Using Hurst Exponent and Machine Learning to Build a Predictive Model for the Jamaica Frontier Market

Abstract

The Jamaica Stock Exchange (JSE) has been defined by Standard and Poor's as a frontier market. It has undergone periods where trading gains exceeded that of major markets such as the London Stock Exchange. The randomness of the JSE was investigated over the period 2001-2014, using statistical tests and the Hurst exponent to reveal periods when the JSE did not follow a random walk. This chapter focuses on machine learning algorithms including decision trees, neural networks and support vector machines used to predict the JSE. Selected algorithms were applied to trading data over a 22 month period for price and trend forecasting and a 12-year period for volume forecasts. Experimental results show 90 % accuracy in the movement prediction with mean absolute error of 0.4 and 0.95 correlation coefficient for price prediction. Volume predictions were enhanced by a discretization method and support vector machine to yield over 70 % accuracy.