ETF predication with machine learning algorithms
There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neu...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/158294 |
| _version_ | 1811686359271735296 |
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| author | Jin, Ziyan |
| author2 | Wong Jia Yiing, Patricia |
| author_facet | Wong Jia Yiing, Patricia Jin, Ziyan |
| author_sort | Jin, Ziyan |
| collection | NTU |
| description | There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neural network to forecast a specific value of ETF price and producing a result with MSE 0.00078. The final step of this article demonstrates how to choose an appropriate index ETF for a certain index. |
| first_indexed | 2024-10-01T04:59:10Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158294 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:59:10Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1582942023-07-07T18:56:02Z ETF predication with machine learning algorithms Jin, Ziyan Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Engineering::Electrical and electronic engineering There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neural network to forecast a specific value of ETF price and producing a result with MSE 0.00078. The final step of this article demonstrates how to choose an appropriate index ETF for a certain index. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-31T07:50:08Z 2022-05-31T07:50:08Z 2022 Final Year Project (FYP) Jin, Z. (2022). ETF predication with machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158294 https://hdl.handle.net/10356/158294 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Jin, Ziyan ETF predication with machine learning algorithms |
| title | ETF predication with machine learning algorithms |
| title_full | ETF predication with machine learning algorithms |
| title_fullStr | ETF predication with machine learning algorithms |
| title_full_unstemmed | ETF predication with machine learning algorithms |
| title_short | ETF predication with machine learning algorithms |
| title_sort | etf predication with machine learning algorithms |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/158294 |
| work_keys_str_mv | AT jinziyan etfpredicationwithmachinelearningalgorithms |