This paper introduces a novel hypothesis in the context of share buybacks, proposing the existence of a "Free Lunch" phenomenon associated with the use of specific execution products. The hypothesis posits that these products can generate consistently positive fees, offering a consistent return...

This paper unveils the unexplored concept of a "Free Lunch" phenomenon within share buybacks, focusing on the inherent structure of buyback products. We propose a novel hypothesis, suggesting that the unique structural characteristics of share buyback transactions may create a consistent...

This paper studies a dynamic market microstructure model, in which a strategic market maker competes with an informed trader. We include the presence of noise traders and limit order traders in our setup. Our model is a N-period model. We give necessary and sufficient conditions for an...

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We...

Since the early beginning of investing as it was commonly seen as a form of gambling for the rich and wealthy, the idea of Harry Markowitz was revolutionising the way of thinking and how portfolios should be constructed. However, today the traditional mean-variance portfolios are still not fully...

Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its...

Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global...

This course on "The Art of Portfolio Management" explores the intersection of finance, mathematics, business, computer science, and economics in the context of portfolio management. The course is designed to provide the audience with a comprehensive understanding of the theoretical foundations,...

Generative Adversarial Networks (GANs) have shown remarkable success as a framework for trainingmodels to produce realistic-looking data. In this work, we propose a GAN to produce realistic real-valued time series, with an emphasis on their application to financial data.Our aim is having a GAN,...

We present an order flow model framework for limit order driven markets. Different from previous models we explicitly model a reference price process that “sweeps” the limit order book as it fluctuates up and down. Our framework allows us to use any stochastic process to model this reference...