3 citations · 11 across the 24 of their papers we have counts for
13 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…
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…
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…
Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting
Anna Perekhodko, Robert Ślepaczuk
Accurate volatility forecasting is essential in banking, investment, and risk management, because expectations about future market movements directly influence current decisions. T…
Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempień, Robert Ślepaczuk
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning a…
Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data
Filip Stefaniuk, Robert Ślepaczuk
The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with di…
Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data
Bartosz Bieganowski, Robert Ślepaczuk
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy p…
The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models
Natalia Roszyk, Robert Ślepaczuk
Predicting the S&P 500 index volatility is crucial for investors and financial analysts as it helps assess market risk and make informed investment decisions. Volatility represents…
LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies
Kamil Kashif, Robert Ślepaczuk
This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm u…
Supervised Autoencoder MLP for Financial Time Series Forecasting
Bartosz Bieganowski, Robert Slepaczuk
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy…