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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
Saved in:
2
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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3
Artificial Neural Networks Performance in WIG20 Index Options Pricing
Wysockia, Maciej
;
Ślepaczuk, Robert
-
2020
Persistent link: https://www.econbiz.de/10012322176
Saved in:
4
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
Saved in:
5
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
Saved in:
6
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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7
Ensembled LSTM with walk forward optimization in algorithmic trading
Chojnacki, Karol
;
Ślepaczuk, Robert
-
2023
Persistent link: https://www.econbiz.de/10014308890
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8
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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9
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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10
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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