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Machine Learning Based Trading Strategy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Based Trading Strategy./
作者:	Lu, Zhen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	101 p.
附註:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
標題:	Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498107
ISBN:	9798534671520

Machine Learning Based Trading Strategy.
Lu, Zhen.

Machine Learning Based Trading Strategy. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 101 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (Master's)--Stevens Institute of Technology, 2021.

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

This thesis explores the machine learning's application in the financial engineering field. The first contribution is building machine learning classification model. The author explored dif- ferent predictors effect on models' prediction and utilized different dimensionality reduction method to filter valuable predictors. Then, the author built different machine learning models and combined their performances together. The second contribution is testifying model's valida- tion. The author applied machine learning models into various real-world applications. Firstly, he combined portfolio analysis and machine learning models together, and made a trading strategy that could improve risk parity portfolio's performance. Secondly, he conducted senti- ment analysis based on tweets and added it into machine learning model. At last, he utilized bootstrapping sampling method to testify model's performance when data was changed.

ISBN: 9798534671520Subjects--Topical Terms:

542899
Finance.
Subjects--Index Terms:

Machine learning

Machine Learning Based Trading Strategy.
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520 $a This thesis explores the machine learning's application in the financial engineering field. The first contribution is building machine learning classification model. The author explored dif- ferent predictors effect on models' prediction and utilized different dimensionality reduction method to filter valuable predictors. Then, the author built different machine learning models and combined their performances together. The second contribution is testifying model's valida- tion. The author applied machine learning models into various real-world applications. Firstly, he combined portfolio analysis and machine learning models together, and made a trading strategy that could improve risk parity portfolio's performance. Secondly, he conducted senti- ment analysis based on tweets and added it into machine learning model. At last, he utilized bootstrapping sampling method to testify model's performance when data was changed.
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650 4 $a Artificial intelligence. $3 516317
650 4 $a Accuracy. $3 3559958
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650 4 $a Noise. $3 598816
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