Measuring information in analyst reports - Martineau, Charles,Zoican, Marius ([University of Toronto - Rotman School of Management], 2021)
How to quantify the informational content of analyst reports? In this short methodological paper, we propose a measure of information contribution (IC), defined in the spirit of Shapley values. We use natural language processing to identify topics for over 90,000 analyst reports for S&P 500 stocks between January 2018 to May 2020. Next, we build the IC measure as the average cosine distance between the topic distribution for a particular report and any subset of competitor reports. A first preliminary finding is that the informational content of reports in
10.2139/ssrn.3925176
Keywords: Graue Literatur; analyst reports; natural language processing; Shapley value; information