Date of Award

January 2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Economics & Finance

First Advisor

Xiao Wang

Abstract

In this paper, I attempt to apply an emotional proxy derived by applying

the Affective Norms for English Words (ANEW) to messages posted to the

Twitter social networking service in order to forecast the movement two stock

market indices: the Dow Jones Industrial Average (DJIA) and the CBOE

Volatility Index (VIX). In contrast to previous works, I have compared the

results of various forecast models employing different sentiment variables, as

well as comparing the neural network approach to more standard logistic re-

gression. Additionally, several of the models used employ an as-yet unique

sentiment proxy, focusing on the average of expressed emotion rather than the

volume of expressed emotion. The results indicate that while there is a distinct

possibility that sentiment variables can assist in accurately forecasting market

movement, the differences in choice of sentiment proxy and forecast method

are less important than anticipated.

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