Financial bubbles and crashes have repeatedly caused economic turmoil notably but not only during the 2008 financial crisis. However, both in the popular press as well as scientific publications, the meaning of bubble is sometimes unspecified. Due to the multitude of bubble definitions, we...

This paper identifies active addresses-to-network value as an additional common risk factor in the returns on cryptoassets. Investigating 652 cryptoassets, I find anomalous returns that increase with active addresses-to-network value ratio, a proxy for the value anomaly. Cryptoassets with a high...

This study empirically tests time-varying asset pricing models in an emerging market with individual stocks. We employ a recently proposed instrumental variables (IV) technique that uses individual stocks as test assets while consistently estimating ex-post risk premiums. This method differs...

We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito's lemma allows for the computation of exact...

Cross-predictability denotes the fact that some assets can predict other assets' returns. I propose a novel performance-based measure that disentangles the economic value of cross-predictability into two components: the predictive power of one asset's signal for other assets' returns...

We evaluate the performance of the Conditional Autoencoder (CAE) model by Gu et al. (2021) in an international context and under economic constraints, such as the exclusion of microcap and illiquid firms, and accounting for transaction costs. The CAE model leverages latent factors and factor...

This novel work is the first study in India to incorporate the Human capital (HC) factor as a six-factor asset-pricing model and presents a robust methodology. The aim of this work is to examine the ability of the six-factor model to capture excess returns using a GMM framework with time periods...

A multivariate normal mean-variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a...

Emerging stock markets are dominated with overconfident individual investors and in those markets short-sale constraints are frequently used to promote market stability. The unique market participants and regulations may influence the formation of stock prices and potentially lead to stock...

We propose a linearization of rule-based algorithms that reveals the most important interactions between characteristics and macroeconomic variables when explaining future stock returns. Our results suggest that the two types of predictors are intertwined, which implies that the relationships...

In March 2020, safe asset markets experienced surprising and unprecedented price crashes. We explain how strategic investor behavior can create such market fragility in a model with investors valuing safety, investors valuing liquidity, and constrained dealers. While safety investors and...

The price-bubble and crash formation process is theoretically investigated in a two-asset equilibrium model. Sufficient and necessary conditions are derived for the existence of average equilibrium price dynamics of different agent-based models, where agents are distinguished in terms of factor...

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered in this Primer on Empirical Asset Pricing include the basic questions in empirical asset pricing, how to make factors, how to use factors in factor models, regressions (time series, cross-sectional,...

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered include the factor zoo, replication in science and in finance, frequentist multiple testing adjustments, a Bayesian model to interpret factor evidence and preserve power in its multiple testing adjustment, and...

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered include an overview of machine learning, supervised and unsupervised learning, and general ML methods such as bias-variance trade-off, hyper-parameter tuning, train-validation-test methods, and feature...

We use deep neural networks to estimate time-varying equity risk premia. The key innovations are the nonlinear and non-parametric generalisation of Fama-Macbeth regressions through partial derivatives of an arbitrary estimator function with respect to its input and the introduction of Jacobian...

Artificial Intelligence (AI) models have been recently studied in finance to discover data patterns. However, generative AI, particularly in image synthesis, remains relatively unexplored. In this paper, we investigate the potential of generative AI to model unknown dynamics using stock chart...

Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of...