  This study focuses on building an algorithmic investment strategy employing a
hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA.
This unique algorithm uses LSTM to produce final predictions but boosts the
results of this RNN by adding the residuals obtained from ARIMA predictions
among other inputs. The algorithm is tested across three equity indices (S&P
500, FTSE 100, and CAC 40) using daily frequency data from January 2000 to
August 2023. The testing architecture is based on the walk-forward procedure
for the hyperparameter tunning phase that uses Random Search and backtesting
the algorithms. The selection of the optimal model is determined based on
adequately selected performance metrics focused on risk-adjusted return
measures. We considered two strategies for each algorithm: Long-Only and
Long-Short to present the situation of two various groups of investors with
different investment policy restrictions. For each strategy and equity index,
we compute the performance metrics and visualize the equity curve to identify
the best strategy with the highest modified information ratio. The findings
conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms
across all the equity indices which confirms the strong potential behind hybrid
ML-TS (machine learning - time series) models in searching for the optimal
algorithmic investment strategies.
