Traditional Asset Pricing Models Research Articles

Anomalies and Multi-Factor Models in the Chinese Market

A stock market anomaly refers to the occurrence of excess returns in stocks that cannot be fully explained by traditional asset pricing models or the efficient market hypothesis. Studying these anomalies allows scholars to gain deeper insights into the stock market and provides investors with opportunities to capitalize on these irregularities for higher returns. In this paper, the size and value factors in the Chinese stock market are empirically tested for the period from January 2002 to December 2021, leading to several conclusions. First, the CH-3 model is found to be more suitable for the Chinese market compared to the FF-3 model. Additionally, the interpretation of anomalies using CH-3 differs from prior research, particularly regarding the CH-4 model, which incorporates a share turnover factor. These differences may stem from potential data mining effects.

Common investor coverage and excess return comovement: Evidence from Seeking Alpha

Investors often cover a limited number of stocks, leading to a certain level of correlation in their information sets. Despite its importance, the role of this correlation in determining the return comovement between stock pairs has been less explored. In this study, we introduce a novel measure of common investor coverage, calculated using the number of co-commentators normalized by the geometric average number of commentators for each stock pair on Seeking Alpha. This measure aims to capture the underlying correlation in investors’ information sets. Our findings reveal a positive relationship between common investor coverage and excess comovement, which cannot be explained by traditional asset pricing models. Moreover, we observe a stronger relationship between common investor coverage and excess return comovement for stocks favored by retail investors, particularly after the implementation of the reward policy in 2011. This suggests that retail investors may play a crucial role in driving this relationship.

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

An Empirical Research on the Performance of Asset Pricing Models in the Chinese A-Share Market

Asset pricing remains a cornerstone of financial field, providing a framework to understand the anticipated returns of asset portfolios and individual assets. Its widespread application spans academic discourse and practical financial methodologies. This study conducts an empirical analysis of the Chinese A-share Market, utilizing transaction data from the A-share market spanning from 1994 to 2024. It examines the applicability of the traditional asset pricing models within the Chinese stock market. Through classic regression analyses of the CAPM, the Fama and French three-factor model (FF3), the Fama and French five-factor model (FF5), this study verifies that the FF5 provides a superior explanation of stock expectations for the A-share market during this period compared to other traditional asset pricing models. The conclusions of this study indicate that, among the CAPM, FF3, and FF5 models, the number of parameters taken into account by the model and its capacity to explain asset returns are positively correlated.

Co-volatility dynamics in global cryptocurrency and conventional asset classes: a multivariate stochastic factor volatility approach

PurposeThis study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.Design/methodology/approachIt used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.FindingsThe study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.OriginalityThis study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.

An Empirical Research of Asset Pricing Models in the US Market under COVID-19

Asset pricing theory has been a key area of research in finance. Since the in-troduction of CAPM, asset pricing models have been very widely used. Re-searchers have discovered many phenomena that cannot be explained by the CAPM model, so they have made many refinements to the asset pricing model and developed various improved versions that can better explain stock returns. The Fama and French three-factor model (FFTFM) and the five-factor model (FFFFM) are two of the most classic and most used asset pricing models. However, as time goes by and financial markets continue to evolve, many scholars have questioned whether traditional asset pricing models are still valid. Starting from the theoretical explanation of CAPM, FFTFM, and FFFFM factor selection, this paper uses the stock market data from 2000 to March 2023 to divide stocks into six portfolios based on their size and book value and conduct regressions according to the above three models respectively to evaluate and compare the recent performance of these models. In addition, this paper also performs separate regressions using data from the novel coronavirus epidemic period in particular to verify the robustness of the model to different market environments. According to the results of this paper, the FFFFM model has the best explanatory power. The role of the factors in the model on stock returns varies with portfolio selec-tion, i.e., firm size and book value, and the market environment also has an impact on the significance of the factors, leaving room for improvement in the model to cope with particular markets, but overall, FFFFM is still effec-tive in explaining present market stock prices.

Conditional autoencoder asset pricing models for the Korean stock market.

This study analyzes the explanatory power of the latent factor conditional asset pricing model for the Korean stock market using an autoencoder. The autoencoder is a type of neural network in machine learning that can extract latent factors. Specifically, we apply the conditional autoencoder (CA) model that estimates factor exposure as a flexible nonlinear function of covariates. Our main findings are as follows. The CA model showed excellent explanatory power not only in the entire sample but also in several subsamples in the Korean market. Also, because of this explanatory power, it can better explain market anomalies compared to the traditional asset pricing models. As a result of examining investment strategies using pricing error, the CA model measures the expected return of stocks better than the traditional asset pricing model. In addition, the CA model indicates that the firm characteristic variables are important in asset pricing conditional on macro-financial states, such as the global financial crisis and the coronavirus disease 2019 pandemic. The result shows that the major variables considered in the explanation of stock returns through the CA model may vary depending on the time. This is expected to provide a broader perspective on asset pricing through the CA model in the future.

Beyond Fama-French Factors: Alpha from Short-Term Signals

Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by applying a combination of signals to a liquid global universe and with advanced buy/sell trading rules that mitigate transaction costs. The composite model consists of short-term reversal, short-term momentum, short-term analyst revisions, short-term risk, and monthly seasonality signals. The resulting alpha is present in out-of-sample and post-publication periods and across regions, translates into long-only applications, is robust to incorporating implementation lags of several days, and is uncorrelated to traditional Fama-French factors.

Is the environmental, social and corporate governance score the missing factor in the Fama-French five-factor model?

Background: Companies are increasingly encouraged to focus on the creation of sustainable value. Financial research institutions evaluate companies’ performance based on pre-established indicators relating to environmental, social and governance (ESG). These scores are intended to inform decisions by equity investors, among others. However, traditional asset pricing models do not include ESG scores.Aim: The purpose of this research is to discover whether the inclusion of ESG scores in the Fama-French five-factor model (FF5F) will improve the model’s predicting power.Setting: Financial research institutions aim to improve the information environment in the South African capital markets. Johannesburg Stock Exchange (JSE)-listed firms are also required to produce integrated reports, emphasising responsible investment.Method: For the largest 40 JSE-listed companies, data over the 5-year period from 2015 to 2019 were employed to compare the predicting power of the FF5F model before and after the inclusion of ESG scores.Results: The results showed that the predictive power of the FF5F model is only marginally improved when the ESG scores are incorporated.Conclusion: These findings indicate that equity returns are not significantly influenced by ESG scores. This research provides the basis for further endeavours on the share-price implications of ESG performance.Contribution: This research contributes to the growing strand of literature on responsible investment and the creation of sustainable value. The research also offers a theoretical contribution by connecting literature on asset pricing with work on sustainability.

Construction and Analysis of Individual Stock Sentiment Anomalies in the A-share Market

Behavioral finance heralds investor sentiment as an anomaly in traditional asset pricing models, and the financing and financing business is an important indicator of individual stock sentiment. In this paper, this paper try to use the growth rate of monthly balance of financing and financing securities as a factor SEN to explain stock returns in the context of Chinese A-share market. This paper finds that market capitalization and SEN have a significant inverse monotonicity, and their information crosses to a large extent to make SEN redundant. This paper argues that the reason for the redundancy of SEN due to market capitalization is that the A-share stock market has set a high margin for financing and financing business, which leads to a weaker liquidity of market transactions and limits the investment choices of rational investors to stocks with relatively small market capitalization. Based on the conclusion, this paper suggests that small investors adopt a strategy of going long on small-cap stocks and short on large-cap stocks.

The use of three-factor model to make financial portfolio and help clients’ decisions

minimizing the risks involved in investing and also increasing the chance of making profits are the most essential two factors in investment. In order to make profits and reduce risks, more and more people tend to find professional managers to make financial portfolios since they are the ones who understand clients’ financial needs and suggest the best and unique investment strategies. During the calculation, we choose the three-factor model over CAPM. The traditional asset pricing model, known formally as the CAPM uses only one variable to describe the returns of a portfolio or stock with the returns of the market as a whole. In addition, generally speaking, the Fama-French Three-Factor Model provides a much better explanation for cross-sectional variations in stock returns in a number of countries. Our paper discusses the advantages and disadvantages of three-factor models compared to CAPM, and the use of three-factor model.

Revisiting the performance of the scaled momentum strategies

PurposeThe main purpose of this study is to enunciate the underlying factors that enhance the performance of scaled momentum strategies.Design/methodology/approachIn previous studies, the negative relationship between the lagged volatility and future return of momentum strategy is exploited to manage the risk. But this negative relationship only holds when volatility is higher, further the volatility is shown to be persistent. The implication of these two characteristics is important and this paper highlights that.FindingsThe higher performance of the scaled momentum strategies for the US market is linked with the length of the investment horizon. The traditional asset pricing models fail to explain this relationship. However, the authors find that the excess variance loaded on the long side of these strategies is one important explanation of this horizon bound performance of these strategies.Practical implicationsThis study highlights that the volatility scaled momentum strategy has higher gains as the investment horizon increases. Therefore, it is an advisable investment strategy for the pension fund industry.Originality/valueMomentum strategy is unique as it fulfils two criteria of performance enhancement through volatility scaling, such as, the persistent in volatility and its negative relationship with the returns. However, the impact on the performance of the negative relationship between volatility and return that only exist in highest volatility related states is not discussed. The authors have shown that this aspect of volatility and return relationship of the momentum strategy has an important bearing on the performance of the volatility scaled momentum strategies.Highlights of the PaperThis study finds that the Sharpe ratios and the alphas of the volatility scaled strategies increase as the investment horizon increases.This is because the volatility series are highly persistent and the negative predictive relationship between the volatility and future momentum returns only exist when the volatility is higher. The impact of these two characteristics of the volatility series on the performance of the scaled momentum strategies is not discussed in the literature.We find that the scaled strategies invest more/less when the volatility of the momentum strategy is lower/higher. By investing less when volatility is higher, the scaled strategies avoid momentum crashes and lessens the contribution of the variance from the short side in the overall variance of these strategies.It is further shown that the higher performance of the volatility scaled strategies, at each investment related horizon can be explained by the higher variance loaded on the long side of such strategies in comparison to the traditional momentum strategy.

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.

A Machine Learning Approach for the Short-term Reversal Strategy

The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

New Evidence to Assess the Asset Pricing Model: An Empirical Investigation Based on Bayesian Network

An extensive, in-depth study of risk factors seems to be of crucial importance in the research of the financial market in order to prevent (or reduce) the chance of developing this return. It represents market anomalies. This study confirms that the [Formula: see text]-factors model is better than the other traditional asset pricing models in explaining individual stock return in the US over the 2000–2017 period. The main focus of data analysis is, on the use of models, to discover and understand the relationships between different factors of risk market anomaly. Recently, Fama and French presented a five-factor model that captures the size, value, profitability, and investment patterns in average stock market returns better than their three-factor model presented previously in 1993. This paper explores a shred of new empirical evidence to assess the asset pricing model through an extension of Fama and French model and a report on applying Bayesian Network (BN) modeling to discover the relationships across different risk factor. Furthermore, the induced BN was used to make inference taking into account sensibility and the application of BN tools has led to the discovery of several direct and indirect relationships between different parameters. For this reason, we introduce additional factors that are related to behavioral finance such as investor’s sentiment to describe a behavior return, confidence index, and herding. It is worth noting that there is an interaction between these various factors, which implies that it is interesting to incorporate them into the model to give more effectiveness to the performance of the stock market return. Moreover, the implemented BN was used to make inferences, i.e., to predict new scenarios when different information was introduced.

Relative performance of religious and ethical investment funds

PurposeThis paper updates the literature regarding the performance of constrained US mutual funds by looking at the relative performance of Christian mutual funds, socially responsible funds and Islamic funds. This paper aims to rank the performance of religious and ethical investment funds.Design/methodology/approachThis study uses monthly returns from 2005 to 2015 to perform traditional asset pricing models as well as data envelopment analysis to determine rank.FindingsIslamic mutual funds outperform socially responsible funds, which then outperform Christian-based mutual funds; these results are also consistent during the latest 2007-2008 crisis period. The results are robust to different performance metrics and benchmarks. Moreover, this paper reports a significant amount of money “left on the table” by investing in constraint funds and disregarding the sin industry which shows an ethical dilemma for investors.Practical implicationsInvestors who seek to invest morally/ethically can be informed of the cost of doing so. They can also compare portfolio with others that have similar holdings and constraints.Originality/valueThis paper not only includes Christian mutual funds in the research but also provides the performance of all constrained assets. It also compares religious funds with “SIN” industry, and thus quantifies the cost of “doing right.”

The role of AI in capital structure to enhance corporate funding strategies

The purpose of this study was to assess if Artificial Intelligence (AI) could be used in the Capital Asset Pricing Model (CAPM) and whether the use of AI could bring a more accurate estimation of expected returns. Cost of capital defines the minimum return expected from any investment made by a firm. Hence for managers to maximise the value of the corporation, it is essential to have an accurate estimation of the cost of capital. For the purpose of analysing securities, the adjusted closing stock prices of 10 high-tech public companies were studied from January 2013 to January 2019. This research assumed that there is a need to predict returns for the next year. Hence one year of historical data was used to calculate traditional CAPM value and also train the Recurrent Neural Networks (RNN) to predict stock prices of the upcoming year. A generic deep learning network architecture was developed with the use of Long Short Term Memory (LSTM) and dropout layers. After calculating the returns using traditional and AI approaches, two methods for calculation of CAPM were proposed and compared. Following the analysis, it was found that the use of AI improved the accuracy of cost of equity estimations by over 60%. The strong ability of the selected deep learning neural network to predict stock prices, increased the accuracy of estimating returns by at least 18%. This study concluded that AI has significant potentials to replace traditional asset pricing models in the near future.

Credit risk – Return puzzle: Evidence from India

Although asset pricing theories predict a positive relation between risk and returns, empirical findings on credit risk-return relationship are mixed. And, observed negative relation between the two in this regard is referred to as credit risk-return puzzle. Using credit rating as a measure of credit risk, we have investigated into the existence of this puzzle in India during July 2011 to March 2019. We have used information for the companies listed on the National Stock Exchange for this purpose. Our results validate the presence of this puzzle in the Indian stock market. Moreover, credit risk is observed to be a systematic risk, which has not been captured in the traditional asset pricing models. We have also observed partial evidence favoring both behavioural and rational pricing explanations—the two widely acknowledged explanations in the literature behind this puzzle. On our further query in this connection, we have not seen any significant change in the puzzle due to the recent enactment of the Insolvency and Bankruptcy Code.

Insurance Cycles, Spanning and Regulation

This paper offers a novel explanation of the financial underwriting cycle in the property-liability insurance industry. By doing so it resolves that significant anomaly in asset pricing theory posed by cycles in the efficient pricing of insurance coverage. In contrast to the reliance on a variety of institutional or capital market failures underlying all previous explanations of this cycle, we directly augment the complete-markets environment of traditional asset-pricing models through the presence of a single source of risk that cannot be fully hedged through existing financial markets. We realistically interpret this source of risk as unforecastable noise in the implementation of insurance regulations. Cycles in the value of underwriting insurance coverage can arise in this simple variant of a standard complete-markets pricing model owing to the effect of such regulatory risk. We offer a sufficient condition for a stable cycle to endogenously exist in market equilibrium and illustrate this condition in the context of a representative insurance firm and a regulator pursuing a countercyclical policy with noisy implementation. Interestingly, while insurance pricing is efficient in the absence of the regulator, cyclic pricing and underwriting profitability can be induced by a countercyclical regulator policy designed to stabilize the very cycle it creates.

Energy shocks pricing model: A non-linear US sectoral based analysis

Traditional asset pricing models have extensively been used; however, determining asset pricing in isolation in the current global environment demands for the inclusion of certain extraneous events. The goal of this study is to measure the impact of disintegrated structural oil shocks, proposed by Kilian (2009) on disaggregated US sectoral returns. Our work is motivated by the influence of global oil shocks on asset returns that are widely documented in the literature. Equity pricing data for nine major US sectors is used on monthly frequency. We apply non-linear Markov Regime Switching framework and report clear regime differences across all sectors. Commonality in behavior for majority of the sectoral returns is observed however consumer discretionary, materials and automobile sector remains sensitive in both regimes contrary to other sectors exhibiting sensitivity in one regime or the other. Results also present robust evidence of oil specific demand shocks implying more uncertainty for US sectoral returns with important investment and policy implication.