Sourcing Alpha in Global Equity Markets: Market Factor Decomposition and Market Characteristics

Subhransu S. Mohanty

Chapter 19 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 737-790 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The sources of risk in a market place are systematic, cross-sectional and time varying in nature. Though the CAPM provides an excellent risk-return framework and the market beta may reflect the risk associated with risky assets, there are opportunities for investors to take advantage of dimensional and time varying return anomalies in order to improve their investment returns. In this paper, we restrict our analysis to return variations linked to market factor anomalies or factor/dimensional beta using the Fama–French 3 factor, Carhart 4 factor, and Asness, Frazzini and Pederson (AFP)’s 5 and 6 factor models. We find significant variations in explaining sources of risk across 22 developed and 21 emerging markets with data over a long period from 1991 to 2016. Each market is unique in terms of factor risk characteristics and market risk as explained by the CAPM is not the true risk measure. Hence, contrary to the risk-return efficiency framework, we find that lower market risk results in higher excess return in 19 out of the 22 developed markets, which is a major anomaly. Although in majority of the markets, the AFP models result in reducing market risk (15 countries) and enhancing Alpha (11 countries), it is very interesting to note that, the CAPM is second only in generating excess returns in the developed markets. We are also conscious of the fact that each market is unique in its composition and trend even over a long time horizon and hence a generalized approach in asset allocation cannot be adopted across all the markets.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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