Hedging Equity Index Strategies with Diversified Algorithmic Strategies

Summary

The paper studies whether strategies built on non-equity assets can diversify algorithmic investment strategies tied to the S&P 500. It compares signals generated from LSTM forecasts, ARIMA-GARCH forecasts, momentum, and contrarian rules. The assets considered include energy commodities, precious metals, cryptocurrencies, and soft commodities. This shifts the diversification question from combining individual assets to combining strategies that trade those assets.

Using data from 2004 to 2022, the study reports that LSTM-based strategies performed better than the other approaches and that a Bitcoin-based strategy was the strongest diversifier for the S&P 500 strategy ensemble. It also examines hourly LSTM signals and reports better results than with daily data. These findings are empirical and specific to the assets, period, and strategy construction examined; the summary provides no detail on transaction costs, risk adjustment, or out-of-sample validation.

Key ideas

• The study evaluates diversification between algorithmic strategies rather than only between individual assets.
• It compares LSTM and ARIMA-GARCH forecasts with momentum and contrarian signals.
• The tests cover several commodity and cryptocurrency strategies alongside an S&P 500 strategy ensemble.
• The authors report that Bitcoin-based strategies provided the strongest diversification for the S&P 500 ensemble.
• Hourly LSTM strategies reportedly outperformed their daily counterparts in the study.

Tags

# 2309.15640


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.