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Machine Learning for Financial Market Forecasting.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Financial Market Forecasting.
Author:	Johnson, Jaya.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	104 p.
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Notes:	Advisor: Wang, Hongming.
Contained By:	Masters Abstracts International84-11.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30486584
ISBN:	9798379491109

Machine Learning for Financial Market Forecasting.
Johnson, Jaya.

Machine Learning for Financial Market Forecasting. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 104 p.

Source: Masters Abstracts International, Volume: 84-11.

Thesis (A.L.M.)--Harvard University, 2023.

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

Stock market forecasting continues to be an active area of research. In recent years machine learning algorithms have been applied to achieve better predictions. Using natural language processing (NLP), contextual information from unstructured data including news feeds, analysts calls and other online content have been used as indicators to improve prediction rates. In this work we compare traditional machine learning methods with more recent ones, including LSTM and FinBERT to assess improvements, challenges and future directions.

ISBN: 9798379491109Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Stock market forecasting

Machine Learning for Financial Market Forecasting.
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245 1 0 $a Machine Learning for Financial Market Forecasting.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 104 p.
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Wang, Hongming.
502 $a Thesis (A.L.M.)--Harvard University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Stock market forecasting continues to be an active area of research. In recent years machine learning algorithms have been applied to achieve better predictions. Using natural language processing (NLP), contextual information from unstructured data including news feeds, analysts calls and other online content have been used as indicators to improve prediction rates. In this work we compare traditional machine learning methods with more recent ones, including LSTM and FinBERT to assess improvements, challenges and future directions.
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650 4 $a Computer science. $3 199325
650 4 $a Finance. $3 183252
653 $a Stock market forecasting
653 $a FinBERT
653 $a Long short term memory
653 $a Natural language processing
653 $a Recurrent neural networks
653 $a Support vector machine
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690 $a 0508
690 $a 0800
710 2 $a Harvard University. $b Extension Studies. $3 966837
773 0 $t Masters Abstracts International $g 84-11.
790 $a 0084
791 $a A.L.M.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30486584