3 citations · 11 across the 24 of their papers we have counts for
9 papers · 1 filter
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…