We reexamine long-term abnormal returns for portfolios sorted on governance characteristics. Firms with strong shareholder rights and firms with weak shareholder rights differ from the population of firms and from each other in how they cluster across industries. Using well-specified tests under...

A tree-structured linear and quantile regression framework is proposed for the analysis and modeling of equity market returns. The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian...

This paper employs a deep learning approach for linking stock market fundamentals to trading signals via neural networks. With an average accuracy of ~54% the model predicts whether markets go up or down on the subsequent trading day. Coupling the prediction to a binary long/cash strategy yields...

The return dynamics of Argentina's main stock index, the SP Mer.Val., show a high level of volatility, signaling a higher degree of downside risk. To hedge against that specific risk, investors could buy put options. However, the Argentinean capital markets lacks variety of hedging contracts....

The purpose of this study is to evaluate the linkage between Environmental, Social, and Governance (ESG) ratings and the stock performance of the top 15 Indian companies with low-ESG-risk as well as the top 15 Indian companies with high-ESG-risk. The existing literature on whether ESG leads to...

Recent developments allow a nonparametric separation of the continuous sample path component and the jump component of realized volatility. The jump component has very different time series properties than the continuous component, and accounting for this allows improved forecasting of future...

Recent developments allow a nonparametric separation of the continuous sample path component and the jump component of realized volatility. The jump component has very different time series properties than the continuous component, and accounting for this allows improved forecasting of future...

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on...