Application of machine learning in quantitative investment strategies on global stock markets
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Cited by:
- Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.
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Keywords
quantitative investment strategies; machine learning; neural networks; regression trees; random forests; support vector machine; technical analysis; equity stock indices; developed and emerging markets; information ratio;All these keywords.
JEL classification:
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-12-12 (Big Data)
- NEP-CMP-2022-12-12 (Computational Economics)
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