英文标题：
《Computing trading strategies based on financial sentiment data using
evolutionary optimization》
---
作者：
Ronald Hochreiter
---
最新提交年份：
2015
---
英文摘要：
In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain stocks. Numerical results for all stocks from the Dow Jones Industrial Average (DJIA) index are presented and a comparison to classical risk-return portfolio selection is provided.
---
中文摘要：
在本文中，我们应用进化优化技术来计算基于金融情绪数据的基于规则的最优交易策略。情绪数据是从社交媒体服务StockTwits中提取的，以适应在线交易社区对某些股票的看涨或看跌程度。本文给出了道琼斯工业平均指数（DJIA）所有股票的数值结果，并与经典的风险收益组合选择进行了比较。
---
分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
--
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--

---
PDF下载：
-->