An Investigation into Opportunities and Challenges for Forex Decision Making Using Machine Learning

The ever-evolving landscape of foreign exchange (FX) trading has seen a surge in the application of artificial intelligence (AI) techniques. In this fast-paced domain, fueled by ever-advancing technology, AI empowers traders to unlock new opportunities by harnessing the vast ocean of available trading data. This has led to the development of robust prediction models that enhance traders’ chances of success in the market. Traditionally, crafting accurate FX prediction models demanded expertise in a diverse range of fields, including quantitative analysis, financial literacy, and programming. However, the growing field of machine learning (ML) offers a more accessible and feasible alternative by leveraging trained algorithms to build remarkably accurate models. This study delves into a comprehensive review of various ML techniques deployed in the FX market, dissecting their diverse potential benefits. Additionally, it classifies the articles based on the types of ML model architectures, input indicators commonly employed, evaluation metrics, specific challenges associated with financial data and tasks, with potential solutions. This categorization aids researchers in staying abreast of cutting-edge advancements and facilitates replicating existing results for benchmarking purposes. Finally, the study concludes by illuminating promising avenues for future research within the research area.