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Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - 2021
Persistent link: https://www.econbiz.de/10012816704
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GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks
Buczyński, Mateusz; Chlebus, Marcin - 2021
Persistent link: https://www.econbiz.de/10012795155
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Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts
Lis, Szymon; Chlebus, Marcin - 2021
Persistent link: https://www.econbiz.de/10012795166
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Towards better understanding of complex machine learning models using explainable Artificial Intelligence (XAI) : case of Credit Scoring modelling
Kłosok, Marta; Chlebus, Marcin - 2020
Persistent link: https://www.econbiz.de/10012322173
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Artificial Neural Networks Performance in WIG20 Index Options Pricing
Wysockia, Maciej; Ślepaczuk, Robert - 2020
Persistent link: https://www.econbiz.de/10012322176
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Predicting prices of S&P500 index using classical methods and recurrent neural networks
Kijewskia, Mateusz; Ślepaczuk, Robert - 2020
Persistent link: https://www.econbiz.de/10012322224
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Fractional differentiation and its use in machine learning
Gajda, Janusz; Walasek, Rafał - 2020
Persistent link: https://www.econbiz.de/10012322246
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A comparison of credit scoring techniques in Peer-to-Peer lending
Dzik-Walczak, Aneta; Heba, Mateusz - 2019
Persistent link: https://www.econbiz.de/10012196591
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LSTM-ARIMA as a hybrid approach in algorithmic investment strategies
Kashif, Kamil; Ślepaczuk, Robert - 2024
Persistent link: https://www.econbiz.de/10014634690
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The hybrid forecast of S&P 500 volatility ensembled from VIX, GARCH and LSTM models
Roszyk, Natalia; Ślepaczuk, Robert - 2024
Persistent link: https://www.econbiz.de/10014634883