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December 19, 2025Advances in Economics Management and Political SciencesOpen Access

Innovative Developments and Risk Challenges of Artificial Intelligence Model in Quantitative Finance for Investment

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Authors

YWY. Richard Wang

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Overview

Review finds AI techniques improve risk assessment and investment modeling in finance, highlighting challenges ahead.

Key Points

  • This paper reviews advancements in AI for quantitative finance and its applications in investment strategies.
  • Reviewed recent AI-enhanced quantitative investing techniques
  • Analyzed deep learning, reinforcement learning, and algorithmic optimization
  • Examined AI models like LSTM networks and XGBoost for investment performance
  • AI models improved stock prediction accuracy to over 97%
  • Kalman Filter achieved nanosecond-level synchronization in networks
  • K-Means clustering accuracy increased to 99.4%, reducing false alarms by 48%

Cite This Study

Y. Richard Wang (2025) studied this question.

synapsesocial.com/papers/69449a892f0218eca9508435https://doi.org/10.54254/2754-1169/2026.ld30582
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends2025
  2. 2Artificial Intelligence in Quantitative Finance: Opportunities, Limitations, and the Future of Investment Decision-Making2026
  3. 3Quantitative Finance and Fintech Research under Artificial Intelligence2024
  4. 4AI-Based Financial Forecasting And Risk Analysis2023
  5. 5Artificial Intelligence in Financial Forecasting and Risk Reduction2025