Robert Slepaczuk

According to our database, Robert Slepaczuk authored at least 20 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of five.

2026

Alternative loss function in evaluation of algorithmic investing strategies based on transformer models.

Jakub Michanków, Pawel Sakowski, Robert Slepaczuk

Pattern Recognit. Lett., 2026

A neural network informer in algorithmic investment strategies on high-frequency bitcoin data.

Filip Stefaniuk, Robert Slepaczuk

Expert Syst. Appl., 2026

2025

Optimal Markowitz portfolio using returns forecasted with time series and machine learning models.

Damian Slusarczyk, Robert Slepaczuk

J. Big Data, December, 2025

Supervised autoencoder MLP for financial time series forecasting.

Bartosz Bieganowski, Robert Slepaczuk

J. Big Data, December, 2025

EXFormer: A Multi-Scale Trend-Aware Transformer with Dynamic Variable Selection for Foreign Exchange Returns Prediction.

Dinggao Liu, Robert Slepaczuk, Zhenpeng Tang

CoRR, December, 2025

Stochastic Volatility Modelling with LSTM Networks: A Hybrid Approach for S&P 500 Index Volatility Forecasting.

Anna Perekhodko, Robert Slepaczuk

CoRR, December, 2025

Alternative Loss Function in Evaluation of Transformer Models.

Jakub Michanków, Pawel Sakowski, Robert Slepaczuk

CoRR, July, 2025

Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data.

Filip Stefaniuk, Robert Slepaczuk

CoRR, March, 2025

LSTM-ARIMA as a hybrid approach in algorithmic investment strategies.

Kamil Kashif, Robert Slepaczuk

Knowl. Based Syst., 2025

Predictive modeling of foreign exchange trading signals using machine learning techniques.

Sugarbayar Enkhbayar, Robert Slepaczuk

Expert Syst. Appl., 2025

Evaluating the Chronos Foundation Model for Daily Stock Index Forecasting.

Stanislaw Laniewski, Robert Slepaczuk

Proceedings of the Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation, 2025

2024

Mean Absolute Directional Loss as a new loss function for machine learning problems in algorithmic investment strategies.

Jakub Michanków, Pawel Sakowski, Robert Slepaczuk

J. Comput. Sci., 2024

Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data.

Bartosz Bieganowski, Robert Slepaczuk

CoRR, 2024

Enhancing literature review with LLM and NLP methods. Algorithmic trading case.

Stanislaw Laniewski, Robert Slepaczuk

CoRR, 2024

Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data.

Zuzanna Kostecka, Robert Slepaczuk

CoRR, 2024

Hedging Properties of Algorithmic Investment Strategies Using Long Short-Term Memory and Time Series Models for Equity Indices.

Jakub Michanków, Pawel Sakowski, Robert Slepaczuk

Proceedings of the Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era, 2024

Predicting Prices Of S&P 500 Index Using Classical Methods and Recurrent Neural Networks.

Mateusz Kijewski, Robert Slepaczuk, Maciej Wysocki

Proceedings of the Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era, 2024

2022

LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index.

Jakub Michanków, Pawel Sakowski, Robert Slepaczuk

Sensors, 2022

Artificial Neural Networks Performance in WIG20 Index Options Pricing.

Maciej Wysocki, Robert Slepaczuk

Entropy, 2022

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index.

Nguyen Vo, Robert Slepaczuk

Entropy, 2022