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The purpose of this paper is to examine the nature and extent of instability of capital asset pricing model (CAPM) beta in a small emerging capital market.

Inter‐period as well as intra beta instability are examined. Inter‐period instability is examined by Mann‐Whitney z‐scores and Blume's regressions. Intra‐period beta instability is examined using Bruesch‐Pagan LM test and Chow break point test. Robustness tests are performed applying time‐varying parameter models.

Beta instability increases with increase in holding (sample) periods. There is evidence of inter‐period as well as intra‐period beta instability. Analysis of the full eight‐year interval reveals a very high incidence of beta instability, namely, about 26 per cent of the individual stocks tested and about 31 per cent of individual stocks have structural break. The extent of beta instability does not significantly decline when corrected for non‐synchronous trading and thin trading as represented by Dimson beta. However, the extent of beta instability is similar to that of developed market. Time‐varying parameter model under Kalman filter approach using AR(1) specification performs better than any other models in terms of in‐sample forecast errors. Dominance of AR(1) approach suggests that stock betas in DSE are time varying, and shocks to the conditional beta have some degree of persistence which ultimately reverts to a mean. This result is in contrast to the findings of Faff et al. revealing the dominance of Random Walk specification in Australian market, suggesting that shocks to stock beta in Australian market persist indefinitely into the future. These contrasting findings may indicate that beta instability in different markets and for different stocks in the same market are of different nature and different models may be suitable for different markets and different stocks in the same market in capturing the time‐varying nature of beta coefficients.

This study covers only 110 stocks of Dhaka Stock Exchange. It can be extended to include more stocks. The study can also be done in other developing markets.

While the issues of beta instability have been extensively explored for developed markets, evidence for emerging markets is less readily available. The present study contributes to the emerging market literature on beta instability by investigating the extent of beta instability and its time‐varying properties in Dhaka Stock Exchange (DSE), Bangladesh. Understanding the systematic risk behaviour of individual stocks in DSE is important for both local and international investors. With the saturation of investment opportunities in developed markets due to their high integration, and with the enhanced deregulation and liberalization of emerging economies, emerging financial markets like DSE provide suitable and a relatively safe investment environment for international investors and fund managers seeking global diversification for better risk‐return trade‐offs. When most of the world markets declined during the recent global financial crisis, stock prices in DSE experienced a continuous rise. This makes it more interesting as an emerging market to study beta instability.

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.

It was early 2015 and executives in iShares' Factor Strategies Group were considering the launch of a new class of exchange-traded funds (ETFs) called smart beta funds. Specifically, the group was considering smart beta multifactor ETFs that would provide investors with simultaneous exposure to four fundamental factors that had shown themselves historically to be significant in driving stock returns: the stock market value of a firm, the relative value of a firm's financial position, the quality of a firm's financial position, and the momentum of a firm's stock price. The executives at iShares were unsure whether there would be demand in the marketplace for such multifactor ETFs, since their value added from an investor's portfolio perspective was unknown. Students will act as researchers for iShares' Factor Strategies Group and conduct detailed analysis of Fama and French's five-factor model and the momentum effect, smart beta ETFs including multifactor ETFs, and factor investing with smart beta ETFs to help iShares make its decision.

Strategic cost structure choices determine how firms divide operating costs between fixed and variable components, and therefore have important implications for financial performance. The purpose of this paper is to examine the effect of operating leverage on equity Betas when managers have discretion over firms' cost structures.

Using panel data for publicly listed trucking firms over years 1994‐2006, market model Betas are regressed on controls and alternatively measured proxies for operating leverage: degree of operating leverage, assets in place and percentage of company employed drivers.

Results of this study generally show positively significant coefficients on all three operating leverage variables.

Operating characteristics of many industries require that firms make substantial investments in long‐lived assets that result in high fixed costs (e.g. depreciation), and for these firms cost structure is exogenously or technologically constrained leaving managers with little discretion. In contrast to these types of firms, the authors examine the effect of operating leverage (OL) on Betas when managers have discretion over firms' cost structures. Trucking firms are a particularly interesting industry group for analyzing the impact of operating OL choices on Beta because distinct strategic cost structure choices are available to the management of trucking firms that result in various degrees of OL throughout the industry.

This paper seeks to examine the association between corporate voluntary disclosure and systematic (market or beta) risk for a sample of Egyptian listed companies.

Using panel data analysis, beta is regressed on the level of voluntary disclosure and the following control variables: dividend payout, asset growth, gearing, firm size and book‐to‐market ratio.

The results generally show a negative relationship between voluntary disclosure level and beta, consistent with predictions of a differential information model and theories about the economic consequences of increased disclosure. The results are dependent on the specification of the model and the market index used to estimate beta, suggesting a need for further research on the link between risk and voluntary disclosure in the context of emerging markets.

The main implication of these results is that more voluntary information about listed companies seems preferable to less in order to reduce the perceived riskiness of a company. This should act as an incentive for listed companies to enhance public disclosure.

This is one of the first studies to explore the economic consequences of increased disclosure in an emerging capital market using panel data analysis. Another distinctive feature is that market betas are estimated using different measures to obtain greater confidence in the overall conclusions.

In this paper we investigate the empirical performance of an alternative beta risk estimator, which is designed to be superior to its conventional counterparts in situations of extreme thin trading. The estimator used is based on the sample selectivity model. The study compares the resultant selectivity-corrected beta to the OLS beta and Dimson Betas. We demonstrate the empirical behaviour of the selectivity corrected beta estimator using a sample of stocks in seven countries from Latin America. The results indicate that the selectivity-corrected beta does correct the downward bias of the OLS estimates and is likely to better estimate stock risk.

Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue.

Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four‐year periods.

The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks.

The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices.

The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.

Recently, due to its passive property, the smart beta has become one of the most interest topics in searching the alpha. In this paper, we attempt to show whether the smart beta strategy generate abnormal excess return, in tradition, which are known as the exclusive property of active fund. Further, we attempt to verify the key drivers of the alpha in the smart beta portfolios. For this purpose, we categorize various smart beta strategies by their scheme for asset picking and risk reduction. Then, based on our categorization, we evaluate and analyze the performance of smart beta strategy in perspective. Our empirical analyses show following results: applying alternative risk reduction scheme to traditional market index portfolio would results in enhanced efficiency; however, without combining any asset picking scheme, the performance of the smart beta portfolio seems explained by the Fama-French 3 factor. Our results lead us to conjecture that it is not the portfolio weighting scheme alone but in association with asset selection scheme that generate significant alpha in the smart beta strategy. In actual practice, our results imply that any passive fund may succeed in seeking the alpha without active strategy, thereby avoiding the risk of market timing and saving the management cost.

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.