英文标题：
《Working Paper: Improved Stock Price Forecasting Algorithm based on
  Feature-weighed Support Vector Regression by using Grey Correlation Degree》
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作者：
Quanxi Wang
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最新提交年份：
2019
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英文摘要：
  With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improves the support vector machine regression algorithm by using grey correlation analysis (GCA) and improves the accuracy of stock prediction. This article first divides the factors affecting the stock price movement into behavioral factors and technical factors. The behavioral factors mainly include weather indicators and emotional indicators. The technical factors mainly include the daily closing data and the HS 300 Index, and then measure relation through the method of grey correlation analysis. The relationship between the stock price and its impact factors during the trading day, and this relationship is transformed into the characteristic weight of each impact factor. The weight of the impact factors of all trading days is weighted by the feature weight, and finally the support vector regression (SVR) is used. The forecast of the revised stock trading data was compared based on the forecast results of technical indicators (MSE, MAE, SCC, and DS) and unmodified transaction data, and it was found that the forecast results were significantly improved.
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中文摘要：
随着人工智能和大数据决策等广泛的工程应用，原本大量繁琐的金融数据处理、处理和分析变得越来越方便和有效。本文旨在提高股票价格预测的准确性。利用灰色关联分析（GCA）对支持向量机回归算法进行了改进，提高了股票预测的准确性。本文首先将影响股票价格变动的因素分为行为因素和技术因素。行为因素主要包括天气指标和情绪指标。技术因素主要包括每日收盘数据和HS 300指数，然后通过灰色关联分析的方法度量关系。交易日内股价与其影响因素之间的关系，并将此关系转化为各影响因素的特征权重。利用特征权重对各交易日的影响因素权重进行加权，最后采用支持向量回归（SVR）。根据技术指标（MSE、MAE、SCC和DS）的预测结果和未修改的交易数据，对修改后的股票交易数据的预测进行了比较，发现预测结果得到了显著改善。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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