Computing Trading Strategies Based on Financial Sentiment Data Using Evolutionary Optimization

被引：7
|
作者：
Hochreiter, Ronald [1 ]
机构：
[1] WU Vienna Univ Econ & Business, Dept Finance Accounting & Stat, Vienna, Austria
来源：
MENDEL 2015: RECENT ADVANCES IN SOFT COMPUTING | 2015年 / 378卷
关键词：
Evolutionary optimization; Sentiment analysis; Technical trading; Portfolio optimization;
D O I：
10.1007/978-3-319-19824-8_15
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：181 / 191
页数：11
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