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Network Science Approaches to Stock ...

Kim, Minjun.

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Network Science Approaches to Stock Market Prediction and Text Classification Problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Network Science Approaches to Stock Market Prediction and Text Classification Problems./
Author:	Kim, Minjun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	94 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
Subject:	Systems science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296045
ISBN:	9798383099001

Network Science Approaches to Stock Market Prediction and Text Classification Problems.
Kim, Minjun.

Network Science Approaches to Stock Market Prediction and Text Classification Problems. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 94 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

Thesis (Ph.D.)--State University of New York at Binghamton, 2024.

This dissertation explores the applicability of network science in solving real-life business problems that involve large-scale complex systems. Specifically, the study investigates how network science can be used in financial market forecasting, decision-making in stock market trading, and conversational AI systems.In the financial market forecasting, the study proposes a new method that utilizes network measurements extracted from S&P 500 networks to improve the predictability of conventional ARIMA models. The study shows that changes in the network strength distributions provide important information on the network's future movements, and the inclusion of network measurements significantly improves the model accuracy. Moreover, the study proposes a network-based Exponential Moving Average (EMA) method for making trading decisions, which outperforms the traditional EMA with an ability to capture more trading opportunities and yield a bigger return at each trade.In the conversational AI area, the study proposes a network community detection-based approach to automatically label text data for solving a classification problem, which outperforms human-labeled data by 2.68-3.75% in classification accuracy. This{A0} approach helps detect mislabeled and ambiguous data points that negatively affect model performance, reducing development time and cost for industries using conversational AI technology.Overall, the study demonstrates that network science can offer significant insights to some of the most critical modeling problems in business applications by providing an additional and powerful perspective to the traditional methodologies. As this dissertation presents, the ability to integrate network science into various business analytics can lead to more informed decision-making, enhanced development efficiency, and a competitive edge in rapidly changing business environments.

ISBN: 9798383099001Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Complex systems

Network Science Approaches to Stock Market Prediction and Text Classification Problems.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 94 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
500 $a Advisor: Sayama, Hiroki.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2024.
520 $a This dissertation explores the applicability of network science in solving real-life business problems that involve large-scale complex systems. Specifically, the study investigates how network science can be used in financial market forecasting, decision-making in stock market trading, and conversational AI systems.In the financial market forecasting, the study proposes a new method that utilizes network measurements extracted from S&P 500 networks to improve the predictability of conventional ARIMA models. The study shows that changes in the network strength distributions provide important information on the network's future movements, and the inclusion of network measurements significantly improves the model accuracy. Moreover, the study proposes a network-based Exponential Moving Average (EMA) method for making trading decisions, which outperforms the traditional EMA with an ability to capture more trading opportunities and yield a bigger return at each trade.In the conversational AI area, the study proposes a network community detection-based approach to automatically label text data for solving a classification problem, which outperforms human-labeled data by 2.68-3.75% in classification accuracy. This{A0} approach helps detect mislabeled and ambiguous data points that negatively affect model performance, reducing development time and cost for industries using conversational AI technology.Overall, the study demonstrates that network science can offer significant insights to some of the most critical modeling problems in business applications by providing an additional and powerful perspective to the traditional methodologies. As this dissertation presents, the ability to integrate network science into various business analytics can lead to more informed decision-making, enhanced development efficiency, and a competitive edge in rapidly changing business environments.
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650 4 $a Statistics. $3 517247
650 4 $a Computer science. $3 523869
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653 $a Business applications
653 $a Stock market
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793 $a English
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