The existing literature suggests that the out-of-sample performance of traditional mean-variance portfolio strategies is not robust, and their performance is even inferior to that of the equal weight strategy. To address this problem, this paper first clarifies that a complete investment process consists of two parts, namely, stock selection and investment weight formulation. Then, we design a stock selection scheme integrating Data Envelopment Analysis (DEA) with multiple data sources, including historical stock trading data, technical indicators, social media data and news data, to assess the investment value of stocks in terms of historical return, asset correlation and investor sentiment performance. In addition, we use Support Vector Machine (SVM) combined with the multi-source data on stocks to predict the stock price movements and combine the obtained stock price movements and the proposed stock selection scheme to construct the portfolio optimization model. Further, we also carry out an out-of-sample test on the proposed stock selection scheme and investment strategies, in which the constituents of CSI 300 index are selected as the test samples. The empirical results show that the proposed stock selection scheme can effectively improve the out-of-sample performance of all investment strategies. Besides, the proposed investment strategy has a better out-of-sample performance compared to the traditional global minimum variance investment strategy, tangency portfolio investment strategy, and equal weight strategy. Finally, we perform a robustness test of the above findings using an additional dataset.

现有文献表明，传统的均值方差投资组合策略的样本外表现并不稳健，其表现甚至还不如等权重策略。为了解决这个问题，本文首先阐明一个完整的投资过程由两部分组成，即选股和投资权重确定。然后，我们设计了一个选股方案，该方案将数据包络分析（DEA）与多个数据源集成在一起，包括历史股票交易数据，技术指标，社交媒体数据和新闻数据，以从历史收益，资产等方面评估股票的投资价值。相关性和投资者情绪表现。此外，我们使用支持向量机（SVM）结合股票的多源数据来预测股票价格走势，并结合获得的股票价格走势和拟议的股票选择方案来构建投资组合优化模型。此外，我们还对拟议的股票选择方案和投资策略进行了样本外测试，其中选择了沪深300指数的成分作为测试样本。实证结果表明，所提出的选股方案可以有效提高所有投资策略的样本外绩效。此外，与传统的全局最小方差投资策略，相切组合投资策略和等权重策略相比，拟议的投资策略具有更好的样本外性能。最后，