英文标题：
《A Big data analytical framework for portfolio optimization》
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作者：
Dhanya Jothimani, Ravi Shankar, Surendra S. Yadav
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最新提交年份：
2018
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英文摘要：
  With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework has been developed to incorporate both structured and unstructured data for portfolio optimization. Portfolio optimization consists of three processes: Asset selection, Asset weighting and Asset management. This framework proposes to achieve the first two processes using a 5-stage methodology. The stages include shortlisting stocks using Data Envelopment Analysis (DEA), incorporation of the qualitative factors using text mining, stock clustering, stock ranking and optimizing the portfolio using heuristics. This framework would help the investors to select appropriate assets to make portfolio, invest in them to minimize the risk and maximize the return and monitor their performance.
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中文摘要：
随着Web 2.0的出现，每天都会产生各种类型的数据。这引发了大数据革命。金融市场中产生了大量结构化和非结构化数据。处理这些数据可以帮助投资者做出明智的投资决策。在本文中，我们开发了一个框架，将结构化和非结构化数据结合起来进行投资组合优化。投资组合优化包括三个过程：资产选择、资产权重和资产管理。该框架建议使用五阶段方法实现前两个过程。这些阶段包括使用数据包络分析（DEA）入围股票、使用文本挖掘合并定性因素、股票聚类、股票排名和使用启发式优化投资组合。该框架将帮助投资者选择合适的资产进行投资组合，对其进行投资以最小化风险和最大化回报，并监控其表现。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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