Modeling Analysts’ Recommendations via Bayesian Machine Learning

Abstract

Individual analysts typically publish recommendations several times per year on the handful of stocks they follow within their specialized fields. How should investors interpret this information? How can they factor in the past performance of individual analysts when assessing whether to invest long or short in a stock? This is a complicated problem to model quantitatively: There are thousands of individual analysts, each of whom follows only a small subset of the thousands of stocks available for investment. Overcoming this inherent sparsity naturally raises the question of how to learn an analyst’s forecasting ability by integrating track-record information from all the stocks the analyst follows; in other words, inferring an analyst’s ability on Stock X from track records on both Stock X and stocks other than X. The authors address this topic using a state-of-the-art computationally rapid Bayesian machine learning technique called independent Bayesian classifier combination (IBCC), which has been deployed in the physical and biological sciences. The authors argue that there are many similarities between the analyst forecasting problem and a very successful application of IBCC in astronomy, a study in which it dominates heuristic alternatives including simple or weighted averages and majority voting. The IBCC technique is ideally suited to this particularly sparse problem, enabling computationally efficient inference, dynamic tracking of analyst performance through time, and real-time online forecasting. The results suggest the IBCC technique holds promise in extracting information that can be deployed in active discretionary and quantitative investment management.

How Can Machine Learning Advance Quantitative Asset Management?

“…The key advantage of applying ML to forecasting fundamentals is that the signal-to-noise ratio tends to be higher. For instance, Bew et al (2019) use ML to extract more value from analyst recommendations, solely relying on analyst data as input. builds on ML to predict earnings surprises, not only leveraging earnings data but also past returns and other technical indicators such as trading volume.…”

Machine Learning for Active Portfolio Management

“…Kozak, Nagel, and Santosh (2020) adopt a shrinkage approach to build Summer 2021 a stochastic discount factor summarizing the joint explanatory power of a large set of stock characteristics. As an alternative to these data-driven approaches, Bew et al (2019) and Papaioannou and Giamouridis (2020) consider expert predictions as model inputs instead of stock characteristics.…”