Boris Groysberg

Hiring Stars and Their Colleagues: Exploration and Exploitation in Professional Service Firms

This paper examines exploration and exploitation in professional service firms by focusing on the individuals who carry out the exploration and exploitation activities. Specifically, we examine the performance of star security analysts who join new firms in exploration versus exploitation roles. We find that stars hired to explore (initiate new activities) experience a short-and long-term performance decline; by contrast, stars who join new firms to exploit (reinforce existing activities) suffer only a short-term drop in performance. Stars hired in exploration roles can preserve some of their performance by moving with a group of colleagues from the originating firm. Investors view the hiring of a star analyst as value-destroying for the hiring firm regardless of whether the firm is hiring to exploit or explore, but the negative reactions are economically more extreme for exploration hires. These findings indicate that, even at the individual level, probabilities of success in exploration activities are lower than in exploitation activities, thereby reinforcing the tendency toward exploitation.

Too Many Cooks Spoil the Broth: How High-Status Individuals Decrease Group Effectiveness

Can groups become effective simply by assembling high-status individual performers? Though an affirmative answer may seem straightforward on the surface, this answer becomes more complicated when group members benefit from collaborating on interdependent tasks. Examining Wall Street sell-side equity research analysts who work in an industry in which individuals strive for status, we find that groups benefited---up to a point---from having high-status members, controlling for individual performance. With higher proportions of individual stars, however, the marginal benefit decreased before the slope of this curvilinear pattern became negative. This curvilinear pattern was especially strong when stars were concentrated in a small number of sectors, likely reflecting suboptimal integration among analysts with similar areas of expertise. Control variables ensured that these effects were not the spurious result of individual performance, department size or specialization, or firm prestige. We discuss the theoretical implications of these results for the literatures on status and groups, along with practical implications for strategic human resource management.

Buy-Side vs. Sell-Side Analysts' Earnings Forecasts

The study reported here is a comparison of the earnings-forecasting performance of analysts at a large buy-side firm with the performance of sell-side analysts in the 1997–2004 period. The tests show that the buy-side analysts made more optimistic and less accurate forecasts than their counterparts on the sell side. The performance differences appear to be partially explained by the buy-side firm’s greater retention of poorly performing analysts and by differences in the performance benchmarks used to evaluate buy-side and sell-side analysts. The 2003 Global Settlement of Conflicts of Interest between Research and Investment Banking raised fundamental questions about the integrity and quality of sell-side research. Regulators had alleged that investment banking fees used to support research induced sell-side analysts to be overly optimistic about the stocks they covered. By limiting the investment banking benefits from sell-side research, an (unintended) consequence of the Global Settlement has been to reduce sell-side research budgets at leading investment banks and to encourage the growth of buy-side research. Buy-side research, which is privately produced and funded, is used only by fund managers at the producing investment firm. As a result, buy-side analysts do not face the potential conflicts of working for investment banking firms and the need to generate commissions encountered by the sell side. Yet, to our knowledge, because of a lack of data on buy-side research, no public investigation of the performance of buy-side analysts has been carried out. We examined analyst earnings forecast optimism and accuracy for buy-side analysts at a large, reputable money management firm relative to the optimism and accuracy of sell-side analysts in the 1997–2004 period. The sample buy-side firm is a top 10–rated money management firm for which fundamental research is an essential part of the stock selection process. From analyst reports provided by the firm for the period July 1997 through December 2004, we collected annual earnings forecasts for each company covered. For sell-side analysts, earnings forecasts came from Thomson Financial’s I/B/E/S database. Our findings indicate that analysts at the buy-side firm made more optimistic and less accurate forecasts than their counterparts on the sell side. As a percentage of actual earnings, the mean (median) buy-side forecasts in the study period are 8–16 percent (3–12 percent) higher than those for the sell side; the mean (median) absolute forecast errors for buy-side analysts are 11–15 percent (4–11 percent) higher than for the sell side. The significant differences in forecast optimism and absolute errors held for all forecast horizons and after controlling for differences in analyst experience, industry specialization, coverage, and firm size. Several factors appear to at least partially explain these findings. First, sell-side firms are less likely than the buy-side firm to retain analysts with weak prior-year earnings forecast accuracy. This factor explains roughly one-third of the buy-side analysts’ relative forecast optimism and one-fifth of their absolute errors. Second, until recently, the buy-side firm did not measure its analysts against the sell side. In contrast, sell-side analysts are regularly measured against each other. Finally, we found a sharp decline in buy-side relative forecast optimism and a decrease in relative forecast accuracy after the enactment of Regulation Fair Disclosure, which is consistent with sell-side analysts’ access to company information being curtailed by the new regulation. We are cautious in interpreting these findings, however, because many other factors affected analysts’ performance during this period. Follow-up tests ruled out several other plausible explanations for the findings. The results were unchanged when we compared the buy-side analysts’ performance with that of analysts at sell-side firms having a comparable number of analysts and breadth of industry coverage, which suggests that the findings are not driven by differences in the buy- and sell-side analysts’ scope of coverage. Moreover, the buy-side analyst forecasts were relatively optimistic, even for newly covered stocks, which indicates that the findings do not simply reflect that coverage of poorly performing companies was stopped by buy-side analysts. Tests of the quality of analysts hired by the buy-side firm from the sell side indicate that the buy-side firm did not hire low-quality sell-side analysts but that the performance of the new analysts deteriorated after they joined the firm. Finally, we found no evidence that the sample investment firm was a poor performer, which could have explained the performance of its analysts. Our findings raise several questions for researchers and practitioners. First, although we have no reason to believe that the sample firm is anything but a strong performer within the industry, a replication of the tests on a broader sample would be interesting. Second, our findings raise questions about the quality of other buy-side research metrics, such as stock recommendations. Finally, it will be interesting to assess whether (and how) services that benchmark buy-side analysts’ research performance to that of analysts at other buy-side firms and to the sell-side affect the quality of buy-side research.

The Stock Selection and Performance of Buy-Side Analysts

Prior research on equity analysts focuses almost exclusively on those employed by sell-side investment banks and brokerage houses. Yet investment firms undertake their own buy-side research and their analysts face different stock selection and recommendation incentives than their sell-side peers. We examine the selection and performance of stocks recommended by analysts at a large investment firm relative to those of sell-side analysts from mid-1997 to 2004. We find that the buy-side firm’s analysts issue less optimistic recommendations for stocks with larger market capitalizations and lower return volatility than their sell-side peers, consistent with their facing fewer conflicts of interest and having a preference for liquid stocks. Tests with no controls for these effects indicate that annualized buy-side Strong Buy/Buy recommendations underperform those for sell-side peers by 5.9% using market-adjusted returns and by 3.8% using four-factor model abnormal returns. However, these findings are driven by differences in the stocks recommended and their market capitalization. After controlling for these selection effects, we find no difference in the performance of the buy- and sell-side analysts’ Strong Buy/Buy recommendations.

What Factors Drive Analyst Forecasts

Using survey data to judge how analyst forecasts are related to evaluations of companies’ industry competitiveness, strategic choices, and internal capabilities, the authors found that analyst forecasts are associated with many of the factors that money managers rate as important in their assessments of analyst contributions. They also found wide variation in ratings consistency across variables among analysts covering the same company. On average, consistency is higher for sell-side analysts than for buy-side analysts. Although extensive research has been conducted on analysts’ earnings forecasts and recommendations, relatively little has been written about the factors that underlie them. In our study, we examined which industry, leadership, and company factors are related to analysts’ forecasts of financial and stock performance. We also examined whether analysts covering the same company make consistent assessments of its industry, leadership, and company capabilities. To study these questions, we used data from a survey of 967 analysts who rated 837 companies on their projected future performance, industry economics, company capabilities, and leadership. Analysts were asked to provide forecasts of growth in revenues, earnings, and stock price, as well as gross margins, for up to three companies they covered. For each company, they were also asked to rate industry, company, and leadership factors that prior research suggests influence future performance. These factors include the competitiveness and growth of each company’s industry, whether it competes primarily on the basis of innovation or price, its strategy execution and communication, its innovativeness, existing financial resources, the quality of its top management, whether management sets high performance standards, and its governance. We found a strong relationship between analysts’ forecasts of a company’s performance and their assessments of its industry growth, industry competition, quality of its management, commitment to high performance expectations, ability to execute strategy, and innovation. We found that several factors are generally unimportant, including governance, transparent strategy communication (especially for buy-side analysts), competition via superior products/services, financial strength, and understanding one’s competitors. Considerable variation in ratings consistency exists across factors among analysts who cover the same company. Analyst ratings are relatively more consistent for company revenue forecasts, balance sheet strength, strategy execution, and strategy communication than for industry competitiveness, forecasted stock appreciation, low-price strategy, and understanding one’s competitors. Consistency is significantly higher for sell-side analysts than for their buy-side peers, perhaps reflecting sell-side pressure to herd. Finally, we found no evidence that analysts are more consistent on financial forecast factors than on internal capability factors.

Does Financial Misconduct Affect the Future Compensation of Alumni Managers

We explore how an organization’s financial misconduct may affect pay for former employees not implicated in wrongdoing. Drawing on stigma theory we hypothesize that although such alumni did not participate in the financial misconduct and they had left the organization years before the misconduct, they experience a compensation penalty. Our results support this prediction. The stigma effect increases in relation to the job function proximity to the misconduct, recency of the misconduct, and an employee’s seniority. Collectively, our results suggest that the stigma of financial misconduct could reach alumni employees and need not be confined to executives and directors that oversaw the organization during the misconduct.

What Factors Drive Director Perceptions of Their Board's Effectiveness?

We use a survey of directors to collect data on their ratings of board effectiveness as well as board internal dynamics and key processes. Controlling for many of the governance metrics examined by prior research, we find that directors’ ratings of their boards’ effectiveness are positively related to measures of board internal dynamics (such as relations with the CEO and senior management, and its ability to perform well as a team), director engagement (reflected in meeting preparedness), and the breadth of the board’s experiences. Tests for a subset of firms that are identified in the survey show that boards rated as ineffective have a higher likelihood of losses one year after the survey, consistent with effective governance enhancing the management of risk.