Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic...

Can environmentally-minded investors impact the cost of capital of green firms even when they invest through financial intermediaries? To answer this and related questions, I build an equilibrium intermediary asset pricing model with three investors, two risky assets, and a riskless bond....

This paper aims to deliver a comprehensive analysis of the theories and concepts that have formed the foundational link between two separate academic fields: finance and economics, resulting in the emergent field of financial economics. The main schools of thought can be divided, with a...

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...

Comparing the Efficient Market Hypothesis and Behavioral Finance, the Adaptive Markets Hypothesis (AMH), which identifies the extremes of these two hypotheses and adapts them to each other, argues that calendar anomalies can coexist, but also focuses on how investor behavior reacts to changing...

This paper examines the pricing effect of cross-sectional patterns in the cryptocurrency market, aiming to enhance the composition of asset pricing factors for a better explanation of cross-sectional variability in cryptocurrency returns.The study utilizes data from 1,160 cryptocurrencies...

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...

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...

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...

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...

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...