國立虎尾科技大學 |

Machine Learning for Financial Market Forecasting.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning for Financial Market Forecasting./
作者:	Johnson, Jaya.
面頁冊數:	1 online resource (104 pages)
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798379491109

Machine Learning for Financial Market Forecasting.
Johnson, Jaya.

Machine Learning for Financial Market Forecasting. - 1 online resource (104 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379491109Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Stock market forecastingIndex Terms--Genre/Form:

554714
Electronic books.

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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Finance. $3 559073
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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710 2 $a Harvard University. $b Extension Studies. $3 1372761
773 0 $t Masters Abstracts International $g 84-11.
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