The application of artificial neural networks to forecast financial time series

IF 0.6 4区 数学 Q2 LOGIC
D González-Cortés, E Onieva, I Pastor, J Wu
{"title":"The application of artificial neural networks to forecast financial time series","authors":"D González-Cortés, E Onieva, I Pastor, J Wu","doi":"10.1093/jigpal/jzae050","DOIUrl":null,"url":null,"abstract":"The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"17 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Logic Journal of the IGPL","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/jigpal/jzae050","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"LOGIC","Score":null,"Total":0}
引用次数: 0

Abstract

The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology.
人工神经网络在金融时间序列预测中的应用
金融市场每天都会产生大量复杂的信息,因此,开发简化决策的系统是一项至关重要的工作。本文提出并测试了几种智能系统,利用十多年的历史数据和五种不同的神经网络架构来预测 IBEX 35 指数的收盘价。首先是多层感知器,然后是简单递归神经网络、门控递归单元网络和两个长短期记忆(LSTM)网络。对这些模型进行分析的结果表明,它们具有强大的预测能力。此外,这项研究的结果还表明,智能系统的应用可以简化金融市场的决策过程,这是一个很大的优势。此外,通过比较各模型的预测结果误差,LSTM 的误差最小,但训练阶段的计算时间较长。LSTM 能够提前准确预测当天的收盘价以及随后一天和两天的价格。总之,实证结果表明,这些模型可以准确预测用于交易目的的金融数据,而智能系统(如 LSTM 网络)的应用代表了金融技术的一大进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.60
自引率
10.00%
发文量
76
审稿时长
6-12 weeks
期刊介绍: Logic Journal of the IGPL publishes papers in all areas of pure and applied logic, including pure logical systems, proof theory, model theory, recursion theory, type theory, nonclassical logics, nonmonotonic logic, numerical and uncertainty reasoning, logic and AI, foundations of logic programming, logic and computation, logic and language, and logic engineering. Logic Journal of the IGPL is published under licence from Professor Dov Gabbay as owner of the journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信