scholarly journals The Role of Factor Strength and Pricing Errors for Estimation and Inference in Asset Pricing Models

2019 ◽  
Author(s):  
M. Hashem Pesaran ◽  
Ron P. Smith
Keyword(s):  
Asset Pricing ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Pricing Errors

Multi-factor asset pricing models in emerging and developed markets

2019 ◽  
Vol 46 (3) ◽  
pp. 360-380
Author(s):  
Vaibhav Lalwani ◽  
Madhumita Chakraborty
Keyword(s):  
Asset Pricing ◽  
Factor Model ◽  
Five Factor Model ◽  
Superior Performance ◽  
International Studies ◽  
Pricing Models ◽  
Content Type ◽  
Developed Markets ◽  
Asset Pricing Models ◽  
Pricing Errors

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.

Models or Stars: The Role of Asset Pricing Models and Heuristics in Investor Risk Adjustment

2020 ◽  
Vol 34 (1) ◽  
pp. 67-107 ◽  
Author(s):  
Richard B Evans ◽  
Yang Sun
Keyword(s):  
Asset Pricing ◽  
Risk Adjustment ◽  
Factor Models ◽  
Strongly Correlated ◽  
Pricing Models ◽  
Asset Pricing Models ◽  
Retail Investors ◽  
Before And After ◽  
Sector Funds

Abstract We examine the role of factor models and simple performance heuristics in investor decision-making using Morningstar’s 2002 rating methodology change. Before the change, flows strongly correlated with CAPM alphas. After, when funds are ranked by size and book-to-market groups, flows become more sensitive to 3-factor alphas (FF3). Flows to a matched institutional sample (same managers/strategies) follow FF3 before and after the change but are unrelated to the CAPM. Placebo tests with sector funds and other factor loadings show no effects. Our results imply that improvements in simple performance heuristics can result in more sophisticated risk adjustment by retail investors.

The role of sorting portfolios in asset-pricing models

2011 ◽  
Vol 21 (18) ◽  
pp. 1381-1396 ◽  
Author(s):  
J. Ernstberger ◽  
H. Haupt ◽  
O. Vogler
Keyword(s):  
Asset Pricing ◽  
Pricing Models ◽  
Asset Pricing Models

Short- and Long-Horizon Behavioral Anomalies and Institutional Investors’ Nudge Behavior: Based on the Data of Listed Chinese Tobacco Companies

2021 ◽  
Vol 7 (5) ◽  
pp. 1904-1922
Author(s):  
Liu Yue ◽  
Liu Tianming
Keyword(s):  
Asset Pricing ◽  
Institutional Investors ◽  
Regulatory Agencies ◽  
Pricing Models ◽  
Healthy Development ◽  
Limits To Arbitrage ◽  
Asset Pricing Models ◽  
Pricing Factors ◽  
The Impact

We use the data of listed tobacco companies in China to study the existence of short- and long-horizon behavioral anomalies and the impact of institutional investors’ behavior on them. We found that the existing asset pricing models cannot explain the short- and long-horizon behavioral anomalies based on tobacco enterprise data. Conversely, the short- and long-horizon behavioral anomalies can explain the exciting asset pricing factors. Compared with existing asset pricing models, behavioral anomalies have a stronger ability to explain anomalies. Behavioral anomalies could pass the cross-sectionally test and strengthened over time. The above results indicate that behavioral anomalies exist in China tobacco enterprisest significantly and are time-varying. We found that the limits to arbitrage and cognitive bias lead to the existence of behavioral anomalies through mechanism tests. Institutional investors did not play the role of price discovery. Instead, their nudge behavior strengthens the short- and long-horizon behavioral anomalies. Therefore, tobacco regulatory agencies should guide listed tobacco companies to broaden information channels to reduce information asymmetry in the market through relevant policies, strengthen the supervision of institutional investors’ bubble riding behavior, and promote the healthy development of the tobacco market.

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