Working Papers

Abstract: We propose, compare, and evaluate a variety of machine learning methods for bond return predictability in the context of regression-based forecasting and contribute to a growing literature that aims to understand the usefulness of machine learning in empirical asset pricing. The main results show that non-linear methods can be highly useful for the out-of-sample prediction of bond excess returns compared to benchmarking data compression techniques such as linear principal component regressions. Also, the empirical evidence show that macroeconomic information has substantial incremental out-of-sample forecasting power for bond excess returns across maturities, especially when complex non-linear features are introduced via ensembled deep neural networks.

Abstract: We study the effect of the predictability of order imbalance on market quality. We measure the degree of predictability by using the predictive likelihood from a dynamic linear model where the dependent variable is the day-ahead order imbalance. Empirically, we show that increasing order imbalance predictability corresponds to significantly higher market liquidity and efficiency. This positive relationship is economically significant: a long-short portfolio based on past predictability generates significant risk-adjusted returns. Predictability of order imbalance measures a cost of asymmetric information that is not captured by traditional measures of adverse selection. The risk factor that is associated with asymmetric information is priced in the cross-section of stock returns, controlling for a variety of conventional sources of systematic risk. These results suggest the existence of a tight link between market microstructure features affecting order imbalance predictability and both market quality and the cost of capital of firms.

Past Academic Work