Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices
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- Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Papers 2309.15640, arXiv.org.
References listed on IDEAS
- Andreas Park & Hamid Sabourian, 2011.
"Herding and Contrarian Behavior in Financial Markets,"
Econometrica, Econometric Society, vol. 79(4), pages 973-1026, July.
- Park, A. & Sabourian, H., 2009. "Herding and Contrarian Behaviour in Financial Markets," Cambridge Working Papers in Economics 0939, Faculty of Economics, University of Cambridge.
- Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
- Dobrynskaya, Victoria, 2019.
"Avoiding momentum crashes: Dynamic momentum and contrarian trading,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
- Victoria Dobrynskaya, 2019. "Avoiding Momentum Crashes: Dynamic Momentum and Contrarian Trading," Proceedings of International Academic Conferences 9912063, International Institute of Social and Economic Sciences.
- Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
- Joel Ong & Dorien Herremans, 2023. "Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning," Papers 2306.13661, arXiv.org.
- Kadoya, Susumu & Kuroko, Takashi & Namatame, Takashi, 2008. "Contrarian investment strategy with data envelopment analysis concept," European Journal of Operational Research, Elsevier, vol. 189(1), pages 120-131, August.
- Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
- Ślepaczuk Robert & Sakowski Paweł & Zakrzewski Grzegorz, 2018. "Investment Strategies that Beat the Market. What Can We Squeeze from the Market?," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(4), pages 36-55, December.
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More about this item
Keywords
machine learning; recurrent neural networks; long short-term memory; algorithmic investment strategies; testing architecture; loss function; walk-forward optimization; over-optimization;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-2023-11-06 (Big Data)
- NEP-CMP-2023-11-06 (Computational Economics)
- NEP-ETS-2023-11-06 (Econometric Time Series)
- NEP-FMK-2023-11-06 (Financial Markets)
- NEP-INV-2023-11-06 (Investment)
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