摘 要
随着金融科技的快速发展，人工智能技术在金融市场的应用日益广泛，特别是在风险预测领域展现出巨大潜力。本研究旨在探讨人工智能技术如何有效提升金融市场风险预测的准确性和效率，为金融机构和投资者提供科学决策支持。研究以深度学习、机器学习及自然语言处理等核心技术为基础，结合金融市场历史数据与实时信息，构建了多维度的风险预测模型。通过对比传统统计方法与人工智能模型的表现，结果表明基于人工智能的方法能够显著提高风险预测的精确度，并具备更强的非线性拟合能力和适应复杂市场环境的能力。此外，本研究创新性地引入了文本情感分析技术，将新闻报道、社交媒体情绪等非结构化数据纳入预测框架，进一步丰富了风险评估的信息来源。
关键词：人工智能技术 风险预测 金融市场
Abstract
With the rapid development of fintech, artificial intelligence technology is increasingly widely used in the financial market, especially in the field of risk prediction, showing great potential. This study aims to explore how AI technology can effectively improve the accuracy and efficiency of financial market risk prediction, and provide scientific decision support for financial institutions and investors. Based on the core technologies such as deep learning, machine learning and natural language processing, combined with the historical data and real-time information of financial market, the research of a multi-dimensional risk prediction model is constructed. By comparing the performance of the traditional statistical methods with the AI model, the results show that the AI-based method can significantly improve the accuracy of the risk prediction, and have a stronger non-linear fitting ability and the ability to adapt to the complex market environment. Moreover, this study innovatively introduced textual sentiment analysis techniques, including unstructured data such as news reporting and social media emotions into the prediction fr amework, and further enriched the information sources for risk assessment.
Keyword:Artificial Intelligence Technology Risk Prediction Financial Market
目 录
1绪论 1
1.1金融市场风险预测的研究背景 1
1.2人工智能技术在金融领域的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与创新点 2
2人工智能技术在风险预测中的理论基础 2
2.1金融风险预测的基本概念 2
2.2人工智能技术的核心原理 3
2.3数据驱动的风险预测模型 3
2.4常见算法在金融领域的应用 4
2.5理论框架的构建与验证 4
3人工智能技术在市场波动预测中的应用 5
3.1市场波动预测的关键问题 5
3.2时间序列分析与深度学习模型 5
3.3自然语言处理在新闻情绪分析中的作用 6
3.4多模态数据融合的技术实现 6
3.5实证案例分析与结果评估 6
4风险预测中的人工智能优化策略 7
4.1模型选择与参数调优方法 7
4.2不确定性建模与鲁棒性提升 7
4.3高频交易中的实时风险监控 8
4.4数据隐私保护与伦理挑战 8
4.5未来发展方向与技术展望 9
结论 9
参考文献 11
致谢 12
