A NEW APPROACH TO CONTROLLING FOR THIN TRADING

Recent research has shown that thin trading can seriously bias beta estimates. Present techniques for controlling this bias in research designs involve the adjustment of OLS betas. This paper presents a new methodology that controls for this bias by forming portfolios where the level of thin trading is held constant, while the difference of another variable, pertinent to a specific research design, is maximized across the portfolios. Directly controlling the level of thin trading avoids reliance on beta adjustment techniques. Further, the linear programming model permits the control of the mean and higher moments of additional variables across portfolios.     

Accounting for the Accuracy of Beta Estimates in CAPM Tests on Assets with Time-varying Risks

This paper advocates two ways to make more efficient use of available information in reducing the bias of the risk premium estimate in two-pass tests of the CAPM. First, explicit modelling of the time-variability of betas can improve the accuracy of the beta forecasts. Second, the cross-sectional information available can be exploited more efficiently using individual stocks instead of portfolios provided that noisy beta predictions are given a smaller weight than more accurate ones. This paper proposes an adjustment of the cross-sectional regressions of excess returns against betas to give larger weights to more reliable beta forecasts. A significant positive relationship between returns and the beta forecast is obtained when the proposed approach is applied to data from the Helsinki Stock Exchange, while the traditional Fama–MacBeth approach as such finds no relationship at all.     

The Time-Series Behavior of IPO Betas

We examine individual IPO betas and provide further evidence that the documented decline in IPO betas results primarily from a seasoning or information effect and not from the delisting of high beta securities. We employ stochastic coefficient regression analysis which permits the estimation of individual IPO betas at all points in time, and therefore avoids disadvantages associated with grouped cross-sectional beta estimates and average individual time-series beta estimates. We find that IPO firms with the lowest betas are more likely to delist, and that individual IPO betas, on average, decline over time which provides support for the information hypothesis.     

Forecasting Beta: How Well Does the 'Five-Year Rule of Thumb' Do?

CAPM betas are generally estimated from historical data and applied to a future period. There is widespread evidence that the CAPM betas vary considerably over time and this raises two questions: can this variation be explained and can it be forecast better than the 'five-year rule of thumb' (i.e using the most recently estimated beta)? We estimate time-varying betas and explain the time-variation in the betas using regression models which we subsequently use for forecasting. We find that forecasting equations have good explanatory power but that their forecasts are dominated, on average, by the five-year rule of thumb.     

The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model

Country risk assessment is central to the international investment, which recently has increasingly focused on emerging markets (EM). In this paper we proxy for country risk in EM by using time-varying beta. We extend existing literature by applying a dynamic conditional correlation GARCH model. After confirming beta is time varying in twenty EM over the period January 1995 to December 2008 we investigate the GARCH (1,1) model and find the t-distribution generates the lowest forecast errors compared to the normal error distribution and a generalised error distribution. In a comparison of previous modelling techniques the results of our modified Diebold-Mariano test statistics suggest that the Kalman Filter model outperforms the GARCH model and the Schwert and Seguin (1990) model. Using a DCC-GARCH model our evidence suggests that considering dynamic betas can improve beta out-of-sample predicting ability and therefore offers potential gains for investors. Finally, we find dynamic betas across EM are strongly associated with each nation's interest rates, US interest rates and the Consumer Price Index (CPI) and to a lesser extent the exchange rates. Our results have some similarities to those in previous studies of developed markets in the economic determinants of time-varying beta but differences exist in the results on best model to forecast time-varying beta. These findings have implications for estimating country risk for investment and risk management purposes in EM.     

Resilience to the financial crisis in customer-supplier networks

Inspired by the Capital Asset Pricing Model (CAPM) beta, we construct customer and supplier betas to separately investigate potentially different properties of downstream and upstream linkages. With the adjacency matrix acting as a ‘filter’ to extract each company's return covariances with its trading partners, the cross-sectional dependence contained in the customer-supplier network is summarized by our betas. We explore how these two betas are related to a company's resilience to the financial crisis of 2008–2009. We observe that a higher customer beta is generally associated with more resilience during the crisis. Therefore, investors could construct the customer beta to gain insights into the relative negative impact of a potential crisis on a stock's performance.     

Markov Chains and Regression toward the Mean

Using the theory of stationary Markov chains, we uncover a previously unknown property of the behavior of betas. Specifically, if the cross-sectional distribution of betas is stationary over time, then the set of firms that remain in an arbitrarily chosen beta interval between one period and the next will not regress toward the mean. This surprising result occurs in spite of the well-known fact that the set of all the firms in the interval will exhibit the regression tendency. Our empirical tests indicate that betas behave in remarkable accordance with this prediction.     

Dividend Announcements and Changes in Beta

This study examines changes in beta risk around dividend announcement dates. The evidence shows that beta changes are strongly influenced by the changing behavior of large and small betas. This implies that researchers who attribute risk changes to specific announcements may need to control their research design for regression order bias. Further research may also be needed to ascertain whether or not this regression order bias plays a significant role in the estimation of abnormal returns in event studies.     

An Examination of Blume and Vasicek Betas

This paper examines the Vasicek and Blume methods for correcting OLS betas. The primary conclusions are that typical applications of Vasicek's method seem to mistakenly equate the prior distribution with the cross-sectional distribution of estimated rather than true betas, that Blume's implicit forecast of any tendency for true betas to regress towards one may not be desirable, that preliminary partitioning of firms into industry type groups (as is typical for Vasicek) is desirable, and that conversion of OLS equity betas to asset betas before applying the correction process is also desirable.     

THIN TRADING AND THE ESTIMATION OF BETAS: THE EFFICACY OF ALTERNATIVE TECHNIQUES

Infrequent trading induces biased estimates of the beta risk coefficient. This paper reports on the efficacy of approaches that seek to correct for this bias and documents the extent of thin trading among New Zealand securities. Parameter estimates free of the thin-trading bias are obtained. These are compared with estimates obtained using ordinary least squares (OLS) applied in the conventional manner to nonsynchronous data, with and without bias-correcting procedures. OLS beta estimates are found to be less biased, more efficient, and as consistent when compared with Dimson or Scholes-Williams estimators. Lower beta estimates are associated with lower trading frequencies.     

Adjusting for Beta Bias: An Assessment of Alternate Techniques: A Note

This paper tests the effectiveness of techniques proposed by: Scholes-Williams; Dimson; Fowler, Rorke, and Jog; and Cohen, Hawawini, Maier, Schwartz, and Whitcomb to control for bias in beta estimates from thin trading and price adjustment delays. Each technique produces beta estimates that reduce the amount of this bias, but the amount of reduction in the best case is only 29%.     

Oil and stock market activity when prices go up and down: the case of the oil and gas industry

We examine the asymmetric effects of daily oil price changes on equity returns, market betas, oil betas, return variances, and trading volumes for the US oil and gas industry. The responses of stock returns associated with negative changes in oil prices are higher than that associated with positive changes in oil prices. Stock risk measured by market beta is influenced more due to oil price decreases than due to oil price increases. On the other hand, oil risk exposures (oil betas) and return variances are more influenced by oil price increases than oil price decreases. The results of our study indicate that oil and gas firm returns, market betas, oil betas, return variances respond asymmetrically to oil price changes. We also find that relative changes in oil prices along with firm-specific factors such as firm size, ROA, leverage, market-to-book ratio (MBR) are important in determining the effects of oil price changes on oil and gas firms’ returns, risks, and trading volumes.     

Understanding industry betas

This paper models and explains the dynamics of market betas for 30 US industry portfolios between 1970 and 2009. We use DCC–MIDAS and kernel regression techniques as alternatives to the standard ex-post measures. We find betas to exhibit substantial persistence, time variation, ranking variability, and heterogeneity in their business cycle exposure. While we find only a limited amount of structural breaks in the betas of individual industries, we do identify a common structural break in March 1998. We propose two practical applications to understand the economic significance of these results. We find the cross-sectional dispersion in industry betas to be countercyclical and negatively related to future market returns. We also find DCC–MIDAS betas to outperform other beta measures in terms of limiting the downside risk and ex-post market exposure of a market-neutral minimum-variance strategy.     

On the County-Level Credit Outcome Beta

In their pioneering work, Musto and Souleles (Journal of Monetary Economics 53(1):59–84, 2006) apply portfolio theory to consumer lending. This paper extends their work by analyzing three county-level credit outcome betas. We use the probability of default calibrated from the credit score, the actual default rate, and the actual bankruptcy rate to compute ‘score’, ‘default’, and ‘bankruptcy’ betas for each U.S. county. The correlation between default and bankruptcy betas is quite low. Counties in states in which a borrower has a right to take action against aggressive collection practices tend to have higher default betas but lower bankruptcy betas. These findings suggest the possibility of an ‘informal bankruptcy’ option for consumers. The effects of county score, default, and bankruptcy betas on the county average revolving credit line per borrower are negative. For small lenders that do not have access to the detailed historical credit files on individual consumers, the county-level beta approach of this paper might be helpful for diversifying portfolios geographically and managing risk on existing accounts.     

Predicting systematic risk: Implications from growth options

In accordance with the well-known financial leverage effect, decreases in stock prices cause an increase in the levered equity beta for a given unlevered beta. However, as growth options are more volatile and have higher risk than assets in place, a price decrease may decrease the unlevered equity beta via an operating leverage effect. This is because price decreases are associated with a proportionately higher loss in growth options than in assets in place. Most of the existing literature focuses on the financial leverage effect: This paper examines both effects. We show, with a simple option pricing model, the opposing effects at work when the firm is a portfolio of assets in place and growth options. Our empirical results show that, contrary to common belief, the operating leverage effect largely dominates the financial leverage effect, even for initially highly levered firms with presumably few growth options. We then link variations in betas to measurable firm characteristics that proxy for the fraction of the firm invested in growth options. We show that these proxies jointly predict a large fraction of future cross-sectional differences in betas. These results have important implications on the predictability of equity betas, hence on empirical asset pricing and on portfolio optimization that controls for systematic risk.     

Cross-sectional analysis of Swedish stock returns with time-varying beta: the Swedish stock market 1983–96

This paper analyses the ability of beta and other factors, like firm size and book-to-market, to explain cross‐sectional variation in average stock returns on the Swedish stock market for the period 1983–96. We use a bivariate GARCH(1,1) process to estimate time-varying betas for asset returns. The estimated variances of these betas, derived from a Taylor series approximation, are used for correcting errors in variables. An extreme bound analysis is utilized for testing the sensitivity of the estimated coefficients to changes in the set of included explanatory variables.
Our results show that the estimated conditional beta is a more accurate measure of the true market beta than the beta estimated by OLS. The coefficient for beta is not significantly different from zero, while the variables book-to-market and leverage have significant coefficients, and the latter coefficients are also robust to model specification. Excluding the down turn 1990–92 from the sample shows that the significance of the risk premium for leverage might be considered as an industry effect during this extreme period. Finally, we find a close dependence between the risk premium for beta and that for size and book-to-market. The omission of each of these variables may cause statistical bias in the estimated coefficient for beta.     

Risk characteristics of closed-end stock fund IPOs

We examine 34 closed-end stock fund initial public offerings (IPOs) during the period of January 1, 1986, through June 30, 1987. We find that the funds have low systematic risk during their first trading months, especially the initial month. We also observe that the funds' beta risk tends to increase as the funds season in aftermarket trading. This pattern is in sharp contrast to new issues by nonfinancial corporations, which have very high initial betas that decline over time.     

The performance of cross-sectional regression tests of the CAPM with non-zero pricing errors

We show that in the presence of non-zero pricing errors, the Fama–MacBeth (FM) cross-sectional regression test is very likely to either reject the Capital Asset Pricing Model (CAPM) when it (almost) holds or accept the model when it grossly fails. We investigate the case when pricing errors are correlated with betas and demonstrate that the test performance depends crucially on the correlation, cross-sectional distribution of betas, and several other parameter values. Even when the CAPM holds exactly (pricing errors are zero) the FM test is equally likely to either reject or accept the model when typical sample sizes are used.     

Bias in estimating the systematic risk of extreme performers: Implications for financial analysis, the leverage effect, and long-run reversals

We show how bias can arise systematically in the beta estimates of extreme performers when long-run return reversals are present and partly, or wholly, due to sign changes in unanticipated factor realizations. Our evidence is consistent with this bias being responsible for the large shifts in the beta estimates of extreme performers, more so than the leverage effect, which has been the predominant explanation in prior literature. Bias in these contemporaneous realized betas, estimated with the same returns that are to be risk adjusted, arises due to the general problem of “overconditioning,” where betas are estimated conditional on information that is not yet known. Several methods for conditioning betas on out-of-sample returns are evaluated and found to be lacking, although some offer improvement under certain circumstances. We also show evidence of this bias in the Fama-French Three-factor loadings of extreme performers. Our findings indicate not only that previous studies of long-run reversals understate contrarian profits but that bias is prevalent in the OLS beta estimates of extreme performers, and this has implications for estimating the cost of capital and measuring long-run performance. We offer recommendations for identifying when this bias is likely present, as well as general methods to correct for it.     

The Conditional Beta and the Cross-Section of Expected Returns

We examine the cross-sectional relation between conditional betas and expected stock returns for a sample period of July 1963 to December 2004. Our portfolio-level analyses and the firm-level cross-sectional regressions indicate a positive, significant relation between conditional betas and the cross-section of expected returns. The average return difference between high- and low-beta portfolios ranges between 0.89% and 1.01% per month, depending on the time-varying specification of conditional beta. After controlling for size, book-to-market, liquidity, and momentum, the positive relation between market beta and expected returns remains economically and statistically significant.