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

TL;DR

This paper introduces a novel ensemble approach using LSTM and other models to hedge equity index portfolios, demonstrating LSTM's superior performance and Bitcoin as an effective diversifier, with improved results at higher data frequencies.

Contribution

The paper presents a new ensemble diversification method for hedging equity indices using multiple models, highlighting LSTM's effectiveness and Bitcoin's diversification potential.

Findings

LSTM-based strategies outperform other models in hedging.

Bitcoin is the most effective diversifier for the S&P 500.

Higher frequency data improves LSTM model performance.

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.