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Modeling the impact of news and soci...

Mo, Sheung Yin Kevin.

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Modeling the impact of news and social media to financial markets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling the impact of news and social media to financial markets./
Author:	Mo, Sheung Yin Kevin.
Description:	243 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Systems science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3715957
ISBN:	9781321942989

Modeling the impact of news and social media to financial markets.
Mo, Sheung Yin Kevin.

Modeling the impact of news and social media to financial markets. - 243 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2015.

The field of behavioral finance studies how the psychology and cognition of real-world investors influence decision-making in an irrational manner. Psychological evidence suggests that emotion and mood play a key role in affecting investors when making financial decisions. The motivation of this dissertation hinges on the growing popularity in the use of news and social media information and their increasing influence on the financial investment community. This dissertation investigates the interplay between news/social sentiment and financial market movement in the form of empirical impact. The underlying belief is that news and social media influence investor sentiment, which in turn drives financial decisions and predicates the upward or downward movement of the financial markets. The current work contributes in the following three areas within behavioral finance and its application: First, it identifies and documents the presence of a financial community on Twitter whose primary interests are consistently aligned with financial market-related knowledge and information. By harnessing the sentiment expressed by the most influential Twitter users within the community, the empirical study proposes a better proxy for quantifying social sentiment and demonstrates its robust predictive power for financial market movements. Second, a significant feedback effect is detected between news sentiment and market returns across the major indices in the U.S. financial market. The dissertation demonstrates that the aggregate sentiment from news articles has a more delayed impact on market returns than that of the reverse (i.e. market returns on news sentiment) within this feedback cycle. Third, this dissertation presents a novel approach to formulate an optimization problem for identifying a trading strategy based on the sentiment feedback strength using genetic programming method. The key intuition behind the feedback strength approach is that collective momentum of the news and tweet sentiment leads to significant market signals, which can be exploited to generate excessive trading profits. The results show that the sentiment feedback-based strategy yields superior risk-adjusted returns over other benchmark strategies. Altogether, this dissertation significantly advances existing empirical knowledge on the impact of sentiment on financial markets and further contributes to the field of financial engineering with an advanced novel trading strategy based on these new findings.

ISBN: 9781321942989Subjects--Topical Terms:

3168411
Systems science.

Modeling the impact of news and social media to financial markets.
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500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Adviser: Steve Y. Yang.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2015.
520 $a The field of behavioral finance studies how the psychology and cognition of real-world investors influence decision-making in an irrational manner. Psychological evidence suggests that emotion and mood play a key role in affecting investors when making financial decisions. The motivation of this dissertation hinges on the growing popularity in the use of news and social media information and their increasing influence on the financial investment community. This dissertation investigates the interplay between news/social sentiment and financial market movement in the form of empirical impact. The underlying belief is that news and social media influence investor sentiment, which in turn drives financial decisions and predicates the upward or downward movement of the financial markets. The current work contributes in the following three areas within behavioral finance and its application: First, it identifies and documents the presence of a financial community on Twitter whose primary interests are consistently aligned with financial market-related knowledge and information. By harnessing the sentiment expressed by the most influential Twitter users within the community, the empirical study proposes a better proxy for quantifying social sentiment and demonstrates its robust predictive power for financial market movements. Second, a significant feedback effect is detected between news sentiment and market returns across the major indices in the U.S. financial market. The dissertation demonstrates that the aggregate sentiment from news articles has a more delayed impact on market returns than that of the reverse (i.e. market returns on news sentiment) within this feedback cycle. Third, this dissertation presents a novel approach to formulate an optimization problem for identifying a trading strategy based on the sentiment feedback strength using genetic programming method. The key intuition behind the feedback strength approach is that collective momentum of the news and tweet sentiment leads to significant market signals, which can be exploited to generate excessive trading profits. The results show that the sentiment feedback-based strategy yields superior risk-adjusted returns over other benchmark strategies. Altogether, this dissertation significantly advances existing empirical knowledge on the impact of sentiment on financial markets and further contributes to the field of financial engineering with an advanced novel trading strategy based on these new findings.
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