中文摘要：

量化选股一直是金融领域研究的热点。随着人工智能技术的空前发展,量化选股方法取得了很大进步。本文构建了基于核主成分遗传算法改进的支持向量回归机人工智能选股模型（KPCA-GA-SVR）,并基于沪深股市股票基本面及交易数据,分别从短期和中长期对其选股性能和预测精度进行了实证分析。主要结论为：①遗传算法（GA）改进的SVR较传统模型预测精度更高,且避免了过度拟合;②与采用主成分降维技术的PCA-GA-SVR模型相比,基于核主成分特征提取的KPCA-GA-SVR模型,具有更好的模型稳健性及预测准确性;③中长期内该模型的预测误差随滑窗长度的增加有降低趋势,且一年期预测精度最高;短期内不同滑窗下,一周的预测效果最佳。本研究对个人投资者的投资决策及国家宏观监控股市动态变化都具积极意义。

英文摘要：

The problem of quantitative stock selection has always been the focus of study in the financial field.With the development of artificial intelligence techniques,great progress has been made in the quantitative stock study.We establish KPCA-GA-SVR artificial intelligence stock selection model based on stock fundamentals and trading data,and evaluate the predicted effect in short term and medium-and-long term stock.The main conclusions are as follows： firstly,genetic algorithm improves the prediction accuracy of SVR model and avoids over fitting compared with the traditional model;secondly,the predicted effect and robustness get better by extending the genetic algorithm with Kernel Principal Component feature extraction than PCA-GA-SVR model;thirdly,in medium-and-long term,the predicted error of the model runs down with the rise of sliding window length and the one-year prediction accuracy is best;in short term,the result of all kinds of sliding window performs well in one week.The study is useful for individual investors’ investment decisions and the government can make full use of it to monitor the smooth fluctuations in the stock market.