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This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compared to Buy and Hold benchmark and evaluated using Performance Metrics, that is Annualized Return Compounded, Maximum Drawdown, Maximum Loss Duration, and three types of Information Ratio. This research uses daily S&P 500 index data ranging from 2000 to 2023. Every strategy was optimized with novel walk forward approach consisting of numerous in sample and out of sample periods. MAC and best performing ML methods were subjected to sensitivity analysis. The results show that LSTM ensembled with XGBoost and MAC yields the most promising results in terms of risk-adjusted returns which suggest further research focused on ensembling of individual ML strategies. Finally, we show that classical methods of technical analysis (that is, MAC) are much less robust and indifferent to change in hyperparameters than machine learning based algorithms, especially LSTM

Author

Listed:
  • Karol Chojnacki

    (University of Warsaw, Faculty of Economic Sciences)

  • Robert Ślepaczuk

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

Abstract

No abstract is available for this item.

Suggested Citation

  • Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2023-15
    as

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    File URL: https://www.wne.uw.edu.pl/download_file/2922/0
    File Function: First version, 2023
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    References listed on IDEAS

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    1. Andrea Frattini & Ilaria Bianchini & Alessio Garzonio & Lorenzo Mercuri, 2022. "Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data," Risks, MDPI, vol. 10(12), pages 1-24, November.
    2. Chlebus Marcin & Dyczko Michał & Woźniak Michał, 2021. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem," Central European Economic Journal, Sciendo, vol. 8(55), pages 44-62, January.
    3. Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
    4. James, F. E., 1968. "Monthly Moving Averages—An Effective Investment Tool?*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 315-326, September.
    5. Sergio Castellano Gómez & Robert Ślepaczuk, 2021. "Robust optimisation in algorithmic investment strategies," Working Papers 2021-27, Faculty of Economic Sciences, University of Warsaw.
    6. 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.
    7. Yizhuo Li & Peng Zhou & Fangyi Li & Xiao Yang, 2021. "An Improved Reinforcement Learning Model Based on Sentiment Analysis," Papers 2111.15354, arXiv.org.
    8. Jakub Drahokoupil, 2022. "Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period," FFA Working Papers 4.006, Prague University of Economics and Business, revised 09 May 2022.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Algorithmic Investment Strategies; Machine Learning; Recurrent Neural Networks; Long Short-Term Memory; XGBoost; Walk Forward Optimization; Trading algorithms; Technical Analysis Indicators;
    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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