Asset Pricing Factors Research Articles

ESG investment performance and global attention to sustainability

We analyze ESG-based investments in stocks across 23 developed markets using daily data from 2004 to 2022. The findings suggest a weak relationship between the ESG ratings and expected returns, with some evidence of modest underperformance of high ESG stocks compared to lower-rated ones in specific periods. This outcome indicates that stock prices have already reflected ESG information, and well-known asset pricing factors can effectively capture the returns of portfolios based on ESG ratings. However, the strength of this relationship depends on global attention to sustainability, where high ESG-rated stocks tend to gain advantages during unexpected attention increases, highlighting the dynamic, nonlinear nature of this relationship.

Product network and origin of common equity factor risks

This paper extends the multisector asset pricing model based on production networks, achieving direct pricing of a firm’s equity assets at a more microscopic level within the product network. An Equilibrium-Aligned PageRank (EA-PR) centrality algorithm is proposed, and it is theoretically proven that EA-PR centrality can reflect the relative value of products and further derive the value of firms. A new equity asset pricing factor is constructed based on the EA-PR centrality of firms. The key findings can be summarized as follows: Firstly, the EA-PR centrality of firms shows a significant positive linear predictability for equity returns, and the returns associated with the EA-PR centrality factor cannot be fully explained by the Fama–French 6 (FF6) factors. Secondly, the EA-PR centrality factor demonstrates significant explanatory power for the FF6 factors and, to some extent, exhibits predictive ability for future returns of the FF6 factors. Thirdly, the product network model can effectively reflect the market competitiveness and growth potential of firms, and firms with high EA-PR centrality have stronger value capture capabilities, improving gross profit margin, cash flow, and investment growth in the future. Finally, this study empirically demonstrates the consistency between the product network model and the pricing theory of the q5 model, which incorporates the expected growth factor.

Testing Illiquidity and Momentum Factors with Asset Pricing: Evidence from Emerging Capital Markets of South Asia

This paper explores the asset pricing factors which pose aggregate risk to the portfolio returns in the Pakistan Stock Exchange (PSX). The purpose is to test the illiquidity and momentum factors along with the well-documented three factors in asset pricing model using Fama and French (2015) and Lam and Tam (2011) for one of the emerging markets of South Asia, i.e., Pakistan, using 20-year monthly data from January 2001 to December 2020. This paper adopts the methodology of Fama and French (2015) as used in the developed market and Lam and Tam (2011) for the emerging market phenomenon, to formulate the portfolio returns based on Size, Book-to-Market(BM), and the Liquidity, and test these returns against the factors causing the return variations. The results based on the 3-factor model, liquidity-adjusted model, and the momentum-based model validate factors of excess market return (CAPM), size, value, liquidity, and momentum as significant risk factors in explaining the variations in portfolio returns of PSX stocks. The portfolio access returns for PSX stocks are adjusted for risk factors with mixed significant response holding average explanatory power. Our results are robust to the GRS F-test.

Comprehensive Asset Pricing Tests in the Korean Stock Market

AbstractWe examine the empirical performance in the Korean stock market of three new asset pricing factor models: the Stambaugh–Yuan (2017) mispricing factor model, the Daniel et al. (2020) three‐factor model, Barillas‐Shanken (2018) six‐factor model and the Hou et al. (2021) q5‐factor model. We find that all factors in these factor models have significantly positive risk premiums and are not explained by the Fama–French six‐factor model. Compared to other prevalent models, the q5 model shows the highest maximum Sharpe ratio, mainly due to its profitability and expected growth factors. Further, the q5 model exhibits superior performance in explaining the returns of 97 anomaly portfolios in the Korean market.

Is there more to asset price linkages in China than meets the eye: Cross-asset momentum and the role of hybrid funds

Within publicly offered funds, hybrid funds combining stocks and bonds offer investors returns while mitigating risks through capital flows between these markets. Our study, analyzing China's financial market data from 2006 to 2022, uncovers a cross-asset momentum relationship between stocks and bonds. Portfolios leveraging cross-asset momentum demonstrate excess returns unexplained by other asset pricing factors. Our analysis highlights the intermediary role of hybrid funds in this cross-asset momentum transmission mechanism. Funds with more flexible asset allocation ratios play a more crucial intermediary role. These insights hold significant implications for investors aiming to optimize asset allocation across diverse markets and for policymakers aiming to bolster China's financial market efficiency.

On the importance of asset pricing factors in the relative valuation

In this study, we examine the implications of multi-factor asset pricing models in corporate valuation, focusing on the relative valuation approach. Our investigation centers around an eight-factor model, which we compare against traditional valuation methods that rely on industry or risk grouping and cross-sectional averages. The purpose of this comparison is to assess the efficacy of incorporating pricing factor variables into the valuation process and to understand their impact on valuation accuracy. The findings of our analysis suggest that the use of the eight-factor model leads to a reduction in estimation errors of valuation multiples, indicating an important improvement in precision compared to conventional valuation methods. Additionally, our research offers insights into the practicality of applying multi-factor models. It contributes to the intersection between empirical asset pricing and corporate finance by providing a detailed evaluation of advanced valuation models and their relevance in improving the accuracy of relative valuation.

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.

Realized semibetas in the Australian stock market

ABSTRACT This study examines the explanatory power of the CAPM and downside risk asset pricing models (the downside beta and the realized semibeta models) for the next-month firm-level cross-sectional stock return variation in the Australian stock market. We show that the CAPM beta, downside beta, semibeta BetaNP, and semibeta BetaNN negatively predict future stock returns, which is inconsistent with the findings in the original study by Bollerslev, Patton, and Quaedvlieg (2022). The BetaNN measures the individual stock movement in the same direction as the downward stock market, while BetaNP measures individual stock downward with the upward stock market. These findings are robust, not subsumed by conventional cross-sectional asset pricing factors, and consistent with the existing Australian downside risk study.

Measuring Intangible Capital with Market Prices

Accounting standards prohibit internally created knowledge and organizational capital from being disclosed on firm balance sheets. As a result, balance sheets exhibit downward biases that have become exacerbated by increasing levels of intangible investments. To offset these biases, researchers must estimate the value of these off-balance sheet intangibles by capitalizing prior flows of research and development (R&D) and selling, general, and administrative (SG&A). In doing so, a set of capitalization parameters must be assumed (i.e., the R&D depreciation rate and the fraction of SG&A that represents a long-lived asset). We estimate these parameters using market prices from firm exits and use them to capitalize intangibles for a comprehensive panel of firms from 1978 to 2017. We then use a series of validation tests to examine the performance of our intangible capital stocks versus those developed from commonly used parameters. On average, our estimates of intangible capital are 15% smaller than estimates from status quo parameters while exhibiting larger variation across industry. Intangible capital stocks derived from exit price parameters outperform existing measures when explaining market enterprise values and identifying human capital risk. Adjusting book values with exit-based intangible capital stocks markedly attenuates well-documented biases in market-to-book and return on equity ratios while increasing the precision of the high-minus-low asset pricing factor. We conclude that our capitalization parameters create intangible stocks that perform equal to or better than status quo measures in various applications. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02058 .

Cross-asset momentum and the hybrid fund transmission mechanism in China's stock and bond markets.

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China's financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market's return, while the momentum in the bond market positively influences the stock market's return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China's financial market.

Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter

Abstract In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-ACB) model from the literature on score-driven models. In the new asset pricing model, named the t generalized ACB (t-GACB) model, a multivariate score-driven filter for the t-distribution drives dynamic interaction effects among the beta coefficients. We present the econometric formulation and statistical inference for the t-GACB model, which we apply to 15 stocks from the United States (US) from 1999 to 2022. In our empirical application, we use the three Fama–French factors as asset pricing factors, and we also use the Volatility Index, TED Spread, and ICE BofA US High Yield Index Option-Adjusted Spread as exogenous explanatory variables that influence the beta coefficients. We compare the statistical and realized tracking error performances of the Gaussian-ACB, t-ACB, and t-GACB models, and we find that the t-GACB model improves the Gaussian-ACB model.

The sustainability factor in asset pricing: Empirical evidence from the Indian market

This study investigates the feasibility of including the sustainability performance of firms in the asset pricing problem. The data of 500 firms from the NIFTY 500 index are used for this study. The stock prices and financial data are downloaded from the CIME database. The sustainability factor is computed using the ESG scores from the Bloomberg database. In order to test the influence of the sustainability factor, the Fama–French Five-Factor model is extended by including the sustainability factor as an additional factor. The dependent variables are the excess returns on 36 size and book-to-market ratio sorted portfolios, 36 size and operating profitability sorted portfolios, and 36 size and investment sorted portfolios. The impact of the sustainability factor on excess portfolio return is tested using the Fama–MacBeth two-pass regression and the Fama–French methodology. The results show that the price of ESG risk (or ESG risk premium) is positive, indicating that firms with lower ESG performance yield more returns than those with higher ESG performance. About one-third of the portfolios witness the significant impact of the sustainability factor on their returns. However, the insignificant relationship in two third of the portfolios between the sustainability factor and excess portfolio returns conveys that in the Indian market, corporate investors have the flexibility to decide on ESG investment. Smaller firms are exposed to a higher ESG risk, and Firms which do not integrate environmental and social costs into their strategies may bear a higher cost of equity.

Reflections on Asset Pricing Factors: A Machine Learning-Based Perspective

In recent years, scholars have explored hundreds of asset pricing factors built upon the foundation of the Fama five-factor model, sparking widespread discussion in the academic community. Simultaneously, the advancement of machine learning techniques has brought innovation to asset pricing factors. This article provides an overview of typical asset pricing factors and the application of machine learning in pricing models. It begins by discussing the construction of new asset pricing factors and then delves into the innovations brought about by machine learning in asset pricing models. The diversity of factors increases the fit of asset pricing models but also presents corresponding challenges. The application of machine learning techniques addresses issues such as overfitting in pricing models, further enhancing model effectiveness. The primary value of this article lies in summarizing various perspectives on asset pricing factors in recent years, exploring the significant applications of machine learning in asset pricing models, and providing a forward-looking view on the development of asset pricing models.

Chasing noise in the stock market: an inquiry into the dynamics of investor sentiment and asset pricing

This study explores the inclusion of sentiment measures as a risk factor in asset pricing. Using UK market data for the period January 1993 to December 2020, we create a new sentiment variable, and construct both raw and clean sentiment indices from a principal component analysis of a variety of literature-acknowledged sentiment proxies. Essentially, the model estimations are categorized into two: first, the study documents the performance of the traditional pricing models on portfolios formed on different characteristics. Second, the study augments the first category by iterating sentiment variables into the model specification. The findings reveal that sentiment-augmented asset pricing models outperform the traditional models in explaining the excess returns of the portfolios. Furthermore, using Hansen & Jagannathan (1997) non-parametric model performance technique, we observe that the sentiment-induced models produce a small distance error compared to the traditional models, thus validating the use of sentiment measures in our pricing mechanism. It is therefore opined that extant asset pricing models may not be sufficient to explain market or pricing anomalies. Investors’ sentiment is an important systematic risk factor that possesses useful information, and by implication, market analysts and stakeholders must take serious cognizance of its propensities when forecasting risk-adjusted returns.

Macroeconomic Announcements: How Announcements Shape Trading Strategies

The Capital Asset Pricing Model (CAPM) is a widely used and tractable model relating financial risk and return. However, it has been less successful when taken to the data. In their seminal paper, Frazzini and Pedersen propose a Betting Against Beta (BAB) factor to take advantage of this fact. In their construction, the BAB factor is a portfolio which longs low-beta stocks and shorts high-beta stocks. Since its construction, the BAB factor has been widely cited and used both in financial academics and in industry. This paper proposes a novel betting on and against beta (BOAB) strategy, which bets on beta on days with macroeconomic announcements and bets against beta on days without. Here, macroeconomic announcement days are defined as days when inflation, unemployment, or interest rate decisions are released. Our findings confirm the persistent positive beta-return relationship observed on macroeconomic announcement days. We show that compared to the BAB strategy, BOAB delivers higher average daily excess returns over the time period 1964 to 2021 when applied to the U.S. stock market. This outperformance of the BOAB strategy is robust to different constructions of the BAB factor and is economically and statistically significant when compared with the usual asset pricing factors such as value, size, and momentum. The outperformance of the BOAB strategy could be used to inform agents’ portfolio allocation choices. Limitations are also discussed.

Conditional autoencoder pricing model for energy commodities

We propose a conditional latent factor asset pricing model for energy commodities (CAE) that uses a modified conditional autoencoder neural network to capture the non-linear relationship between latent factors and factor loadings. In addition to spot prices, we incorporate 127 macroeconomic and 598 energy information characteristics to extract the factor loadings. The empirical results demonstrate the high-quality performance of the model in out-of-sample testing. Furthermore, by analyzing characteristic importance, we find that energy information characteristics, particularly coal, electricity, and crude oil and natural gas resource development, play a dominant role in explaining the excess returns of energy commodities.

ESG controversy as a potential asset-pricing factor

We examine whether ESG and ESG controversy scores are pricing factors that systemically affect the risk-return relationship of stocks. We test their additional explanatory power after controlling for other well-known risk factors. ESG controversy risk factor explains the cross-section of stock market returns. Investors exposed to ESG controversies demand a risk premium whereas ESG risk is regarded as idiosyncratic.

Asymmetric and Lag Effects of Industry Risk Factors on the Malaysian Oil and Gas Stocks

This study examines asymmetric and the lag effects of oil price, gas price, and exchange rate on stock performance of the Malaysian oil and gas sub-industries. Using company-level data and multi factor asset pricing models, this study found that oil price, gas price, exchange rate, and common systematic risk factors such as market, size, and book-to-market have significant effects on the stock performance of Malaysian oil and gas sub-industries, but these exposures remain heterogeneous. Oil price and exchange rate show strong asymmetrical impacts on stock performance when decomposed into oil price increases (decreases) and exchange rate depreciation (appreciation). Increases in oil price and exchange rate depreciation have a greater impact on stock performance of oil and gas industry than decreases in oil price and exchange rate appreciation. Oil price, gas price, and exchange rate exhibit lag effects on the performance of oil and gas stocks. The findings have important asset pricing implications for profitable investment and portfolio strategies based on oil and gas risk factors for oil-exporting country.JEL Classifications: C13; C58; G10; G12; G14; Q4

Climate change and blue returns: Evidence from Niche firms in China

Environmental degradation and climate change require a coordinated effort to mitigate their impacts. Water and ocean-related companies are impacted by climate change, and their stock returns may be influenced by factors related to the circular economy. We study this phenomenon by selecting a comprehensive sample of blue firms listed on the two main stock exchanges in China. Our assessment shows that the blue returns are influenced negatively by the sea temperature anomaly and positively related to climate change performance. The results remained robust after controlling for conventional asset pricing factors. These findings have important implications for investors and policymakers.

Is There a Replication Crisis in Finance?

ABSTRACTSeveral papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication that leads to different conclusions. The majority of asset pricing factors (i) can be replicated; (ii) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio; (iii) work out‐of‐sample in a new large data set covering 93 countries; and (iv) have evidence that is strengthened (not weakened) by the large number of observed factors.