14 papers
When the Fed Speaks: Dynamics and Forecasts of the Volatility Surface
Lukasz Adamski, Robert Slepaczuk
Our primary goal is to forecast and empirically examine the evolution of the implied volatility (IV) surface, with particular focus on the dates of scheduled meetings of the Federa…
Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
Damian Lebiedź, Robert Ålepaczuk
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurre…
Volatility Forecasting and Return Prediction under Market Regimes: Evidence from High-Frequency Chinese Equity Data
Xinyue Fang, Robert Ålepaczuk
This study investigates whether regime-dependent volatility forecasting and machine-learning-based return prediction can be jointly integrated to improve both statistical forecasti…
Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting
Andrei Bysik, Robert Ålepaczuk
This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs. Usi…
Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
Kamil Kashif, Robert Ålepaczuk
This study develops and evaluates a deep reinforcement learning framework for dynamic portfolio allocation across global equity markets. The Soft Actor-Critic algorithm is used to…
Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks
Szymon Lis, Robert Ålepaczuk, PaweÅ 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…