LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

TL;DR

This paper presents a hybrid LSTM-ARIMA model for algorithmic investment that combines neural networks and traditional time series methods, demonstrating superior performance across major equity indices.

Contribution

Introduces a novel hybrid LSTM-ARIMA approach for investment strategies, integrating residuals to enhance prediction accuracy and outperform existing models.

Findings

LSTM-ARIMA outperforms other models across all tested indices.

Hybrid model improves risk-adjusted returns.

Effective for both Long-Only and Long-Short strategies.

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 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.