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person:"Koskela, Erkki"
type_genre:"Non-commercial literature"
~isPartOf:"Discussion paper / Economics series / Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"
~isPartOf:"Discussion paper series / IZA"
~isPartOf:"Working papers"
~person:"Güth, Werner"
~person:"Ślepaczuk, Robert"
~subject:"Neural networks"
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Koskela, Erkki
Güth, Werner
Ślepaczuk, Robert
Chlebus, Marcin
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Michańków, Jakub
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Discussion paper / Economics series / Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
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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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2
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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3
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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4
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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5
A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy
Baranochnikov, Illia
;
Ślepaczuk, Robert
-
2022
Persistent link: https://www.econbiz.de/10013473692
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6
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:
7
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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