Investigating the Role of Machine Leaning and Blockchain for Forecasting Stock Market Trends

Abstract

There is a sudden deluge of evolutionary approaches in the domain of data processing and computation, which are significantly affecting several facets of applications globally. Artificial intelligence, machine learning and Blockchain happen to be at the forefront of evolutionary computation beating conventional approaches. Finance applications currently are heavily reliant of data driven models. Stock trend analysis happens to be one such approach, which lays the foundation for forecasting decisions to be made. The leeway in such applications is critically small as minimal inaccuracies in forecasting may lead to major losses. This paper presents the current perspective in terms of evolutionary algorithms such as artificial intelligence and Blockchain and how they are transforming software development, the application of machine learning algorithms to regression problems. Finally, the stock trend analysis based on in and out of sample datasets has been performed for standard S&P datasets. A comparative analysis with previous work clearly indicates the improved performance of the proposed work with respect to baseline approaches in the domain.