| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 02040 | |
| Number of page(s) | 4 | |
| Section | Machine Learning and Energy Industry Structure Forecast Analysis | |
| DOI | https://doi.org/10.1051/e3sconf/202021402040 | |
| Published online | 07 December 2020 | |
Predicting S&P 500 Market Price by Deep Neural Network and Enemble Model
The Oakwood School, GREENVILLE, NC, USA
The method to predict the movement of stock market has appealed to scientists for decades. In this article, we use three different models to tackle that problem. In particular, we propose a Deep Neural Network (DNN) to predict the intraday direction of SP500 index and compare the DNN with two conventional machine learning models, i.e. linear regression, support vector machine. We demonstrate that DNN is able to predict SP500 index with relatively highest accuracy.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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