Systematic Risk-Factors among U.S. Stock Market Sectors

The Capital Asset Pricing Model (CAPM) and its extensions are a family of empirical asset pricing models which partition risk as either systematic (market-wide) or idiosyncratic (stock-specific). Examples of systematic risk-factors include the market return, company size, and company value. Within the framework of the CAPM-family of models, it is assumed that the effects of these systematic risk-factors are homogenous among sectors. This paper develops an extension to the CAPM relaxing this assumption, by directly comparing these systematic risk-factors at the sector-level. Utilizing CRSP and Compustat data, systematic risk-factor premiums are estimated for each sector, which demonstrates heterogeneity, with respect to sector. An analysis of means and statistical significance reveals that a separate stock-picking strategy is necessary within each individual sector, and that there exist factors that are irrelevant to some sectors altogether. The estimated sector premiums are utilized to develop a GICS Ten-Factor Model, which has superior explanatory power amongst the CAPM-family. The GICS Model has an average Adjusted-R2 of 27%, compared to the CAPM which has a value of 15.5%. It is then demonstrated that the GICS Model is superior to the CAPM-family in regard to high-Beta Portfolio construction-with a Sharpe Ratio of 0.61 compared to the CAPM which has a value of 0.42. This paper demonstrates that systematic risk-factors are heterogeneous among sectors, and details how this information is materially useful to investors

Multi-Industry Simplex : A Probabilistic Extension of GICS

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.Comment: 17 pages, 10 figure

A “Quant” Approach to Predicting Revenue Growth

The primary task for an active equity investor is to identify stocks with positive expected future returns. In general, this is a very difficult task, as stock returns are a function of many inputs, including size, value, and profitability. To predict company profitability, it is necessary to develop models for company revenue and company costs - as they are independent functions. This study attempts to model the former, by developing a two-stage algorithm to estimate the probability that a company will report quarterly revenue growth. This is done using a combination of Time Series and Bayesian statistical techniques. In the first stage, a Logistic Auto-Regressive Moving Average (LARMA) model is used to estimate a prior probability that a company will report revenue growth, based on historical quarterly data from the Compustat database. A limitation of this prior estimate is that it cannot incorporate data that is more recent than the last quarterly report (three months ago), during which the probability of revenue growth may have changed. There is an attempt to remedy this problem in the second stage of the algorithm, which utilizes Bayesian conditioning to update our prior estimate based on the proportion of similar companies which reported revenue growth in the past month. This study aims to identify whether this Bayesian conditioning significantly improves the accuracy of our prediction. This project is currently a work-in-progress, though sufficient results are expected by the time of the conference

The Developing Economist, Volume 5

Contents: Moral Sentiments and Altruism in Game Theory // by Joshua Liebmann, Truman State University (p. 9) -- Estimating Minority Benefit from Government Assistance in the US Mortgage Market // by Maksim Papenkov, State University of New York at Albany (p. 41) -- Do High Equity Valuations Predict Recessions? // by Ariel Goldszmidt, The University of Chicago (p. 64) -- Does Snowfall Influence Ski Visitation to Resorts in Colorado? An Empirical Analysis Uncovering the Relationship // by Alex Vergara, The University of Colorado, Boulder (p. 91) -- The Transferability of Human Capital: A Case Study of Refugees in the United States // by Sarang D Murthy, University of Vermont (p. 112) -- A Sequential Game-Theoretic Approach to Student Learning Time // by Luther Yap, University of Cambridge (p. 147) -- Power, Expected Value, and Excessive Risk-Taking // by Johnny Fulfer, Eastern Oregon University (p. 195)Economic