"You might imagine that it's harder to analyze two stocks than it is to analyze one, and you would be right. In particular, it's harder to analyze the second stock, because you are tired from analyzing the first" - Matt Levine
Decreased research accuracy
Mr. Levine brings levity and insight to the stock markets each day with his Money Stuff blog. In his most recent post "Index Rules and Analyst Fatigue" he discusses the challenges faced on a daily basis by human stock researchers and analysts. This is not necessarily new information; however, there is an increasing body of research which delves into the challenges presented when individuals are required to do repetitive (yet intense) tasks for extended periods of time. One of the more recent research endeavors discussing these challenges is "Decision Fatigue and Heuristic Analyst Forecasts" by David A. Hirshleifer, Ben Lourie and Siew Hong Teoh of the University of California, Irvine and Yaron Levi of the University of Southern California. Quite possibly the most interesting take away from this paper is the finding "that forecast accuracy declines over the course of a day as the number of forecasts the analyst has already issued increases". This is a psychological phenomena known as decision fatigue.
Combating research fatigue
These insights are not necessarily unexpected and cannot be attributed as a downfall to any research analyst. We all face decision fatigue in many aspects of our lives. When attempting to gain true insight and a market edge with information - the pertinent question is "How can human research be supplemented to decrease the effects of decision fatigue?" One answer to this question is the use of artificial intelligence (AI) systems to perform research. Putting AI to use analyzing market data, big data, text, news, etc to mine for insights eliminates any variety of human fatigue whether mood based, physical or mental. Not only is outright fatigue addressed but so are other biases such as mood. By utilizing a systematic computing based approached to analysis of text, for example, alternate data users benefit from a system which always evaluates domain specific phrases or words in exactly the same way instead of being influenced by environmental factors such as survivorship bias, physical or mental state, or mood.
More about RelateTheNews
At RelateTheNews we have built a robust domain specific sentiment analysis engine which combats these research challenges utilizing AI. RelateTheNews provides independent research in the form of news sentiment analysis for capital markets alternate market data. Global hedge funds, asset managers, and buy side participants apply this data to manage risk and generate alpha. Sell side firms also utilize RelateTheNews data to enhance and extend their research data offerings to their clients. Contact us to address your alternate market data and independent research data needs.