Speculative Betas

The risk and return trade‐off, the cornerstone of modern asset pricing theory, is often of the wrong sign. Our explanation is that high‐beta assets are prone to speculative overpricing. When investors disagree about the stock market's prospects, high‐beta assets are more sensitive to this aggregate disagreement, experience greater divergence of opinion about their payoffs, and are overpriced due to short‐sales constraints. When aggregate disagreement is low, the Security Market Line is upward‐sloping due to risk‐sharing. When it is high, expected returns can actually decrease with beta. We confirm our theory using a measure of disagreement about stock market earnings.     

Macroeconomic Forces and Mutual Fund Betas

This paper develops and tests a model in which fund betas are linearly related to changes in macroeconomic factors. Tests using monthly returns for 171 mutual funds over 1978–1991 were run. Results indicate that equity funds betas, on average, are negatively related to inflation changes and default risk premia while bond fund betas, on average, are negatively related to changes in risk-free rates, industrial production growth, and the term structure. Betas for passive portfolios, however, are not related to the macroeconomic factors examined.     

Consumption Betas and Backwardation in Commodity Markets

This paper examines the relationship between commodity consumption betas and realized commodity futures contract risk premiums. A linear relationship between risk premiums and consumption betas is developed based on a consumption oriented CAPM. The parameters of this linear model are estimated using fourteen commodities.     

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.     

Time-Varying Betas of German Stock Returns

The market model assumes stock returns to be a linear function of the market return. However, there is considerable evidence that the beta stability assumption commonly used when estimating the market model is invalid. In this paper we account for beta instability in German stock returns by allowing the coefficients to vary over time in estimation. For time-varying beta estimation we rely on the Flexible Least Squares approach, the Random Walk Model and Moving Window Least Squares. Due to our results time-varying estimation fits the data considerably better than time-invariant estimation and, hence, increases the efficiency of beta based risk measurement.Acknowledgements: The authors thank Stefan Mittnik, Christian Pigorsch, an anonymous referee and the editor for constructive comments.     

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.     

On the Cross-sectional Relation between Expected Returns and Betas

There is an exact linear relation between expected returns and true “betas” when the market portfolio is on the ex ante mean-variance efficient frontier, but empirical research has found little relation between sample mean returns and estimated betas. A possible explanation is that market portfolio proxies are mean-variance inefficient. We categorize proxies that produce particular relations between expected returns and true betas. For the special case of a zero relation, a market portfolio proxy must lie inside the efficient frontier, but it may be close to the frontier.     

Improved Forecasting of Mutual Fund Alphas and Betas

This paper proposes a simple back testing procedure that isshown to dramatically improve a panel data model's ability toproduce out of sample forecasts. Here the procedure is usedto forecast mutual fund alphas. Using monthly data with an OLSmodel it has been difficult to consistently predict which portfoliomanagers will produce above market returns for their investors.This paper provides empirical evidence that sorting on the estimatedalphas populates the top and bottom deciles not with the bestand worst funds, but with those having the greatest estimationerror. This problem can be attenuated by back testing the statisticalmodel fund by fund. The back test used here requires a statisticalmodel to exhibit some past predictive success for a particularfund before it is allowed to make predictions about that fundin the current period. Another estimation problem concerns theuse of a single statistical model for all available mutual funds.Since no one statistical model is likely to fit every fund,the result is a great deal of misspecification error. This papershows that the combined use of an OLS and Kalman filter modelincreases the number of funds with predictable out of samplealphas by about 60%. Overall, a strategy that uses very modestex-ante filters to eliminate funds whose parameters likely deriveprimarily from estimation error produces an out of sample risk-adjustedreturn of over 4% per annum.     

Estimating the Dynamics of Mutual Fund Alphas and Betas

This article develops a Kalman filter model to track dynamicmutual fund factor loadings. It then uses the estimates to analyzewhether managers with market-timing ability can be identifiedex ante. The primary findings are as follows: (i) Ordinary leastsquares (OLS) timing models produce false positives (nonzeroalphas) at too high a rate with either daily or monthly data.In contrast, the Kalman filter model produces them at approximatelythe correct rate with monthly data; (ii) In monthly data, thoughthe OLS models fail to detect any timing among fund managers,the Kalman filter does; (iii) The alpha and beta forecasts fromthe Kalman model are more accurate than those from the OLS timingmodels; (iv) The Kalman filter model tracks most fund alphasand betas better than OLS models that employ macroeconomic variablesin addition to fund returns.     

The Regression Tendencies of Betas: A Reappraisal

This paper reexamines the regression tendencies of beta. We show that common assertions in the literature about regression tendencies go well beyond the facts established by Blume. We analyze betas during the 1926–1985 period and examine the tendencies of betas to change. Extreme betas do tend to move toward the mean. However, betas near the mean in one period tend to move away from the mean. As a result, the distribution of betas is approximately stationary over time.     

Good News,Bad News,Volatility, and Betas

We investigate the conditional covariances of stock returns using bivariate exponential ARCH (EGARCH) models. These models allow market volatility, portfolio-specific volatility, and beta to respond asymmetrically to positive and negative market and portfolio returns, i.e., “leverage” effects. Using monthly data, we find strong evidence of conditional heteroskedasticity in both market and non-market components of returns, and weaker evidence of time-varying conditional betas. Surprisingly while leverage effects appear strong in the market component of volatility, they are absent in conditional betas and weak and/or inconsistent in nonmarket sources of risk.     

Biased Auctioneers

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.     

Changes in Factor Betas and Risk Premiums Over Varying Market Conditions

We show risk exposures and premiums associated with the Chen, Roll, and Ross (1986) risk factors change over time and depend on stock market and business cycle condition. Findings also indicate that factor risk premiums change sign between January and non-January, especially during bull markets. These findings serve as a caveat for portfolio managers who allocate assets to match desired exposures to key macroeconomic risk factors.     

Information Risk and Fair Values: An Examination of Equity Betas

Using a sample of U.S. financial institutions, we exploit recent mandatory disclosures of financial instruments designated as fair value level 1, 2, and 3 to test whether greater information risk in financial instrument fair values leads to higher cost of capital. We derive an empirical model allowing asset‐specific estimates of implied betas, and find evidence that firms with greater exposure to level 3 financial assets exhibit higher betas relative to those designated as level 1 or level 2. We further find that this difference in implied betas across fair value designations is more pronounced for firms with ex ante lower‐quality information environments: firms with lower analyst following, lower market capitalization, higher analyst forecast errors, or higher analyst forecast dispersion. Overall, the results are consistent with a higher cost of capital for more opaque financial assets, but also suggest that differences in firms' information environments can mitigate information risk across the fair value designations.     

Biased voluntary disclosure

We provide a bridge between the voluntary disclosure and the earnings management literature. Voluntary disclosure models focus on managers’ discretion in deciding whether or not to provide truthful voluntary disclosure to the capital market. Earnings management models, on the other hand, concentrate on managers’ discretion in deciding how to bias their mandatory disclosure. By analyzing managers’ disclosure strategy when disclosure is voluntary and not necessarily truthful, we show the robustness of voluntary disclosure theory to the relaxation of the standard assumption of truthful reporting. We also demonstrate the sensitivity of earnings management theory to the commonly made mandatory disclosure assumption.     

Tests of Asset Pricing with Time-Varying Expected Risk Premiums and Market Betas

Tests of asset-pricing models are developed that allow expected risk premiums and market betas to vary over time. These tests exploit the relation between expected excess returns and current market values. Using weekly data for 1963 through 1982 on ten common stock portfolios formed according to equity capitalization, a single-risk-premium model is not rejected if the expected premium is time varying and is not constrained to correspond to a market factor. Conditional mean-variance efficiency of a value-weighted stock index is rejected, and the rejection is insensitive to how much variability of expected risk premiums is assumed.     

On the Cross-Sectional Relation between Expected Returns, Betas, and Size

In this paper, I set up scenarios where the mean-variance capital asset pricing model is true and where it is false. Then I investigate whether the coefficients from regressions of population expected excess returns on population betas, and expected excess returns on betas and size, allow us to distinguish between the scenarios. I show that the coefficients from either ordinary least squares or generalized least squares regressions do not allow us to tell whether the model is true or false.     

Interest Rate Skewness and Biased Beliefs

Conditional skewness of Treasury yields is an important indicator of the risks to the macroeconomic outlook. Positive skewness signals upside risk to interest rates during periods of accommodative monetary policy and an upward-sloping yield curve, and vice versa. Skewness has substantial predictive power for future bond excess returns, high-frequency interest rate changes around Federal Open Market Committee announcements, and survey forecast errors for interest rates. The estimated expectational errors, or biases in beliefs, are quantitatively important for statistical bond risk premia. These findings are consistent with a heterogeneous-beliefs model in which one of the agents is wrong about consumption growth.