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A machine‐learning analysis of the rationality of aggregate stock market forecasts

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  • Christian Pierdzioch
  • Marian Risse

Abstract

We use a machine‐learning algorithm known as boosted regression trees (BRT) to implement an orthogonality test of the rationality of aggregate stock market forecasts. The BRT algorithm endogenously selects the predictor variables used to proxy the information set of forecasters so as to maximize the predictive power for the forecast error. The BRT algorithm also accounts for a potential non‐linear dependence of the forecast error on the predictor variables and for interdependencies between the predictor variables. Our main finding is that, given our set of predictor variables, the rational expectations hypothesis (REH) cannot be rejected for short‐term forecasts and that there is evidence against the REH for longer term forecasts. Results for three different groups of forecasters corroborate our main finding.

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  • Christian Pierdzioch & Marian Risse, 2018. "A machine‐learning analysis of the rationality of aggregate stock market forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 642-654, October.
    • Handle: RePEc:wly:ijfiec:v:23:y:2018:i:4:p:642-654
    DOI: 10.1002/ijfe.1641
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    Cited by:

    1. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    2. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.

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