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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/158294 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
|---|