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Can you really predict markets with ...
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ProQuest Information and Learning Co.
Can you really predict markets with Twitter?
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Can you really predict markets with Twitter?/
作者:
Plenzick, Christopher.
面頁冊數:
1 online resource (48 pages)
附註:
Source: Masters Abstracts International, Volume: 56-02.
標題:
Economics. -
電子資源:
click for full text (PQDT)
ISBN:
9781369547511
Can you really predict markets with Twitter?
Plenzick, Christopher.
Can you really predict markets with Twitter?
- 1 online resource (48 pages)
Source: Masters Abstracts International, Volume: 56-02.
Thesis (M.S.)--The University of North Dakota, 2016.
Includes bibliographical references
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 regression. 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.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369547511Subjects--Topical Terms:
555568
Economics.
Index Terms--Genre/Form:
554714
Electronic books.
Can you really predict markets with Twitter?
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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 regression. 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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