Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices

Jakub Michańków, Paweł Sakowski and Robert Ślepaczuk
Additional contact information
Paweł Sakowski: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance

No 2023-25, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data.

Keywords: machine learning; recurrent neural networks; long short-term memory; algorithmic investment strategies; testing architecture; loss function; walk-forward optimization; over-optimization (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-fmk and nep-inv
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.wne.uw.edu.pl/download_file/3347/0 First version, 2023 (application/pdf)

Related works:
Working Paper: Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2023-25

Access Statistics for this paper

More papers in Working Papers from Faculty of Economic Sciences, University of Warsaw Contact information at EDIRC.
Bibliographic data for series maintained by Marcin Bąba ().