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https://repository.iimb.ac.in/handle/2074/19699
Title: | Developing a rating methodology for stock analysts covering FMCG sector in India | Authors: | Kunche, Subhash Nandi, Abhishek |
Keywords: | Equity analysts;FMCG sector;Stock analysts | Issue Date: | 2017 | Publisher: | Indian Institute of Management Bangalore | Series/Report no.: | PGP_CCS_P17_008 | Abstract: | Equity analysts play a critical role in maintaining the integrity of financial markets by critically evaluating management performance, from both a short term and a long term perspective. In this manner, they provide guidance to brokers and institutional clients, both sell side and buy side. Hence, rating the performance of the analyst is necessary to ensure continued active and unbiased coverage. In the absence of publicly available rating systems for analysts in India, we tried to develop a model for rating analysts from key brokerage houses. For our analysis, we chose the FMCG sector, one of the key contributors to the economy with a stable growth trajectory. The key dimensions chosen for establishing the ratings were prediction accuracy, performance relative to market, accuracy of recommendations and performance relative to other analysts. We quantified these dimensions by developing suitable algorithms and assigning parameters to these algorithms based on a combination of market data analysis and judgment. The model was applied to analyst coverage of selected companies from the FMCG sector over a period of 5 years. Firstly, a rating was obtained for the analyst coverage of a single company, and then the average of ratings across all companies were taken to obtain an overall rating for the coverage of the FMCG sector. After the overall ratings were obtained, we segregated the analysts as Star Analyst, Average Performing Analyst and Under Performing Analyst. In the final part, we try to understand what led to the success of the Star Analysts and under performance of some analysts. We then propose to make the ratings for individual dimensions available to the investors so that they can make an informed decision according to their risk profile. Finally, we enlist a few other dimensions, the addition of which will make the model more robust going forward. | URI: | https://repository.iimb.ac.in/handle/2074/19699 |
Appears in Collections: | 2017 |
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