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LSTM-ARIMA as a hybrid approach in algorithmic investment strategies
Kashif, Kamil
;
Ślepaczuk, Robert
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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
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3
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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4
Hedging properties of algorithmic investment strategies using long short-term memory and time series models for equity indices
Michańków, Jakub
;
Sakowsk, Paweł
;
Ślepaczuk, Robert
-
2023
Persistent link: https://www.econbiz.de/10014448237
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5
The performance of time series forecasting based on classical and machine learning methods for S&P 500 index
Uzzal, Maudud Hassan
;
Ślepaczuk, Robert
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2023
Persistent link: https://www.econbiz.de/10014305886
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6
Ensembled LSTM with walk forward optimization in algorithmic trading
Chojnacki, Karol
;
Ślepaczuk, Robert
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2023
Persistent link: https://www.econbiz.de/10014308890
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7
A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy
Baranochnikov, Illia
;
Ślepaczuk, Robert
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2022
Persistent link: https://www.econbiz.de/10013473692
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8
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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9
The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index
Thi Thu Giang Nguyen
;
Ślepaczuk, Robert
-
2022
Persistent link: https://www.econbiz.de/10013474013
Saved in:
10
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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