基于沪深300成分股的量化投资策略研究
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摘要
本文以沪深300指数成分股为股票池,构建出一个能持续战胜市场的量化选股模型。第一步先从基本面入手,通过多因子打分模型筛选出50只长期优势股,对应的上市公司经营状况良好,具有一定投资价值,但短期内可能受市场震荡影响,未必在一周之内有上涨表现。在第二步引入支持向量分类算法对长期优势股展开技术分析,从中选出本周上涨概率最大的10只优势精选股买入。该模型在2015—2017年累计收益率达73.03%,年化收益率为20.05%,夏普比率为0.54,远超同期沪深300指数的业绩表现。
Components of CSI 300 are used as a stock pool to construct a quantitative stock selection model that can continuously beat the market.The first step starts with the fundamentals and 50 longterm dominant stocks are filtered out through a multi-factor scoring model.These companies are in good operating conditions,but they may be affected by market shocks in the short term.Therefore,in the second step,the support vector machine is introduced to analyze the long-term dominant stocks,and the top 10 stocks with the highest probability of rise are selected to hold.The cumulative return rate of the model during the period of 2015-2017 reaches 73.03%,the annualized rate of return is 20.05%,and the Sharpe ratio is 0.54,far exceeding the performance of CSI 300 over the same period.
Components of CSI 300 are used as a stock pool to construct a quantitative stock selection model that can continuously beat the market.The first step starts with the fundamentals and 50 longterm dominant stocks are filtered out through a multi-factor scoring model.These companies are in good operating conditions,but they may be affected by market shocks in the short term.Therefore,in the second step,the support vector machine is introduced to analyze the long-term dominant stocks,and the top 10 stocks with the highest probability of rise are selected to hold.The cumulative return rate of the model during the period of 2015-2017 reaches 73.03%,the annualized rate of return is 20.05%,and the Sharpe ratio is 0.54,far exceeding the performance of CSI 300 over the same period.
引文
[1] SHARPE W F.Capital asset prices:a theory of market equilibrium under conditions of risk[J].Journal of Finance,1964,19(3):425-442.DOI:10.1111/j.1540-6261.1964.tb02865.x.
[2]孙尚芝.中国证券市场存在Alpha吗?:基于中小板的实证[J].商,2015(38):205.
[3] DeBONDT W F M,THALER R.Does the stock market overreact[J].Journal of Finance,1985,40(3):793-805.DOI:10.1111/j.1540-6261.1985.tb05004.x.
[4] JEGADEESH N,TITMAN S.Returns to buying winners and selling losers:implications for stock market efficiency[J].Journal of Finance,1993,48(1):65-91.DOI:10.1111/j.1540-6261.1993.tb04702.x.
[5]罗洪浪,王浣尘.中国股市动量策略和反向策略的赢利性[J].系统工程理论方法应用,2004,13(6):495-499,503.DOI:10.3969/j.issn.1005-2542.2004.06.004.
[6]刘毅.因子选股模型在中国市场的实证研究[D].上海:复旦大学,2012.
[7]朱晨曦.我国A股市场多因子量化选股模型实证分析[D].北京:首都经济贸易大学,2017.
[8] FAN A,PALANISWAMI M.Stock selection using support vextor machines[C]//Proceedings of International Joint Conference on Neural Networks:Volume 3.Piscataway NJ:IEEE Press,2001:1793-1798.DOI:10.1109/IJCNN.2001.938434.
[9] YU Lean,WANG Shouyang,LAI K K.Mining stock market tendency using GA-based support vector machines[J].Proceedings of the First International Workshop on Internet and Network Economics:LNCS Volume 3828.Berlin:Springer,2005:336-345.DOI:10.1007/11600930_33.
[10] VAPNIK V N.Statistical learning theory[M].Hoboken,NJ:Wiley,1998.
[2]孙尚芝.中国证券市场存在Alpha吗?:基于中小板的实证[J].商,2015(38):205.
[3] DeBONDT W F M,THALER R.Does the stock market overreact[J].Journal of Finance,1985,40(3):793-805.DOI:10.1111/j.1540-6261.1985.tb05004.x.
[4] JEGADEESH N,TITMAN S.Returns to buying winners and selling losers:implications for stock market efficiency[J].Journal of Finance,1993,48(1):65-91.DOI:10.1111/j.1540-6261.1993.tb04702.x.
[5]罗洪浪,王浣尘.中国股市动量策略和反向策略的赢利性[J].系统工程理论方法应用,2004,13(6):495-499,503.DOI:10.3969/j.issn.1005-2542.2004.06.004.
[6]刘毅.因子选股模型在中国市场的实证研究[D].上海:复旦大学,2012.
[7]朱晨曦.我国A股市场多因子量化选股模型实证分析[D].北京:首都经济贸易大学,2017.
[8] FAN A,PALANISWAMI M.Stock selection using support vextor machines[C]//Proceedings of International Joint Conference on Neural Networks:Volume 3.Piscataway NJ:IEEE Press,2001:1793-1798.DOI:10.1109/IJCNN.2001.938434.
[9] YU Lean,WANG Shouyang,LAI K K.Mining stock market tendency using GA-based support vector machines[J].Proceedings of the First International Workshop on Internet and Network Economics:LNCS Volume 3828.Berlin:Springer,2005:336-345.DOI:10.1007/11600930_33.
[10] VAPNIK V N.Statistical learning theory[M].Hoboken,NJ:Wiley,1998.