Market Beta Research Articles

Physical climate change risk and ESG green premium

ABSTRACT We build an asset pricing model that incorporates climate change risk and firms’ ESG engagement to analyse their interactive effects on equity returns. Our model shows that a firm’s ESG engagement has three channels for influencing stock returns: its direct impact on resisting climate change risk, indirect reduction in systematic risk and attraction of green preference, and the empirical evidence shows that both risk–return relation and ESG engagement are priced. We also point out an inevitably negative and convex relation between the market Sharp Ratio and stocks market beta in an economy with climate change risk.

Comparative Analysis of Portfolio Management Models: Markowitz, CAPM, and Multi-Factor Models

This paper presents a comparative analysis of three foundational portfolio management models: the Markowitz Efficient Frontier, the Capital Asset Pricing Model (CAPM), and Multi-Factor Models. It aims to explore their theoretical foundations, empirical applicability, and practical usage to assess their strengths and limitations in financial portfolio management. The Markowitz model is appreciated for its detailed risk-return optimization framework but criticized for its reliance on historical data and computational demands. The CAPM simplifies risk assessment through market beta, yet its assumptions of market efficiency and a risk-free rate are viewed as impractical under current market conditions. Multi-Factor Models address some limitations of the CAPM by incorporating various risk factors, offering enhanced explanatory power but also adding complexity in factor selection.The study concludes that while no single model is universally superior, the choice of model should depend on the specific needs of the portfolio manager, including risk-return objectives and market environment. The paper suggests a blended or adaptive approach to model selection, highlighting the need for flexibility in response to evolving market dynamics and advances in computational finance.

An industry-level analysis of a pandemic's impact on stock market risk

We examine stock market risk surrounding the height of the COVID-19 pandemic at the industry level. We find that low-beta industries experienced a significant increase in relative risk at the worst possible time, while high-beta industries saw a reduction in relative risk. Shocks to market beta were not accompanied by offsetting changes in loadings on other factors, suggesting that risk didn't merely shift from one factor to another. Risk dynamics differed substantially from those surrounding the height of the 2008 financial crisis. Our results bear important implications for investors, firms, financial intermediaries and regulators in the event of another pandemic.

Understanding co-movements based on heterogeneous information associations

Both systematic and idiosyncratic information dissemination contribute to assets co-movement. To proxy for co-movement based on heterogeneous information diffusion, we construct two-layer network structures in terms of the correlations of systematic and idiosyncratic returns for Fama–French 49 industry portfolios. We further delve into the co-movement structures by studying their dynamics and interactions in terms of topological properties at both the system and individual levels. Our findings reveal the following: (1) Co-movement structures exhibit temporal changes and are closely associated with major risk events. (2) At the system level, there is time-varying mutual Granger causality between co-movement structures, particularly during or after recession periods. (3) At the individual level, we identify statistically significant but economically insignificant interplay between the two co-movement structures. Moreover, the co-movement structure based on systematic information diffusion is mainly influenced by industry-level characteristics, including idiosyncratic momentum, idiosyncratic volatility, and market beta. These results deepen our understanding of asset co-movement structures and bear significance for asset allocation and risk management practices.

Estimating Stock Market Betas via Machine Learning

Abstract Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

Holding Period Effects in Dividend Strip Returns

Abstract We estimate short-term dividend strip prices from 27 years of S&P 500 index options data (1996-2022). We use option-implied interest rates when estimating strip prices and longer holding period returns to mitigate measurement error. We find that Sharpe ratios for short-term strips are similar to or higher than Sharpe ratios for the market. Short-term strips also have a low market beta and a positive alpha. Over the business cycle, realized term premiums (ie, the difference between market and strip returns) and the term structure of Sharpe ratios move countercyclically, whereas the term structure of alphas moves procyclically.

Simply Better Market Betas around the Globe

Simply Better Market Betas around the Globe

RankFIRST: Visual Analysis for Factor Investment By Ranking Stock Timeseries.

In the era of quantitative investment, factor-based investing models are widely adopted in the construction of stock portfolios. These models explain the performance of individual stocks by a set of financial factors, e.g., market beta and company size. In industry, open investment platforms allow the online building of factor-based models, yet set a high bar on the engineering expertise of end-users. State-of-the-art visualization systems integrate the whole factor investing pipeline, but do not directly address domain users' core requests on ranking factors and stocks for portfolio construction. The current model lacks explainability, which downgrades its credibility with stock investors. To fill the gap in modeling, ranking, and visualizing stock time series for factor investment, we designed and implemented a visual analytics system, namely RankFIRST. The system offers built-in support for an established factor collection and a cross-sectional regression model viable for human interpretation. A hierarchical slope graph design is introduced according to the desired characteristics of good factors for stock investment. A novel firework chart is also invented extending the well-known candlestick chart for stock time series. We evaluated the system on the full-scale Chinese stock market data in the recent 30 years. Case studies and controlled user evaluation demonstrate the superiority of our system on factor investing, in comparison to both passive investing on stock indices and existing stock market visual analytics tools.

An Analytic Solution to the Mean-Variance Equilibrium: Is the Market Beta a Valuable Tool?

An Analytic Solution to the Mean-Variance Equilibrium: Is the Market Beta a Valuable Tool?

Market power and systematic risk

AbstractWe examine the impact of product market competition on firms' systematic risk. Using a measure of total product market similarity, we document a strong negative relationship between market power and market betas. The effect more than triples in the most recent period of low competition. Anticompetitive mergers result in a significant reduction in market betas. Firms facing less competition seem to be partially insulated from systematic discount‐rate shocks. Lower equity costs therefore imply that market power is partly self‐perpetuating.

The Effects of Energy Prices on Oil-Gas Sectoral Stock Returns for BRIC Countries: Evidence from Space State Models

This paper examines the effects of oil and natural gas prices on the oil and gas sectors of the BRIC countries (Brazil, Russia, India, and China) over the period over from 20013 to 2022. Unlike previous studies, it employs a time-varying capital asset pricing model based on the estimation of state-space mode. In brief, the findings highlight significant changes in the asset-pricing model parameters across all countries, indicating the limitations of using time-invariant estimates. Specifically, Brazil shows the highest volatility in oil price risk, followed by Russia, both being oil-exporting countries, while market beta values remain relatively stable. Time-varying estimates further suggest that natural gas parameters are relatively lower and less significant than those of oil prices. The Russian-Ukrainian conflict's energy crisis adversely affects the performance of oil and gas sectoral stock returns. This war has had a negative and significant impact on China's oil-gas stock return.

Market-based private equity returns

Using the universe of business development companies (BDCs), a unique publicly traded segment of U.S. Private Equity (PE), for the period 1998–2017, we provide the first in-depth examination of their performance and risk-adjusted characteristics and compare our results to contrasting evidence derived from recently developed time series proxies for unlisted PE returns. BDCs exhibit zero alpha, beta of one, and significant exposure to SMB, HML, and CMA factors of 0.5, 0.7, and -0.3, respectively. BDC performance and market beta are sensitive to fund size and leverage. We provide evidence that BDC returns capture both the asset selection and PE ownership elements of the unlisted PE investment strategy. Finally, an event study analysis shows that NAV disclosures become informative only after the adoption of the Statement of Financial Accounting Standards 157 (SFAS 157). We posit that BDCs provide a readily available market-based PE benchmark for use by regulators, market participants, and academics.

Risk culture in corporate innovation

In this study, we utilize stock market beta to examine the impact of corporate risk culture on innovation within U.S. firms. Our results reveal that firms with a pronounced high-risk culture invest more in innovation and generate greater innovation outcomes. These firms tend to employ overconfident or generalist managers and offer them incentive compensation to spur innovation. Further analysis underscores that the influence of risk culture on innovation occurs predominantly in innovative industries. Our findings highlight that firms with a high-risk culture excel in seizing innovative growth opportunities.

Market beta coefficient and enterprise risk management: A literature review

One of the significant factors in the valuation of publicly listed firms is their market beta coefficient, commonly utilised in the capital asset pricing model (CAPM) as a proxy for stock volatility directly affecting market value. This article’s primary purpose is to explore the theoretical basis for future empirical research into the relationship between the market beta coefficient and enterprise risk management (ERM). The author explores academic literature about various researched variables affecting the market beta coefficient in the context of the neoclassical capital asset pricing model, which was founded on the premise of an efficient market hypothesis.

Disclosure Similarity and Future Stock Return Comovement

Existing research often assumes that firms’ financial reporting choices influence their return comovement with other firms. We examine the validity of that assumption. First, we provide initial evidence suggesting that similarity in two firms’ disclosures not only predicts but influences future return comovement between those two firms. Second, we show that this predictive ability aggregates to the market level; disclosure similarity can be used to estimate more accurate forward-looking market betas. Taken together, these two results suggest that firms’ reporting decisions can influence their firms’ betas even in the absence of changes to capital structure or operations. This paper was accepted by Suraj Srinivasan, accounting. Funding: The authors are grateful for financial support from the Brigham Young University Marriott School of Business and the Fisher College of Business, Ohio State University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4915 .

Night trading: Lower risk but higher returns?

AbstractThis paper demonstrates that overnight returns are subject to highly persistent biases and examines the profitability of overnight‐only investments in that context. Overnight returns tend to exceed their intraday counterparts, and the paper first reconciles these patterns by introducing a model that factors in systematic biases. This model identifies one‐fifth of stocks as having positive and statistically significant overnight biases. Investing overnight in these stocks in the next year yields twice the market's return for a third of the market beta. Results also have implications for daytime investors as these stocks underperform intraday. Implementation costs and issues are discussed.

The macroeconomic announcement premium and information environment

The quality of information environment has impact on the market risk premium and the expected risk reduction on macroeconomic announcement days. The risk premium is high when the risk is high as in standard asset pricing models, while the risk premium is low when the prevailing information environment is poor. The same is true for the expected risk reduction. These effects extend to market factor premiums (i.e., the premium associated with market betas) on various sets of portfolios and have a connection with business cycles. The findings are consistent with the notion that poor information environment hampers the effectiveness of learning.

Income elasticity of demand and stock market beta

AbstractSystematic risk, or beta, measures stock price variability in the overall stock market. A considerable body of literature focuses on estimating beta. To the best of our knowledge, there is, however, a lack of definitive research on the impact of income elasticity of demand on stock market beta. This study is the first to examine this relationship using 659 publicly traded firms from 47 industries in South Korea from 2001 to 2020. To estimate the value of the stock market beta, we employ an econometric model with a fixed effects‐two stage least squares approach and use industry concentration as an instrumental variable to deal with the endogeneity problem in the estimation. The overall objective of this study is to investigate the influence of income elasticity of demand on stock market beta.

The Time Secret of Chinese A-Share Systematic Risk: Overnight and Intraday

ABSTRACT Based on all daily A-share data from January 2006 to December 2021, this study examines the relationship between overnight and intraday returns and market beta using Fama-Macbeth regressions and panel regressions to explore the reasons for the weak correlation between systematic risk and full-day returns in the Chinese A-share market. The results show that the overnight risk premium of the Chinese A-share market is significantly negative. The intraday risk premium is negligible when portfolios are sorted by beta, industry and Book-to-Market ratios, and stock characteristics. The slope of the securities market line (SML) tends to zero due to the interaction of the uncertain intraday risk premium and the negative correlation between overnight returns and market risk.

Examining significance of “downside beta” as a measure of risk – evidence from Indian equity market

PurposeMany studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.Design/methodology/approachThis is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.FindingsThe study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.Practical implicationsThe empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.Originality/valueThis is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.