LSTM-ARIMA as a hybrid approach in algorithmic investment strategies

Kamil Kashif · Robert Ślepaczuk

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

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 combination of LSTM and ARIMA leverages the strengths of both models, with LSTM effectively capturing non-linear patterns in the data, while ARIMA contributes by modeling linear relationships and accounting for autocorrelations, thereby enhancing the overall predictive power and accuracy of the hybrid model. The base LSTM-ARIMA strategy was then compared to RandomForest-ARIMA and Buy&Hold strategy. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data spanning from January 2000 to August 2023. The architecture of testing is based on the walk-forward procedure which is applied 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 combining 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 (( I R ∗ ∗ )). 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.

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References

2009Time series analysis forecasting and control 2018A Comparison of ARIMA and LSTM in Forecasting Time Series 2014Stock Price Prediction Using the ARIMA Model 2017Stock market's price movement prediction with LSTM neural networks 2018NSE Stock Market Prediction Using Deep-Learning Models 2022Predicting stock market index using LSTM 2014Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices 2017Predicting Stock Prices Using LSTM

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