國立虎尾科技大學 |

Machine Learning for Stock Prediction Based on Fundamental Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning for Stock Prediction Based on Fundamental Analysis./
Author:	Huang, Yuxuan.
Description:	1 online resource (71 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
Subject:	Neural networks. -
Online resource:	click for full text (PQDT)
ISBN:	9798845490087

Machine Learning for Stock Prediction Based on Fundamental Analysis.
Huang, Yuxuan.

Machine Learning for Stock Prediction Based on Fundamental Analysis. - 1 online resource (71 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.Eng.Sc.)--The University of Western Ontario (Canada), 2019.

Includes bibliographical references

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. In this thesis, we prepared 22 years' worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision making regarding to stock investment.

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

Mode of access: World Wide Web

ISBN: 9798845490087Subjects--Topical Terms:

1011215
Neural networks.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning for Stock Prediction Based on Fundamental Analysis.
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245 1 0 $a Machine Learning for Stock Prediction Based on Fundamental Analysis.
264 0 $c 2019
300 $a 1 online resource (71 pages)
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338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-03.
500 $a Advisor: F. , Capretz Luiz;Danny, Ho.
502 $a Thesis (M.Eng.Sc.)--The University of Western Ontario (Canada), 2019.
504 $a Includes bibliographical references
520 $a Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. In this thesis, we prepared 22 years' worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision making regarding to stock investment.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Neural networks. $3 1011215
650 4 $a Finance. $3 559073
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0508
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Western Ontario (Canada). $3 1184598
773 0 $t Masters Abstracts International $g 84-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29246274 $z click for full text (PQDT)