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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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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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Machine learning in algorithmic trading strategy optimization - implementation and efficiency
Ryś, Przemysław; Ślepaczuk, Robert - Uniwersytet Warszawski / Wydział Nauk Ekonomicznych - 2018
Persistent link: https://www.econbiz.de/10011981706
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Predictive modeling of foreign exchange trading signals using machine learning techniques
Enkhbayar, Sugarbayar; Ślepaczuk, Robert - 2024
Persistent link: https://www.econbiz.de/10014634708
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Improving realized LGD approximation : a novel framework with XGBoost for handling missing cash-flow data
Kostecka, Zuzanna; Ślepaczuk, Robert - 2024
Persistent link: https://www.econbiz.de/10014634855
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Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index
Kryńska, Katarzyna; Ślepaczuk, Robert - 2022
Persistent link: https://www.econbiz.de/10013473995
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Application of machine learning in algorithmic investment strategies on global stock markets
Grudniewicz, Jan; Ślepaczuk, Robert - In: Research in international business and finance 66 (2023), pp. 1-24
Persistent link: https://www.econbiz.de/10014463386
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Mean absolute directional loss as a new loss function for machine learning problems in algorithmic investment strategies
Michańków, Jakub; Sakowski, Paweł; Ślepaczuk, Robert - 2023
Persistent link: https://www.econbiz.de/10014448222
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The performance of time series forecasting based on classical and machine learning methods for S&P 500 index
Uzzal, Maudud Hassan; Ślepaczuk, Robert - 2023
Persistent link: https://www.econbiz.de/10014305886
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Ensembled LSTM with walk forward optimization in algorithmic trading
Chojnacki, Karol; Ślepaczuk, Robert - 2023
Persistent link: https://www.econbiz.de/10014308890