Capital Asset Pricing Model Beta Research Articles

A perfect storm in the financial market

This study provides a model explaining how small changes in asset prices may disrupt an entire financial market. Based on the capital asset pricing model (CAPM), our model implies that during a market crash, asset price changes affect the relative distribution of the CAPM betas of individual assets and force all tradable assets to co-move. Using US stock market data, our empirical results are consistent with the model’s predictions. Overall, the study aids understanding of the price patterns of assets during substantial market downturns, such as financial crises.

The effect of FinTech investments on listed banks: Evidence from an Italian sample

This paper analyses whether and how investments in financial technology (FinTech) affect performance, risk, and value of listed Italian banks. This paper tests the effect of return on equity (ROE) and capital asset pricing model (CAPM) Beta coefficient — and, secondly, of the price-to-book value (PBV) ratio, on a sample of 17 Italian listed banks from 2013 to 2019, representing the largest institutions operating in the Italian banking industry. The FinTech variable is declined into two different statuses: digital active banks and digital-focused banks. The study adds useful insights to the positive effects of innovation on banks’ value, in a market, like the Italian one, where investments in FinTech have spread in recent years. Controlling for other financial statements and market variables, the presence of FinTech investments does not affect the CAPM Beta coefficient, while the relationship is positive and significant with ROE for digital active banks only, and with the PBV for digital-focused banks. These results confirm a positive effect on performance for banks investing in FinTech, while greater expectations from investors and a positive effect on bank value creation are significant for digital-focused banks only.

Beta measurement with high frequency returns

Analysis with high frequency returns has become a core part of modern financial econometrics. Particularly in the measurement and forecasting of variance, covariance, correlation and Capital Asset Pricing Model (CAPM) beta. This paper studies CAPM beta measurement with high frequency returns and evaluates trade-offs between bias and variability from different approaches. Our main finding is that the increasing of the return sampling frequency to a suitably high level with the inclusion of a lead and lag in the beta estimation, can result in substantial improvements in the bias and variability trade-off, relative to standard realized beta estimators with returns over a range of sampling frequencies.

Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM (beta) coefficient and model constant are allowed to vary on a binary basis with any degradation or improvement in the country’s international trade competitiveness, and meanwhile a new variable is added to the models to represent the portfolio’s sensitivity to excess returns on the governance portfolio (BIST Governance) over the market. Some robust and Bayesian linear models have been derived using the monthly capital gains between December 2009 and December 2019 of four leading index portfolios. A crude measure is then introduced that we think can be used in assessing governance quality of portfolios. This is called governance quality score (GQS). Our robust regression findings suggest both superiority of conditional models assuming varying beta coefficients over static model proposals and significant impact of corporate governance quality on portfolio returns. The Bayesian model proposals, however, exhibited robust findings that favor the static model with fixed beta estimates and were lacking in supporting significance of corporate governance quality.

Beta-Anomaly: Evidence from the Indian Equity Market

The study investigates the existence of beta-anomaly in the Indian equity market. A growing body of literature shows that financial markets do not reward the risk (defined in terms of standard deviation or market beta). These results (known as ‘low-risk anomaly’ and ‘low-beta-anomaly’) are quite puzzling from the theoretical perspective. This study presents an empirical test of the capital asset pricing model (CAPM) in the Indian equity market to examine the existence of low risk/low beta anomaly. The study covers 650 actively traded stocks for a period of 189 months from July 2002 to March 2018, and the Fama–MacBeth procedure has been used for testing CAPM. The beta-shorted portfolios are formulated using both equal and value-weights. The results of the study were found robust after controlling for outliers and correcting the bias in standard errors, as suggested by Petersen (in Rev Financ Stud 22(1):435–480, 2009). We confirm the presence of ‘low-beta-anomaly’ in India. A non-linear relationship was found between CAPM beta and expected returns. This relationship follows a quadratic function, where expected returns initially increase with beta and then start declining, resulting in the negative risk premium for high-beta portfolios.

Analysis of the Relationship of Risk and Return Using the Capital Asset Pricing Model (CAPM) Method at Kompas 100 for the 2015-2019 Period

This research aimed to investigate the relationship between risk and return on Kompas 100 shares using the Capital Asset Pricing Model (CAPM) approach from 2015 to 2019. The sample amounted to 52 companies registered in Kompas 100. This study used a quantitative approach. The data used includes the closing price of shares and the Composite Stock Price Index (IHSG) for 4 years (1 January 2015 - 31 December 2019) and the risk-free rate, which is calculated using the interest rate on Bank Indonesia Certificates ( SBI) issued by the Bank Indonesia. The results of testing the relationship with the simple correlation coefficient of CAPM calculation, Beta, and CAPM predicted return has a significant positive relationship. If beta increases, the expected return will increase, and vice versa. If Beta goes down, the expected return will go down. Of the 52 sample companies, 33 companies deserve to be used as investment destinations and purchase their shares.

Analysis of the Relationship of Risk and Return Using the Capital Asset Pricing Model (CAPM) Method at Kompas 100 for the 2015-2019 Period

This research aimed to investigate the relationship between risk and return on Kompas 100 shares using the Capital Asset Pricing Model (CAPM) approach from 2015 to 2019. The sample amounted to 52 companies registered in Kompas 100. This study used a quantitative approach. The data used includes the closing price of shares and the Composite Stock Price Index (IHSG) for 4 years (1 January 2015 - 31 December 2019) and the risk-free rate, which is calculated using the interest rate on Bank Indonesia Certificates ( SBI) issued by the Bank Indonesia. The results of testing the relationship with the simple correlation coefficient of CAPM calculation, Beta, and CAPM predicted return has a significant positive relationship. If beta increases, the expected return will increase, and vice versa. If Beta goes down, the expected return will go down. Of the 52 sample companies, 33 companies deserve to be used as investment destinations and purchase their shares.

Beta Measurement and Forecasting with High Frequency Returns

Analysis with high frequency returns has become a core part of modern financial econometrics. Particularly in the measurement and forecasting of variance, covariance, correlation and Capital Asset Pricing Model (CAPM) beta. This paper studies CAPM beta measurement and forecasting with high frequency returns and evaluates trade-offs between bias and variability from different approaches. Our main finding is that the increasing of the return sampling frequency to a suitably high level with the inclusion of a lead and lag in the beta estimation, can result in substantial improvements in the bias and variability trade-off, relative to standard realized beta estimators with returns over a range of sampling frequencies.

Cross-Section of Asset Returns: Emerging Markets and Market Integration

Asset pricing in its essence is a very controversial topic. Despite numerous research papers criticising traditional approaches, such as linear factor models, practitioners as well as academics repeatedly return to the milestone models such as the Capital Asset Pricing Model (CAPM), mainly due to their attractive simplicity. This article focuses on the risk-return relationship by comparing the power of traditional and alternative asset pricing models in explaining the cross-section of asset returns. The focus is on unconditional models, commonly used among investors and equity analysts. This paper is based on the research performed by Estrada in 2004 and it extends his approach by introducing the use of GMM. The results suggest that for Emerging markets' investors should give preference to total risk measures over systematic risk measures. Within the category of systematic risk measures, downside beta proved its superiority to traditional CAPM beta. The results can be attributed to delayed integration process, partially justified by the lower FDI and portfolio investments into Emerging markets.

Estimating the implied risk premium of U.S.-listed shipping firms

ABSTRACTWhile the water transportation industry is perceived as risk-laden, expected return estimates for shipping firms, based on the capital asset pricing model (CAPM), are found to be ‘remarkably low.’ We apply the implied cost of capital (ICC), defined as the constant discount rate that equates share prices and future cash flows, as an alternative means of shipping expected returns and use these estimates instead, as well as in conjunction with the CAPM to provide fresh evidence on the risk and return characteristics of the shipping industry. An aggregate measure of ICC reveals that the median risk premium of shipping firms between 1983 and 2013 is in the range of 0.94% to 0.08%. The expected return of shipping firms tracks a long-term moving average of the S&P 500 index; nevertheless, shipping returns are below the index return at similar, and at times lower, levels with the 10-year treasury yield. The expected equity premium of the shipping industry implied in the cross-section of ICC estimates and CAPM betas ranges between 1.4% and 0.2%. The persistence of a ‘shipping return paradox’ against a variant of expected return methodologies may indicate the pricing of higher moments in the return distribution of shipping firms beyond the mean-variance framework.

A Quantitative Investigation of the Time-varying Beta of the International CAPM: The Case of North American and European Equity Portfolios

This paper investigates the time-invariant and the time-varying betas of the international capital asset pricing model (CAPM) for North American and European equity portfolio returns over the period from August 1, 1990 to June 30, 2016. Our quantitative examinations using the full vector-half (VECH) model reveal the following interesting evidence. First, we find that (1) the time-invariant international CAPM beta value for North American equity portfolio returns and that for European equity portfolio returns, which are derived from the standard ordinary least squares (OLS) method, are both close to one. In contrast, our examinations find that (2) the time-varying international CAPM betas of North American equity portfolio returns are lower than one before 1996, and slightly higher than one after 1996. Moreover, our investigations further reveal that (3) the time-varying international CAPM betas of European equity portfolio returns are lower than one before around 2004, and clearly higher than one after around 2004.

The time traveller’s CAPM

ABSTRACTThis empirical study comprises six emerging market portfolios and five industry replicating portfolios from the USA, using data from 2005 to 2014. The purpose of this study is to test the ability of the ex-ante beta against the ex-post beta using six different generalised autoregressive conditional heteroscedasticity (GARCH) models and the machine learning artificial neural network (ANN) to construct the ex-ante models. Whereas most studies use GARCH models for ultra-short forecasts, little is known about long-run forecasts using these models. The ANN model is also trained to learn the capital asset pricing model (CAPM) to see if the computer can improve upon the traditional model. This study uses a one month (five week) forecast window in all its sample and subsample horseraces, 55 races in all. The ability to forecast the CAPM beta parameters, travel to the future, was found to be more accurate than the historical method across all 11 markets for the three three-year, two five-year and one full 10-year sample periods. Using out-of-sample testing (backtests), the SGARCH was the best predictor overall in developed markets while the IGARCH was in emerging markets.

Is the CAPM a Biased Estimator of Market Risk?

The Capital Asset Pricing Model (CAPM) is often used to determine an appropriate return on equity for regulated utilities. However, the CAPM will sometimes produce unrealistically high or low results during times of excess market volatility. I hypothesize that these results do not indicate a failure in the model itself. Instead, they are caused by the traditional implementation of the CAPM and the underlying definition of risk used by many financial theorists.My paper uses 1997-2001 data to provide an empirical comparison of betas generated in a manner consistent with the capital asset pricing model (CAPM betas) to betas generated using a method based on market losses (“Alternative Betas”). I use psychological literature and a mathematical example to suggest that when a loss-based notion of risk is used, only those observations in which the market declines constitute market risk; and that the CAPM is a systematically biased estimator of market risk because the variability of gains receive the same weight as the variability of losses.In the CAPM world, there are only two types of risk: market risk (measured by beta), and firm-specific risk; with the assumption that only market risk will be rewarded since firm specific risk can be diversified away. The CAPM assumes that the market is mean-variance efficient; predicts that the constant term will be 0; and predicts that a security's risk premium is proportional to both beta and the risk premium of the market portfolio (Bodie, 1996). Mathematically, the risk premium is equal to β[E(rM) − rf], where rM is the market return and rf is the risk-free rate.In the 30 years since the initial publication of the CAPM, there have been numerous criticisms of the model in the financial literature. Studies have suggested that the CAPM is not testable because the composition of the true market portfolio is unknown (Roll, 1977); the constant term is positive and significant (Miller and Scholes, 1972); the estimated risk premium is significantly lower than the actual risk premium (Black, Jensen, and Scholes, 1972); non-systematic risk has a significant effect on excess returns (Lintner, 1965b); and the expected return-beta relationship is not fully consistent with empirical observations (Fama and MacBeth, 1973).

Entropy-based financial asset pricing.

We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative measure of risk. Our results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return – entropy system. For empirical investigation we use daily returns of 150 randomly selected securities for a period of 27 years. Our regression results show that entropy has a higher explanatory power for the expected return than the capital asset pricing model beta. Furthermore we show the time varying behavior of the beta along with entropy.

From Risk-Based Betas to Value-at-Risk: Estimating Monetary Risk with Market Data

In the Capital Asset Pricing Model (CAPM) the β-parameter is related to the risk level of an asset and takes on values ranging around 1. I argue that β is also a function of the monetary risks the asset is exposed to, hence monetary risk can be estimated from betas. On the other hand, if we have an estimate of the monetary risk magnitude we can determine the asset’s beta. I call it risk-based beta since it’s derived from a risk measure, as opposed to the CAPM beta which is derived from market prices movements.Often value-at-risk (VaR) and conditional value-at-risk (CvaR) are used as the monetary measures of risk. This paper maintains that VaR and CVaR can be estimated from betas.In the last paragraph i propose the use of the resh-indices. They quantify monetary risk using CAPM betas and are put in relation to the value of total assets, capitalization and liquidity. The resh-indices measure market-wide or sector-wide risk and can be used as benchmarks. This paper doesn’t deal with the distribution of beta, therefore it lacks the basics for practical use of the resh-index. Obviously I’m investigating the subject and will provide my findings shortly.The magnitude of monetary risk can be assessed by insiders only, the model put forward in this paper provides an easy way to estimate risk from market data available to the public. Moreover when an inside accountant assesses the VaR or total monetary risk she has no benchmark to refer to, the resh-indices provide such benchmark.

Public Utility Beta Adjustment and Biased Costs of Capital in Public Utility Rate Proceedings

The Capital Asset Pricing Model (CAPM) is commonly used in public utility rate proceedings to estimate the cost of capital and allowed rate of return. The beta in the CAPM associates risk with estimated return. However, an empirical analysis suggests that the commonly used Blume CAPM beta adjustment is not appropriate for electric and electric and gas public utility betas, and may bias the cost of common equity capital in public utility rate proceedings.

A time-varying perspective on the CAPM and downside betas

In the current study, we focus on the capital asset pricing model (CAPM) beta and downside betas. The empirical results of market index returns in the international samples of 23 developed countries exhibit significant differences between the CAPM and downside betas, indicating that these models capture distinct risks. Considering autocorrelation variance, the DCC downside betas (HW-beta and HR-beta) more effectively explain the expected stock market returns than does the CAPM beta.

Conditional Relation between Systematic Risk and Returns in the Conventional and Downside Frameworks: Evidence from the Indonesian Market

Unconditional pricing models fail to support a positive risk–return trade-off. When excess market return is negative an inverse relationship between the capital asset pricing model (CAPM) beta and equal-weighted and value-weighted portfolio return is observed. To accommodate market movement in the pricing model, two volatility regimes (high/low) is delineated by specifying a threshold on conditional market volatility estimated via a generalised autoregressive conditional heteroscedasticity (GARCH) process. In the low volatility regime, the CAPM beta risk premium and the downside beta risk premium are negative. This observation is robust to the level of the threshold used and is more pronounced in value-weighted portfolios. When the market condition and market movement is incorporated together as conditioning variables, a strong relationship between CAPM beta and return is uncovered. JEL Classification: G12

Multistage investment, systematic risk premium and CAPM beta: empirical evidence from product development

Recent theoretical literature suggests that the magnitude of the systematic risk premium for a multistage investment project is subject to various forces that may cause the premium to change across stages. To test this hypothesis, I investigate whether Capital Asset Pricing Model (CAPM) beta differs by product development stage in the biotechnology industry. To this end I estimate CAPM beta for various stages of drug development using Full-Information Beta (FIB) technique. Findings indicate that early stage drug development projects have higher CAPM beta than drugs in later stages of development or in production and marketing. The beta appears to decrease monotonically as a project approaches completion.

Corporate investment appraisal with possibilistic CAPM

The Capital Asset Pricing Model (CAPM) is a useful tool in the estimation of the equity cost in the cost of capital computation. This work proposes a method to limit problems arising from the CAPM assumptions. The main tools are the possibilistic mean and the possibilistic variance/covariance of fuzzy numbers as they are introduced by Carlsson and Fullér (2001) [3]. The results of this method are a possibilistic CAPM beta value and a possibilistic value for the market premium. These values lead to a possibilistic set-up of CAPM and then to the computation of the after tax Weighted Average Cost of Capital (WACC). The data for this work is fuzzy and derives from experts in investments and specialists in “deciphering” the information flowing from financial markets. Finally, a computational algorithm of five steps and a numerical example are presented for thorough comprehension of the proposed method.