Stock Market Feature Selection Using Orthogonal Array

There are challenges to analyzing huge volumes of data in the financial sector. How to handle big financial data intelligently is among one of the most important topics faced by researchers and practitioners. The stock market data are too large or complex to be dealt with by traditional data-processing application software. In this research, we propose using the orthogonal array to systematically generate pairs of input data fields for the Machine Learning model developed in our previous works. Trials in the automated wealth management industry (e.g. Robo-Advisors) have increased with the introduction of newer data analysis tools and technology applications. This has resulted in new methods, variables, and ideations being considered for optimal predictive analysis in the stock, bond, and cryptocurrency markets. Large data sets used in conjunction with machine learning are telling and predictive for different points in time. Our research attempts to understand which input factors will affect the stock market the most. As a result, we are expecting to reduce the volume of data needed to supply our machine learning model.