2024, Vol. 5, Issue 1, Part B
Ai meets FinTech: Dynamic portfolio optimization for smarter, faster, and safer investments
Author(s): Arunkumar Medisetty
Abstract: The integration of Artificial Intelligence (AI) into financial technology (FinTech) has significantly reshaped the landscape of investment management, offering personalized, data-driven portfolio optimization solutions that adapt to real-time market conditions and investor risk preferences. Traditional portfolio strategies—such as Mean-Variance Optimization (MVO) and Buy-and-Hold—often lack the agility and predictive accuracy required in today’s volatile, data-rich financial environments. This study presents an AI-driven investment portfolio optimization framework that combines deep learning, sentiment analysis, and reinforcement learning to enhance asset allocation decisions. The proposed modular pipeline includes five key components: data collection and preprocessing, hybrid market prediction using LSTM and NLP-based sentiment analysis, dynamic investor risk profiling via unsupervised learning, portfolio optimization through reinforcement learning agents, and real-time rebalancing supported by performance feedback.
A proof-of-concept was tested on a five-year historical dataset (2018-2023) covering 50 diversified assets across equities, ETFs, and cryptocurrencies. Comparative analysis with traditional strategies—including MVO, Equal-Weighted, Black-Litterman, and Buy-and-Hold—demonstrates that the AI model achieved superior outcomes. The reinforcement learning engine produced the highest annual return (14.5%) and Sharpe ratio (1.38) while maintaining the lowest volatility (11.3%) and maximum drawdown (-12.1%). Notably, the inclusion of sentiment data improved signal precision by over 12%, and the model showed robust adaptability during periods of market stress, such as the COVID-19 pandemic. This study not only confirms the potential of AI in optimizing financial portfolios but also addresses challenges related to transparency, turnover control, and regulatory compliance. The results establish a foundation for next-generation FinTech platforms that are intelligent, ethical, and investor-centric.
DOI: 10.33545/27076571.2024.v5.i1b.168Pages: 114-121 | Views: 696 | Downloads: 412Download Full Article: Click Here
How to cite this article:
Arunkumar Medisetty.
Ai meets FinTech: Dynamic portfolio optimization for smarter, faster, and safer investments. Int J Comput Artif Intell 2024;5(1):114-121. DOI:
10.33545/27076571.2024.v5.i1b.168
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