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Long abstract (Advanced research project / June 14, 2012) Corresponding Author: Reto Cueni, University of Zurich: [email protected] Bold Analysts or Bold Banks: Do Institutional Factors Drive Herding Behavior?
The last financial crisis provides many examples of financial analysts who expressed their pessimistic view of forthcoming market developments and got mobbed and sidelined until they resigned or even got dismissed (see, e.g. The Economist 2009, Robinson 2009).1 The financial industry benefits most if the market prices of securities rise or are presumed to rise. Thus, the financial industry’s best interests are served when a positive economic outlook persists and negative forecasts are rejected (Michaely and Womack 1999, Francis and Philbrick 1993). We subsume all the various negative consequences (e.g., stress, repression, or dismissal) an analyst has to bear when issuing a negative or deviant forecast under the term costs of non-­‐herding From an economic point of view, the existence of the costs of non-­‐herding leads to a waste of resources because it generates an information-­‐asymmetry in favor of positive ratings about the future economic trends. This asymmetry generates distorted asset and resource allocations. In financial markets, there is ample evidence that such an asymmetry exists (Hong and Kacperczyk 2010, Clement and Tse 2005, Hong and Kubik 2003, Welch 2000, Graham 1999). Additionally, Hong et al. (2000) and Clement and Tse (2005) show that there is an overall pressure on analysts to herd with the consensus (i.e. the average of all the earnings forecasts of other analysts covering the same stock). This herding behavior of analysts due to the costs of non-­‐herding amplifies the loss of genuine information in financial markets and is thus responsible for an even larger misallocation of resources, as attested during the last financial crisis. To avoid this waste of resources in the financial industry, we need to understand what drives the herding behavior of analysts. Anecdotes suggest that institutional 1 In Bloomberg Markets Magazine, three Equity Analysts tell their stories of being punished by their peers and supervisors due to the fact that they forecasted a negative economic trend in their industry sectors; ex-­‐post, all of them executed a correct analysis (Robinson 2009). 1 / 4 factors of brokerage houses affect the forecasting behavior of their analysts beyond individual analyst characteristics (e.g., cognitive abilities or experience). Furthermore, we hypothesize that the pressure to herd is not the same for all the analysts covering different stocks. Specifically for analysts covering the financial sector, we expect the pressure to be the highest because of two reasons. First, these analysts have the highest exposure to owners, principals or senior executives of their brokerage houses, as they have a deep knowledge about the financial industry. Therefore these analysts are under stronger supervision and thus face more pressure to meet their principals or directors demands than analysts not covering the financial sector. Second, a negative forecast issued on a stock of a particular bank can harm a brokerage’s business relation to this bank as well as it might raise the chance of a downturn in the industry the analysts himself is working in. Using a large and unique panel data set, we analyze if and to what extent institutional factors in brokerage houses influence the herding behavior of their equity analysts. Second, we examine if analysts covering the financial sector do face a higher pressure to herd and how institutional factors influence the analyst’s behavior when covering different sectors. The data consists of more than 1’300’000 observations of earnings estimates, which are provided from more than 10’000 analysts working with over 700 different brokerage houses between the years 2000 and 2009. We follow the estimation procedure by Clement and Tse (2005) and identify the “bold” analysts (those not herding) and the ones that herd. This measure of analysts’ behavior is then linked with the institutional factors of the brokers they are working with. We obtain data for various dimensions of brokerage houses such as the ownership structure and different measures of the size of the organization.2 2 We use data on analyst forecasts from the Institutional Brokerage Estimation System (IBES), data on corporate governance from the Institutional Shareholder Service (ISS), and data on other broker characteristics from Compustat. 2 / 4 First, we find that institutional factors of the brokerage houses have a significant and substantial influence on the analysts’ forecasting behavior, controlling for individual characteristics used in previous literature on analysts’ behavior. We find a sizeable and significant effect of ownership structure (privately vs. publicly held broker) on the herding behavior of analysts. Second, we can reproduce the finding of Clement and Tse (2005), that “bold” forecasts are indeed more accurate than herding forecasts, controlling for individual analysts’ characteristics. Third, we are able to test the hypothesis that analysts covering the financial industry face the highest costs of non-­‐herding. We split the various earnings estimates into groups according to the Standard Industrial Classification (SIC) divisions of the US Department of Labor (e.g. Manufacturing or Services). We find the most significant and sizeable effects of institutional factors for estimates of stocks from the division “Finance, Insurance, And Real Estate”. This evidence supports our hypothesis that analysts covering stocks from certain sectors face higher costs of non-­‐herding than analysts covering other sectors and that the institutional factors we analyze influence mostly the herding behavior of analysts covering the financial sector. The insights we gain from our study shall help to understand how institutional factors in organizations can shape herding behavior and inform the investors and regulators about the factors that support a more genuine forecasting behavior of analysts. This should lead to a reduction of the information-­‐asymmetry in favor of positive ratings about the future economic trends and thus to a more efficient asset and resource allocation in financial markets. 3 / 4 References Clement, M.B., Tse, S.Y., 2005. Financial analyst characteristics and herding behavior in forecasting. The Journal of Finance 60, 307-­‐341 Francis, J., Philbrick, D., 1993. Analysts' decisions as products of a multi-­‐task environment. Journal of Accounting Research 31, 216-­‐230 Graham, J.R., 1999. Herding among Investment Newsletters: Theory and Evidence. The Journal of Finance 54, 237-­‐268 Hong, H., Kacperczyk, M., 2010. Competition and bias. The Quarterly Journal of Economics 125, 1683-­‐1726 Hong, H., Kubik, J.D., 2003. Analyzing the analysts: Career concerns and biased earnings forecasts. The Journal of Finance 58, 313-­‐351 Hong, H., Kubik, J.D., Solomon, A., 2000. Security analysts' career concerns and herding of earnings forecasts. The Rand journal of economics 31, 121-­‐144 Michaely, R., Womack, K.L., 1999. Conflict of interest and the credibility of underwriter analyst recommendations. Review of Financial Studies 12, 653-­‐686 Robinson, E., 2009. Research Renegades. In: Bloomberg Markets Magazine, pp. 32-­‐44. Bloomberg Finance L.P., New York The Economist, 2009. Please do feed the bears. In: TheEconomist, pp. 16-­‐16 Welch, I., 2000. Herding among security analysts. 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