关于股票价格预测的文献综述:从统计模型到深度学习方法
Pham Hoang Vuong1,2, Lam Hung Phu1, Tran Hong Van Nguyen3
1Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam.
Science progress
|March 15, 2024
概括
本研究分析了用于股票价格预测的统计,机器学习和深度学习模型. 混合模型显示出提高金融市场预测准确性的前景.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 金融计量经济学 金融计量经济学
背景情况:
- 准确的股价预测对于财务决策至关重要.
- 传统方法面临的挑战是市场波动和非线性动态.
- 人工智能的进步为改善预测模型提供了新的途径.
研究的目的:
- 提供各种股票价格预测方法的全面分析.
- 讨论统计,机器学习和深度学习模型的优点和局限性.
- 探索混合模型在提高预测准确度方面的潜力.
主要方法:
- 统计模型的审查,如自行回归集成移动平均线 (ARIMA).
- 分析机器学习算法,包括人工神经网络和随机森林.
- 研究深度学习架构,如卷积神经网络 (CNN) 和循环神经网络 (RNN).
- 综合不同的预测技术的混合模型的评估.
主要成果:
- 统计模型是高效的,但与非线性数据和长期预测有限.
- 机器学习和深度学习模型擅长捕捉复杂的非线性模式.
- 混合模型通过利用各种优势来提高预测准确性的潜力.
结论:
- 没有单一的模型是普遍优越的;模型选择取决于具体的预测需求.
- 混合方法为改进股票价格预测提供了一个有希望的方向.
- 需要进一步的研究来优化和验证先进的预测技术.
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