國立高雄大學圖資館 |

登入

回首頁

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.
作者:	Grisham, Ian L.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	80 p.
附註:	Source: Masters Abstracts International, Volume: 85-03.
附註:	Advisor: Bennett, Brian.
Contained By:	Masters Abstracts International85-03.
標題:	Support vector machines.
電子資源:	http://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:

679056
Support vector machines.

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.
LDR:02232nmm a2200361 4500 001 655807
005 20240414211936.5
006 m o d
007 cr#unu||||||||
008 240620s2023 ||||||||||||||||| ||eng d
020 $a 9798380257428
035 $a (MiAaPQ)AAI30551932
035 $a (MiAaPQ)EastTennessee5739
035 $a AAI30551932
040 $a MiAaPQ $c MiAaPQ
100 1 $a Grisham, Ian L. $3 966915
245 1 0 $a Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines and Bidirectional Encoders from Transformers.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 80 p.
500 $a Source: Masters Abstracts International, Volume: 85-03.
500 $a Advisor: Bennett, Brian.
502 $a Thesis (M.Sc.)--East Tennessee State University, 2023.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0069.
650 4 $a Support vector machines. $3 679056
650 4 $a Computer science. $3 199325
650 4 $a Finance. $3 183252
650 4 $a Web studies. $3 708690
690 $a 0800
690 $a 0984
690 $a 0508
690 $a 0646
710 2 $a East Tennessee State University. $3 212799
773 0 $t Masters Abstracts International $g 85-03.
790 $a 0069
791 $a M.Sc.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30551932