Hedging Properties of Algorithmic Investment Strategies using Long
  Short-Term Memory and Time Series models for Equity Indices
Jakub Michańków Paweł Sakowski Robert Ślepaczuk
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.