News and Sentiment Analysis of the European Market with a Hybrid Expert Weighting Algorithm

Social Computing (SocialCom), 2013 ASE/IEEE International Conference on , pp.391-396

6 Pages Posted: 27 Jan 2014

See all articles by Germán G. Creamer

Germán G. Creamer

Stevens Institute of Technology, School of Business; Columbia University - Department of Computer Science

Yong Ren

Stevens Institute of Technology - School of Business

Yasuaki Sakamoto

AXA Direct Japan

Jeffrey V. Nickerson

Stevens Institute of Technology - School of Business

Date Written: September 8-14, 2013

Abstract

This paper proposes a hybrid human machine system based on an expert weighting algorithm that combines the responses of both humans and machine learning algorithms. The general topic of the paper is the use of the crowd to interpret text, and the power of that interpretation to predict future events. This topic is addressed through an experiment, in which news sentiment is evaluated by crowds and experts in different configurations. Their classifications are used as training sets for machine learning algorithms, including one that weights both machine and human predictions. The testing is done based on Thomson Reuters news stories and the returns of the stocks mentioned right after the stories appear.

The hybrid expert weighting algorithm forecasts asset returns similar to the different versions of the trained and crowd groups because it combines the best results of the machine learning algorithms with human answers. The forecast of the expert weighting algorithm does not always show the best performance in comparison with the other learning algorithms; however its performance is very similar to the best algorithm in most cases. From a cognitive perspective, the capacity of the expert weighting algorithm to select dynamically the best expert according to its previous performance is consistent with an evolving collective intelligence: the final decision is a combination of the best individual answers -- some of these come from machines, and some from humans.

Keywords: Computational finance, text analysis, crowdsourcing, cognitive modeling, machine learning

JEL Classification: C53, C63, G12, G14, F30,C92

Suggested Citation

Creamer, Germán G. and Ren, Yong and Sakamoto, Yasuaki and Nickerson, Jeffrey V., News and Sentiment Analysis of the European Market with a Hybrid Expert Weighting Algorithm (September 8-14, 2013). Social Computing (SocialCom), 2013 ASE/IEEE International Conference on , pp.391-396. Available at SSRN: https://ssrn.com/abstract=2385613 or http://dx.doi.org/10.2139/ssrn.2385613

Germán G. Creamer (Contact Author)

Stevens Institute of Technology, School of Business ( email )

1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)

HOME PAGE: http://www.creamer-co.com

Columbia University - Department of Computer Science ( email )

New York, NY 10027
United States

Yong Ren

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Yasuaki Sakamoto

AXA Direct Japan ( email )

Japan

Jeffrey V. Nickerson

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

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