  This work is concerned with autoregressive prediction of turning points in
financial price sequences. Such turning points are critical local extrema
points along a series, which mark the start of new swings. Predicting the
future time of such turning points or even their early or late identification
slightly before or after the fact has useful applications in economics and
finance. Building on recently proposed neural network model for turning point
prediction, we propose and study a new autoregressive model for predicting
turning points of small swings. Our method relies on a known turning point
indicator, a Fourier enriched representation of price histories, and support
vector regression. We empirically examine the performance of the proposed
method over a long history of the Dow Jones Industrial average. Our study shows
that the proposed method is superior to the previous neural network model, in
terms of trading performance of a simple trading application and also exhibits
a quantifiable advantage over the buy-and-hold benchmark.
