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This paper investigates the role of economic disagreement in the cross-sectional pricing of individual stocks. Economic disagreement is quantified with ex ante measures of cross-sectional dispersion in economic forecasts from the Survey of Professional Forecasters (SPF), determining the degree of disagreement among professional forecasters over changes in economic fundamentals.

The authors introduce a broad index of economic disagreement based on the innovations in the cross-sectional dispersion of economic forecasts for output, inflation and unemployment so that the index is a shock measure that captures different aspects of disagreement over economic fundamentals and also reflects unexpected news or surprise about the state of the aggregate economy. After building the broad index of economic disagreement, the authors test out-of-sample performance of the index in predicting the cross-sectional variation in future stock returns.

Univariate portfolio analyses indicate that decile portfolios that are long in stocks with the lowest disagreement beta and short in stocks with the highest disagreement beta yield a risk-adjusted annual return of 7.2%. The results remain robust after controlling for well-known pricing effects. The results are consistent with a preference-based explanation that ambiguity-averse investors demand extra compensation to hold stocks with high disagreement risk and the investors are willing to pay high prices for stocks with large hedging benefits. The results also support the mispricing hypothesis that the high disagreement beta provides an indirect way to measure dispersed opinion and overpricing.

Most literature measures disagreement about individual stocks with the standard deviation of earnings forecasts made by financial analysts and examines the cross-sectional relation between this measure and individual stock returns. Unlike prior studies, the authors focus on disagreement about the economy instead of disagreement about earnings growth. The authors' argument is that disagreement about the economy is a major factor that would explain disagreement about stock fundamentals. The authors find that disagreement in economic forecasts does indeed have a significant impact on the cross-sectional pricing of individual stocks.

This paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.

A conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.

The results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.

This is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.

This paper aims to examine the contemporary association between accounting information and a number of measures of systematic (beta) risk that incorporate dynamic market features. The goal is to determine the fundamental accounting drivers of beta and to assess whether their explanatory variable power has changed or declined over time.

Beta estimates are calculated using adjustments for thin‐trading, central tendency, leverage, and time variance. Accounting risk variables are derived from theoretical foundations and prior empirical research, and classified as operating, financial or growth.

Results show a strong association between accounting variables (operating and growth) and systematic risk that is consistent over time, but with some industry and size differences and possible country effects. Accounting variables are able to capture dynamic risk shifts and generally are able to outperform naïve M‐GARCH and industry betas in predicting next year's systematic risk.

Internal management and external decision making enable the development of more efficient ex‐post risk measures, isolating actual risk determinants rather than just determining the level of risk, overcoming the problem that conventional ex‐post measures cannot be used for non‐listed entities, initial public offering firms, or those that do not have sufficient trading history, reduces the noise found in traditional risk estimates that rely on historical security returns, and the development of trading and valuation strategies.

This is the first paper that assesses the association between a range of dynamic risk measures and accounting variables and tests whether this long‐run association has changed over time.

This paper aims to investigate the intervalling effect bias in ETFs' systematic risk expressed by beta. The authors' findings reveal the existence of a significant intervalling effect on ETFs' beta obtained by the ordinary least squares method (OLS). Also investigated is the impact of ETFs' capitalization on beta. Results provide evidence that small cap ETFs have greater betas than large cap ETFs. Results also reveal that the OLS beta of all ETFs increases when the return interval is lengthened regardless of capitalization. The impact of ETFs' trading activity on systematic risk is assessed too. Findings give evidence that the OLS betas of the ETFs that trade infrequently are biased downwards while the beta of the frequently traded ETFs is biased upwards. Finally, the paper reveals a strong intervalling effect on ETFs' tracking error.

The authors employ a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange to test whether beta estimates change when the return interval measurement changes. Their data cover a maximum period of ten years starting from September 16, 1998 using daily, weekly and monthly return data. The authors estimate beta applying three alternative methods: the market model applied with the OLS method, the Scholes and Williams model (SW beta) and the Dimson model (Dim beta).

Results indicate that the average beta of ETFs derived by the OLS method increases when the return interval increases. The differences among the daily, weekly and monthly OLS betas are statistically significant at the 1 per cent level. This finding implies a strong intervalling effect bias in ETFs' OLS beta. On the other hand, the authors did not find any statistically significant differences in daily, weekly and monthly Scholes and Williams and Dimson betas. Moreover, results show that the daily and weekly OLS and Scholes and Williams betas and weekly OLS and Dimson betas are significantly different from each other.

In this paper using a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange, the authors have examined various issues concerning: the intervalling effect bias in ETFs' systematic risk, the relation between beta and capitalization of ETFs, the relation between beta and trading frequency of ETFs and, finally, the intervalling effect bias in ETFs' tracking error. While the literature on intervalling effect on securities' beta and the relation between systematic risk and market value and trading activity is voluminous, this is the first attempt to examine these issues with respect to ETFs.

The purpose of this paper is to estimate the Beta Risk Coefficient of 32 listed companies (shares), which are included in the Ghana Stock Exchange (GSE All Share Index).

This research investigated some of the issues that can affect beta estimates (the measurement of returns, the choice of market index used, the length of estimation period, the sampling interval, the issue of normality, autocorrelation, the effect of thin trading, seasonality and stability) by using 32 listed companies. The methodology used was the Market model and some of the beta estimation techniques used included Scholes‐Williams' beta, Dimson's beta, and Fowler‐Rorke's beta.

The empirical results generally confirm the evidence by various researchers in the literature reviewed. However, the tests for the effect of thin trading and the effect of seasonality reject the null hypothesis (H_o: β_Monday=β_Tuesday=β_Wednesday=β_Thursday=β_Friday).

The study has about 95 per cent originality since the authors went into the field to gather all the data needed and did all the analysis.

Using Japan REITs stock data, this paper examines the risk‐return relations conditional on up and down markets periods. The results show that beta is significantly and positively (negatively) related to realized returns in up (down) markets before and after controlling for extra risk factors. The same conditional results are found for unsystematic risk and total risk, providing evidence that investors do not hold well‐diversified portfolios. Though skewness is significantly priced, the coefficients are unexpectedly positive (negative) in up (down) markets, indicating that investors dislike positively skewed portfolios and would ask for compensation if they are required to hold them during up markets. One possible reason is that the investors have a poor concept of skewness and/or they are too aggressive during bullish markets and so they ignore the benefit of positive skewness. Subsidiary results highlight that there is no seasonal effect in the conditional relation between beta/unsystematic risk/total risk/skewness and returns. This study is the first comprehensive study of the risk‐return relations in Japan REITs market, which provides out‐of‐sample evidence relative to earlier tests on Asian and international stock markets. The findings give important insights and provide useful guidance on investing in Japan REITs market.

The purpose of this paper is to empirically estimate industry beta in Indian stock market with three alternative models and compare the accuracy of forecasting error to find the most suitable model for time-varying beta estimation.

The paper applies the standard regression model, Kalman filter model, other statistical approaches and secondary material.

The paper finds that the existence of dynamic beta in Indian market. The results also indicate systematic risk or beta of Indian industries is susceptible to the global economic effect. Finally, the Kalman filter generates the lower forecasting error compared to the other method for almost all the industries.

The accurate estimation of beta which is a measure of systematic risk helps investors to make investment decision easier. The implication of this result is important for finance practitioners such as portfolio managers, investment advisors and security analysts. This study will help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.

This paper reliably estimate industry portfolio beta for India. The time-varying beta is estimated using Kalman filter method which is rarely applied in Indian literature. This paper contributes by extending the knowledge of existing literature by introducing a new data set with Indian data which is not affected by the “data snooping” bias. This study will also help to determine the country risk with respect to the global index and analyze the global financial market integration effect on India.

This study examines the ability of fundamental summary measure Pr to predict earnings change for the subsequent year, the association of Pr and stock returns, and the relationship between Pr and risk factors beta and size. Pr is a probability index generated by logistic model and financial statement data. Beta effect is minimized by grouping firms into beta portfolios while size is controlled through incorporating size as an independent variable in the regression models. Evidence from the study indicates that Pr has a strong ability to predict future earnings change and has a positive and significant association with adjusted market returns, after controlling for beta. Pr's association with adjusted market returns is mitigated when beta and size are controlled simultaneously.

The purpose of this paper is to examine the relationship between beta and returns in the Athens stock exchange (ASE), taking into account the difference between positive and negative market excess returns' yields.

The data were taken from DataStream database and the sample period consists of 12 years divided into four six‐year periods such that the test periods do not overlap. Regression analysis is applied, using both the traditional (unconditional) test procedure and the conditional approach.

The estimation of return and beta without differentiating positive and negative market excess returns produces a flat unconditional relationship between return and beta. However, when using the conditional capital asset pricing model (CAPM) and cross‐sectional regression analysis, the evidence tends to support the significant positive relationship in up market and a significant negative relationship in down market.

The small number of listed companies in the ASE led to the inclusion of the financial and insurance companies in the sample, and to the formation of a small number of portfolios. The same research methodology could be applied to individual stocks of the ASE and with the exclusion of all financial companies.

The results tend to support the existence of a conditional CAPM relation between risk and realized return trade‐off.

Reviews previous research on the nature of beta and investigates the stochastic structure of time‐varying beta in Hong Kong, Malaysia and Singapore using the bi‐variate GARCH‐in‐mean model and fractional tests. Develops mathematical models and applies them to 1989‐1998 daily data from all three stock markets. Presents the results, which suggest, in contrast to other findings, that all three time‐varying betas are slowly mean‐reverting (long memory).