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

Christian Pierdzioch and Marian Risse

International Journal of Finance & Economics, 2018, vol. 23, issue 4, 642-654

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.

Date: 2018
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