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Influence Propagation Modeling and A...

Zhang, Li.

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Influence Propagation Modeling and Applications in Finance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Influence Propagation Modeling and Applications in Finance./
Author:	Zhang, Li.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.
Contained By:	Dissertation Abstracts International78-01A(E).
Subject:	Finance. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140630
ISBN:	9781339960265

Influence Propagation Modeling and Applications in Finance.
Zhang, Li.

Influence Propagation Modeling and Applications in Finance. - 117 p.

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.

To appropriately use the information from social media for finance-related problems is typically challenging to both finance and data mining. Traditional schemes in finance focus on identifying the trading activities and financial events that generate asset abnormal returns, while the usage of data typically only covers regular events such as earning announcements, financial statements, and new stock issuance. Related data-driven implementations mainly focus on developing trading strategies using social media data, while the results usually lack theoretical explanations. This work is designed to fill the gap between the usage of social media data and financial theories, with comprehensive evaluations using real-world data from social media and the stock market.

ISBN: 9781339960265Subjects--Topical Terms:

542899
Finance.

Influence Propagation Modeling and Applications in Finance.
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100 1 $a Zhang, Li. $3 729094
245 1 0 $a Influence Propagation Modeling and Applications in Finance.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-01(E), Section: A.
500 $a Advisers: Yuefan Deng; Svetlozar Rachev.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.
520 $a To appropriately use the information from social media for finance-related problems is typically challenging to both finance and data mining. Traditional schemes in finance focus on identifying the trading activities and financial events that generate asset abnormal returns, while the usage of data typically only covers regular events such as earning announcements, financial statements, and new stock issuance. Related data-driven implementations mainly focus on developing trading strategies using social media data, while the results usually lack theoretical explanations. This work is designed to fill the gap between the usage of social media data and financial theories, with comprehensive evaluations using real-world data from social media and the stock market.
520 $a A Degree of Social Attention (DSA) framework is developed based on a newly proposed influence propagation model, by leveraging on the vast social networks data, to bring profound impacts on research and practice in finance including market efficiency analysis. For each stock, the framework dynamically generates a DSA measurement that would accurately reflect the price shock. Specially, the topological structure of a social network is able to be modeled as well as the self-influence of each social media user. Furthermore, the market influence of the current DSA as well as the effects of historical ones on different stocks are estimated.
520 $a The essential relationship is verified between social media activities and the stock market movement by testing the semi-strong-form efficient market hypotheses. And then it is confirmed that the effectiveness of our framework in the implementation of stock shock ranking. The results suggest that considering historical DSAs improve the model's performance of fitting abnormal returns in terms of the statistical significance as well as the ranking accuracy. I also develop a new method to estimate social attention of stocks with sentiment analysis and the results show that the newly proposed measurement of social attention would significantly improve the forecasting power of our framework.
590 $a School code: 0771.
650 4 $a Finance. $3 542899
650 4 $a Economic theory. $3 1556984
650 4 $a Mathematics. $3 515831
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710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 1684041
773 0 $t Dissertation Abstracts International $g 78-01A(E).
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791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140630