being the number of analysts following the firm. The advantage of using our measure is that it considers the magnitude of prior deficit that the analyst has to make up for while the alternative measure does not. Conceptually, the magnitude of prior deficit warrants consideration because it may affect the extent to which the boldness of forecasts is changed. Nevertheless, untabulated results show that we obtain similar findings using this alternative ranking measure.
Our inferences are the same if industry classification is based on Global Industry Classifications Standard (GISC) system.
If there is only one quarterly observation in a half-year, then half-year value is equal to the only quarterly value. The underlying assumption is that tournament outcomes are determined by the average of quarterly earnings forecast accuracy rankings in a year. A possible alternative is that the tournament outcomes are determined by annual earnings forecast accuracy ranking. Our conclusion is robust to this alternative because annual earnings equal sum of four quarterly earnings and analysts who issue accurate quarterly earnings forecasts are likely to issue accurate annual earnings forecasts.
Untabulated results show that the distributions of the scaled variables are generally comparable to those reported in Clement and Tse (2005).
An alternative way to compute half-year values is to obtain the sum of two quarterly values and then scale it to range from 0 to 1. For example, to compute half-year Firm-level interim accuracy ranking, we obtain the sum of the absolute forecast errors of the two quarterly forecasts and then scale it to range from 0 to 1 by using the formula\( Firm - level\;accuracy\;ranking_{ijq} = \frac{{AFE\;\max_{jq} - AFE_{ijq} }}{{AFE\;\max_{jq} - AFE\;\min_{jq} }} \), t refers to each half-year. Our results are robust to this alternative approach.
We base this inference on the fact that accuracy ranking measures are scaled to range from 0 for lowest level of accuracy ranking among all analysts providing a forecast for a given firm in a particular half-year, to 1 for the highest level of the accuracy ranking among all analysts providing a forecast for a given firm in that half-year.
Similar to analyst earnings forecast sample, we only keep the last recommendation an analyst issues in each half-year.
Our results are qualitatively similar when we measure stock picking ability by taking the 10-day/60-day size-adjusted return starting from the recommendation issuance date.
Our untabulated results show that the ability to bring in investment banking business is less important as a performance measure after the Global Research Settlement in 2003. We change its weight to 0 in the brokerage-level composite ranking after 2003 and obtain similar results.
Different from our earlier model, we do not control for ΔFrequency. ΔFrequency represents the change in the scaled measure of the forecasting frequency and is the same as the change in the interim firm-level frequency ranking. If we include both Industry-level interim composite ranking (which is a linear transformation of the interim frequency ranking) and ΔFrequency (the change in the interim frequency ranking) in the same regression, there is a severe multicollnearity problem as a result of the high correlation between the two.
The numbers of observations are different in the three regressions since half-year values could be constructed even if there is only one quarterly observation in that half-year, while quarterly values could only be constructed for each specific quarter. So there are greater number of observations in regression (2), compared with regression (1) and regression (3).
The Baker and Wurgler (2006) index was originally designed to measure investor sentiment. By construction, it captures a variety of market activity signals including banking-related (e.g., initial public offering volume, first-day IPO returns, and equity share in new issues) and commission-related (e.g., average monthly turnover on NYSE-listed stocks) variables.
References
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61, 1645–1680.
Beyer, A., Cohen, D., Lys, T., & Walther, B. R. (2010). The financial reporting environment: Review of the recent literature. Journal of Accounting and Economics, 50, 296–343.
Bognanno, M. L. (2001). Corporate tournaments. Journal of Labor Economics, 19, 290–315.
Brown, K. C., Harlow, W., & Starks, L. T. (1996). Of tournaments and temptations: An analysis of managerial incentives in the mutual-fund industry. Journal of Finance, 51, 85–110.
Chen, S., & Matsumoto, D. A. (2006). Favorable versus unfavorable recommendations: the impact on analyst access to management-provided information. Journal of Accounting Research, 44, 657–689.
Cichello, M., Fee, C., Hadlock, C., & Sonti, R. (2009). Promotions, turnovers, and performance evaluation: Evidence from the careers of division managers. The Accounting Review, 84, 1119–1143.
Clement, M. B., & Tse, S. Y. (2005). Financial analyst characteristics and herding behavior in forecasting. Journal of Finance, 60, 307–341.
Das, S., Guo, R. J., & Zhang, H. (2006). Analysts’ selective coverage and subsequent performance of newly public firms. Journal of Finance, 61, 1159–1185.
Dugar, A., & Nathan, S. (1995). The effects of investment banking relationships on analysts’ earnings forecasts and investment recommendations. Contemporary Accounting Research, 12, 131–161.
Ehrenberg, R., & Bognanno, M. (1990). Do tournaments have incentive effects? Journal of Political Economy, 98, 1307–1324.
Francis, J., & Philbrick, D. (1993). Analysts’ decisions as products of a multi-task environment. Journal of Accounting Research, 31, 216–231.
Gleason, C. A., & Lee, C. (2003). Analyst forecast revisions and market price discovery. The Accounting Review, 78, 193–225.
Green, J. R., & Stokey, N. L. (1983). A comparison of tournaments and contracts. Journal of Political Economy, 91, 349–364.
Groysberg, B., Healy, P., & Maber, D. (2011). What drives sell-side analyst compensation at high-status investment banks? Journal of Accounting Research, 49, 969–1000.
Grund, C., Hocker, J., & Zimmermann, S. (2010). Risk taking behavior in tournaments: Evidence from the NBA. Working paper. University of Wurzburg. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1570430.
Hong, H., & Kubik, J. (2003). Analyzing the analysts: Career concerns and biased earnings forecasts. The Journal of Finance, 58, 313–352.
Hong, H., Kubik, J., & Solomon, D. (2000). Security analysts’ career concerns and herding of earnings forecasts. Rand Journal of Economics, 31, 121–144.
Kadous, K., Mercer, M., & Thayer, J. (2009). Is there safety in numbers? The effect of forecast accuracy and forecast boldness on financial analysts’ credibility with investors. Contemporary Accounting Research, 26, 933–968.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts’ access to management and survival. Journal of Accounting Research, 44, 965–999.
Kempf, A., & Ruenzi, S. (2008). Tournaments in mutual-fund families. Review of Financial Studies, 21, 1013–1036.
Koski, J. L., & Pontiff, J. (1999). How are derivatives used? Evidence from the mutual-fund industry. Journal of Finance, 54, 791–816.
Lazear, E., & Oyer, P. (2009). Personnel economics. Handbook of Organizational Economics. Available at http://faculty-gsb.stanford.edu/oyer/wp/handbook.pdf.
Lazear, E., & Rosen, S. (1981). Rank-order tournaments as optimum labor contracts. Journal of Political Economy, 89, 841–864.
Leone, A. J., & Wu, J. (2007). What does it take to become a superstar? Evidence from institutional investor rankings of financial analysts, working paper, University of Miami. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=313594.
Lin, H., & McNichols, M. (1998). Underwriting relationship, analysts’ earnings forecasts and investment recommendations. Journal of Accounting and Economics, 25, 101–128.
Loh, R. K., & Mian, G. M. (2006). Do accurate earnings forecasts facilitate superior investment recommendations? Journal of Financial Economicms, 80, 455–483.
Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic theory. Oxford, UK: Oxford University Press.
McLaughlin, K. (1988). Aspects of tournament models: A survey. Research in Labor Economics, 9, 225–256.
Michaely, R., & Womack, K. (1999). Conflict of interest and the credibility of underwriter analyst recommendations. Review of Financial Studies, 12, 653–686.
Mikhail, M., Walther, B., & Willis, R. (1999). Does forecast accuracy matter to security analysts? The Accounting Review, 74, 185–191.
Nalebuff, B., & Stiglitz, J. (1983). Prizes and incentives: Towards a general theory of compensation and competition. Bell Journal of Economics, 14, 21–43.
Orphanides, A. (1996). Compensation incentives and risk-taking behavior: evidence from mutual funds. Board of Governors of the Federal Reserve System Finance and Economics Discussion Series 96–21. Available at http://www.federalreserve.gov/Pubs/Feds/1996/199621/199621pap.pdf.
Prendergast, C. (1999). The provision of incentives in firms. Journal of Economic Literature, 37, 7–63.
Ramnath, S., Rock, S., & Shane, P. (2008). The financial analyst forecasting literature: A taxonomy with suggestions for further research. International Journal of Forecasting, 24, 34–75.
Rosen, S. (1986). Prizes and incentives in elimination tournaments. American Economic Review, 76, 701–715.
Stickel, S. E. (1992). Reputation and performance among security analysts. Journal of Finance, 47, 1811–1836.
Talylor, J. D. (2003). Risk-taking behavior in mutual-fund tournaments. Journal of Economic Behavior & Organization, 50, 373–383.
Wu, J., & Zang, A. (2009). What determine financial analysts’ career outcomes during mergers? Journal of Accounting and Economics, 47, 59–86.
Acknowledgments
The authors acknowledge helpful comments from an anonymous referee, Patricia Dechow (the editor), Bin Ke, Hilary Gilles, Clive Lennox, Oliver Li, Jiang Luo, Jun-Koo Kang, and seminar participants at Fudan University, Nanyang Business School, Shanghai Jiaotong University, and Shanghai University of Finance and Economics. Huifang Yin acknowledges the financial support from the National Natural Science Foundation of China (Grant Number: 71302076, 71202003, and 71272012) and MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (No. 11JJD790008).
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Yin, H., Zhang, H. Tournaments of financial analysts. Rev Account Stud 19, 573–605 (2014). https://doi.org/10.1007/s11142-013-9255-6
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DOI: https://doi.org/10.1007/s11142-013-9255-6