東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Machine Learning Based Trading Strategy.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Based Trading Strategy./
Author:	Lu, Zhen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	101 p.
Notes:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
Subject:	Finance. -
Online resource:	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.
LDR:01927nmm a2200325 4500 001 2348593
005 20220912135613.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798534671520
035 $a (MiAaPQ)AAI28498107
035 $a AAI28498107
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lu, Zhen. $3 3687956
245 1 0 $a Machine Learning Based Trading Strategy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 101 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Cui, Zhenyu.
502 $a Thesis (Master's)--Stevens Institute of Technology, 2021.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0733.
650 4 $a Finance. $3 542899
650 4 $a Artificial intelligence. $3 516317
650 4 $a Accuracy. $3 3559958
650 4 $a Deep learning. $3 3554982
650 4 $a Datasets. $3 3541416
650 4 $a Regression analysis. $3 529831
650 4 $a Financial analysis. $3 3687957
650 4 $a Discriminant analysis. $3 560438
650 4 $a Normal distribution. $3 3561025
650 4 $a Noise. $3 598816
650 4 $a Time series. $3 3561811
650 4 $a Principal components analysis. $3 565921
650 4 $a Sentiment analysis. $2 lcstt $3 3266790
650 4 $a Power. $3 518736
650 4 $a Experiments. $3 525909
650 4 $a Neural networks. $3 677449
650 4 $a Support vector machines. $3 2058743
650 4 $a Classification. $3 595585
650 4 $a Methods. $3 3560391
650 4 $a Correlation analysis. $3 3684579
650 4 $a Algorithms. $3 536374
650 4 $a Decision trees. $3 827433
653 $a Machine learning
653 $a Trading strategies
690 $a 0508
690 $a 0800
710 2 $a Stevens Institute of Technology. $b Financial Engineering. $3 3181482
773 0 $t Masters Abstracts International $g 83-01.
790 $a 0733
791 $a Master's
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498107