A Deep Neural Network Based Model for Jane Street Market Prediction
(2021) 2nd IEEE International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021 In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021 p.303-306- Abstract
The price trend of stocks directly affects the economic interests of investors, and also affects and reflects the country's macroeconomic policies, so it has received widespread attention. The formation and fluctuation of stock prices are not only restricted by various economic and political factors, but also influenced by investment psychology and trading techniques. But in fact, stock prices are not only closely related to the internal financial status of listed companies, but also related to the overall stock market conditions and even the overall economic operation. Due to the many factors that affect stock price fluctuations, using traditional regression analysis models to predict is not only complicated but also less accurate. In... (More)
The price trend of stocks directly affects the economic interests of investors, and also affects and reflects the country's macroeconomic policies, so it has received widespread attention. The formation and fluctuation of stock prices are not only restricted by various economic and political factors, but also influenced by investment psychology and trading techniques. But in fact, stock prices are not only closely related to the internal financial status of listed companies, but also related to the overall stock market conditions and even the overall economic operation. Due to the many factors that affect stock price fluctuations, using traditional regression analysis models to predict is not only complicated but also less accurate. In this paper, we propose an algorithm based on deep neural networks to build stock prediction models. This model is better than other models in predicting stock trends. Specifically, the return value of our neural network model is 3137 higher than the Xgboost algorithm and 4692 higher than the Lightgbm algorithm.
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- author
- Guo, Shuting ; Wang, Haoshan ; Jiahao, Lin and Chen, Xi
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Data Mining, Deep learning algorithms, Neural Networks
- host publication
- 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021
- series title
- 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021
- article number
- 9390063
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2nd IEEE International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021
- conference location
- Nanchang, China
- conference dates
- 2021-03-26 - 2021-03-28
- external identifiers
-
- scopus:85104410820
- ISBN
- 9780738131221
- DOI
- 10.1109/ICBAIE52039.2021.9390063
- language
- English
- LU publication?
- no
- id
- 2c87248d-17b1-49ed-8f9a-3c69888c3ac8
- date added to LUP
- 2021-05-03 18:09:07
- date last changed
- 2022-04-27 01:51:52
@inproceedings{2c87248d-17b1-49ed-8f9a-3c69888c3ac8,
abstract = {{<p>The price trend of stocks directly affects the economic interests of investors, and also affects and reflects the country's macroeconomic policies, so it has received widespread attention. The formation and fluctuation of stock prices are not only restricted by various economic and political factors, but also influenced by investment psychology and trading techniques. But in fact, stock prices are not only closely related to the internal financial status of listed companies, but also related to the overall stock market conditions and even the overall economic operation. Due to the many factors that affect stock price fluctuations, using traditional regression analysis models to predict is not only complicated but also less accurate. In this paper, we propose an algorithm based on deep neural networks to build stock prediction models. This model is better than other models in predicting stock trends. Specifically, the return value of our neural network model is 3137 higher than the Xgboost algorithm and 4692 higher than the Lightgbm algorithm.</p>}},
author = {{Guo, Shuting and Wang, Haoshan and Jiahao, Lin and Chen, Xi}},
booktitle = {{2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021}},
isbn = {{9780738131221}},
keywords = {{Data Mining; Deep learning algorithms; Neural Networks}},
language = {{eng}},
pages = {{303--306}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021}},
title = {{A Deep Neural Network Based Model for Jane Street Market Prediction}},
url = {{http://dx.doi.org/10.1109/ICBAIE52039.2021.9390063}},
doi = {{10.1109/ICBAIE52039.2021.9390063}},
year = {{2021}},
}