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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.

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.

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 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 aims to propose that a Neave-Worthington Match Test for Ordered Alternatives is a simple, non-parametric test that can be used to consider Gibrat’s law. Whether the law, that states that the proportional rate of growth is independent of absolute size, is supported by regional house price growth rates is considered. The Match Test is further used to test the applicability of beta-convergence and dual economy models to a house price context.

The Match Test relates an actual rank order with an expected one. Gibrat’s law implies house price growth rates are independent of the absolute price levels. Beta-convergence posits that growth rates are inversely related to the initial price level. With a divergent system, there is a direct relationship between size-order and growth rates. As such, the Match Test is used to test alternative models of size-growth relationship.

Rather than convergence, there is a tendency to diverge across the UK, but not in Eire. That said, the size of growth shocks is related to price level on the upswing of a price cycle, but not in the down. Assigning the high-priced regions of the two islands into core and the rest into a periphery, total matching is dominated by the capital cities’ growth. The sigma-convergence observed in British house prices is likely to be associated with slower beta-divergence, not a convergent system. The law of Gibrat is not found to apply in a regional house price context.

This work only covers two countries and nineteen regions. Gibrat’s law in regional house prices may be better examined using a multi-country analysis.

As the law of Gibrat is not found to apply in a regional house price context and core-regions appearing to dislocated, this has interesting implications for growth trend analysis and the claim of cointegration, which should be explored further. In particular, the level-growth relationship in the cyclical price upswing points to a ratcheting of differentials between high and low house price regions. The common trends in the long run may result from corrective periodic crashes. Not an ideal mechanism for policymakers.

To the best of the author’s knowledge, this paper makes a novel use of the Neave-Worthington test in the realm of regional convergence-divergence and in the first consideration of the law of Gibrat in a house price context across two countries.

This study aims to investigate the effect of product market competition on the exposure of firms’ returns to consumption fluctuations (C-CAPM beta).

The C-CAPM beta comes from a regression of a stock’s returns against consumption growth, with controls for the Fama–French three factors and momentum. The Herfindahl–Hirschman index of concentration measures competition, with other measures like deregulation and tariff reductions used for robustness tests. Industries are categorized using different SIC digits, with the NAICS measure used for robustness tests. The C-CAPM beta is regressed to competition, with appropriate control variables, to find its relationship.

Higher levels of competition reduces the C-CAPM beta. The results are consistently robust to different measures of product market competition and industry identification.

Product market competition influences the sensitivity of systematic risk, as measured by the C-CAPM beta, to consumption, such that higher levels of competition reduce systematic risk.

This research contributes to a literature that admittedly is still murky, as the relationship between competition and systematic risk is still unsettled. No study (to the authors’ knowledge) examines the effect of competition on firms’ exposure to consumption. This research adds to the literature on the role of competition in risk, specifically with respect to consumption.

Prior work claims that the CAPM is mis‐specified based on evidence that beta and idiosyncratic variables such as size, book‐to‐market, and price‐earnings ratios combine to explain average cross‐sectional variation in stock returns. This paper set out to reexamine this research by employing more rigorous statistical methods to control for beta shifts.

TIMVAR program is employed to control for risk shift. Clean betas are used to reexamine the methods used in Fama and French (1992).

The paper shows that, even if the CAPM generates expected returns, the mentioned idiosyncratic variables may be correlated with expected returns. Moreover, if beta is measured with error, then it is possible for idiosyncratic variables to enter as explanatory variables. Evidence is provided indicating that, as measurement error in beta is reduced, then the role of beta in explaining cross‐sectional returns increases, while the role of idiosyncratic variables diminishes.

Beta continues to be a significant variable to measure the risk of securities.

This paper employs a novel approach (TIMVAR program) to detect and control for structural changes in linear regression. This approach has been widely used in quality control by industries. The audience of the paper includes both academics and practitioners.