A comprehensive analysis of LSTM techniques for predicting financial market

Abstract

The “Financial Engineering” has revolutionized the financial industry by integrating mathematics, finance, economics, statistics, and computational tools to solve complex problems like risk management and portfolio optimization. This interdisciplinary approach has given rise to “AI in Finance”, merging the quantitative techniques of financial engineering with AI’s data-driven capabilities. In this survey, we explore concrete examples of their real-world impact. One such example involves a leading investment firm using machine learning algorithms to analyze market sentiments for informed trading decisions, resulting in significant returns. Additionally, AI-driven credit scoring models are expanding financial access by accurately assessing creditworthiness, especially for underserved populations. Natural language processing algorithms are also employed to parse financial news and social media data, providing investors with timely insights to navigate volatile markets effectively. These advancements highlight the transformative potential of financial engineering and AI in finance. By optimizing investment strategies and mitigating risks, they drive innovation and resilience in today’s dynamic financial landscape. From algorithmic trading to credit risk assessment and market sentiment analysis, the fusion of these disciplines is reshaping traditional paradigms and shaping the future of finance.