Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

Machine Learning algorithms have been widely used and proven effective in financial markets. In this paper, we introduced a Machine Learning model set trained on the residual factors from the Fama-French three-factor model (Fama and French, 1992) to find significant alpha factors. To include...

This thesis presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM embedding. The model is built to further stabilize the performance of the...

Lotteries are a curious phenomenon in financial markets, as they seem to contradict traditional utility models that predict rational behavior under uncertainty. Despite this, lotteries continue to attract the interest of many investors who knowingly or unknowingly trade their expectations of...

Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short...

We cardinally and ordinally rank distribution functions (CDFs). We present a new class of statistics, maximal adjusted quantiles, and show that a statistic is invariant with respect to cardinal shifts, preserves least upper bounds with respect to the first order stochastic dominance relation,...

The central question addressed in this note is whether it is better to sell (and re-purchase) appreciated assets now and pay today's long-term capital gains tax rate, or wait to realize gains in the future and pay a likely higher capital gains tax rate. The authors argue that a framework based...

We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

Factor investing has been around for several decades, backed by an enormous body of literature, and yet it is still surrounded by much confusion and debate. Some of the rhetoric and myths have existed for a long time, while others have arisen in response to the difficult performance from 2018 to...

A generative model is a statistical model of the joint probability distribution. We built a generative model for univariate time series in finance using a Variational Autoencoder (VAE) neural network architecture. We test the model in SP500 and the Heston Model widely used for option pricing and...