Factor Models, Machine Learning, and Asset Pricing

2021 ◽  
Author(s):  
Bryan T. Kelly ◽  
Dacheng Xiu
Keyword(s):  
Machine Learning ◽  
Asset Pricing ◽  
Factor Models

scholarly journals The impact of liquidity and size premium on equity price formation in Serbia

2012 ◽  
Vol 57 (195) ◽  
pp. 43-78 ◽  
Author(s):  
Jelena Minovic ◽  
Bosko Zivkovic
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Firm Size ◽  
Market Value ◽  
Asset Returns ◽  
Factor Models ◽  
Price Formation ◽  
Good Tool ◽  
Equity Price ◽  
The Impact

The goal of this paper is to examine the impact of an overall market factor, the factor related to the firm size, the factor related to the ratio of book to market value of companies, and the factor of liquidity risk on expected asset returns in the Serbian market. For this market we estimated different factor models: Capital Asset Pricing Model (CAPM by Sharpe, 1964), Fama-French (FF) model (1992, 1993), Liquidity-augmented CAPM (LCAPM) by Liu (2006), and combination LCAPM with FF factors. We used daily data for the period from 2005 to 2009. Using a demanding methodology and complex dataset, we found that liquidity and firm size had a significant impact on equity price formation in Serbia. On the other hand, our results suggest that the factor related to the ratio of book to market value of companies does not have an important role in asset pricing in Serbia. We found that Liu?s two factor LCAPM model performs better in explaining stock returns than the standard CAPM and the Fama-French three factor model. Additionally, Liu?s LCAPM may indeed be a good tool for realistic assessment of the expected asset returns. The combination of the Fama-French model and the LCAPM could improve the understanding of equilibrium in the Serbian equity market. Even though previous papers have mostly dealt with examining different factor models of developed or emerging markets worldwide, none of them has tested factor models on the countries of former Yugoslavia. This paper is the first to test the FF model and LCAPM with FF factors in the case of Serbia and the area of ex-Yugoslavia.

Using Stocks or Portfolios in Tests of Factor Models

2019 ◽  
Vol 55 (3) ◽  
pp. 709-750 ◽  
Author(s):  
Andrew Ang ◽  
Jun Liu ◽  
Krista Schwarz
Keyword(s):  
Asset Pricing ◽  
Risk Premia ◽  
Factor Models ◽  
Idiosyncratic Volatility ◽  
Standard Errors ◽  
Coefficient Estimates ◽  
Pricing Models ◽  
Cross Sectional ◽  
Factor Loadings ◽  
Asset Pricing Models

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

scholarly journals Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xi Sun ◽  
Yihao Chen ◽  
Yulin Chen ◽  
Zhusheng Lou ◽  
Lingfeng Tao ◽  
...  
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Return ◽  
Model Comparison ◽  
Factor Model ◽  
Factor Models ◽  
Comparison Results ◽  
Distributional Assumptions ◽  
Stock Return Anomalies ◽  
T Distribution

Factor models provide a cornerstone for understanding financial asset pricing; however, research on China’s stock market risk premia is still limited. Motivated by this, this paper proposes a four-factor model for China’s stock market that includes a market factor, a size factor, a value factor, and a liquidity factor. We compare our four-factor model with a set of prominent factor models based on newly developed likelihood-ratio tests and Bayesian methods. Along with the comparison, we also find supporting evidence for the alternative t-distribution assumption for empirical asset pricing studies. Our results show the following: (1) distributional tests suggest that the returns of factors and stock return anomalies are fat-tailed and therefore are better captured by t-distributions than by normality; (2) under t-distribution assumptions, our four-factor model outperforms a set of prominent factor models in terms of explaining the factors in each other, pricing a comprehensive list of stock return anomalies, and Bayesian marginal likelihoods; (3) model comparison results vary across normality and t-distribution assumptions, which suggests that distributional assumptions matter for asset pricing studies. This paper contributes to the literature by proposing an effective asset pricing factor model and providing factor model comparison tests under non-normal distributional assumptions in the context of China.

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