Advanced Artificial Intelligence-Driven Financial Forecasting Models: Enhancing Market Trend Prediction and Investment Risk Management through Real-Time Validation and Comprehensive AI Integration

Dev Kumar, K. Raghuveer, J. Veni, L. Jothibasu, K. Mithun Krishna, S. Sathyakala

2025

Abstract

With the changeable impact investing world, traditional methods of forecasting are getting overtaken with the complexity of the global trends. The scope of this article is to: I) Propose a holistic end-to-end artificial intelligence (AI)-informed financial prediction approach not only to outperform previous studies on real-time data analysis, model interpretability, and multi-market generalizability, but also to contribute to the evaluation and understanding of the AI models for the task of stock price prediction. Contrary to the existing work which is often limited to credit risk modelling or provide theoretical intuition, we work with deep learning architectures like LSTM and transformer-based models for a more accurate prediction of the market. The proposed model strikes a balance between predictive accuracy and transparency by considering ethical issues, interpretability and regulatory concerns. The model’s effectiveness has been empirically verified in trend forecasting and investment risk assessment on various financial indices. By doing so, this research is not only tech-nically pushing forward the frontier of AI forecasting models, but is also, in theory, of practical importance to those who are involved in financial decision making in the face of a complex market environment.

Paper Citation

in Harvard Style

Kumar D., Raghuveer K., Veni J., Jothibasu L., Krishna K. and Sathyakala S. (2025). Advanced Artificial Intelligence-Driven Financial Forecasting Models: Enhancing Market Trend Prediction and Investment Risk Management through Real-Time Validation and Comprehensive AI Integration. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 288-293. DOI: 10.5220/0013862800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Dev Kumar and K. Raghuveer and J. Veni and L. Jothibasu and K. Krishna and S. Sathyakala},
title={Advanced Artificial Intelligence-Driven Financial Forecasting Models: Enhancing Market Trend Prediction and Investment Risk Management through Real-Time Validation and Comprehensive AI Integration},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={288-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013862800004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Advanced Artificial Intelligence-Driven Financial Forecasting Models: Enhancing Market Trend Prediction and Investment Risk Management through Real-Time Validation and Comprehensive AI Integration
SN - 978-989-758-777-1
AU - Kumar D.
AU - Raghuveer K.
AU - Veni J.
AU - Jothibasu L.
AU - Krishna K.
AU - Sathyakala S.
PY - 2025
SP - 288
EP - 293
DO - 10.5220/0013862800004919
PB - SciTePress