金融预测的深度学习

V. Unadkat, Parth Sayani, Pratik Kanani, Prachi Doshi
{"title":"金融预测的深度学习","authors":"V. Unadkat, Parth Sayani, Pratik Kanani, Prachi Doshi","doi":"10.1109/ICCSDET.2018.8821178","DOIUrl":null,"url":null,"abstract":"Finance is one of the main pillars on which an economy grows. Having a precise understanding of how the financial market works corroborates greater monetary returns. Stock Markets are established for the general public to own a piece of a company and state an ownership whereas the company gains funds from the public which are used for the expansion of the business and return profits to the investor based on the amount of investment. However choosing which company’s stock to buy is the most challenging task. Exhaustive research and study about the company, their way of working, history, their place in the market, their holdings, projects, etc is required to get an idea about their present performance and future. However no one can predict how a company will perform in the future but an estimation can be achieved by studying various parameters. Machine Learning and Deep Learning techniques have proven their mettle in analysing and prediction data with state of the art results. Though without any intention to predict the exact future of a company, these techniques can be extremely helpful in getting an estimation which bolster our choice rather than just trusting our intuition. Recurrent Neural Network and Long-Shot Term Memory (LSTM) are used to get an approximate estimation of the future of a stock. This would help us in choosing the right stock and thus improve the return of investment.","PeriodicalId":157362,"journal":{"name":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Deep Learning for Financial Prediction\",\"authors\":\"V. Unadkat, Parth Sayani, Pratik Kanani, Prachi Doshi\",\"doi\":\"10.1109/ICCSDET.2018.8821178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Finance is one of the main pillars on which an economy grows. Having a precise understanding of how the financial market works corroborates greater monetary returns. Stock Markets are established for the general public to own a piece of a company and state an ownership whereas the company gains funds from the public which are used for the expansion of the business and return profits to the investor based on the amount of investment. However choosing which company’s stock to buy is the most challenging task. Exhaustive research and study about the company, their way of working, history, their place in the market, their holdings, projects, etc is required to get an idea about their present performance and future. However no one can predict how a company will perform in the future but an estimation can be achieved by studying various parameters. Machine Learning and Deep Learning techniques have proven their mettle in analysing and prediction data with state of the art results. Though without any intention to predict the exact future of a company, these techniques can be extremely helpful in getting an estimation which bolster our choice rather than just trusting our intuition. Recurrent Neural Network and Long-Shot Term Memory (LSTM) are used to get an approximate estimation of the future of a stock. This would help us in choosing the right stock and thus improve the return of investment.\",\"PeriodicalId\":157362,\"journal\":{\"name\":\"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSDET.2018.8821178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSDET.2018.8821178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

金融是经济增长的主要支柱之一。对金融市场如何运作有一个精确的理解,可以证实更高的货币回报。股票市场的建立是为了让公众拥有公司的一部分,并表明所有权,而公司从公众那里获得资金,用于扩大业务,并根据投资额将利润返还给投资者。然而,选择购买哪家公司的股票是最具挑战性的任务。详尽的研究和研究公司,他们的工作方式,历史,他们在市场上的地位,他们的持股,项目等都需要得到一个关于他们现在的表现和未来的想法。然而,没有人可以预测公司未来的表现,但可以通过研究各种参数来估计。机器学习和深度学习技术已经用最先进的结果证明了它们在分析和预测数据方面的能力。虽然没有任何意图预测公司的确切未来,但这些技术在获得一个支持我们选择的估计方面非常有帮助,而不是仅仅相信我们的直觉。利用递归神经网络和长镜头记忆(LSTM)对股票的未来进行近似估计。这将有助于我们选择正确的股票,从而提高投资回报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Financial Prediction
Finance is one of the main pillars on which an economy grows. Having a precise understanding of how the financial market works corroborates greater monetary returns. Stock Markets are established for the general public to own a piece of a company and state an ownership whereas the company gains funds from the public which are used for the expansion of the business and return profits to the investor based on the amount of investment. However choosing which company’s stock to buy is the most challenging task. Exhaustive research and study about the company, their way of working, history, their place in the market, their holdings, projects, etc is required to get an idea about their present performance and future. However no one can predict how a company will perform in the future but an estimation can be achieved by studying various parameters. Machine Learning and Deep Learning techniques have proven their mettle in analysing and prediction data with state of the art results. Though without any intention to predict the exact future of a company, these techniques can be extremely helpful in getting an estimation which bolster our choice rather than just trusting our intuition. Recurrent Neural Network and Long-Shot Term Memory (LSTM) are used to get an approximate estimation of the future of a stock. This would help us in choosing the right stock and thus improve the return of investment.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信