To confront the challenge that disaster risk is “dark matter” in finance, we construct an objective measure of disaster risk, which is able to predict half of GDP crashes in a sample of 20 advanced economies between 1870 and 2021. Despite this significant predictability, we find no...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

Empirical evidence suggests that investor protection has significant effects on ownership concentration and asset prices. We develop a dynamic asset pricing model to address the empirical regularities and uncover some of the underlying mechanisms at play. Our model features a controlling...

We exploit the merger between BlackRock and Barclays Global Investors to study how changes in expected ownership concentration affect the investment behavior of funds and the cross-section of stocks worldwide. We find that funds with open-end structures and a large exposure to commonly-held...

With the continuous questioning of the efficient market hypothesis and the booming development of behavioral finance theory, the basic framework of financial economics becomes increasingly blurred. Meanwhile, the adaptive market hypothesis proposed by Lo (2004), underscoring time-varying market...

This paper finds that the majority of stock price movements remain unexplained after controlling for both public and private information. This suggests that economists' inability to explain asset price movements is the result of either noise or naive asset pricing models.

In this paper I study the relationship between rationality and asset prices when agents have heterogeneous and incorrect beliefs about future events. Using the fully rational pricing as a benchmark, I show that when agents behave according to the Subjective Generalized Kelly rule (Bottazzi et...

This study develops an agent-based computational stock market model in which each trader’s buying and selling decisions are endogenously determined by multiple factors: namely, firm profitability, past stock price movement, and imitation of other traders. Each trader can switch from being a...