Learning, Portfolio Complexity and Informational Asymmetry in Forecasts of Sell-Side Analysts

• Main Author: Melquiades Pereira de Lima Júnior
• Format: Article
• Language: English
• Published: FUCAPE Business School 2017-01-01
• Series: BBR: Brazilian Business Review
• Subjects: financial analysts; target price forecast; stock recommendations; learning; portfolio complexity
• Online Access: http://www.redalyc.org/articulo.oa?id=123050102001

The aim of this study was to analyze the association of learning and complexity in the target price forecasts and sell-side analysts’ recommendations on the BM&FBovespa. The sample comprised forecasts of 195 stocks, 75 brokers and 569 analysts between 2005 and 2013, analyzed by linear models with panel data. Our results suggest that the experience with the stock, with the sector and complexity of the portfolio confirmed the learn by doing, but the overall experience showed contradictions due to information asymmetry. Despite anchoring in their peers, analysts achieved significant returns, but showed forecasts with low accuracy. Therfore, we concluded that more experienced analysts may intentionally contradict themselves in an attempt to bias the market. Finally, we suggest the development of less biased analyst rankings in order to increase the competitiveness and quality in the results of the analyzesThe aim of this study was to analyze the association of learning and complexity in the target price forecasts and sell-side analysts’ recommendations on the BM&FBovespa. The sample comprised forecasts of 195 stocks, 75 brokers and 569 analysts between 2005 and 2013, analyzed by linear models with panel data. Our results suggest that the experience with the stock, with the sector and complexity of the portfolio confirmed the learn by doing, but the overall experience showed contradictions due to information asymmetry. Despite anchoring in their peers, analysts achieved significant returns, but showed forecasts with low accuracy. Therfore, we concluded that more experienced analysts may intentionally contradict themselves in an attempt to bias the market. Finally, we suggest the development of less biased analyst rankings in order to increase the competitiveness and quality in the results of the analyzes.