Financial markets provide a natural quantitative lab for understanding some of the most advanced human behaviours. Among them is the invention and use of mathematical tools known as financial instruments. Besides money, the two most fundamental financial instruments are bonds and equities. More...

Securities selection attempts to distinguish prospective winners from losers conditional on beliefs and available information. This article surveys relevant academic research on this subject, including work about the combining of forecasts (Bates and Granger 1969), the Black-Litterman model...

We develop a behavioral theory of real options that relaxes the informational and behavioral assumptions underlying applications of financial options theory to real assets. To do so, we augment real option theory's focus on uncertain future asset values (prospective uncertainty) with feedback...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...

Many consumption-based models succeed in matching long lists of asset price moments. We propose an alternative, full-information Bayesian evaluation that decomposes the price-dividend ratio (p/d) into contributions from long-run risks, habit, and a residual. We find that long-run risks account...

It has been in the literature since 1963 when Mandelbrot published The Variation of Certain Speculative Prices that returns on equity securities have heavy tails. In a 2014 article, Harris derives a mathematical reason these tails must be heavy. This proof in turn excludes mean-variance finance...

I examine whether incorporating economically-motivated prior information yields more accurate forecasts of industry costs of equity. I find that incorporating the long-run mean of the CAPM parameters and the industry characteristics in the cross section produces more accurate parameter...

The Intertemporal Capital Asset Pricing Model (ICAPM) predicts that an unobservable factor capturing changes in expected market returns should be priced in the cross section. My Bayesian framework accounts for uncertainty in the intertemporal risk factor and gauges the effects of prior...

We introduce a hierarchical Bayes approach to model conditional firm-level alphas as a function of firm characteristics. Our empirical framework is motivated by growing concerns in the literature regarding the reliability of inferences from portfolio-based methods. In our initial tests, we...