Antecedents and Consequences of Financial Analyst Turnover

Transcription

1 Antecedents and Consequences of Financial Analyst Turnover Emad Mohd Accounting and Financial Management Services MGD-317 DeGroote School of Business McMaster University 1280 Main Street West Hamilton, Ontario L8S 4M4 Canada Tel: ext Siva Nathan Associate Professor School of Accountancy Robinson College of Business Georgia State University Atlanta, GA Tel: January 2005 We thank Institutional Brokers Estimate System (I/B/E/S), which is a service of Thomson Financial, Inc. for providing us the financial analysts earnings forecasts data. The data has been provided as part of a broad academic program to encourage earnings expectations research. We thank workshop participants at Georgia State University and Emory University for their comments. We thank Rodger Griffeth and Julie Hotchkiss for their comments and their insights into the management and labor economics literature on employee turnover. We also thank Larry Brown, George Benston and Paul Simko for their comments. The second author visited the Goizueta Business School at Emory University while preparing the first draft of this paper.

2 Antecedents and Consequences of Financial Analyst Turnover Abstract We examine the factors leading to turnover among sell-side financial analysts and the consequences of turnover. We distinguish between two types of turnover, voluntary and involuntary. We define voluntary (involuntary) when analysts leave their employment at one brokerage firm and find (do not find) employment at another brokerage firm. We examine the analyst and brokerage firm specific factors leading to analyst turnover, separately for voluntary and involuntary turnovers. We find that job performance is positively (negatively) related to voluntary (involuntary). This finding is consistent with Jackofsky s (1984) theory predicting a curvilinear U-shaped relationship between performance and turnover. For voluntary turnover, we examine analysts performance and job conditions at the new brokerage firm and relate these to the factors leading to turnover. We find that these analysts move to larger brokerage firms and become more accurate. They have lighter workload at the new brokerage firm as they follow fewer firms and industries. Our results suggest that good (bad) performance is rewarded (punished), where more accurate analysts move to larger brokerage houses and have lighter workload and less accurate analysts leave the profession. 1

3 Antecedents and Consequences of Financial Analyst Turnover 1. Introduction This study examines the factors leading to turnover among sell-side financial analysts and the consequences of turnover. We distinguish between two types of turnover, voluntary and involuntary. We define voluntary turnover when analysts leave their employment at one brokerage firm and find employment at another brokerage firm. When analysts leave their employment at one brokerage firm and are not employed at another brokerage firm covered by I/B/E/S, we define this turnover as involuntary. Using the management and labor economics literatures, we examine the analyst and brokerage firm specific factors leading to analyst turnover, separately for voluntary and involuntary turnovers. Moreover, we examine analysts performance and job conditions at the new brokerage firm and relate these to the factors leading to turnover. This study adds to the extensive management and labor economics literatures on employee turnover, focusing specifically on the sell-side financial analyst profession whose turnover characteristics have not been extensively studied. Most of the empirical studies on employee turnover in other professions use survey data, while we examine whether the existing employee turnover theories hold for the analyst profession using archival empirical data. While some studies have used archival data to examine analyst turnover (e.g., Hong and Kubik 2003; Mikhail et al. 1999), we provide a more extensive analysis of the relation between factors related to analyst performance and turnover. Additionally, our study is the first to examine analysts performance after turnover. 2

4 Understanding the causes and consequences of employee turnover in the various professions is very important. The costs involved with employee turnover are extremely high both for the employer and the employee. For the employer, these costs include recruitment and training costs for newly hired replacements, productivity losses, and the demoralization of the remaining employees. This is particularly true for key employees, like sell-side analysts in a brokerage firm. In addition, turnover of high performers is likely to be dysfunctional for the organization. In service industries such as brokerage firms, clients (individual and institutional investors) might abandon a firm if their attachment to a firm had been based on relationships with an employee (sell-side analyst). Because of the costs involved, management of a brokerage firm would be interested in understanding what leads to analyst turnover and the consequences of such turnover. 1 For employees, the costs of turnover include loss of prestige, cost of job search, cost of geographic relocation, and costs of adjusting to the new employer. The employee would like to avoid turnover if possible, thus understanding what leads to turnover can help the employee take steps to prevent it. However, not all employee turnover is necessarily negative. For the employer, involuntary turnover can be a mechanism to displace poor performers, to infuse new blood, to prevent stagnation and complacency, and to facilitate change and innovation. For the employee, voluntary turnover might lead to better compensation, more prestige, and better working conditions and lower workload with the new employer. Thus, for both the employer and the employee there are both costs and benefits of turnover. An organization that loses a disproportionately high number of its good performers would have more cause for concern than 1 There is evidence of some extreme negative consequences of employee turnover. For example, Lane et al. (1996) find that high turnover firms are less likely to survive. 3

5 one that loses predominantly poor performers. In other words, the organizational consequences of turnover are dependent on who leaves and who stays. Using earnings forecast accuracy as a measure of job performance, we find that analysts involved in voluntary turnovers (defined as moving from one brokerage firm to another) tend to have relatively superior job performance prior to the turnover. We also find that these analysts, compared to analysts involved in involuntary turnover, have lower experience (at the firm- and industry-level), but they follow more companies and industries and they revise their forecasts more frequently. It is not surprising that the involuntary turnover analysts lose their jobs given their bad performance despite their greater experience and lighter workload. For analysts who switch brokerage firms, we compare job performance at the new brokerage firms vis-à-vis the old brokerage firms. We find after the switch that these analysts forecasts become relatively more accurate. While these analysts move to larger brokerage firms, their workload becomes lighter as they follow fewer firms and industries, suggesting that their superior performance allowed them to negotiate a lighter workload at the new brokerage firm. Although there has been prior research on sell-side analyst turnover, this study makes contributions in several ways. First, our study is the first to apply the theories of employee turnover to the analyst profession. Specifically, we use Jackofsky s (1984) theory, which predicts a curvilinear relationship between performance and turnover, where turnover will be relatively high for both very poor performers and very good performers. Turnover is high for very poor performers primarily due to involuntary turnover and is high for relatively good performers primarily due to voluntary turnover. We assume that if an analyst leaves the I/B/E/S database it is an involuntary turnover, and if an analyst switches to another brokerage firm and remains on the I/B/E/S database it is a voluntary turnover. Our findings are consistent with this 4

6 theory. Furthermore, we use the management and labor economics literatures to test variables, in addition to job performance, which have been shown to predict employee turnover. Second, this is the first paper on analyst turnover to use the classification of voluntary and involuntary turnover. Third, we examine job performance and the values of other variables that predict turnover at the new brokerage firm and relate them to signs these factors in the turnover regression. The remainder of the paper proceeds as follows. Section 2 discusses the extant turnover literature. This section starts with discussion of the management and labor economics literatures on employee turnover, which are directly relevant for this study. It then discusses the current literature on analyst turnover. Section 3 discusses the variables explaining voluntary and involuntary analyst turnover. Section 4 describes the data, section 5 describes the methodology, section 6 presents the results, and section 7 presents the summary and conclusions. 2. Literature Review 2.1 Management and Labor Economics Literatures on Employee Turnover Jackofsky (1984) hypothesizes that job performance and turnover have a U-shaped curvilinear relationship. Specifically, Jackofsky predicts that performance and turnover are inversely related for poor performers, who experience an actual or perceived threat of dismissal, and positively related for good performers who will enjoy numerous external employment opportunities leading to increased ease of movement. McEvoy and Cascio (1987) argue that if Jackofsky s U-shaped curvilinear relationship between performance and turnover is true, we should expect performance and involuntary turnover to be inversely related, and performance and voluntary turnover to be positively related. Our empirical tests in the present study are based 5

7 on this prediction. 2 Many studies support Jackofsky s theory (e.g., Trevor et al. 1997; Williams and Livingstone 1994; Schwab 1991). For example, Schwab (1991) finds a positive (negative) relationship between research performance, measured as the number of citations in the Social Science Citation Index, and turnover for tenured (untenured) professors. Since the departure of tenured professors is almost certainly voluntary, and the departure of untenured professors is most likely involuntary, Schwab s results are consistent with Jackofsky s theory. The relationship predicted by Jackofsky between performance and turnover is also dependent on other factors. First, a positive relationship between performance and voluntary turnover is expected only if employee performance in one organization is observable to, and demanded by, other organizations in the relevant external labor market (Dreher 1982). It is reasonable to assume that for sell-side analysts their job performance (based on research reports, earnings forecasts and investment recommendations) is observable by other brokerage firms and valued by them. Second, the performance-turnover relationship is dependent on the organizational reward system. Dreher (1982) argues that high performers will be less likely to leave if their own organization recognizes and rewards their performance. Similarly, Steers and Mowday (1981) predict that high performance will lead to increased expectation of rewards (promotions, salary growth), which will lead to increased turnover only if those expectations are not met. We attempt to control for these moderator variables in the design of our study. In addition to job performance, many factors lead to employee turnover. Hom and Griffeth (1995) (p. 94), Lane and Parkin (1998) and Williams and Livingstone (1994) argue that higher organizational commitment among employees leads to lower turnover. Tenure with the 2 Theories predicting a positive relationship between job performance and voluntary turnover also exist in the labor economics literature. For example, Kahn and Longhofer (1993) develop an economic equilibrium model of voluntary turnover in the labor market under conditions of asymmetric information. They show that voluntary turnovers increase in periods of high output (job performance), without postulating exogenous price rigidity. 6

8 employer is frequently used as a measure of organizational commitment. Chevalier and Ellison (1999) and Hom and Griffeth (1995) (p. 39) argue that turnover is higher among younger employees. Since experience is positively correlated with age, an employee s experience in the profession is frequently used as a proxy for age in the turnover literature. Hom and Griffeth (1995) (p. 61), Idson (1996), Price and Mueller (1986), and Weiss (1984) argue that employer size is frequently related to turnover, but the direction of the relationship varies across professions. Hom and Griffeth (1995) (p. 42) and McLaughlin (1990) argue that the quality of peergroup relationships and work environment are related to turnover. Employees will be inclined to leave an organization when experiencing an unpleasant environment and poor relationship with their peers. The employee turnover ratio in an organization is frequently used as a proxy for the quality of peer-group relationship and work environment. Goldsmith (1997), Hom and Griffeth (1995) (pp. 61, 94), and Price and Mueller (1986) argue that heavy workload can lead to job stress and burnout, which in turn leads to turnover. In the case of analysts, the number of companies and industries followed by an analyst can be used as measures of the analyst s workload (Clement 1999). Hom and Griffeth (1995) (pp ) and Steel and Ovalle (1984) show that intentions to seek alternatives to the current job or intentions to quit are among the best predictors of actual departures. Analysts who are harboring thoughts of quitting their current brokerage firm and moving to another will be distracted and not motivated to update their earnings forecasts frequently. As a result, just prior to actual departure from the brokerage firm we expect their earnings forecasts to be relatively old. 7

9 2.2 Analyst Turnover Literature Mikhail et al. (1999) (MMW hereafter) examine the relation between analyst turnover and the accuracy of the earnings forecast (one year) prior to turnover. Using quarterly earnings forecast and the Zacks database of analysts earnings forecasts, they find that analysts are more likely to leave if their forecast accuracy is lower than their peers. They find no association between the probability of turnover and raw forecast accuracy. In their study, MWW included analysts who switched to another brokerage firm (followed by Zacks) and those who leave Zacks. A maintained assumption of the MWW study is that all analyst turnovers are involuntary, irrespective of whether the analyst leaves or remains in the Zacks database. In other words, MWW do not differentiate between voluntary and involuntary turnover, which is a major contribution of our study. Using I/B/E/S data, Karamanou (1999) examines whether brokerage firms offer their research department analysts turnover incentives to bias their forecasts. She extends the model in MWW, by breaking up their accuracy measures into their optimistic and pessimistic parts. She finds that the probability of demotion (promotion) is lower (higher) when the forecast error is optimistic. Demotion (promotion) is defined as an analyst moving to a smaller (larger) brokerage firm. Hong and Kubik (2003) find that relatively good (poor) earnings forecasting performance increases the probability that an analyst moves from a low (high) status to a high (low) status brokerage firm. They also find that more optimistic analysts are more likely to move to a higher status brokerage firms. Hong et al. (2000) test theories of reputation and herd behavior in the labor market for security analysts and find that for a given level of earnings forecast accuracy, inexperienced analysts are more likely to be terminated than experienced analysts. Controlling 8

10 for forecast accuracy, they are also more likely to be terminated for bold forecasts that deviate from the consensus. None of the above studies tests Jackofsky s (1984) curvilinear hypothesis on the relationship between performance and turnover, while we directly examine this hypothesis. We also have a more comprehensive theory of turnover using variables previously shown in the management and labor economics literatures to predict employee turnover. Thus, we are able to examine the incremental effect of one variable over and above the others, which the previous studies did not do. We find that all these variables incrementally predict analyst turnover over and above job performance. For analysts who switch brokerage firms we examine the job performance (earnings forecast accuracy) of the analyst immediately after the switch. None of the above studies perform this analysis. Finally, for the switch analysts we examine the values of the variables predicting turnover, in the period immediately after the switch and relate the changes in their values of to the sign of their slope coefficients in the turnover prediction model. This is the first study to undertake this type of analysis. 3. Variables Explaining Voluntary and Involuntary Analyst Turnover The accuracy of an analyst s earnings forecast is used as a measure of job performance. Based on Jackofsky s theory we expect that turnover will be negatively (positively) related to performance for involuntary (voluntary) turnovers. 3 To control for the moderating effects of the organizational reward system, we use the size of the companies followed by the analyst as a measure of the prestige and status enjoyed by the analyst within the brokerage firm. Hong and Kubik (2003) use this variable for the same purpose. For the voluntary turnover analysts we 3 The profitability of analysts recommendation is an alternative performance measures. However, Mikhail et al. (1999) find no association between the profitability of recommendations and turnover. 9

11 expect that analysts who follow larger companies to be more likely to switch brokerage firms because they enjoy more prestige and status and thus more likely to come to the attention of other brokerage firms. We have no predictions regarding the relationship between size of companies followed and involuntary turnover and leave it as an empirical issue. We use the sales figure of companies followed as a measure of size. 4 The number of years the analyst has been employed with the brokerage firm is used as a proxy for organizational commitment. For voluntary turnover we expect tenure to be negatively related to the probability of turnover. Since the brokerage firm initiates involuntary turnover, we make no predictions regarding the relationship between tenure and involuntary turnover and leave it as an empirical issue. Since data on an analyst s age is not publicly available, the experience of the analyst within the profession is used as a proxy for age. For involuntary turnovers, we expect turnover to be negatively related to an analyst s experience. For voluntary turnovers, the direction of the relationship is unclear. The management and labor economics literatures predict that older employees (with more experience) are less likely to switch to another employer. However, Clement (1999) and Mikhail et al. (1999) have shown that there is a positive relationship between an analyst s experience and earnings forecast accuracy. Thus, more experienced analysts are more likely to get job offers from other firms because they are more accurate. Therefore, the relationship between experience and voluntary turnover is an empirical issue. To measure employer size we use the number of analysts employed by the brokerage firm. Since in the management and labor economics literatures provide mixed evidence on the 4 Results are insensitive to other measures of size such as market capitalization and the number of analysts following. 10

12 direction of the relationship between employer size and turnover, we do not have a prediction about the direction of this relationship for analysts. To measure the quality of peer-group relationships within the brokerage firm we use the analyst turnover ratio within the firm; the higher the analyst turnover ratio, the worse the peergroup relationship within the firm. For voluntary turnovers, we expect a positive relationship between the brokerage firm s analyst turnover ratio and the probability of voluntary turnover. For involuntary turnovers, if the firm is in the process of weeding out poor performers we should also expect a positive relationship between the firm s analyst turnover ratio and the probability of involuntary turnover. To measure an analyst s workload, we use the number of companies and the number of industries followed by the analyst. Clement (1999) uses these variables for the same purpose. For voluntary turnover, we expect a positive relationship between turnover and workload since these analysts seek to improve their work conditions at the new brokerage firm. We have no predictions regarding the relationship between workload and involuntary turnover and leave it as an empirical issue. To measure the analysts intention to seek alternative opportunities or to quit the current job, we use the age of their earnings forecast and the number of times they revised their forecasts during the year. Analysts who intend to quit will not be motivated to update their forecasts frequently in light of new information, thus their forecasts will be older. For voluntary turnovers, we expect a positive relation between turnover and forecast age and a negative relation between turnover and the number of forecast revisions. Since the employer initiates involuntary turnovers, we do not make any predictions regarding the relationship between forecast age and the probability of involuntary turnover. However, if the analyst has been 11

13 performing poorly for several periods, there is a possibility that they have thought about quitting or anticipated that they will be fired because of their poor performance. This will result in loss of motivation to update forecasts. We leave this as an empirical question. 4. Data All the data for this study are obtained from the I/B/E/S database and our sample spans the 18-year period The I/B/E/S database has a code that identifies the brokerage firm employing the analyst and a code that uniquely identifies each analyst. For each analyst in the I/B/E/S database we identify changes in the brokerage firm code for that analyst. We assume that a change in the broker code for an analyst indicates that the analyst has switched brokerage firms. If an analyst s earnings forecasts are no longer on the I/B/E/S database we assume that the analyst s employment with the brokerage firm has ended. We acknowledge the limitations to these classifications of voluntary and involuntary turnover. It is possible that an analyst was fired from a brokerage firm but managed to find employment with another brokerage firm that supplies forecasts to I/B/E/S. In this case, although the turnover is involuntary we would misclassify it as voluntary. Similarly, it is possible that an analyst who is currently employed by a brokerage firm that supplies forecasts to I/B/E/S, receives a job offer from and moves to a brokerage firm that does not supply forecasts to I/B/E/S. It is also possible that an analyst resigned from a brokerage firm and did not seek employment with another brokerage firm because of personal reasons, or because he received an attractive job offer in a related profession like portfolio management, investment banking, consulting, or corporate management. In these cases, although the turnovers are voluntary we would misclassify them as involuntary. Although we do not rule out these misclassification possibilities, we conjecture that these cases will be 12

14 rare. These alternative paths leading to voluntary and involuntary analyst turnover introduce noise in our data and bias against finding significant results. Although not being able to directly observe the reason for analyst turnover is a limitation of the archival method used in this study, this method has several advantages. The job performance of the analyst can be measured with great precision using the analyst s earnings forecast data. Measuring job performance is problematic using survey data. Furthermore, variables like employee workload and employer size are measured with better precision under the archival method than under the survey method. Finally, the archival method enables us to measure employee (analyst) and employer (brokerage firm) specific variables after the turnover. This is very difficult under the survey method, since it is almost impossible for an employer or a researcher to track down employees after their employment with the organization has ended. An examination of the analysts switching brokerage firms revealed that some of them switched brokerage firms back and forth. We assume that these analysts furnish forecasts to more than one brokerage firm at the same time. We exclude these analysts from the voluntary turnover sample because switching firms back and forth does not constitute turnover. We also observe that some analysts have switched brokerage firms more than once. The results that follow are insensitive to the inclusion of these analysts, therefore, we report the results with these analysts included. Table 1 presents the numbers of: (1) all analysts in the I/B/E/S database, (2) all analysts who switched brokerage firms, (3) analysts who switched only once, and (4) analysts who left the I/B/E/S database for each year of our study. It is interesting to notice that 86.8 percent of the analysts have been involved in turnover (voluntary and involuntary) during the sample period. About 15 percent of analysts were involved in voluntary turnover, of which about 73 percent 13

15 have changed brokerage firms more than once. The number of analysts switching brokerage firms (once) ranges from a low of 95 (7) in 1984 to a high of 300 (82) in 1990 (2000). Analysts who left the I/B/E/S database account for about 83% of turnover analysts and about 72% of all analysts, suggesting high attrition in the analysts profession. 5. Methodology We use earnings forecast accuracy to measure the analyst s job performance. We measure forecast accuracy as the absolute value of the standardized analyst s earnings forecast error, where the earnings forecast error equals actual annual earnings reported minus the annual earnings forecast of the analyst. We standardize this measure by the absolute value of the actual earnings. The absolute value of this ratio, multiplied by 1, is a measure of the analyst s earnings forecast accuracy and is denoted by ACCUR. By construction, higher values of this variable mean more accurate forecasts. McEvoy and Cascio (1987) argue that the effects of performance on turnover are more noticeable if the measurements of performance and turnover are temporally close. In a meta-analysis using five studies they show that studies with a short time span (one year or less) in the measurement of performance prior to turnover show a stronger relationship between these variables than studies with a longer time span. Therefore, if an analyst issues more than one forecast for a company for a year, we keep the last forecast. We measure the size of the company as the annual total sales (Compustat item # 12). This variable is denoted as COSIZE. 5 We denote the number of companies followed by the analyst each year as NCOS. We define industries as the 2-digit SIC code and denote the number of industries followed by an analyst as NIND. We use the number of analysts employed by the brokerage firm as a proxy for the size of the brokerage firm (BFSIZE). We measure analyst experience at three levels. Firm-level experience, denoted as FEXP, is the number of years the 14

16 analyst has issued a forecast for the firm. Industry-level experience, denoted as IEXP, is the number of years the analyst has issued forecasts for firms in that industry. We define industries using the two-digit SIC code. The experience of the analyst that we use as a proxy for analyst s age is general experience (GEXP), measured as the number of years the analyst has been listed on the I/B/E/S database. The age of the earnings forecast (FAGE) is the number of days between the analyst s earnings forecast date and the company s fiscal year-end. It should be noted that this variable has negative values, when the analyst s forecast date is after the fiscal year-end of the company. It is common for analysts to make forecasts after the fiscal-year end, right up to the earnings announcement date. Tenure with the brokerage firm (TENURE) is the number of years the analyst has been employed at the brokerage firm. Finally, the brokerage firm s analyst turnover ratio (BFTURN) is the number of analysts who departed the brokerage firm (voluntarily or involuntarily) in the year prior to the turnover, divided by the total number of analysts employed by the brokerage firm in the year prior to turnover. We measure the turnover ratio in the year prior to turnover to ensure that the turnover analyst has the opportunity to observe the value of this variable at the time of the turnover decision. This is particularly important for turnovers that take place at the beginning of the year. To facilitate comparison between firms, we employ the methodology of Mikhail et al. (1999) to calculate the relative values of each variable. For example, the relative accuracy for an analyst is calculated by ranking the analyst s raw accuracy (ACCUR) relative to that of all other analysts following the same company in the same year. A rank of n, representing the number of analysts following the company in that year, corresponds to the most accurate analyst. This rank is then divided by the number of analysts following the company in that year, n. Thus, this 5 Alternative measures such as market capitalization and analysts following yield similar result. 15

17 standardized rank ranges from 1/n to 1, with high levels corresponding to more accurate analysts, and represents an analyst s rank accuracy for a given company for a given year relative to his competitor analysts. The relative values of all other variables are calculated in a similar manner, with high values of the relative variable corresponding to high values for the raw variable. The relation between turnover and the predictor variables is tested using the following binomial logistic regression model: 6 Prob (TURNOVER = 1) = logit (a 0 + a 1 ACCUR + a 2 FAGE+ a 3 COSIZE+ a 4 BFSIZE + a 5 GEXP + a 6 NIND + a 7 NCOS + a 8 REVISE + a 9 TENURE + a 10 BFTURN) (1) where, TURNOVER is a binary indicator variable that equals one if the analyst was involved in a turnover, and zero otherwise. Independent variables are measured in relative terms. To test Jackofsky s curvilinear hypothesis, we estimate the above logistic regression model separately for voluntary and involuntary turnovers Results 6.1. Before Turnover Table 2 presents the means of the raw measures in Panel A and relative measures in Panel B of all variables separately for voluntary and involuntary turnovers. All the variables are 6 Schwab (1991) (footnote 3) points that the use of logistic regression as opposed to ordinary-least-squares regression is preferable here for several reasons. First, dichotomous dependent variables are seldom linearly related to independent variables, resulting in biased in OLS estimates. Second, error variables resulting from dichotomous dependent variables is not homoskedastic, as assumed in OLS, so estimates are inefficient. Finally, although observed values of the dependent variable can only take the values zero and one, OLS predicted values can be greater than one and less than zero, thus nonsensical in a probability sense. Use of logistic regression mitigates all these problems. 7 This is similar to the technique used in Schwab (1991) to test whether Jackofsky s curvilinear hypothesis holds for college professors. Another technique used to test the curvilinear hypothesis is to introduce a quadratic performance measure into the model, in addition to the linear performance measure. If the quadratic term is significant then we conclude that a curvilinear effect exists (Williams and Livingstone 1994). However, in this study such a model will be very complex since we have other variables in our study, in addition to job performance, to predict turnover. Furthermore, we have differing predictions on the effects of these variables for voluntary and involuntary turnovers. 16

18 measured in the period immediately prior to turnover. Relative measures represent the percentile for the turnover analysts relative to all other analysts following the same firms. For example, voluntary turnover analysts forecast accuracy is in the 58 th percentile, while the forecast accuracy for involuntary turnover analysts is in the 47 th percentile. Forecast accuracy is significantly higher for voluntary turnover analysts than for involuntary turnover analysts, both in raw and relative measures. This result is consistent with Jackofsky s theory that very good performers move to other jobs, while very bad performers lose their jobs. Descriptive statistics for other variables depict an interesting picture. Involuntary turnover analysts have enjoyed more favorable conditions at their brokerage firms prior to losing their jobs. They had greater experience at all levels, followed fewer firms and industries, and worked for larger brokerage firms, which usually provide better resources than smaller brokerage firms. Despite these favorable conditions, involuntary turnover analysts did not work hard enough as they revised their forecasts less frequently and their forecasts were almost two hundred days old by the fiscal year-end, which resulted in them issuing earnings forecasts that are less accurate than those of their peers. Table 3 presents the results of the logistic regression analysis for both involuntary and voluntary turnover. The estimated coefficient on ACCUR is negative and significant for involuntary turnover and positive and significant for voluntary turnover, suggesting that analysts who quit the profession tend to be relatively less accurate (bad performers) and analysts who move to another firm tend to be relatively more accurate (good performers). This result is consistent with Jackofsky s hypothesis. Therefore, for this study running the logistic regression model separately for voluntary and involuntary turnovers is the simplest, most direct and most reliable way to test Jackofsky s curvilinear hypothesis. 17

19 We use the variable FAGE and REVISE to proxy for analyst s intention to quit. When analysts intend on quitting, they tend not to update their forecasts, therefore, we expect a positive (negative) relation between FAGE (REVISE) and voluntary turnover. We do not have predictions for involuntary turnover. The positive coefficient on FAGE (REVISE) in the voluntary turnover regression is consistent (inconsistent) with our predictions. A potential interpretation is that voluntary turnover analysts have revised their forecasts frequently before they intended to leave and stopped after that, therefore their forecasts are old. The positive (negative) coefficient in FAGE (REVISE) in the involuntary regression can be interpreted in two ways; either analysts sensed that they will be fired, hence stopped updating their forecasts, or these analysts got fired because they did not update their forecasts frequently. We predict that analysts who follow larger companies are more likely to switch brokerage firms, because they enjoy more prestige and status and thus more likely to come to the attention of other brokerage firms. The positive and significant coefficient on COSIZE in the voluntary turnover regression supports our prediction. The positive and significant coefficient on COSIZE in the involuntary turnover regression suggests that when following larger firms, analysts are under more pressure to perform well. Failure to perform when following larger firms increases the probability of being fired. Since the evidence on the relation between the employer size and turnover is mixed, we do not have a prediction on the sign of BFSIZE. The negative (positive) coefficient on BFSIZE in the involuntary (voluntary) regression suggests that the smaller (larger) the brokerage firm the more likely the analyst will be fired (move to another brokerage firms). These results suggest that smaller brokerage firms are less likely to keep bad performers, while analysts working for large brokerage firms have a better chance at moving to other firms. 18

20 We predict that the analyst age is negatively related to involuntary turnover. We use GEXP as proxy for analyst age. Consistent with our prediction, the coefficient of GEXP in the involuntary turnover regression is negative and significant at the one percent level. We also find a positive association between GEXP and voluntary turnover. This result is consistent with the literature that documents a positive relation between analyst experience and forecast accuracy (Clement 1999; Mikhail et al. 1997), suggesting that more experienced analysts are more likely to get job offers from other firms because they are more accurate. The number of companies and industries followed, NCOS and NIND, are used as a proxy for workload (Clement 1999). We predict a positive association between workload and voluntary turnover since analysts seek to improve their work conditions. Consistent with this prediction, the coefficients on NCOS and NIND are positive and significant in the voluntary turnover regression. For involuntary turnover, the coefficients on NCOS and NIND are both negative and significant, suggesting that the responsibilities of bad performers have been reduced prior to their termination. Analysts who are committed to their brokerage firms are less likely to leave. We use TENURE as a proxy for organizational commitment, and we expect a negative relation between TENURE and voluntary turnover. Since brokerage firms initiate involuntary turnover, we do not have a prediction for TENURE in the involuntary turnover regression. The negative coefficient on TENURE in the voluntary turnover regression supports our prediction. The positive coefficient on TENURE in the involuntary turnover regression suggests that brokerage firms do not consider the number of years the analyst worked for the firm when they fire them. Finally, we predict a positive association between turnover (voluntary and involuntary) and BFTURN, the turnover ratio at the brokerage firm. We use BFTURN to proxy for the 19

21 quality of peer-group relationships within the brokerage firms. The positive coefficient on BFTURN in the voluntary turnover regression suggests that the poor peer-group relationships within the brokerage firms drive analysts to leave. The positive coefficient on BFTURN in the involuntary turnover regression suggests that the brokerage firm is in the process of weeding out poor performers After Turnover We follow voluntary turnover analysts at the new brokerage firm and relate the factors that led to turnover at the new brokerage firm. Table 4 presents the means of the raw measures in Panel A and relative measures in Panel B of all variables separately for voluntary turnover analysts before and after the switch to the new brokerage firm. All the variables are measured over a period of one year before and after the switch. The most notable result is that the improvement in raw forecast accuracy at the new brokerage firms is not significant. However, relative forecast accuracy improves significantly after the switch. Before the switch, analysts forecast accuracy was in the 58 th percentile, and it moved up to the 60 th percentile after the switch. FAGE after the switch, both in raw and relative measures, is significantly lower than before the switch, indicating that analysts forecasts become more recent at the new brokerage firm. The size of the new brokerage firms, BFSIZE, is larger only in raw terms, suggesting that analyst move to larger brokerage firms. Although analyst general experience, GEXP, becomes greater at the new brokerage firm by construction, firm- and industry-level experience, FEXP and IEXP, do not necessarily increase as the analyst moves to another brokerage firm. In our sample, FEXP decreases when the analyst moves to another brokerage firm, suggesting that analysts do not follow the same firms they used to follow before the switch. IEXP, however, increases at the new brokerage 20

22 firm, indicating that analysts keep following the same industries they followed before the switch. These results suggest that good performers are needed at the new brokerage firms for their industry expertise. The work conditions at the new brokerage firm improve for analysts. First, they move to larger brokerage firms, which have more resources available to the analysts (Clement 1999). Second, the workload is significantly lower at the new brokerage firms. Before the switch, analysts followed about twenty companies and 4.7 industries. The results of logistic regression indicate that workload is positively related to turnover. After the switch, the analysts follow about 14.5 companies and 3.7 industries. The reduction in workload at the firm- and industrylevel is significant in both raw and relative terms. Third, analysts revise their forecasts less frequently at the new brokerage firm. Although forecast frequency is positively related to forecast accuracy (Jacob et al. 1999), the performance of turnover analysts is improved by working for larger brokerage firms, following fewer firms and industries, and issuing more recent forecasts. Summary and Conclusions This study examines the factors leading to turnover among sell-side financial analysts and the consequences of turnover. We distinguish between two types of turnover, voluntary and involuntary. We define voluntary (involuntary) turnover when an analysts leaves their employment at one brokerage firm and find (do not find) employment at another brokerage firm. We use the I/B/E/S database to identify turnover analysts and classify the turnovers as voluntary and involuntary. Using the management and labor economics literatures, we examine the analyst and brokerage firm specific factors leading to analyst turnover, separately for voluntary and 21

23 involuntary turnovers. For voluntary turnover, we examine the job conditions at the new brokerage firm and relate that to the factors leading to turnover. We find that prior to the turnover, voluntary turnover analysts tend to have relatively better job performance than involuntary turnover analysts. We also show that these analysts tend to (1) follow relatively smaller companies more companies and industries, (2) have relatively greater tenure with the brokerage firm, (3) have less experience, (4) issue relatively more recent forecasts, (5) revise their forecasts more often, and (6) work for brokerage firms that have relatively high analyst turnover. We find that voluntary (involuntary) turnover is positively (negatively) related to performance, measured as earnings forecast accuracy. This result is consistent with Jackofsky's (1984) theory predicting a curvilinear U-shaped relationship between job performance and turnover. We also find that voluntary turnover is positively related to forecast age, size of companies followed, brokerage firm size, analysts general experience, workload, frequency of forecast revisions, and the turnover at the brokerage firm. Voluntary turnover is negatively related to the analyst s tenure at the brokerage firm. Results indicate that involuntary turnover is positively related to forecast age, the size of companies followed, the analyst tenure with the brokerage firm, and the turnover at the brokerage firm. Involuntary turnover is negatively related to brokerage firm size, analysts general experience, workload, and the frequency of forecast revisions. For analysts who switch brokerage firms, we compare the job performance and work conditions at the new brokerage firms vis-à-vis the old brokerage firms. We find that after the switch the turnover analysts earnings forecasts become significantly more accurate. We also find that as these analysts move to larger brokerage firms, they do not follow the same firms they 22

24 used to follow at the old brokerage, but they follow the same industries. This result suggests that analyst industry experience is more valuable to the new brokerage firm than firm experience. Finally, we find that the workload at the new brokerage firm is significantly lighter and they revise their forecasts less frequently, suggesting improved work condition. 23

25 References Chevalier, J., and G. Ellison Career concerns of mutual fund managers. The Quarterly Journal of Economics 114 (2): Clement, M. B Analyst forecast accuracy: Do ability, resources, and portfolio complexity matter? Journal of Accounting and Economics 27 (3): Dreher, G. F The role of performance in the turnover process. Academy of Management Journal 25 (1): Goldsmith, M Retain your top performers. Executive Excellence 14 (11): Hom, P. W., and R. W. Griffeth Employee turnover. Cincinnati, OH: South-Western College Publishing. Hong, H., and J. D. Kubik Analyzing the analysts: Career concerns and biased earnings forecasts. The Journal of Finance 58 (1): Hong, H., J. D. Kubik, and A. Solomon Security analysts' career concerns and herding of earnings forecasts. The Rand Journal of Economics 31 (1): Idson, T. L Employer size and labor turnover. Edited by S. W. Polachek. Vol. 15, Research in labor economics. Greenwich, CT: JAI Press. Jackofsky, E. F Turnover and job performance: An integrated process model. Academy of Management Review 9 (1): Jacob, J., T. Z. Lys, and M. A. Neale Expertise in forecasting performance of security analysts. Journal of Accounting and Economics 28 (1): Kahn, C. M., and S. D. Longhofer An equilibrium model of quits under optimal contracting. European Economic Review 37 (6): Karamanou, I Turnover incentives and the bias in analyst earnings forecasts: An extension of Mikhail, Walther and Willis, Working Paper, University of Cyprus. Lane, J., A. G. Isaac, and D. W. Stevens Firm heterogeneity and worker turnover. Review of Industrial Organization 11 (3): Lane, J., and M. P. Parkin Turnover in an accounting firm. Journal of Labor Economica 16 (4): McEvoy, G. M., and W. F. Cascio Do good or poor performers leave? A meta-analysis of the relationship between performance and turnover. Academy of Management Journal 30 (4): McLaughlin, K. J A theory of quits and layoffs with efficient turnover. Journal of Political Economy 99 (1):1-29. Mikhail, M. B., B. Walther, and R. H. Willis Do security analysts improve their performance with experience? Journal of Accounting Research 35 (Supplement): Mikhail, M. B., B. R. Walther, and R. H. Willis Does forecast accuracy matter to security analysts? The Accounting Review 74 (2): Price, J. L., and C. W. Mueller Absenteeism and turnover of hospital employees. Greenwich, CT: JAI Press. Schwab, D. P Contextual variables in employee performance-turnover relationships. Academy of Management Journal 34 (4): Steers, R. M., and R. T. Mowday Employee turnover and post-decision accommodation process. In Research in organizational behavior, edited by B. M. S. a. L. L. Cummings. Greenwich, CT: JAI Press. 24

26 Trevor, C. O., B. Gerhart, and J. W. Boudreau Voluntary turnover and job performance: Curvilinearity and the moderating influences of salary growth and promotions. Journal of Applied Psychology 82 (1): Williams, C. R., and L. P. Livingstone Another look at the relationship between performance and voluntary turnover. Academy of Management Journal 37 (2):

27 Table 1 Number (Percentage) of Analysts Switching Brokerage Firms and Analyst Leaving the IBES Database Analysts Who Switched Brokerage Firms Analysts Who Switched Only Once Analysts Leaving the I/B/E/S Database All Analysts on Year I/B/E/S , (4.3%) 7 (0.3%) 470 (21.4%) , (3.9%) 8 (0.4%) 452 (20.3%) , (4.4%) 11 (0.5%) 367 (16.3%) , (5.6%) 9 (0.4%) 522 (21.7%) , (5.1%) 12 (0.5%) 408 (17.6%) , (9.5%) 25 (1.0%) 441 (16.9%) , (11.4%) 33 (1.3%) 444 (16.9%) , (10.5%) 24 (1.0%) 306 (13.1%) , (7.1%) 25 (1.1%) 253 (11.3%) , (6.7%) 16 (0.6%) 215 (8.7%) , (6.4%) 23 (0.8%) 305 (10.6%) , (5.7%) 26 (0.8%) 431 (13.7%) , (5.4%) 24 (0.7%) 420 (12.0%) , (5.6%) 38 (1.0%) 499 (12.6%) , (5.5%) 43 (1.0%) 708 (16.3%) , (6.2%) 71 (1.6%) 782 (17.7%) , (5.7%) 82 (1.8%) 978 (21.6%) , (3.2%) 41 (0.9%) 1,055 (23.8%) Total 12, (15.0%) 518 (4.1%) 9056 (71.8%) 26

28 Table 2 Descriptive Statistics in the Period Immediately Prior to Voluntary and Involuntary Analyst Turnover Panel A: Raw Measures Panel B: Relative Measures Variable Voluntary Involuntary t-value Voluntary Involuntary t-value ACCUR *** *** FAGE *** *** BFSIZE *** * FEXP *** *** GEXP *** *** IEXP *** *** NCOS *** *** NIND *** *** TENURE *** *** BFTURN *** *** COSIZE *** *** REVISE *** *** All variables in this table are measured in the period immediately prior to the analysts switching brokerage firms or leaving the I/B/E/S database. The relative value of a variable is calculated using the raw value of a variable for a turnover analyst, relative to the raw value of that variable for all other analysts following the same company in the same year. To compute the relative value, all analysts following a company in a year are ranked from 1 to n, where n is the number of analysts following the company and corresponds to the highest value for that value. Each rank is then divided by n. This standardized rank measure will now range from 1/n to 1, with high ranks corresponding to high values for that variable. ACCUR is the analyst s earnings forecast accuracy, measured as the absolute value of the difference between the actual earnings per share of the company and the analyst s forecast of the company s earnings per share, and divided by the absolute value of actual earnings per share. This ratio is then multiplied by 1. FAGE is the age of the analyst s earnings forecast, measured as the number of days between the forecast date and the end of the company s fiscal year. A negative (positive) for this variable indicates that the forecast was issued after (before) the end of the fiscal year. BFSIZE is the size of the brokerage firm, measured as the number of analysts employed by the brokerage firm. FEXP is the firm-specific experience, measured as the number of years the analyst has issued a forecast for the firm. GEXP is the professional experience of the analyst. It is measured as the number of years the analyst has submitted forecasts to I/B/E/S. IEXP is the industry-level experience, measured as the number of years the analyst has issued a forecast for firms belonging to the industry. Industry if defined as the two-digit SIC code NCOS is the number of companies followed by the analyst. NIND is the number of industries followed by the analysts, defined as the two-digit SIC code. TENURE is the number of years the analyst has been employed with the brokerage firm. 27