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This paper presents a simple rational expectations model of intertemporal asset pricing. It shows that state-independent heterogeneous risk aversion of investors is likely to generate declining aggregate relative risk aversion. This leads to predictability of asset returns and high and persistent volatility. Stock market crashes may be observed if relative risk aversion differs strongly across investors. Then aggregate relative risk aversion may sharply increase given a small impairment in fundamentals so that asset prices may strongly decline. Changes in aggregate relative risk aversion may also lead to resistance and support levels as used in technical analysis. For numerical illustration we propose an analytical asset price formula.

We study return predictability of stock indexes of blue chip firms and smaller hightechnology firms in Germany, France, and the United Kingdom during the second half of the 1990s. We measure return predictability in terms of first-order autocorrelation coefficients, and find evidence for return predictability of stock indexes of smaller hightechnology firms, but no evidence for return predictability of stock indexes of blue chip firms. Our findings suggest that a leading candidate for explaining the economic sources of return predictability of stock indexes of smaller high-technology firms is transaction costs.

The paper reports on studies of return predictability of stock indexes of blue-chip firms and high-technology firms in Germany, France and the UK during the second half of the 1990s. Return predictability was measured in terms of first-order autocorrelation coefficients, and evidence was found for the return predictability of stock indexes of high-technology firms, but not for the return predictability of stock indexes of blue-chip firms. These findings suggest that a candidate for explaining the economic sources of the return predictability of these stock indexes of high-technology firms is transaction costs in the form of the costs of gathering and processing information in new technological fields.

We consider a random utility extension of the fundamental Lucas (1978) equilibrium asset pricing model. The resulting structural model leads naturally to a likelihood function. We estimate the model using U.S. asset market data from 1871 to 2000, using both dividends and earnings as state variables. We find that current dividends do not forecast future utility shocks, whereas current utility shocks do forecast future dividends. The estimated structural model produces a sequence of predicted utility shocks which provide better forecasts of future long-horizon stock market returns than the classical dividend-price ratio.

I use a time-varying parameter model in order to study the predictability of monthly real stock returns in Germany over the period 1880–1913. I find that the extent to which returns were predictable underwent significant changes over time. Specifically, predictability of returns, as measured by their first-order autocorrelation coefficient, was positive most of the time. It tended to be significant during extended periods of stock market decline, but not during periods of stock market increase. I argue that this time-pattern of predictability of returns is consistent with feedback effects of futures trading on the spot market.

International audience; This paper uses a stylised asset-pricing model to show that sunspots may cause asset returns to be predictable, a widely documented feature of many speculative markets. This result parallels and extends previous works showing that sunspots render asset prices excessively volatile.

We consider a random utility extension of the fundamental Lucas (1978) equilibriumasset pricing model. The resulting structural model leads naturally to a likelihoodfunction. We estimate the model using U.S. asset market data from 1871 to2000, using both dividends and earnings as state variables. We find that current dividendsdo not forecast future utility shocks, whereas current utility shocks do forecastfuture dividends. The estimated structural model produces a sequence of predictedutility shocks which provide better forecasts of future long-horizon stock market returnsthan the classical dividend-price ratio.KEYWORDS: Randomutility, asset pricing, maximumlikelihood, structuralmodel,return predictability, We consider a random utility extension of the fundamental Lucas (1978) equilibrium asset pricing model. The resulting structural model leads naturally to a likelihood function. We estimate the model using U.S. asset market data from 1871 to 2000, using both dividends and earnings as state variables. We find that current dividends do not forecast future utility shocks, whereas current utility shocks do forecast future dividends. The estimated structural model produces a sequence of predicted utility shocks which provide better forecasts of future long-horizon stock market returns than the classical dividend-price ratio.

I use a time-varying parameter model in order to study the predictability of monthly real stock returns in Germany over the period 1880?1913. I find that the extent to which returns were predictable underwent significant changes over time. Specifically, predictability of returns, as measured by their first-order autocorrelation coefficient, was positive most of the time. It tended to be significant during extended periods of stock market decline, but not during periods of stock market increase. I argue that this timepattern of predictability of returns is consistent with feedback effects of futures trading on the spot market.

One of the most important findings in empirical finance has been the fact that returns are not i.i.d. Predictability, or time variation in the conditional distribution of returns, is one of the basic ingredients of asset pricing and portfolio choice models nowadays. Under the current renewed interest in its implications for portfolio management, there is a growing literature on the computation of optimal portfolios for a given utility function and a given estimation of a particular model of returns. But there is an obvious problem with that approach. If the estimated model is not an accurate description of the distribution of returns the conclusions may be misleading. The approach in this paper is to study the properties of a particular model by means of the evaluation of its joint density forecasts of stock and bond returns. The focus is not on model testing, the procedure is based instead on out-of-sample checks of real-time density forecasting rules against realizations of returns. That is the relevant context for portfolio management. In addition, two important and controversial questions are addressed for each model: time-variation in risk premia and parameter uncertainty.

This paper analyzes whether the price-output ratio (the cpy-ratio) predicts real stock returns in twelve OECD countries. The cpy-ratio is a ratio of a share price to a macroeconomic variable. Traditionally, either ratios of purely financial indicators, ratios of purely macroeconomic indicators, or ratios of macroeconomic indicators to wealth have been used to predict returns. However, if share prices are mean reverting, and thus contain a predictable component, and predictability of returns is related to the macroeconomic environment that ultimately determines the investment opportunities, a ratio of a share price to a macroeconomic variable could be believed to predict returns. The analyses reveal that the cpy-ratios do indeed predict future stock returns in most of the countries that are studied.

This Paper analyses the relation between momentum strategies (strategies that buy stocks with high returns over the previous three to 12 months and sell stocks with low returns over the same period) and turnover (number of shares traded divided by the number of shares outstanding) for the German stock market. Our main finding is that momentum strategies are more profitable among high-turnover stocks. In contrast to US evidence, this result is driven mainly by winners: high-turnover winners have higher returns than low-turnover winners. We present various robustness checks, long-horizon results, evidence on seasonality, and control for size-, book-to-market-, and industry-effects. We argue that our results are useful to empirically evaluate competing explanations for the momentum effect.

This dissertation examines whether simultaneously conditioning on the multidimensional characteristics of information signals can help predict returns that are of economic significance. We use large price changes, public announcements, and large volume increases to proxy for the magnitude, dissemination, and precision of information signals. Abnormal returns following large price change events are found to be unimportant. As we condition on other characteristics of information signals, the abnormal returns become large. Large price change events accompanied by both a public announcement and an increase in volume have a 20-day abnormal return of almost 2% for positive events and -1.68% for negative events. The type of news provides further refinement. If the news relates to earnings announcements, management earnings forecasts, or analyst recommendations then the 20-day abnormal returns becomes much larger: ranging from 3% to 4% for positive events and about -2.25% for negative events. For these news events, we also find that the underreaction is greater for positive (negative) event firms that underperformed (overperformed) the market in the prior period, earning 20-day post-event abnormal returns of 4.85% (-3.50%). This evidence is consistent with the Barberis, Shleifer, and Vishny (1998) model of investor sentiment that suggests that investors are slow to change their beliefs. The evidence from our sample does not provide much support for strategic trading models under information asymmetry. Finally, an out-of-sample trading strategy generates 20-day post-event statistically significant abnormal return of 2.18% for positive events and -2.40% for negative events. Net of transaction costs, the abnormal returns are a statistically significant 1.04% for positive events and a statistically significant -1.51% for negative events. Ph. D.

This paper uses an extension of the equilibrium model of Lucas (1978) to study the valuation of options on the market portfolio with return predictability, endogenous stochastic volatility and interest rates. Equilibrium conditions imply that the mean-reverting of the rate of dividend growth induces the predictable feature of the market portfolio. Although the actual drift of the price for the market portfolio does not explicitly enter into the option price formula when the equivalent martingale pricing principle is used, parameters underlying the predictable feature affect option prices through their influence on endogenized volatility and interest rates. Equilibrium conditions also review that there is strong interdependence between the equilibrium price process for the market portfolio and its volatility process, both of which are induced by the process for aggregate dividend. Closed-form pricing formulas for options on the market portfolio incorporate both stochastic volatility and stochastic interest rates. With realistic parameter values, numerical examples show that stochastic volatility and stochastic interest rates are both necessary for correcting the pricing biases generated by the Black-Scholes model. In addition, Closed-form solutions for European bond option prices are obtained, which encompass the Vasicek (1977) model and the Cox-Ingersoll-Ross (1985) model. In this sense, the current model provides a consistent way to price options written on the market portfolio and the bonds.

Order flow imbalance refers to the difference between market buy and sell orders during a given period. This paper is the first study to examine effects of order flow imbalance on returns of stocks traded on the German Xetra trading system on a daily basis. In contrast to previous studies on other markets, we control for unobserved effects using a fixed-effects panel regression. For the concurrent (or conditional) relation between order imbalance and returns, our results confirm those of the literature. For the question of return predictability from past order imbalances (unconditional relation), our results are partly confirmatory. In addition, we provide evidence for size and liquidity effects and analyze changes in imbalance effects during the financial crisis.

Stock returns in emerging market economies exhibit patterns that are distinctively different from developed countries: returns are noted to be highly volatile and autocorrelated, and long horizon returns are predictable. While these stylized facts are well established, the assumption underlying the distribution of returns is less understood. In particular, the empirical literature continues to rely on the normality assumption as a starting point, and most asset pricing models tend to overstretch this point. This paper questions the rationale behind this supposition and proceeds to test more formally for normality using multivariate joint test for skewness and kurtosis. Additionally, the paper extends the literature by examining a number of empirical regularities for Brazil, Russia, India, China and South Africa (the BRICS for short). Our main findings are that the distribution of stock returns for the BRICS exhibits peakedness with fatter and longer tails, and this is invariant to both the unit of measurement and the time horizon of returns. Volatility clustering is prevalent in all markets, and this decays exponentially for all but Brazil. The relationship between risk and return is found to be significant and risk premiums are prevalent in our sample.

This thesis examines predictability and seasonality in the cross-section of stock returns. The first chapter, titled ``Infrequent Rebalancing, Return Autocorrelation, and Seasonality,'' shows that a model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross-sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross-section of stock returns, which can help explain available empirical evidence. The second chapter, titled ``Seasonalities in Anomalies,'' investigates return seasonalities in a set of well-known anomalies in the cross-section of U.S. stocks returns. A January seasonality goes beyond a size effect and strongly affects most anomalies, which can even switch sign in January. Both tax-loss selling and firm size are important in explaining the turn-of-the-year pattern. Return seasonality exists outside of January, with respect to the month of the quarter. Small stocks earn abnormally high average returns on the last day of each quarter, which significantly affects size, idiosyncratic volatility, and illiquidity portfolios. The results have implications for the interpretation and analysis of many anomalies, such as asset growth and momentum. The third chapter, titled ``The Cross-Section of Intraday and Overnight Returns,'' uses a thirty-year sample of U.S. stock returns to document substantial cross-sectional variation in returns over the trading day and overnight. Market closures have a large impact on returns. Small and illiquid stocks earn high average returns in the last thirty minutes of trading. In contrast, large and liquid stocks perform poorly at this time. I find support for institutional and information asymmetry theories. But these theories do not fully explain the cross-sectional evidence. Portfolios based on other characteristics, such as beta and idiosyncratic volatility, earn their return gradually throughout the trading day–contrary to the market and a benchmark based on random portfolios. These portfolios also tend to incur large negative returns overnight, consistent with mispricing at the open.

The response of corporate bond credit spreads to three exogenous macro-shocks -- oil supply, investment-specific technology, and government spending -- is large, significant, and a mirror image of macroeconomic activity. This counter-cyclicality is largely driven by credit risk premia and translates into significant return predictability. Equity risk premia exhibit similar responses, providing external validity. Information rigidities and leverage play a key role in the transmission of the shocks. Since causal evidence linking macro-shocks to credit markets is scarce and recent work highlights the real effects of credit fluctuations, our findings contribute to understanding the joint dynamics of credit markets and the macroeconomy.