Do Words Matter: Predicting IPO Performance from Prospectus Sentiment

被引:1
|
作者
Ly, Howard [1 ]
Khanh Nguyen [2 ]
机构
[1] NYU, Courant Inst Math Sci, New York, NY 10003 USA
[2] Tufts Univ, Coll Arts & Sci, Medford, MA 02155 USA
关键词
Natural language processing; Sentiment analysis; Machine Learning; Initial Public Offering; Apache Spark; TWITTER SENTIMENT; TEXTUAL ANALYSIS; MEDIA;
D O I
10.1109/ICSC.2020.00061
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Initial Public Offerings (IPOs) are essential to an investor's portfolio. Due to the incredible amount of risk surrounding them, IPOs are a good investment for investors looking for high-risk, high-reward stocks. We attempt to alleviate the risk that investors face by introducing a novel framework that uses sentiment analysis to forecast the first 3, 5, 10, 20, and 30 days price movement of an IPO through analyzing its prospectus (Form S-1). The prospectus is a formal document filed to the SEC that details the investment offering to the public as well as provides an important inside look into the IPO, and we illustrate that models trained on the prospectus's sentiment can predict whether an IPO's stock price would increase or decrease from opening day at an accuracy of up to 9.6% higher than chance.
引用
收藏
页码:307 / 310
页数:4
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