Factor Pricing Models Research Articles

Deep factor asset pricing with policy guidance based on multi-source heterogeneous information

We propose a novel latent factor pricing model to extract latent pricing factors and corresponding factor loadings from multi-source heterogeneous information through a deep learning architecture. Notably, we pioneer the extraction of policy pricing factors from China’s national strategies (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using natural language processing and a dynamic topic model. The proposed mixed-frequency deep factor asset pricing (MIDAS-DF) model learns from mixed-frequency heterogeneous data and captures nonlinear joint patterns between inputs and outputs, providing more nuanced insights into asset pricing. The empirical analyses of the Chinese A-share market from January 1, 2003 to July 31, 2022 show that the MIDAS-DF model outperforms competing models in pricing individual stocks, various test portfolios, and investment portfolios. The results also demonstrate that low-frequency policy information anchors long-term pricing trends, while high-frequency market and sentiment information refine short-term pricing accuracy. They work together to enhance the pricing performance.

Adaptive Testing for Alphas in Conditional Factor Models with High Dimensional Assets

This article focuses on testing for the presence of alpha in time-varying factor pricing models, specifically when the number of securities N is larger than the time dimension of the return series T. We introduce a maximum-type test that performs well in scenarios where the alternative hypothesis is sparse. We establish the limit null distribution of the proposed maximum-type test statistic and demonstrate its asymptotic independence from the sum-type test statistics proposed by Ma et al. Additionally, we propose an adaptive test by combining the maximum-type test and sum-type test, and we show its advantages under various alternative hypotheses through simulation studies and two real data applications.

Is illiquidity priced in an international factor pricing model? A dynamic panel data application with robust IV

AbstractIn the setting of a dynamic panel data framework, we investigate the international five‐factor Fama–French (2017) model augmented with traditional illiquidity factors (Amihud, Journal of Financial Markets, 2002, 5, 31–56; Amihud, Critical Finance Review, 2019, 8, 203–221; Pástor and Stambaugh, Journal of Political Economy, 2003, 111, 642–685; Pástor and Stambaugh, Critical Finance Review, 2019, 8, 277–299) to determine if any of these factors are priced. Since illiquidity measures are endogenous, we propose an algorithm that generates robust instruments which are combined with a GMM estimator to cope with both the endogeneity issues surrounding illiquidity and other eventual specification errors. In this dynamic framework, we generally find that the most significant factors correspond to market and size but illiquidity may matter depending on the level of the beta. We find that illiquidity has more impact on returns in expansion than in recession. However, the bid‐ask spread seems to behave differently from the other illiquidity measures.

A factor pricing model based on double moving average strategy

The heterogeneous market hypothesis states that there are different types of investors with different investment term-structures in the stock market. Based on the hypothesis, we analyze the trading strategies of moving averages with different term structures, and we find that the trading strategies formed by the 1-month short-term moving average with 3-, 6-, 9-, 12-, 18-, and 24-month long-term moving averages can gain significant excess returns in the Chinese market. Accordingly, this paper adopts the moving average factor formed by the method of Liu’s to expand the current four-factor pricing model of the Chinese market. The results of spanning regression show that the introduction of the short-term average with a period of 1-month and the long-term moving average with a period of 3- and 12-month can significantly improve the pricing power. The GRS test developed by Gibbons et al. (1989) also shows that the augmented six-factor model with moving average factors can pass the test at the significance level of 5%, and the average absolute value of intercept terms decreases by about 50% compared with the results of the four-factor model. At the same time, under different macro-state dependence, the overall performance of the augmented six-factor model with moving averages is still better than that of the four-factor pricing model proposed by Liu et al.’s. Our main contribution is to introduce double moving average factors to capture the behaviors of investors’ different term structures and add these factors to the most competitive asset pricing model for enhancing the pricing power. It is of great significance to supplement the pricing model in the Chinese market.

Adaptive Testing for Alphas in High-Dimensional Factor Pricing Models

This article proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Because the market’s inefficient pricing is likely to occur to a small fraction of exceptional assets, we develop a testing procedure that is particularly powerful against sparse signals. Based on the high-dimensional Gaussian approximation theory, we propose a simulation-based approach to approximate the limiting null distribution of the test. Our numerical studies show that the new procedure can deliver a reasonable size and achieve substantial power improvement compared to the existing tests under sparse alternatives, and especially for weak signals.

Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance

This paper proposes a new parametric approach for estimating linear factor pricing models with dynamic risk premia. Time-varying risk prices and exposures follow an observation-driven updating scheme that reduces the one-step-ahead prediction error from a cross-sectional factor model at the current observation. This agnostic approach is particularly useful in situations where predictors are unknown or of uncertain quality. Updating schemes for elliptically distributed returns are derived and propose cross-sectional regression errors as driving sequence for the parameter dynamics. Estimation and inference are performed by likelihood maximization. A simulation study confirms that the novel method is capable of filtering and predicting substantial risk price movements. The empirical performance of the method is illustrated by an application to a panel of equity portfolios.

A factor pricing model based on machine learning algorithm

We adopt Discrete Wavelet Transform (DWT) and Support Vector Regression (SVR) algorithm to predict stock returns and form a time-varying Machine Learning (ML) factor based on the predicted returns for improving the classical asset pricing in the Chinese stock market from 2000 to 2020. The results show that the sorted portfolios formed by the predicted return rates can obtain significant excess returns in the Chinese market. Furthermore, our research shows that incorporating the ML factor into the CH4 and FF5 model can improve the pricing power significantly. It indicates that the ML factor can complement the traditional pricing models. We also find the performance of factor models depend on macroeconomy and market sentiment, that is, the better the macroeconomic and stock market performance, the stronger the pricing power of the factor models in the Chinese market. Additionally, we explore whether ML factor falls under short-term, median-term, and long-term momentum and reversal factors, and our analysis demonstrates that the ML factor is more effective than momentum and reversal factors.

Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text

Abstract We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities

Abstract This article considers tests of alpha in linear factor pricing models when the number of securities, N, is much larger than the time dimension, T, of the individual return series. We focus on class of tests that are based on Student’s t-tests of individual securities which have a number of advantages over the existing standardized Wald type tests, and propose a test procedure that allows for non-Gaussianity and general forms of weakly cross-correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. We also show that the proposed test can account for some limited degree of pricing errors allowed under Ross’s arbitrage pricing theory condition. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe–Lintner capital asset pricing model and Fama–French three and five factor models are found mainly during the period of Great Recession (2007M12–2009M06).

Green bonds as a bridge to the UN sustainable development goals on environment: A climate change empirical investigation

AbstractThe United Nations Sustainable Development Goals (SDGs) made an urgent call for action by all the countries across the globe, with an aim to end poverty, improve health and education, reduce inequality, and spur economic growth – all of this is intended to be achieved while tackling climate change and working to protect environment and preserve earth. However, these goals cannot be achieved unless money is mobilised to finance climate change mitigation and adaptation efforts across the world. In response, various manifestations of green bonds have appeared in the market and these are considered as a bridge to the achievement of the SDGs – this is because climate mitigation and adaptation are integral to successful implementation of the SDGs. Using the Capital Asset Pricing Model, Fama–French Three Factor, Carhart Four Factor and Fama–French Five Factor pricing models, our study provides empirical evidence that the announcement of green bonds issuance lead to positively abnormal return on stocks. We divided our dataset into two parts. The first part of the dataset is from 01/01/2013 to 30/06/2018 and later part analyses the period from 01/07/2018 to 30/06/2022. The consistent results highlight the firms' and investors' efforts towards climate action (SDG13) and strongly suggest that green bonds play an important role as a bridge to the SDGs.

A One-Sided Refined Symmetrized Data Aggregation Approach to Robust Mutual Fund Selection

We consider the problem of identifying skilled funds among a large number of candidates under the linear factor pricing models containing both observable and latent market factors. Motivated by the existence of non-strong potential factors and diversity of error distribution types of the linear factor pricing models, we develop a distribution-free multiple testing procedure to solve this problem. The proposed procedure is established based on the statistical tool of symmetrized data aggregation, which makes it robust to the strength of potential factors and distribution type of the error terms. We then establish the asymptotic validity of the proposed procedure in terms of both the false discovery rate and true discovery proportion under some mild regularity conditions. Furthermore, we demonstrate the advantages of the proposed procedure over some existing methods through extensive Monte Carlo experiments. In an empirical application, we illustrate the practical utility of the proposed procedure in the context of selecting skilled funds, which clearly has much more satisfactory performance than its main competitors.

New evidence on Bayesian tests of global factor pricing models

To identify the best global factor pricing model is crucial in international asset pricing literature. This study adopts the Bayesian methods of Chib et al. (2020b) and Chib and Zeng (2020) to estimate and compare 14,322 Gaussian and Student-t distributed global factor pricing models. We find strong evidence that Student-t distributed models significantly outperform Gaussian distributed models in both in-sample and out-of-sample tests. This finding highlights the importance of using the Student-t distributions to model the fat tails in global risk factor data. Analysis reveals that the best global factor pricing model is a Student-t distributed factor model with three degrees of freedom and seven risk factors including the six factors of Fama and French (2018) and the betting against beta (BAB) factor of Frazzini and Pedersen (2014). Our results are robust for different estimation samples and in both relative and absolute pricing performance tests.

Green Incentive in Stock Market in China from the Perspective of Enterprise Green Information Disclosure

China's green financial system is developing towards the goal of green sustainability, which depends on the sustainable development of green enterprises. This paper identifies the green degree of enterprises from the perspective of enterprise green information disclosure, divides green enterprises and non green enterprises, and empirically tests whether there is green incentive in China's stock market, so as to judge whether China's stock market can allocate resources effectively. Firstly, based on the concept of green sustainable development, green factor and green efficiency factor are constructed, which are introduced into the three factor model of Chinese version respectively, and the four factor model of Chinese version is constructed. Secondly, take the A-share listed companies in Shanghai and Shenzhen from 2009 to 2021 as the sample for empirical analysis to study the green incentive phenomenon and influencing factors in China's stock market. It is found that the Chinese version of the four factor model can better fit China's stock market than the existing factor pricing model. There is a green incentive effect in China's stock market, that is, the stock market produces higher excess return for green enterprises, and the higher the green efficiency of enterprises, the higher the excess return.

Capital Structure Priority Effects in Durations, Stock-Bond Comovements, and Factor Pricing Models

Abstract We show theoretically and empirically that the durations of corporate securities are monotonically related to their capital structure priority, with equity often having a negative duration. The magnitude of this effect increases with firm leverage. We use these insights to challenge existing results on stock-bond comovements and factor pricing. For example, though overlooked, higher leverage and lower priority reduce the correlation between corporate security and government bond returns, and these variables explain time-series and cross-sectional variation in correlations; traditional market model regressions significantly understate corporate bond betas; and regressions on standard term and default factors dramatically overstate interest rate and default risk. (JEL G12, G13)

High-dimensional test for alpha in linear factor pricing models with sparse alternatives

We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the time dimension of the return series, and the alternative hypothesis is sparse, i.e. the alpha vector is sparse. As a general alternative, we suggest to combine the max-of-squares type test and a sum-of-squares type test, to benefit from the power advantages of both tests. We apply the two proposed tests to the monthly returns on securities in the Chinese and the U.S. stock markets, respectively under the Fama–French three-factor model, and confirm the usefulness of the proposed tests in detecting the presence of alpha.

Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China

ABSTRACT This paper examines the performance of three famous factor pricing models in markets of bull, bear, and consolidation in China. Empirical results show that these models explain the time-series variations in portfolio returns in bearish market reasonably well, but fail to explain the cross-sectional variations. Another two findings are revealed by instability tests. First, the three models are more unstable in trending (i.e., bearish and bullish) markets under time-series regression due to the higher stock price synchronicity. Second, greater instability causes the unitary parameter estimates less reliable and brings about difficulties in explaining the cross-sectional portfolio returns in trending markets.

Understanding momentum and reversal

Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary significantly over time, even at short horizons over which the momentum phenomenon operates (one year), and this variation captures reliable conditional risk premia missed by other factor models commonly used in the literature. Our estimates of conditional risk exposure can explain a sizable fraction of momentum and long-term reversal returns and can be used to generate even stronger return predictions.

Optimisation of Time-Varying Asset Pricing Models with Penetration of Value at Risk and Expected Shortfall

This study aims to apply value at risk (VaR) and expected shortfall (ES) as time-varying systematic and idiosyncratic risk factors to address the downside risk anomaly of various asset pricing models currently existing in the Pakistan stock exchange. The study analyses the significance of high minus low VaR and ES portfolios as a systematic risk factor in one factor, three-factor, and five-factor asset pricing model. Furthermore, the study introduced the six-factor model, deploying VaR and ES as the idiosyncratic risk factor. The theoretical and empirical alteration of traditional asset pricing models is the study’s contributions. This study reported a strong positive relationship of traditional market beta, value at risk, and expected shortfall. Market beta pertains its superiority in estimating the time-varying stock returns. Furthermore, value at risk and expected shortfall strengthen the effects of traditional beta impact on stock returns, signifying the proposed six-factor asset pricing model. Investment and profitability factors are redundant in conventional asset pricing models.

Factor models with many assets: Strong factors, weak factors, and the two-pass procedure

This paper re-examines the problem of estimating risk premia in unconditional linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are local-to-zero, we show that the conventional two-pass estimation procedure delivers inconsistent estimates of the risk premia. We propose a new estimation procedure based on sample-splitting instrumental variables regression. The proposed estimator of risk premia is robust to weak included factors and to the presence of strong unaccounted cross-sectional error dependence. We prove the consistency of the new estimator, establish asymptotically valid inferences using Wald statistics, verify performance of the new procedure in simulations, and revisit some empirical studies.

Multilayer Network Risk Factor Pricing Model

This paper proposes a multilayer network risk factor pricing model to depict the impact of interactions between stocks on excess stock returns by constructing the network risk factor based on the stock multilayer network and introducing it to the traditional three-factor pricing model. According to China’s stock market data, we find that compared with the traditional three-factor model, the multilayer network risk factor pricing model can achieve higher fitting degree. Meanwhile, the multilayer network risk factor has a significant positive impact on the excess stock returns in most cases.