基于支持向量机的多因子融合量价数据的选股策略的研究  

Research on Stock Selection Strategy with the Fusion of Data of Multi-Factor, Volume and Price Based on Support Vector Machine

作　　者：胡新烜 曹丽[1] 姜毅[2] 

机构地区：[1]浙江理工大学计算机科学与技术学院(人工智能学院),浙江 杭州 [2]浙江理工大学经济管理学院,浙江 杭州

出　　处：《电子商务评论》2024年第3期7271-7281,共11页E-Commerce Letters

摘　　要：针对中证1000成分股的投资策略构建,提出了基于支持向量机的多因子融合量价数据选股策略,应用于实时或模拟市场数据的回测中,以评估策略的有效性。本文对基于支持向量机的多因子选股模型进行改进,将量价数据融合到选股模型中,在多因子选股模型筛选出的股票基础上,进一步融合量价数据再次筛选,以期望获得更优的收益。回测结果表明,加入量价数据与未加入量价数据的模型对比,策略收益率提高了4.83%,策略年化收益率提高了11.43%,策略累积收益率与夏普比率显著优于多因子选股策略,最大回撤比之减小或者略高,预测涨跌更为接近实际股票涨跌趋势。实验结果表明:基于支持向量机的多因子融合量价数据选股策略应用在量化投资上,是十分有效的。In response to the construction of investment strategy for the constituent stocks of China Securities 1000, a stock selection strategy with the fusion of data of multi-factor, volume and price based on support vector machine is proposed which is applied to real-time or simulated market data backtesting, in order to evaluate the effectiveness of the strategy. We improve the multi-factor model of stock selection based on support vector machine, integrate the volume and price data into the model of stock selection, and further fuse the volume and price data to screen the stocks selected by the multi-factor model of stock selection to obtain more optimal returns. According to the backtest results, compared with the model with or without the addition of volume and price data and the model, the strategy return rate increases by 4.83%, the strategy annual return rate increases by 11.43%, the strategy cumulative return to sharp ratio is significantly better than that of the multi-factor stock selection strategy, and the maximum retracement ratio decreases or is slightly higher. The improved forecast moves more closely to the actual trend of stock movements. The results show th

关 键 词：SVM算法 量化投资 多因子选股模型 SuperMind平台 

分 类 号：F42[经济管理—产业经济]