Artificial Intelligence in Trading the Financial Markets

Abstract

Purpose: The purpose of this study is to review the methods that are used to construct Artificial Intelligence (AI) algorithmic trading systems. Design/Methodology/Approach: A review approach of the existing knowledge was used. Findings: We find that there are various methodologies that are used by researchers and practitioners when they contract algorithmic trading systems. Some of the systems combine data from the financial markets alone and some methods combine financial data with social media data. The ability of computerized algorithms to integrate a large set of data and react almost immediately according to it, does not come without risks of accelerating downtrends in times of panic in the financial market and therefore those systems must be institutionally monitored by the regulating authorities. Practice Implication: This study enables readers to understand the major methodologies that are used to predict trends of financial assets prices. The research identifies and explains the complexity of methods that helps traders to improve their trading results. Originality Value: No past study has summarized the major methodologies that are used to construct and optimize trading results.