Improved Methods for Tests of Long-Run Abnormal Stock Returns

We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness-adjusted t -statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series t -statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous.     

Sticky Valuations,Aggregation Effects,and Property Indices

Previous studies on real estate smoothing have generally focused on the second moment of returns for individual properties. Although this body of research has developed plausible reasons for explaining the observed lower risk associated with real estate, no explanation has, however, been offered to account for the large difference in serial correlation at the individual property level compared with the index level. This article addresses this issue and also offers an explanation for the difference in serial correlation observed with different frequency real estate indices. Employing the framework developed by Holbrook Working (1960), we argue that the high levels of serial correlation typically observed in real estate indices results from a combination of random and sticky appraisals that induce cross-correlations between the component returns. Using the concept of sticky values we question the results of Lai and Wang (1998) in which they argue that the variance of appraisal-based returns should always be greater than true returns. We argue that a pragmatic conclusion regarding volatility should be conditioned on the underlying stochastic processes. We draw a distinction between serial cross-sectional and temporal sticky appraisal processes that influence smoothing at the index and individual property levels. Our results indicate that smoothing does not appear to be a serious issue at the individual property level. However, when different appraisal processes are aggregated into an index the underlying cross-correlation between those processes can induce high levels of smoothing.     

Accruals quality vis-à-vis disclosure quality: Substitutes or complements?

The impact of accruals quality and disclosure quality on stock returns is a topical issue in market-based accounting research. Most of the debate is centred on their incremental ability to predict future earnings. Recent studies suggest that higher information risk proxied by either lower accruals quality or lower disclosure quality results in higher stock returns. This paper examines the relationship between accruals quality and disclosure quality, and investigates whether they are complements or substitutes in explaining the time-series variation in portfolio returns. Applying portfolio groupings, we find a positive association between accruals quality and disclosure quality, suggesting that firms with higher disclosure quality engage less in earnings management and have higher accruals quality. Asset pricing tests show that an accruals quality factor and a disclosure quality factor explain the time-series variation in the excess returns of similar sets of portfolios. This suggests that they contain similar information and confirms the substitutive nature of accruals quality and disclosure quality factors.     

Changes in the international comovement of stock returns and asymmetric macroeconomic shocks

We study whether asymmetric macroeconomic shocks help to explain changes in the international comovement of monthly stock returns in major industrialized countries over the period 1975–2004. Based on a time-varying parameter model, we trace out how the pattern of international comovement of stock returns changed over time. In order to identify asymmetric macroeconomic shocks, we estimate vector-autoregressive models. The results of estimating time-series regression models and panel-data models indicate that changes in the international comovement of stock returns are not systematically linked to macroeconomic shocks.     

Institutional trading and stock returns

In this study, we explore the dynamics of the relation between institutional trading and stock returns. We find that stock returns Granger-cause institutional trading (especially purchases) on a quarterly basis. The robust and significant causality from equity returns to institutional trading can be largely explained by the time-series variation of market returns, that is, institutions buy more popular stocks after market rises. Stock returns appear to be negatively related to lagged institutional trading. A further analysis of the behavior of trading and the returns of the traded stocks reveals evidence that stocks with heavy institutional buying (selling) experience positive (negative) excess returns over the previous 12 months.     

Foreign exchange risk and the predictability of carry trade returns

This paper provides an empirical investigation of the time-series predictive ability of foreign exchange risk measures on the return to the carry trade, a popular investment strategy that borrows in low-interest currencies and lends in high-interest currencies. Using quantile regressions, we find that higher market variance is significantly related to large future carry trade losses, which is consistent with the unwinding of the carry trade in times of high volatility. The decomposition of market variance into average variance and average correlation shows that the predictive power of market variance is primarily due to average variance since average correlation is not significantly related to carry trade returns. Finally, a new version of the carry trade that conditions on market variance generates performance gains net of transaction costs.     

Dynamic principal component CAW models for high-dimensional realized covariance matrices

We propose a new dynamic principal component CAW model (DPC-CAW) for time-series of high-dimensional realized covariance matrices of asset returns (up to 100 assets). The model performs a spectral decomposition of the scale matrix of a central Wishart distribution and assumes independent dynamics for the principal components' variances and the eigenvector processes. A three-step estimation procedure makes the model applicable to high-dimensional covariance matrices. We analyze the finite sample properties of the estimation approach and provide an empirical application to realized covariance matrices for 100 assets. The DPC-CAW model has particularly good forecasting properties and outperforms its competitors for realized covariance matrices.     

Long-Term Market Overreaction: The Effect of Low-Priced Stocks

Conrad and Kaul (1993) report that most of De Bondt and Thaler's (1985) long-term overreaction findings can be attributed to a combination of bid-ask effects when monthly cumulative average returns (CARs) are used, and price, rather than prior returns. In direct tests, we find little difference in test-period returns whether CARs or buy-and-hold returns are used, and that price has little predictive ability in cross-sectional regressions. The difference in findings between this study and Conrad and Kaul's is primarily due to their statistical methodology. They confound cross-sectional patterns and aggregate time-series mean reversion, and introduce a survivor bias. Their procedures increase the influence of price at the expense of prior returns.     

Arbitrage pricing with information

The Arbitrage Pricing Theory is extended to a setting where investors possess information about future asset returns. A no-arbitrage pricing restriction is obtained with arbitrage conditioned on an investor's information. The pricing restriction contains unconditional factor loadings and either conditional or unconditional expected returns. Thus, tests of the theory can be based solely on time-series estimates of unconditional moments. Additional tests based on conditional expected returns are also appropriate.     

Are Housing Price Cycles Driven by Irrational Expectations?

This paper investigates the extent to which condominium apartment prices are set in an efficient asset market. Unlike previous work that focuses on the time-series properties of measures of excess returns, the analysis is framed in terms of the changes in observable house prices over time. More precisely, the paper develops and applies a test of the joint null hypothesis of rational expectations, perfect markets, and no risk premium in the Vancouver condominium apartment market. The empirical results provide significant evidence against the joint null hypothesis. On average, ex post house price changes move in a direction opposite to their rational expectation. This approach offers a methodological advantage over the standard efficiency literature and is shown to provide a more powerful test of market efficiency than conventional return regressions. Another contribution of the paper is to characterize the time-series properties of deviations of condominium prices from those predicted by the risk-neutral rational expectations model, using cointegration and random coefficients techniques. Deviations in house price changes from their (risk-neutral) rational expectations are time varying, stationary, and related to the stage of the real estate price cycle.     

Momentum, Business Cycle, and Time-varying Expected Returns

A growing number of researchers argue that time-series patterns in returns are due to investor irrationality and thus can be translated into abnormal profits. Continuation of short-term returns or momentum is one such pattern that has defied any rational explanation and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time-varying expected returns as an explanation for momentum payoffs.     

Consumption-based equity valuation

Using a CCAPM-based risk-adjustment model, we perform yearly valuations of a large sample of stocks listed on NYSE, AMEX, and NASDAQ over a 30-year period. The model differs from standard valuation models in the sense that it adjusts forecasted residual income for risk in the numerator rather than through a risk-adjusted cost of equity in the denominator. The risk adjustments are derived based on assumptions about the time-series properties of residual income returns and aggregate consumption rather than on historical stock returns. We compare the performance of the model with several implementations of standard valuation models, both in terms of median absolute valuation errors (MAVE) and in terms of excess returns on simple investment strategies based on the differences between model and market prices. The CCAPM-based valuation model yields a significantly lower MAVE than the best performing standard valuation model. Both types of models can identify investment strategies with subsequent excess returns. The CCAPM-based valuation model yields time-series of realized hedge returns with more and higher positive returns and fewer and less negative returns compared with the time-series of realized hedge returns based on the best performing standard valuation model for holding periods from 1 to 5 years. In a statistical test of 1-year-ahead excess return predictability based on the models’ implied pricing errors, the CCAPM-based valuation model is selected as the better model. Using the standard series of aggregate consumption and the nominal price index, a reasonable level of relative risk aversion, and calibrated growth rates in the continuing value at each valuation date, the CCAPM-based valuation model produces small risk adjustments to forecasted residual income and low continuing values. Compared with standard valuation models, it relies less on estimated parameters and speculative elements when aggregating residual earnings forecasts into a valuation.     

INTERTEMPORAL ASSET PRICING WITHOUT CONSUMPTION DATA: EMPIRICAL TESTS

In this paper I conduct tests of an intertemporal asset pricing model using variables that forecast stock returns as the risk factors. I document that the forecasting variables are priced so that expected excess returns are related to their conditional covariances with the forecasting variables. The variability in the covariance risk fails to explain the cross-sectional and time-series variation in expected stock returns. Evidence rejects restrictions on the prices of covariance risk imposed by the model with constant volatilities. I also find that an extended model that allows time-varying conditional volatilities is misspecified.     

MACROECONOMIC VARIABLES AND SEASONAL MEAN REVERSION IN STOCK RETURNS

In this paper I investigate whether seasonal mean reversion in stock portfolio returns is related to common macroeconomic risk factors. I decompose excess returns into explained and unexplained returns using a multifactor pricing model. The explained excess returns exhibit January mean reversion; the unexplained excess returns do not. The mean reversion can be attributed to the components of return related to unexpected inflation, bond default premium, and market risk. The results do not depend on the time-series properties of the portfolio betas. Bond default premia and excess market returns are mean reverting in January.     

Sentiment and stock returns: The SAD anomaly revisited

Widely-cited research by Kamstra et al. (2003) argues that changes in mood resulting from Seasonal Affective Disorder (SAD) drive changes in investor risk aversion and cause seasonal patterns in aggregate stock returns around the world. In this paper we reexamine the so-called SAD effect by replicating and extending Kamstra et al. (2003). We study the psychological underpinnings of the SAD hypothesis and show that the time-series predictions of the SAD model do not correspond to the seasonal patterns in depression found in the general population. We also investigate the cross-sectional prediction that SAD has a greater effect on stock markets in countries where SAD is more prevalent and find no relation between the prevalence of SAD and stock returns. Finally, we document that the SAD effect is mechanically driven by an overlapping dummy-variable specification and higher returns around the turn of the year.     

How asymmetric is U.S. stock market volatility?

This paper explores differences in the impact of equally large positive and negative surprise return shocks in the aggregate U.S. stock market on: (1) the volatility predictions of asymmetric time-series models, (2) implied volatility, and (3) realized volatility. Following large negative surprise return shocks, both asymmetric time-series models (such as the EGARCH and GJR models) and implied volatility predict an increase in volatility and, consistent with this, ex post realized volatility normally rises as predicted. Following large positive return shocks, asymmetric time-series models predict an increase in volatility (albeit a much smaller increase than following a negative shock of the same magnitude), but both implied and realized volatilities generally fall sharply. While asymmetric time-series models predict a decline in volatility following near-zero returns, both implied and realized volatility are normally little changed from levels observed prior to the stable market. The reasons for the differences are explored.     

Active management, fund size, and bond mutual fund returns

Conventional wisdom holds that bonds are relatively homogenous investments compared to equities. Consequently, factors that explain variation in returns among bond mutual funds may differ in magnitude from those for equity mutual funds. In this study, a time-series cross-sectional analysis is employed to investigate the relationship between a bond fund's risk-adjusted return and specific fund attributes. Results indicate that a bond fund's past performance does not predict future performance and that bond fund managers are generally ineffective at increasing risk-adjusted returns. However, unlike equity mutual funds, bond mutual funds do appear to enjoy economies of scale.     

Consumer Confidence and Asset Prices: Some Empirical Evidence

We explore the time-series relationship between investor sentimentand the small-stock premium using consumer confidence as a measureof investor optimism. We estimate the components of consumerconfidence related to economic fundamentals and investor sentiment.After controlling for the time variation of beta, we study thetime-series variation of the pricing error with sentiment. Overthe last 25 years, investor sentiment measured using consumerconfidence forecasts the returns of small stocks and stockswith low institutional ownership in a manner consistent withthe predictions of models based on noise-trader sentiment. Sentimentdoes not appear to forecast time-series variation in the valueand momentum premiums. (JEL G10, G12, G14)     

The Global Determinants of Direct Office Real Estate Returns

We investigate the determinants of direct office real estate returns by analyzing rents, capital appraisals, and total returns. A recently compiled global database of major cities in Asia, Europe, and the United States provides a unique opportunity to give a macro-view on the effects of economic growth and supply and demand factors on nominal real estate returns. The global database provides quarterly observations from 1986 to 1999. To address the smoothness problem of appraisal-based price data and regulated rents, we employ the Generalized Method of Moments to estimate a dynamic panel-data model. The model allows us to combine the cross-sectional and time-series dimension in our quarterly data. We find that gross domestic product, inflation, unemployment, vacancy rate, and the available stock all have an effect on real estate returns.