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LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

Author

Listed:
  • Kamil Kashif

    (University of Warsaw, Faculty of Economic Sciences)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning)

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 boost 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 spanning from January, 2000 to August, 2023. The architecture of testing is based on the walk-forward procedure which is applied for hyperparameter tunning phase that uses using 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 in order to present 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 what confirms strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the optimal algorithmic investment strategies.

Suggested Citation

  • Kamil Kashif & Robert Ślepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Working Papers 2024-07, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2024-07
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    File URL: https://www.wne.uw.edu.pl/download_file/4196/0
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    References listed on IDEAS

    as
    1. Illia Baranochnikov & Robert Ślepaczuk, 2022. "A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy," Working Papers 2022-21, Faculty of Economic Sciences, University of Warsaw.
    2. Vikram Bali & Ajay Kumar & Satyam Gangwar, 2020. "A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 11(3), pages 13-30, July.
    3. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
    4. Bui, Quynh & Ślepaczuk, Robert, 2022. "Applying Hurst Exponent in pair trading strategies on Nasdaq 100 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    5. Mateusz Kijewski & Robert Ślepaczuk, 2020. "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers 2020-27, Faculty of Economic Sciences, University of Warsaw.
    6. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    7. Rama K. Malladi & Prakash L. Dheeriya, 2021. "Time series analysis of Cryptocurrency returns and volatilities," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(1), pages 75-94, January.
    8. Sergio Castellano Gómez & Robert Ślepaczuk, 2021. "Robust optimisation in algorithmic investment strategies," Working Papers 2021-27, Faculty of Economic Sciences, University of Warsaw.
    9. Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(1), pages 1-60, February.

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    More about this item

    Keywords

    Deep Learning; Recurrent Neural Networks; Algorithmic Investment Strategy; LSTM; ARIMA; Hybrid/Ensemble Models; Walk-Forward Process;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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