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

TL;DR

This paper introduces an autoregressive support vector regression model utilizing Fourier features and a turning point indicator to predict local extrema in financial time series, demonstrating improved trading performance over previous neural network approaches.

Contribution

The paper presents a novel autoregressive support vector regression approach for turning point prediction, outperforming neural network models in financial data analysis.

Findings

Superior trading performance compared to neural network models

Quantifiable advantage over buy-and-hold strategy

Effective prediction of local extrema in financial series

Abstract

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.