Stability Classification of Stock Market Using Temporal Rule-Based Classification

Abstract

Abstract Economists have sought to predict stock market prices for decades with varying degrees of success. This study classifies stocks according to their stability in two consequent financial quarters (depending on whether the majority of stocks remain in the same stability group, which can indicate forecastability). However, classifying temporal information like stock market data using available methods produces complicated rules that cannot be easily interpreted by human experts. To reduce this complication a new approach of rule-based temporal classification is used. The method combines human-provided rules with machine optimisation to produce classes that can be easily interpreted by experts, enabling them to comprehend the complicated temporal dimension of the data. Rules provided by human experts might be via generalization of the temporal data using statistical functions like standard deviation and averages. Initially, each rule will have a range of possible values that can be reduced to a single one. For this study, we classify the stability of stock market data of the S&P 500 for two constitutive financial quarters to test if stocks have the same stability or not. The results show that the classes for stock markets are different beyond random chance, which might be an indication of the viability of forecasting stock markets depending on their old trends.