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Machine Learning for Financial Market Forecasting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning for Financial Market Forecasting.
作者:	Johnson, Jaya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	104 p.
附註:	Source: Masters Abstracts International, Volume: 84-11.
附註:	Advisor: Wang, Hongming.
Contained By:	Masters Abstracts International84-11.
標題:	Computer science.
電子資源:	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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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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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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792 $a 2023
793 $a English
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