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基于深度学习的股票价格预测研究.docx
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基于深度学习的股票价格预测研究.docx
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摘 要
- I -
基于深度学习的股票价格预测研究
杨扬
- II -
摘 要
随着经济的发展,中国股票市场的规模持续扩大,早已成为金融投资的重要部
分,掌握股票市场的变化规律无论是对监管者还是投资者都具有极其重要的意义。
正因如此,人们不断探索着股票市场的变化规律,其中使用深度学习预测股价是当
前国内国际研究与应用的热点。
本文首先从有效市场假说和分形市场假说两个角度讨论了中国股票市场的有效
性,说明股票市场具有复杂的非线性特征。其次,结合股票市场特征对比了当前的
预测方法,认为深度学习在股价预测中更具优势。接着,基于深度学习中的长短期
记忆网络进行股价预测实验。通过对比试验,本文得出了长短期记忆网络在预测股
价方面比三层全连接网络更有实际意义的结论,同时发现了多日数据作为输入变量
较单日数据更加准确,增加训练数据在一定程度上能提高准确率,且模型的预测准
确率能达到 68%。最后,本文重新选取了 10 支股票进行预测,以此进一步验证模型
的效果。预测结果的平均准确率为 62%,且能为绝大多数股票带来了正向效益,说
明了模型具有适应性,进一步证明了深度学习在股价预测方面的意义。
关键词:股价预测;人工神经网络;深度学习;长短期记忆网络
Abstract
- III -
Abstract
With the development of economy, the scale of China's stock market continues to
expand, which has already become an important part of financial investment. It is of great
significance for both regulators and investors to master the changing rules of the stock
market.
Firstly, this paper discusses the efficiency of China's stock market on the basis of
efficient market hypothesis and fractal market hypothesis, which shows that the stock
market has complex nonlinear characteristics. Secondly, combining with the
characteristics of the stock market, this paper compares the current forecasting methods.
Obviously deep learning has more advantages in stock price forecasting. Then, the stock
price prediction experiment is carried out based on Long Short-term Memory Network
(LSTM) in deep learning. The prediction results show that LSTM is more meaningful than
the 3-layer fully connected network in predicting the stock price. The prediction accuracy
of the model can reach 68%, and it has certain prediction ability. Finally, this paper selects
10 more stocks for prediction, with an average accuracy of 62%. The prediction of the
model brings positive benefits to 9 stocks out of 10, which indicates that the model has
applicability and further proves the significance of deep learning in stock price prediction.
Keywords: Stock Price Forecasting;ANN;Deep Learning;LSTM
- IV -
目 录
摘 要 ..................................................................................................................................I
Abstract ...............................................................................................................................II
目 录................................................................................................................................III
第 1 章 绪 论....................................................................................................................1
1.1 课题背景及研究的目的和意义..............................................................................1
1.1.1 课题背景.............................................................................................................1
1.1.2 研究的目的和意义.............................................................................................1
1.2 国内外研究现状.......................................................................................................2
1.2.1 中国股票市场有效性相关文献综述.................................................................2
1.2.2 神经网络相关文献综述.....................................................................................3
1.2.3 国内外研究现状评述.........................................................................................6
1.3 研究内容和方法.......................................................................................................7
1.3.1 研究内容.............................................................................................................7
1.3.2 研究方法.............................................................................................................7
第 2 章 相关概念及理论介绍............................................................................................8
2.1 股票市场相关理论...................................................................................................8
2.1.1 有效市场假说.....................................................................................................8
2.1.2 分形市场假说.....................................................................................................9
2.2 深度学习相关理论.................................................................................................10
2.2.1 人工神经网络简介...........................................................................................10
2.2.2 循环神经网络简介...........................................................................................12
2.2.3 长短期记忆网络简介.......................................................................................13
目 录
- V -
2.3 本章小结.................................................................................................................14
第 3 章 股价预测与深度学习..........................................................................................16
3.1 股价预测的方法.....................................................................................................16
3.1.1 技术分析法.......................................................................................................16
3.1.2 基本面分析法...................................................................................................16
3.1.3 统计分析法.......................................................................................................17
3.1.4 非线性预测法...................................................................................................17
3.2 股票价格预测中的问题.........................................................................................17
3.2.1 股价数据的特征...............................................................................................17
3.2.2 预测方法比较...................................................................................................18
3.3 深度学习的兴起.....................................................................................................19
3.3.1 深度学习的意义...............................................................................................19
3.3.2 长短期记忆网络的优势...................................................................................20
3.4 深度学习的具体应用.............................................................................................21
3.4.1 数据挖掘...........................................................................................................21
3.4.2 量化投资...........................................................................................................21
3.5 本章小结.................................................................................................................22
第 4 章 深度学习在股价预测中的实证研究..................................................................23
4.1 数据获取与处理.....................................................................................................23
4.1.1 数据获取...........................................................................................................23
4.1.2 归一化处理.......................................................................................................23
4.2 深度学习构建模型.................................................................................................24
4.2.1 实验设置...........................................................................................................24
4.2.2 模型结构设计...................................................................................................24
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