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Multi-factor Sentiment Analysis for ...

ProQuest Information and Learning Co.

Multi-factor Sentiment Analysis for Gauging Investors Fear.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Multi-factor Sentiment Analysis for Gauging Investors Fear./
Author:	Tai, Ichihan.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438278677

Multi-factor Sentiment Analysis for Gauging Investors Fear.
Tai, Ichihan.

Multi-factor Sentiment Analysis for Gauging Investors Fear. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (D.Engr.)--The George Washington University, 2018.

Includes bibliographical references

The Chicago Board Options Exchange Volatility Index (VIX) is widely used to gauge investor fear and measures the "insurance premiums" of the stock market. Traditionally, VIX prediction was done using time-series analysis models (e.g., GARCH), and some attempt was made to predict it using sentiment analysis approach. However, traditional sentiment analysis is focused on authors' sentiment (sentiment expressed in news authors' word choices), and this Praxis will demonstrate that adding other factors (e.g., similarity, readability) into the traditional authors' sentiment model will improve VIX prediction results. This Praxis research leverages natural language processing (NLP) and machine learning (ML) techniques to build a VIX prediction model.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438278677Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Multi-factor Sentiment Analysis for Gauging Investors Fear.
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245 1 0 $a Multi-factor Sentiment Analysis for Gauging Investors Fear.
264 0 $c 2018
300 $a 1 online resource (111 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Advisers: Amir H. Etemadi; Ebrahim Malalla.
502 $a Thesis (D.Engr.)--The George Washington University, 2018.
504 $a Includes bibliographical references
520 $a The Chicago Board Options Exchange Volatility Index (VIX) is widely used to gauge investor fear and measures the "insurance premiums" of the stock market. Traditionally, VIX prediction was done using time-series analysis models (e.g., GARCH), and some attempt was made to predict it using sentiment analysis approach. However, traditional sentiment analysis is focused on authors' sentiment (sentiment expressed in news authors' word choices), and this Praxis will demonstrate that adding other factors (e.g., similarity, readability) into the traditional authors' sentiment model will improve VIX prediction results. This Praxis research leverages natural language processing (NLP) and machine learning (ML) techniques to build a VIX prediction model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Finance. $3 559073
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0508
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Engineering Management. $3 1179162
773 0 $t Dissertation Abstracts International $g 79-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10844773 $z click for full text (PQDT)

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