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Stock Trend Prediction : = Based on ...

University of California, Los Angeles.

Stock Trend Prediction : = Based on Machine Learning Methods.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Stock Trend Prediction :/
Reminder of title:	Based on Machine Learning Methods.
Author:	Song, Yuan.
Description:	1 online resource (43 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355811285

Stock Trend Prediction : = Based on Machine Learning Methods.
Song, Yuan.

Stock Trend Prediction :Based on Machine Learning Methods. - 1 online resource (43 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--University of California, Los Angeles, 2018.

Includes bibliographical references

Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. I chose stock price indicators from 20 well-known public companies and calculated their related technical indicators as inputs, which are the Relative Strength Index, the Average Directional Movement Index, and the Parabolic Stop and Reverse. Experimental results show that recurrent neural network outperforms in time-series related prediction. Especially for gated recurrent units, its accuracy rate is around 5% higher than support vector machine and eXtreme gradient boosting.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355811285Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Stock Trend Prediction : = Based on Machine Learning Methods.
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100 1 $a Song, Yuan. $3 1195602
245 1 0 $a Stock Trend Prediction : $b Based on Machine Learning Methods.
264 0 $c 2018
300 $a 1 online resource (43 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Yingnian Wu.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2018.
504 $a Includes bibliographical references
520 $a Nowadays, people show more and more enthusiasm for applying machine learning methods to finance domain. Many scholars and investors are trying to discover the mystery behind the stock market by applying deep learning. This thesis compares four machine learning methods: long short-term memory (LSTM), gated recurrent units (GRU), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) to test which one performs the best in predicting the stock trend. I chose stock price indicators from 20 well-known public companies and calculated their related technical indicators as inputs, which are the Relative Strength Index, the Average Directional Movement Index, and the Parabolic Stop and Reverse. Experimental results show that recurrent neural network outperforms in time-series related prediction. Especially for gated recurrent units, its accuracy rate is around 5% higher than support vector machine and eXtreme gradient boosting.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Los Angeles. $b Statistics 0891. $3 1183048
773 0 $t Masters Abstracts International $g 57-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10751871 $z click for full text (PQDT)

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