We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage...

This article provides an overview of existing community-contributed commands for executing event studies. I assess which command(s) could have been used to conduct event studies that have appeared in the past ten years in three leading accounting, finance and management journals. The older...

This paper discusses issues related to GPU for economic problems. It highlights new methodologies and resources that are available for solving and estimating economic models and emphasizes situations when they are useful and others where they are impractical. Two examples illustrate the...

The paper demonstrates the power of alternative data. Relying on the indicators obtained by mining online publicly available news articles, authors analyze their impact on Bitcoin returns. This research shows that in the first quarter of 2022 Bitcoin returns could be explained by the sentiment...

The estimation of the volatility with high-frequency data is plagued by the presence of microstructure noise, which leads to biased measures. Alternative estimators have been developed and tested either on specific structures of the noise or by the speed of convergence to their asymptotic...

Corporate earnings announcements unpack large bundles of information that should, if markets are efficient, almost surely trigger jumps in stock prices immediately after the news release. Testing this implication is difficult in practice because most earnings announcements occur in the...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I introduce an affine jump-diffusion model, which accounts for both the factor structure of asset returns and that of the variance of idiosyncratic returns. The estimation is...

Noisy markets need extensive descriptions that are noisy themselves, such as deep regression trees that capture many data-local nonlinear anomalies and that do not require arbitrary weighting schemes like traditional linear multifactor models often do. Simple tools allow extraction of general...