We use high-frequency tick data to study stylized facts of the return and volatility dynamics of the nine most liquid cryptocurrencies. Factor structures exist in both returns and volatility, but the explanatory power from the common factor is much stronger for volatility. The factor structures...

We present a machine learning approach to firm valuation that requires only historical accounting data as input. The machine learning model generates a median absolute percentage error of 17.2% in out-of-sample firm value predictions. The model out-performs a sample of final-year finance...

This paper develops a rare disaster asset pricing model with EZ preferences, in particular including the impact of macroeconomic consequences of the COVID-19 disaster. I estimate the probability of disaster, disaster states, and the duration of disaster to shed light on the frequency and size of...

There is a gap in the existing literature for models linking prices in prediction markets with those for financial markets. We bridge this gap using a model based on the assumption that a binary political event has a constant effect on the difference of the conditional expectations of financial...

Artificial intelligence (AI), powered by machine learning algorithms, is capable of extracting information efficiently from big data and, therefore, has great potential for improving financial decision-making. In this chapter, we summarize several important applications of AI in this context....

Asset returns exhibit grouped heterogeneity, and a “one-size-fits-all” model has been elusive empirically. This paper proposes a Bayesian Clustering Model (BCM) combining Bayesian factor selection and panel tree for asset clustering. The Bayesian model marginal likelihood guides the tree...

The Internet Appendix collects additional empirical results supporting the main text. We show that our imputation results are robust to different masking mechanisms and over time. We also confirm that our imputation results are robust with respect to filters based on market capitalization, share...

This paper studies the implied rate of return, in particular during COVID-19 to see whether and how it is affected by firms’ resilience. The research investigates the cross-sectional heterogeneity in discount rates based on resilience. Specifically, the novelty of the paper is to provide...

We propose and evaluate a variety of penalized regression methods for forecasting and economic decision making in a data-rich environment under parameter uncertainty. Empirically, we explore the statistical and economic performance across different asset classes such as stocks, bonds, and...

Basic asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent an anomaly due to frictions or behavioral biases. We propose two complementary, simple-to-use tests to assess whether risk can explain differences in...