利用q -学习技术改进基于MAD的股票交易定量预测

Pengcheng Zhao, Siyuan Yin, B. V. D. Kumar, Teh Jia Yew
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引用次数: 0

摘要

股票是企业股份的凭证,代表股份公司股东的所有权。股票交易是一个动态的、复杂的工程,股票历史数据是一个非线性的、高噪声的时间序列。考虑到股票交易的风险性,为保证股票交易的安全性,提供一种最优预测方法至关重要。传统的股票交易预测精度不足,因此本研究尝试使用强化学习模型进行大数据下的股票交易变化预测。本文提出了中位数绝对偏差法(MAD)和q -学习模型来构建更有效的预测模型。基于纳斯达克综合指数(NASDAQ Composite, ^IXIC)数据的仿真结果表明,该方法能够更好地帮助股票预测。该方法存在一定的局限性,目前使用的是传统计量模型和强化学习模型的结合,存在一定的效率问题。然而,本文的研究对股票预测分析的理论方面提供了很好的延伸,可以为投资者提供新的研究方法和视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving quantitative stock trading prediction based on MAD using Q-learning technology
Stocks are a proof of shares in a corporate enterprise and represent the ownership of the stockholder in the joint stock company. Stock trading is a dynamic and complicated project, stock history data is a non-linear and highly noisy time series. Considering the risky nature of stock trading, it is essential to provide an optimal prediction method to ensure stock trading security. The accuracy of traditional stock trading prediction is insufficient, so this study attempts to use reinforcement learning models for stock trading change prediction under big data. This paper proposes the median absolute deviation method (MAD) and Q-learning model to build a more effective prediction model. The simulation results based on NASDAQ Composite (^IXIC) data show that the new method can better help predict stocks. The method has some limitations and currently uses a combination of traditional econometric models and reinforcement learning models, which have some efficiency issues. However, the research in this paper provides a good extension to the theoretical aspects of stock prediction analysis and can provide investors with new research methods and perspectives.
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