Performance evaluation of analyst recommendations

Abstract

One aspect of India’s much publicised growth story that has attracted tremendous interest has been the booming stock market. With advances in technology that have facilitated ease of market trading, the number of domestic investors in the retail segmenti has increased exponentially over the past decade. This has also fuelled a plethora of brokerage houses and financial newspapers offering equity research recommendations. While there has been lot of study done in the US and European markets to understand how the recommendations of equity analysts have performed, we were not able to find any research paper pertaining to the Indian equity markets. Hence the focus of our study is to study how the recommendations made on Indian equities fared in the long term. Semi-strong form of market efficiency clearly states that stock prices rapidly adjust to publicly available information so no abnormal returns can be generated over the long run. Most prior studies indicate that analyst reports promotes market efficiency by disseminating information to investors which they wouldn’t be privy of, due to lack of access and effort required. Analysts gather information and then systematically research on different stocks in order to ascertain if current market price of the stock is overpriced or underpriced with respect to the intrinsic fair value of the company to arrive at their recommendations. We have considered here the one year horizon recommendations which is the most common time frame used. This study should be of interest to both investors and the analyst research houses. From investor perspective, given the fact that there are many paid subscriptions to brokerage research houses, it will help them understand if their investments based on recommendations generate real value over time. Also by dissecting the type of stocks the analysts choose for making recommendations, we try to identify the inherent biases, which can be removed to aid in making better recommendations in terms of improved predictive returns.