摘 要
随着金融市场的复杂性不断提升，传统量化投资策略逐渐难以满足动态环境下的高精度预测需求，而机器学习技术的引入为优化投资决策提供了新的可能性。本研究旨在探讨机器学习算法在量化投资策略中的应用，并通过改进模型架构与参数调优实现策略性能的显著提升。研究选取了多种主流机器学习算法，包括支持向量机、随机森林、梯度提升决策树以及深度学习模型，并结合金融市场数据特征进行针对性优化。通过对历史交易数据的分析，构建了基于多因子选股和市场趋势预测的投资框架，同时引入强化学习方法以适应非平稳市场环境。实验结果表明，优化后的机器学习模型在回测阶段展现出更高的收益风险比和更稳定的预测能力，尤其是在处理高频数据时表现出显著优势。此外，本研究提出了一种融合多模型预测结果的集成方法，有效降低了单一模型的过拟合风险，提升了整体策略的鲁棒性。研究的主要贡献在于揭示了机器学习技术在量化投资领域的潜力，并通过实证分析验证了其在复杂市场条件下的适用性，为未来相关研究提供了理论支持与实践参考。
关键词：机器学习；量化投资；多因子选股；强化学习；模型集成
Abstract
As the complexity of financial markets continues to increase, traditional quantitative investment strategies are gradually unable to meet the demand for high-precision predictions in a dynamic environment. The introduction of machine learning techniques offers new possibilities for optimizing investment decision-making. This study aims to explore the application of machine learning algorithms in quantitative investment strategies and achieve significant improvements in strategy performance through enhanced model architectures and parameter optimization. A variety of mainstream machine learning algorithms were selected, including support vector machines, random forests, gradient boosting decision trees, and deep learning models, with targeted optimizations based on the characteristics of financial market data. By analyzing historical trading data, an investment fr amework was constructed that integrates multifactor stock selection and market trend prediction, while reinforcement learning methods were introduced to adapt to non-stationary market environments. Experimental results demonstrate that the optimized machine learning models exhibit higher risk-adjusted returns and more stable predictive capabilities during backtesting, particularly showing significant advantages in handling high-frequency data. Additionally, this study proposes an ensemble method that combines predictions from multiple models, effectively reducing the risk of overfitting associated with single models and enhancing the overall robustness of the strategy. The primary contribution of this research lies in uncovering the potential of machine learning technologies in the field of quantitative investment and validating their applicability under complex market conditions through empirical analysis, providing theoretical support and practical references for future related studies.
Keywords: Machine Learning; Quantitative Investment; Multi-factor Stock Selection; Reinforcement Learning; Model Integration
目 录
1绪论 1
1.1机器学习与量化投资的背景分析 1
1.2研究意义 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2机器学习算法在量化投资中的适用性分析 2
2.1常见机器学习算法概述 2
2.2不同算法在量化投资中的适用场景 3
2.3数据特征对算法选择的影响 3
2.4算法性能评估指标体系构建 4
2.5算法适用性实证分析 4
3机器学习驱动的量化投资策略设计 5
3.1量化投资策略的基本框架 5
3.2基于监督学习的预测型策略开发 5
3.3基于无监督学习的组合优化策略 6
3.4强化学习在交易决策中的应用 6
3.5混合模型策略的设计与实现 7
4机器学习在量化投资中的优化与挑战 7
4.1模型过拟合问题及其解决方案 8
4.2高频数据处理与计算效率优化 8
4.3噪声数据对模型性能的影响及应对 9
4.4风险控制机制的设计与改进 9
4.5伦理与监管挑战的思考 10
结论 11
参考文献 12
致 谢 13
