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

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