Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/20028
Title: Predictive power of analyst recommendations in the Indian market
Authors: Agarwal, Shrey 
Gupta, Shivam 
Keywords: Equity markets;Predictive power;Indian market;Stock markets;Investment
Issue Date: 2019
Publisher: Indian Institute of Management Bangalore
Series/Report no.: PGP_CCS_P19_160
Abstract: This study examines the predictive content of aggregate analyst recommendations in Indian equity markets. Analyst reports are one of the most important sources of information for investors to understand major corporate decisions and create their opinions regarding investment in the equity. For a past few decades, economists globally have become increasingly interested in examining whether these aggregate analyst recommendations actually have any additional information content and whether the investors can construct profitable investment strategies based on these forecasts and advice. Our methodology focusses on two major aspects of analyst recommendations, namely: consensus rating and consensus estimates. Analyst consensus ratings show that whether, on average, analysts are recommending “buy”, “hold” or “sell” for that security. Consensus estimates are average of earnings estimates (like Revenue, Net Income, EPS, etc.) forecasted by various analysts quarterly/semi-annually/annually. The methodology described below tests if the aggregate recommendations by analysts contain some market-level information not incorporated in the prices. We have used analyst forecasts for various large-cap Indian companies on a QoQ and YoY basis to analyse the predictive power of the various consensus measures. The study proceeds as follows: Section 2 provides the literature review providing summary of existing literature regarding the information content of analyst ratings, estimates, target prices and its dispersions, done mostly for western & developed markets.. Section 3 provides the methodology used with Section 4 describing the data used. Section 5 provides a snapshot of the results generated using the data described above and the R code of the methodology describes in Section 3. Finally, Section 6 and Section 7 gives limitations of this study and future directions the study could take to completely understand the relevance of analyst recommendations as a tool for generating abnormal results.
URI: https://repository.iimb.ac.in/handle/2074/20028
Appears in Collections:2019

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