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This work is concerned with autoregressive prediction of turning points infinancial price sequences. Such turning points are critical local extremapoints along a series, which mark the start of new swings. Predicting thefuture time of such turning points or even their early or late identificationslightly before or after the fact has useful applications in economics andfinance. Building on recently proposed neural network model for turning pointprediction, we propose and study a new autoregressive model for predictingturning points of small swings. Our method relies on a known turning pointindicator, a Fourier enriched representation of price histories, and supportvector regression. We empirically examine the performance of the proposedmethod over a long history of the Dow Jones Industrial average. Our study showsthat the proposed method is superior to the previous neural network model, interms of trading performance of a simple trading application and also exhibitsa quantifiable advantage over the buy-and-hold benchmark.

Autoregressive short-term prediction of turning points using support vector regression