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Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach

Author

Listed:
  • Yue-Jun Zhang

    (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China)

  • Han Zhang

    (Business School, Hunan University, Changsha 410082, China; Center for Resource and Environmental Management, Hunan University, Changsha 410082, China)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

Abstract

Forecasting of the artificial intelligence index returns is of great significance for financial market stability and the development of artificial intelligence industry. To provide investors more reliable reference in terms of artificial intelligence index investment, this paper selects the Nasdaq CTA Artificial Intelligence and Robotics (AI) Index as the research target, and proposes novel hybrid methods to forecast the AI index returns by considering its nonlinear and time-varying characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method to decompose the AI index returns, and combines the least square support vector machine approach together with the particle swarm optimization (PSO-LSSVM) method and the generalized autoregressive conditional heteroskedasticity (GARCH) model to construct novel hybrid forecasting methods. The empirical results indicate that: first, the decomposition and integration models usually produce superior forecasting accuracy than the single forecasting models, due to the complicated feature of the non-decomposed data. Second, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-GARCH model) which combines the advantage of traditional econometric models and machine learning techniques can yield the optimal forecasting performance for the AI index returns.

Suggested Citation

  • Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021. "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers 202182, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202182
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    More about this item

    Keywords

    AI index return forecasting; PSO-LSSVM model; GARCH model; Decomposition and integration model; Combination model;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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