3 citations · 11 across the 24 of their papers we have counts for
26 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…
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…
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…
Alternative Loss Function in Evaluation of Transformer Models
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
The proper design and architecture of testing machine learning models, especially in their application to quantitative finance problems, is crucial. The most important aspect of th…
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…
Generalized Mean Absolute Directional Loss for Machine Learning Trading Models
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
The article presents and evaluates a custom loss function designed specifically for machine learning models used in algorithmic trading. Regardless of the selected asset class and…
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…
Enhancing literature review with LLM and NLP methods. Algorithmic trading case
Stanisław Łaniewski, Robert Ślepaczuk
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we ide…
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…
Construction and Hedging of Equity Index Options Portfolios
Maciej Wysocki, Robert Ślepaczuk
This research presents a comprehensive evaluation of systematic index option-writing strategies, focusing on S&P500 index options. We compare the performance of hedging strategies…
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…
Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data
Zuzanna Kostecka, Robert Ślepaczuk
The scope for the accurate calculation of the Loss Given Default (LGD) parameter is comprehensive in terms of financial data. In this research, we aim to explore methods for improv…
Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market
Adam Korniejczuk, Robert Ślepaczuk
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machin…
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…
Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices
Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to di…