Search results

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.

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.

As the economic and financial characteristics of countries change, so would be their betas and correlations of their investment returns with that of the U.S. Such changes are expected to be particularly significant for emerging market nations as they strive for rapid industrialization and modernization. OLS estimator for the beta coefficient would not be the Best Linear Unbiased Estimator (BLUE) if beta is non‐stationary or changes from period to period. This paper proposes a special type of time weighted least square method (TWLS), which assigns greater weights on the regression errors in more recent periods, for estimating the current beta. This TWLS approach can tackle the problem of intertemporal heteroscedasticity and thus yields a beta that is more efficient. The breakthrough lies on the viability of the method without a‐priori knowledge or estimation of the values of the weights. This yields a significant practical advantage since the weights are unobservable in the real world. Since the Time Weighted Method estimator is the coefficient estimator of beta value for the latest period in the sample, statisticians who base their forecasts on the beta estimates derived from the Time Weighted Least Square can expect to outperform those relying on beta values obtained from conventional estimation. We use a sample of daily returns of thirty‐one emerging markets stock over the period of January 1, 2000 through December 31, 2002. We find that most of the tstatistics for the variances are significant at the 95 per cent level, indicating that the Var(s)’s are not zero for nearly every emerging‐markets. This implies that the betas for these markets do shift over time.

In order to develop our understanding of valuation models and so extend this to encompass the important area of performance measurement and its interpretation, it is essential to have a framework which will enable such developments to take place. This paper presents a theoretical model based on a certainty equivalent approach which enables the market risk of individual properties and portfolios to be assessed on an expectations basis. The data requirements for using the model are not onerous and with simple extensions it can be adapted to cope with changes in risk that occur when variations in the lease structure are anticipated. Understanding the influence of systematic or market risk is essential if our understanding of valuation is to improve. Systematic risk is the single most important factor which determines the premium which should be allowed to compensate for risk. This aspect has been largely ignored in the property literature with the result that risk premium figures are frequently assumed to be constant across all sectors and properties. This paper derives a model which attempts to overcome some of these problems. Due to data limitations empirical tests of the model cannot be regarded as conclusive. However, those tests that have been carried out suggest that the model could be used for estimating the required rate of return of both sectors and individual properties. It also has considerable potential in estimating growth expectations for groups of properties and can thus be used in the decision‐making process. Much, however, remains to be done.

In this paper we empirically investigate the tendency for beta risk to mean‐revert across industries. Using a sample of Australian stocks over the ten‐year period 1989 to 1998, our key results are as follows. We generally observe evidence of a mean reversion tendency — in particular, this seems most appropriate for the Gold, Energy, Finance and Consumer industry groupings. Moreover, there is some evidence that the mean reversion of beta is different across industries. Furthermore, we see that the maximum mean reversion beta occurs for the Gold industry — a value of approximately 1.4 (1.6) for the OLS (Scholes‐Williams) beta analysis. On the other hand, the minimum mean reversion beta based on the ‘All Stocks’ OLS analysis occurs for Miscellaneous Industries with a value of 0.4, while a similar minimum mean reversion beta based on the Scholes‐Williams analysis occurs for the Consumer industry grouping.

The capital asset pricing model has failed to explain the effect of systematic risk (referred to as beta) on actual stock market returns. Accordingly, this study analyzes daily returns by splitting it into overnight and daytime returns. The study analysis empirically confirms a positive relationship between overnight returns and beta and a negative relation between daytime returns and beta. Furthermore, this paper aims to determine that empirical results are mostly the same with three different beta calculations, namely, daily, overnight and daytime returns. The study concludes that beta on overnight returns has the strongest explanatory power and is statistically significant.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

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.

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.