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A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction

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  • Orte, Francisco
  • Mira, José
  • Sánchez, María Jesús
  • Solana, Pablo

Abstract

In this study, a price prediction model for futures markets of crypto assets is presented. Random Forest was used to study three scenarios as a function of input variables: technical indicators, candlestick patterns and both simultaneously. In turn, the model parameters, the time intervals, and the most suitable investment horizons were studied. In addition to showing the results from the model, a one-year out-of-sample prediction was simulated. The entire year of 2020 was chosen because the three possible stock market scenarios occurred in this year: a sideways market, a bear market resulting from the global pandemic and an end-of-year bull market. Last, this out-of-sample simulation was analyzed as a real operation, that is, by retraining the model after each new collection of data, so that the model had the maximum information at all times. In conclusion, using candlestick patterns instead of technical indicators, improves the efficiency of the results.

Suggested Citation

  • Orte, Francisco & Mira, José & Sánchez, María Jesús & Solana, Pablo, 2023. "A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction," Research in International Business and Finance, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:riibaf:v:64:y:2023:i:c:s027553192200215x
    DOI: 10.1016/j.ribaf.2022.101829
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    Cited by:

    1. Xu, Yuhong & Zhao, Xinyao, 2024. "How does node centrality in a financial network affect asset price prediction?," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    2. Feng, Lingbing & Qi, Jiajun & Lucey, Brian, 2024. "Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    4. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Random Forest; Cryptocurrencies; Bitcoin; Technical indicators; Candlestick patterns;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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