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Predicting High-Cap Tech Stock Polar...

Grisham, Ian L.

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Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers./
Author:	Grisham, Ian L.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	80 p.
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Support vector machines. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30551932
ISBN:	9798380257428

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.
Grisham, Ian L.

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 80 p.

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.Sc.)--East Tennessee State University, 2023.

This item must not be sold to any third party vendors.

The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model's ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance.

ISBN: 9798380257428Subjects--Topical Terms:

2058743
Support vector machines.

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.
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502 $a Thesis (M.Sc.)--East Tennessee State University, 2023.
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520 $a The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model's ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance.
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