The Role of AI and Big Data in Predictive Financial Analytics and Investment Strategies
DOI:
https://doi.org/10.64137/XXXXXXXX/IJFEMS-V1I1P104Keywords:
Artificial intelligence, Big Data, Predictive analytics, Investment strategies, Machine learning, Financial forecasting, Portfolio optimization, Sentiment analysis, Risk managementAbstract
It is reshaping the financial industry through revolutionizing predictive analytics as well as investment strategies by the use of Artificial Intelligence (AI) and Big Data analytics. Traditionally, analytical techniques no longer suffice to process the huge volume, velocity and variety of financial data that are being generated daily. In this context, AI algorithms, particularly those that incorporate Machine Learning (ML), deep learning, and Natural Language Processing (NLP), are effective tools that enable the development of models for making real-time decisions and managing risk. This paper attempts to establish the convergence of AI and Big Data in the financial sector, especially in predictive financial analytics and investment strategies. They enable forecasting of accurate markets, assessing risk, optimization of portfolios, detecting fraud, and sentiment analysis. A review of Big Data platforms, including Hadoop and Spark, and methodological frameworks such as supervised learning, unsupervised learning, and reinforcement learning, is provided. The practical applications and limitations are illustrated through a comprehensive literature review, methodological analysis, and case study. A significant improvement in the accuracy of the forecast and investments' return presented in the results confirms the importance of applying AI and Big Data in modern financial infrastructures
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