Asset Pricing Theory Research Articles

THEORIES OF FINANCIAL INTERMEDIATION: EVALUATION AND EMPIRICAL RELEVANCE

Objective: The process of credits transfer is often facilitated by the financial intermediaries, including banks and non-bank institutions. his article offers an insight on the various theories of intermediation. The first scrutinises the various theoretical conceptualizations found in varied models which demonstrate the roles of intermediaries. The second offers a concise discuss on the relevance of the theories to current situation of intermediation and intermediaries in Nigeria. Method: The paper is based on theoretical reviews. The study used the approach to appraise the issues on financial intermediation and of intermediaries. Result and Discussion: The study finds that financial intermediation is fast changing as new types of economic players enter and new financial tools evolve. Moreso, that the financial markets have become more dominated by extensive intermediaries. We note that the development cryptocurrencies have largely affected the ways of intermediation, and this has effect on asset pricing theory. Implications: The theory and evidence on financial intermediation have important implications for the capital market. Originality: The study offers information related to theoretical perspectives on financial intermediation and intermediaries. The value of the paper is evidenced by the fact that it provides background on financial intermediation and intermediaries. Recommendations: The paper concludes that other means of intermediation that involve decentralised exchanged are continuously developing and this has implications for the current theory of intermediation. The study recommends the need for development of more details theory that consider the nature and effect of decentralised finance and decentralised exchange on the optimality of capital allocation.

The Left-tail Risk and the Cross-section of Stock Returns: Evidence from China

As a matter of fact, left-tail risk is a common risk in stock market. This study examines the linear correlation considering Value-at-Risk (VaR) and future stock returns within context of the Chinese main board stocks, particularly during periods marked by significant left-tail risk. Processing data from March 2014 to March 2024, this study concentrate to daily and monthly cross-section returns, along with other financial statistics. A suitable Fama-Macbeth regression using in this paper to exam the significance of left-tail risk. The key focus is to investigate the effect of VaR concerning future stock returns under downside market conditions, as indicated by the recent 23% drop in China’s benchmark CSI 300 index. The regression analysis indicates a significant negative correlation between VaR and future stock returns, suggesting that higher potential losses as indicated by VaR correspond with lower future returns. This outcome is contrary to traditional asset pricing theories which posit a positive risk-return relationship. The findings will attract attention of investors to consider extreme risk management of public stocks and contributes to the ongoing discussion on risk assessment in financial markets by highlighting the importance of integrating robust risk management tools like VaR in investment strategies, especially in volatile markets.

Mapping the Intellectual Structure of Asset Pricing: A Bibliometric Study

This paper presents a bibliometric analysis of research on asset pricing and identifies and highlights the most significant authors, keywords, articles, and journals based on a systematic literature review and Bibliometric analysis of 915 documents published over 33 years (1989-2022), obtained from the web of science database, to discover the noticeable landscape and research horizons in the field of asset pricing theory. This descriptive study demonstrates an upward trend in ”asset pricing’ papers in business and finance journals. According to the report, the United States is the leading contributor to the ”asset pricing” study, followed by the Netherlands, the United Kingdom, Canada, and France. The authors, papers, and citation-based analyses show that Acharya vv; Albuquerque R, Aih, and Acciaio B are the most effective and influential asset pricing researchers, followed by Affleck Graves J and Akdeniz L. In terms of restrictions, our research is limited to the Web of Science database. Investigations from other databases could be used in future studies. We’ve also limited Bibliometric analysis to a few dimensions. Future research would look at networking from a different perspective. In our analysis, we limited ourselves to simply looking at scientific articles. Despite these flaws, we feel the study has research and management implications

MIT and the Origins of the Modern Theory of Asset Pricing

This article argues that it was no coincidence that one of the places in which the modern theory of finance was developed was the Massachusetts Institute of Technology. Not only had it attracted Paul Samuelson, who had a long-standing interest in the problem, but its academic culture as a science-focused engineering school that fostered a competitive atmosphere and close relations between its economics department and business school was conducive to the development of such theories.

Excessive financialization and “Original Sin Theory”: Redemption from corporate reputation

Empirical studies suggest that excessive financialization that firms invest in a high proportion of financial assets will have a negative impact on firm value. This goes against the capital asset pricing theory, which suggests that high risk can produce high expected return. However, there is no explanation for the negative effect. We have empirically found that financial investments can yield higher expected returns compared with physical investments at any level, which aligns with the capital asset pricing theory. However, excessive financialization can harm a company’s reputation, which can be measured through product and service competitiveness, research and development output, and corporate social responsibility. As corporate reputation is an important intangible asset, excessive financialization has a negative impact on the overall firm value. Furthermore, excessive financialization has a greater negative impact on corporate reputation for firms with high financial leverage and sensitivity to economic policy uncertainty.

Multifactor conditional equity premium model: Evidence from China's stock market

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

Bank credit, consumption risk, and the cross-section of expected returns

Although the consumption capital asset pricing model has an unmatched theoretical purity for assets’ risk, the empirical failure of the model stands as a central finding in asset pricing. In an effort to resuscitate consumption-based asset pricing model, we develop and estimate new conditional consumption capital asset pricing models with bank credit growth and bank credit cycle as conditioning variables. Rather than assuming the constant factor loadings of asset returns on consumption growth, we model time-varying consumption beta as a function of bank credit growth or bank credit cycle. We find that our specifications explain satisfactorily the variation in stock returns across the 25 Fama–French portfolios. Moreover, the strict statistical tests on risk price and explanatory power show that the models compare favorably with other renowned asset pricing models. These findings document that the dispersion of expected returns is the outcome of the degree of reward for consumption risk, thereby being consistent with asset pricing theory.

A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management

We propose a novel portfolio model integrating the Fama–French three-factor model into the Black–Litterman framework, enabling efficient investment strategies. The model surpasses traditional benchmarks, significantly increasing alpha, minimizing estimation error, and improving diversification. Performance improvements are shown by a tripled Sharpe ratio and doubled Certainty Equivalent Return compared to standard models. It maintains stability across different parameters and economic climates, leveraging improved weight adjustment to reduce estimation errors and withstand market volatility. It provides a new perspective for portfolio construction, leveraging long-term insights from asset pricing theory with significant implications.

Dynamic Connectedness between Crypto and Conventional Financial Assets: Novel Findings from Russian Financial Market

In the dynamic landscape of the Russian digital economy and increasing financial openness, crypto assets have emerged as influential players in the financial market. The geopolitical and economic developments after a conflict with Ukraine have presented formidable challenges in the shape of financial and trade sanctions, coupled with the suspension from the SWIFT banking system, has plunged the Russian economy into a precarious situation. The current study delves into the network spillover effects between a prominent crypto asset and various financial assets including equity, exchange rates, crude oil, gold, and commodity futures using daily data from January 01, 2018, to August 31, 2023. The purpose of the study is to provide empirical and theoretical insights into countering the impact of sanctions on Russia, proposing a pragmatic solution for the Russian financial market. The research methodology involves the application of network spillover estimation and value-at-risk analysis. Notably, the findings expose a robust association between crypto and financial assets, where crypto assets play a pivotal role in transmitting risk within the financial landscape. While their impact on other financial assets remains relatively subdued, short-term correlations exhibit volatile fluctuations, often marked by sharp increases in downside risk. Theoretical implications follow the portfolio theory of asset pricing, with extreme risk spillover originating from long-run fluctuations in the crypto market, impacting market sentiment and elevating risk propagation in the Russian financial market. These results carry practical significance for payment and receipt processes, as well as trading activities with foreign countries, presenting essential insights for policymakers and investment decision-makers.

A Review of the Applicability of Pricing Models in the Stock Market

The capital asset pricing model (CAPM) model has always been the core theory of asset pricing and is widely used in the world as a finance tool in the securities market, but it is not fully effective for the economic market due to the current state of development in some countries. This paper compares and analyses the CAPM model and its improved and alternative models based on existing literature and statistical data. This paper first explains the CAPM model, analyses its formula, and then describes the disadvantages and shortcomings of the CAPM model. Then it gives examples of other replaceable models, first explains those models, then lists the advantages of these models compared with the CAPM model as well as some of their shortcomings, and finally concludes through the comparison of these models to the CAPM model that although the CAPM model may not be applicable to the market in many cases, it can be replaced by other models, and even then there is no one-size-fits-all model that can replace all the market models, and conclusions need to be made on a case-by-case basis.

Emerging Industry Development and Information Transmission in Financial Markets: Evidence from China's Renewable Energy

Financial constraint theory and asset pricing theory elucidate the distinct correlation between equity capital cost and strategic investment from different perspectives, encompassing both the intricate uncertainty of the business environment and the efficacy of transmitting financial market information. The burgeoning development of China's renewable energy industry amidst this complex backdrop provides an exceptional empirical context for investigation. Therefore, we select Chinese renewable energy enterprises as samples, considering both internal perceived and external cognized business environment uncertainty, to examine the role played by these factors in influencing equity cost on strategic investment. The findings reveal that changes in such uncertainties result in a ‘saddle’ characteristic. This insight facilitates a comprehensive comprehension of the information conveyed by fluctuations in equity capital cost within emerging industries, along with its underlying mechanisms and limitations.

Climate change news risk and corporate bond returns in China

By constructing a climate change news index based on the textual analysis of newspapers and estimations of the covariance of bond returns, this paper investigates whether climate change news risk is “priced in” in Chinese corporate bonds. We find that bonds with a higher climate change news beta are associated with lower future returns. Moreover, the low climate change news beta premium originates from the transition news beta. Additionally, the low climate change news beta premium is more obvious during times of heightened climate change news risk, which is consistent with the asset pricing theory of intertemporal hedging demand.

Is the cash-returns relationship risk induced?

Whether there is a cash anomaly and what drives the cash–returns relationship are unclear. By constructing a cash risk factor, this study explores the cash–returns relationship and the mechanisms underlying it. We find that the cash factor is pervasive and captures co-movement in stock returns, indicating a significant cash–returns relationship. The factor loading cannot predict returns after controlling for the cash characteristic and has a negative relationship with the implied cost of capital. Also, the cash factor does not reflect future macroeconomic risk. These results are not consistent with rational capital asset pricing theories and cast doubts on the role of the risk explanation while supporting the role of the mispricing effect in the cash–returns relationship.

Idiosyncratic volatility and return: A finite mixture approach

Using finite mixture normal regressions, we find that the relation between idiosyncratic volatility (IVOL) and realized return is negative for one latent group and positive for the other latent group. The intercept is larger for the group with a positive relation than for the group with a negative relation; the negative relation concentrates in firms with large negative realized return in the bottom two quintiles and the positive relation concentrates in firms with large positive realized return in the top two quintiles; the likelihood of having a negative relation is positively related to price-to-value ratio estimates. Realized return has a prominent mispricing-correction component that is negative (positive) for overvalued (undervalued) firms and decreases (increases) with the degree of overvaluation (undervaluation) corrected. Overall, our findings are consistent with the positive relation between IVOL and mispricing. Our findings suggest that an overall negative relation between IVOL and realized return is driven by the negative relation between IVOL and the mispricing-correction component from overvalued firms. Rationally determined expected return cannot be negative. Evidently, an overall negative relation between IVOL and realized return does not contradict the prediction of rational asset pricing theories.

Scale or Yield? A Present-Value Identity

Abstract We propose a loglinear present-value identity in which investment (“scale”), profitability (“yield”), and discount rates determine a firm’s market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (1) Both investment and profitability are important contributors to the value spread and stock return news variance. (2) Any cross-sectional return predictability has a mirror image in cash-flow fundamentals, providing asset pricing theories with additional moments to match. (3) The investment spread significantly improves the predictability of time-series variation in the value premium and justifies the poor performance of value in recent years.

The Q-Measure Dynamics of Forward Rates

I review how the theoretical modeling of the dynamics of forward rates in the context of derivatives pricing has evolved over time. I review the theoretical developments from the short rate models of the 1980s to the stochastic-volatility extensions of the SABR model. I argue that how the theory developed can be understood only by taking into account the institutional setting of derivatives trading and that the modeling choices were motivated to a surprisingly large extent by how the market evolved. I conclude with an assessment of which of these theoretical contributions have had a lasting and meaningful effect on the financial theory of asset pricing.

Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach

Low-risk pricing anomalies, characterized by lower returns in higher-risk stocks, are prevalent in equity markets and challenge traditional asset pricing theory. Previous studies primarily relied on linear regression methods, which analyze a limited number of factors and overlook the advantages of machine learning in handling high-dimensional data. This study aims to address these anomalies in the Chinese market by employing machine learning techniques to measure systematic risk. A large dataset consisting of 770 variables, encompassing macroeconomic, micro-firm, and cross-effect factors, was constructed to develop a machine learning-based dynamic capital asset pricing model. Additionally, we investigated the differences in factors influencing time-varying beta between state-owned enterprises (SOEs) and non-SOEs, providing economic explanations for the black-box issues. Our findings demonstrated the effectiveness of random forest and neural networks, with the four-layer neural network performing best and leading to a substantial rise in the excess return of the long–short portfolio, up to 0.36%. Notably, liquidity indicators emerged as the primary drivers influencing beta, followed by momentum. Moreover, our analysis revealed a shift in variable importance during the transition from SOEs to non-SOEs, as liquidity and momentum gradually replaced fundamentals and valuation as key determinants. This research contributes to both theoretical and practical domains by bridging the research gap in incorporating machine learning methods into asset pricing research.

Stock market anomalies and machine learning across the globe

We identify the characteristics and specifications that drive the out-of-sample performance of machine-learning models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant monthly value-weighted (long-short) returns of around 1.8–2.2%, and a vast majority of tested models outperform a linear combination of predictors (our baseline factor benchmark) by a substantial margin. Composite predictors based on machine learning have long-short portfolio returns that remain significant even with transaction costs up to 300 basis points. By comparing 46 variations of machine-learning models, we find that the models with the highest return predictability apply a feed-forward neural network or composite predictors, with extending rolling windows, including elastic net as a feature reduction, and using percent ranked returns as a target. The results of our nonlinear models are significant across several classical asset pricing models and uncover market inefficiencies that challenge current asset pricing theories in international markets.

Power enhancement for testing multi-factor asset pricing models via Fisher’s method

Testing multi-factor asset pricing models is instrumental for asset pricing theory and practice. However, due to the accumulation of errors in estimating high-dimensional parameters, traditional quadratic-form tests such as the Wald test perform poorly against the sparse alternative hypothesis, i.e., a few mispriced assets. Fan et al. (2015b) introduced a powerful testing procedure by adding a power enhancement component to the Wald test statistic and proved power enhancement properties. To provide an alternative to their methodology, we first instantiate the power enhancement component by introducing a new maximum-form test statistic and then study the asymptotic joint distribution of the Wald test statistic and the maximum test statistic. We prove that these two test statistics are asymptotically independent. Given their asymptotic independence, we propose a new power-enhanced testing procedure to combine their respective power based on Fisher’s method (Fisher, 1925). Theoretically, we prove that the new power-enhanced test retains the desired nominal significance level and achieves asymptotically consistent power against more general alternatives. Furthermore, we demonstrate the finite-sample performance of our proposed power-enhanced test in both simulation studies and an empirical study of testing market efficiency using asset returns of the Russel-2000 portfolio.

Is the Beta Anomaly Real? A Correction in Existing Theories of Cost of Capital and Asset Pricing

Researchers argue about the beta anomaly and related anomalies in the capital market based on existing theories of asset pricing. This article shows that the observed beta anomaly is added due to the mathematical errors, inconsistencies, and limitations in existing theories. We propose a general theory for central concepts in asset pricing, including beta and cost of capital, that holds for growth, taxes, and risky debt. Our theory addresses observed beta-related anomalies and other phenomena, and provides a clearer taxonomy for ongoing research and a step toward resolving several issues. The findings are highly significant for researchers and firms. JEL Codes: G32, G12, G11, G35