UC San Diego

This dissertation presents three stand-alone contributions to the fields of theoretical and empirical asset pricing. The first chapter presents a theoretical model in which the attention investors pay to the stock market varies over time. This feature is obtained by introducing information costs into a continuous-time model of asset allocation with time-varying investment opportunities. My model explains why investors do not trade uniformly through time. It also rationalizes why agents do not modify their portfolio allocations gradually with the arrival of new information, but rather alternate extended periods of inertia (no-trade) with brief moments of action where asset allocations are updated according to the current state of the economy. The second chapter analyzes the role of information in the context of financial market predictions. I employ a novel semi-parametric method known as Boosted Regression Trees (BRT) to forecast stock returns and volatility at the monthly frequency. The framework allows me to generate forecasts on the basis of a large set of predictor variables without incurring over- fitting related problems. My results indicate that expanding the conditioning information set results in greater out-of-sample predictive accuracy compared to the standard models proposed in the literature and that the forecasts generate profitable portfolio allocations even when market frictions are considered. The third chapter (co-authored with Allan Timmermann) analyzes the limitations of parametric models in evaluating the relation between risk and return. By taking advantage of the flexible and semi-parametric nature of Boosted Regression Trees, we find evidence of a nonmonotonic relation between conditional volatility and expected stock market returns. At low and medium levels of conditional volatility there is a positive risk-return trade-off, but this relation is inverted at high levels of volatility. This finding helps explain the absence of a consensus in the empirical literature on the sign of the risk-return trade-off. We propose a new measure of risk based on the conditional covariance between observations of a broad economic activity index and stock market returns. Using this broader covariance-based risk measure, we find clear evidence of a positive and monotonic risk-returns trade- off