深度学习驱动的股票市场预测：复杂特征与高效表现

深度学习
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"《基于深度学习的股票市场预测》探讨了在现代金融市场中，股票市场预测的重要性，尤其是股票价格波动预测作为一项极具吸引力的研究课题。传统的机器学习方法，如单隐藏层神经网络和支持向量机，由于其浅层结构限制，对于有限样本和计算资源的效率以及处理复杂问题的能力存在局限。深度学习技术因其深度非线性网络结构的优势，能有效解决这些问题。 深度学习的核心在于其通过学习多层次的抽象表示，能够捕捉输入数据的分布式表示，展现出强大的特征提取能力。这种方法特别适用于那些目标函数具有丰富内在含义的复杂任务，如股票价格预测。实验中，研究者使用了香港交易所2001年的交易数据作为测试案例，结果显示深度学习模型在股票价格预测方面展现了良好的样本特征抽取能力，能够揭示出样本的内在规律，并产生了相对满意的预测结果。 该研究由赵志勇、王峰和李元香合作完成，赵志勇专注于机器学习和智能计算领域，王峰是武汉大学软件工程国家重点实验室的副教授，负责机器学习和算法交易研究。他们的研究不仅关注理论构建，还结合实际应用，旨在推动股票市场预测的精确度提升。 本文的关键词包括深度学习、股票市场预测、机器学习以及多源数据处理。整体来看，这篇论文展示了深度学习作为一种强大的工具，在金融领域特别是股票市场预测中的潜力和价值，预示着未来在这一领域将有更深入的应用和发展。"

Machine Learning Techniques for Stock Prediction

2012-11-09 上传
Machine Learning Techniques fo rStock Prediction Vatsal H.Shah

Deep Learning Networks for Stock Market Analysis and Prediction

2019-07-06 上传
We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine—on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

deepstock, 技术实验用深度学习打败股票市场.zip

2019-09-17 上传
deepstock, 技术实验用深度学习打败股票市场 DeepStock技术实验用深度学习打败股票市场。实验基于日新闻标题分析的深度学习股票新闻标题与DJIA指数之间相关性的尝试。在中更详细地说明这个幻灯片。基于深度学习的自动交易机器人基于公司价格历史的公司股价预测。递

Deep learning networks for stock market analysis and prediction_ Methodology

2018-04-23 上传
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

Deep learning for stock market prediction from financial news articles.pdf

2021-05-13 上传
这份标题为《Deep learning for stock market prediction from financial news articles》的文档，是关于使用深度学习方法从财经新闻标题和一组技术指标预测标普500指数日内行情的研究。文档讨论了深度学习在金融...

Deep Learning for Event-Driven Stock Prediction

2021-02-21 上传
We propose a deep learning method for event-driven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. ...

Deep-Reinforcement-Learning-And-Technical-Analysis-Applied-to-the-Stock-Market:深度强化学习和技术分析应用于股票市场-BI MASTER-商业智能硕士-最终项目-CCE PUC-Rio-2021年4月

2021-04-23 上传
该系统是根据深度强化学习（Deep Reinforcement Learning-DRL）的概念开发的，可以预测股票交易时段的顺序过程中的股票价格。 根据股票的先前历史价值来训练DRL代理。 在此培训阶段，DRL代理遍历值的时间序列，调整...

stock-market-scraper：从https：finance.yahoo.com提取历史股票市场数据

2021-02-04 上传
stock-market-scraper是一种命令行工具，可从下载csv和json格式的所有历史股票数据。 这仅用于教育和研究目的。 不要过度使用此脚本。 它将负载放在服务器上。该自述文件仅可通过Pyhton Script进行大规模下载。 ...

Python Machine Learning Blueprints 2nd Edition

2019-03-10 上传
Who this book is for ...Use Machine Learning to Forecast the Stock Market Classifying Images with Convolutional Neural Networks Building a Chatbot Build a Recommendation Engine What's next?

Soft Computing in Machine Learning(PDF)

2018-03-06 上传
Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis....
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