6 papers
MLP-Initialized Reinforcement Learning for Exit Timing in SPX Short-Straddle Portfolios
Jianning Wang, Robert Ślepaczuk, Paweł Sakowski
This paper studies exit timing for SPX short-straddle portfolios using a two-stage supervised and reinforcement-learning framework. Historical episodes first generate ex-post optim…
Randomized Neural Networks for estimation of exposure profiles and Credit Valuation Adjustment (CVA) for American Equity Options
Isidro Moroso Varona, Jakub Michańków, Paweł Sakowski
This paper studies the use of randomized neural networks for the estimation of exposure profiles and unilateral CVA of American options within a Monte Carlo framework. The analysis…
Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks
Szymon Lis, Robert Ślepaczuk, Paweł Sakowski
This paper investigates whether short-term market overreactions can be systematically predicted and monetized as momentum signals using high-frequency emotional information and mod…
Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
Zofia Bracha, Paweł Sakowski, Jakub Michańków
This paper explores the application of deep Q-learning to hedging at-the-money options on the S\&P~500 index. We develop an agent based on the Twin Delayed Deep Deterministic Polic…
Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Paweł Sakowski
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovari…
Can Artificial Intelligence Trade the Stock Market?
Jędrzej Maskiewicz, Paweł Sakowski
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (…