3 citations · 11 across the 24 of their papers we have counts for
10 papers · 1 filter
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…
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 Ślepaczuk
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 Ślepaczuk
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 Ślepaczuk
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 Ślepaczuk
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 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…
A novel approach to trading strategy parameter optimization using double out-of-sample data and walk-forward techniques
Tomasz Mroziewicz, Robert Ślepaczuk
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading…
Explainable Patterns in Cryptocurrency Microstructure
Bartosz Bieganowski, Robert Ślepaczuk
We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive i…
EXFormer: A Multi-Scale Trend-Aware Transformer with Dynamic Variable Selection for Foreign Exchange Returns Prediction
Dinggao Liu, Robert Ślepaczuk, Zhenpeng Tang
Accurately forecasting daily exchange rate returns represents a longstanding challenge in international finance, as the exchange rate returns are driven by a multitude of correlate…