Multi-factor asset pricing models in emerging and developed markets

Abstract

Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

The Impacts of The Systematic, Idiosyncratic Risks and Market Sentiment on China A-Shares Performances

“…Among the countries understudied; United States, Australia, Canada, and China, the application of FF5 model performs the best, while for the remaining 6 markets, all 3 models did not perform significantly different. Based on the above research conclusions, Lalwani and Chakraborty (2019) recommended the FF3 model as the most efficient, parsimonious and robust model, but nevertheless, the author still expected to find a better model to explain the China's stock market. Zheng et al (2020) studied on multi-factor models of stock returns in 9 Asian markets, including China, Japan, and South Korea.…”

“…Therefore, the author expects that there could have a new better model to explain the China A-shares market. Lalwani and Chakraborty (2019) conducted a study comparing the effects of FF3, Carhart 4-factor, and FF5 models in developed and developing countries. Among the countries understudied; United States, Australia, Canada, and China, the application of FF5 model performs the best, while for the remaining 6 markets, all 3 models did not perform significantly different.…”

Enhancing portfolio management using artificial intelligence: literature review

“…Two decades later, this model has been extended to the five-factor model (Fama and French, 2015 ), which includes profitability and investment of the firm in addition to market factor, firm size and value, aiming to describe better the variation in equity prices that the three-factor model does not capture. Over a considerable time, these models have been extensively tested empirically by numerous studies aiming to adequately price the equity returns in both developed and emerging markets (Kubota and Takehara, 2018 ; Lalwani and Chakraborty, 2019 ; Mosoeu and Kodongo, 2020 ). The evidence shows, for example, (Mohanty, 2019 ), that each market is unique in its composition and trend even over a long time horizon, and hence, a generalized asset pricing model cannot be adopted across all the markets.…”

An empirical investigation of investor sentiment and volatility of realty sector market in India: an application of the DCC–GARCH model

“…An asset pricing model gives the relationship between impacting factors and returns (Lalwani and Chakraborty 2020 ). The traditional theory of asset pricing by Sharpe ( 1964 ) gives the linear relationship between returns and market risk (β).…”