Using Recommendations for Trade Returns Prediction with Machine Learning

Abstract

Automatically predicting stock market behavior using machine learning and/or data mining technologies is quite a challenging and complex task due to its dynamic nature and intrinsic volatility across global financial markets. Forecasting stock behavior solely based on historical prices may not perform well due to continuous, dynamic and in general unpredictable influence of various factors, e.g. economic status, political stability, voiced leaders’ opinions, emergency events, etc., which are often not reflected in historic data. It is, therefore, useful to look at other data sources for predicting direction of market movement. The objective of ISMIS 2017 Data Mining Competition was to verify whether experts’ recommendations can be used as a reliable basis for making informed decisions regarding investments in stock markets. The task was to predict a class of a return from an investment in different assets over the next three months, using only opinions given by financial experts. To address it, the trading prediction is formulated as a 3-class classification problem solved within supervised machine learning domain. Specifically, a hybrid classification system has been developed by combining traditional probabilistic Bayesian learning and Extreme Learning Machine (ELM) based on Feed-forward Neural Networks (NN). Assuming feature space narrowed down to just the latest experts recommendations probabilistic and ELM classifiers are trained and their outputs fed to train another baseline ELM classifier. The outputs from baseline classifiers are combined by voting at the decision level to generate final decision class. The presented hybrid model achieved the prediction score of 0.4172 yielding $8^{th}$ place out of 159 teams competing in the ISMIS’ 2017 competition.