Search Results
Report
The microstructure of cross-autocorrelations
This paper examines the mechanism through which the incorporation of information into prices leads to cross-autocorrelations in stock returns. The lead-lag relation between large and small stocks increases with lagged spreads of large stocks. Further, order flows in large stocks significantly predict the returns of small stocks when large stock spreads are high. This effect is consistent with the notion that trading on common information takes place first in the large stocks and is then transmitted to smaller stocks with a lag, suggesting that price discovery takes place in the large stocks.
Working Paper
Jackknifing stock return predictions
We show that the general bias reducing technique of jackknifing can be successfully applied to stock return predictability regressions. Compared to standard OLS estimation, the jackknifing procedure delivers virtually unbiased estimates with mean squared errors that generally dominate those of the OLS estimates. The jackknifing method is very general, as well as simple to implement, and can be applied to models with multiple predictors and overlapping observations. Unlike most previous work on inference in predictive regressions, no specific assumptions regarding the data generating process ...
Report
Loss aversion, asymmetric market comovements, and the home bias
Loss aversion has been used to explain why a high equity premium might be consistent with plausible levels of risk aversion. The intuition is that the different utility impact of wealth gains and losses leads loss-averse investors to behave similarly to investors with high risk aversion. But if so, should these agents not perceive larger gains from international diversification than standard expected-utility preference agents with plausible levels of risk aversion? They might not, because comovements in international stock markets are asymmetric: Correlations are higher in market downturns ...
Journal Article
Boomer retirement: headwinds for U.S. equity markets?
Historical data indicate a strong relationship between the age distribution of the U.S. population and stock market performance. A key demographic trend is the aging of the baby boom generation. As they reach retirement age, they are likely to shift from buying stocks to selling their equity holdings to finance retirement. Statistical models suggest that this shift could be a factor holding down equity valuations over the next two decades.
Working Paper
Asset returns with earnings management
The paper investigates stock return dynamics in an environment where executives have an incentive to maximize their compensation by artificially inflating earnings. A principal-agent model with financial reporting and managerial effort is embedded in a Lucas asset-pricing model with periodic revelations of the firm's underlying profitability. The return process generated from the model is consistent with a range of financial anomalies observed in the return data: volatility clustering, asymmetric volatility, and increased idiosyncratic volatility. The calibration results further indicate that ...
Report
Equity premium predictions with adaptive macro indexes
Fundamental economic conditions are crucial determinants of equity premia. However, commonly used predictors do not adequately capture the changing nature of economic conditions and hence have limited power in forecasting equity returns. To address the inadequacy, this paper constructs macro indexes from large data sets and adaptively chooses optimal indexes to predict stock returns. I find that adaptive macro indexes explain a substantial fraction of the short-term variation in future stock returns and have more forecasting power than both the historical average of stock returns and commonly ...
Report
Stock returns and volatility: pricing the short-run and long-run components of market risk
We decompose the time series of equity market risk into short- and long-run volatility components. Both components have negative and highly significant prices of risk in the cross section of equity returns. A three-factor model with the market return and the two volatility components compares favorably to benchmark models. We show that the short-run component captures market skewness risk, while the long-run component captures business cycle risk. Furthermore, short-run volatility is the more important cross-sectional risk factor, even though its average risk premium is smaller than the ...
Report
Performance maximization of actively managed funds
Ratios that indicate the statistical significance of a fund's alpha typically appraise its performance. A growing literature suggests that even in the absence of any ability to predict returns, holding options positions on the benchmark assets or trading frequently can significantly enhance performance ratios. This paper derives the performance-maximizing strategy--a variant of buy-write--and the least upper bound on such performance enhancement, thereby showing that if common equity indexes are used as benchmarks, the potential performance enhancement from trading frequently is usually ...
Working Paper
Financial leverage, corporate investment, and stock returns
This paper presents a dynamic model of the firm with risk-free debt contracts, investment irreversibility, and debt restructuring costs. The model fits several stylized facts of corporate finance and asset pricing: First, book leverage is constant across different book-to-market portfolios, whereas market leverage differs significantly. Second, changes in market leverage are mainly caused by changes in stock prices rather than by changes in debt. Third, when the model is calibrated to fit the cross-sectional distribution of book-to-market ratios, it explains the return differences across ...
Working Paper
The information content of high-frequency data for estimating equity return models and forecasting risk
We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted from high-frequency data. For this purpose, we introduce asymptotically exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient estimates lead in turn to substantial gains for forecasting various risk measures at horizons ranging from a ...