基于生成对抗网络的人工市场模拟定量调整

Masanori Hirano, K. Izumi
{"title":"基于生成对抗网络的人工市场模拟定量调整","authors":"Masanori Hirano, K. Izumi","doi":"10.1109/ICA55837.2022.00009","DOIUrl":null,"url":null,"abstract":"This study focuses on parameter tuning of artificial market simulations and aims to replace the traditional qualitative evaluation metrics based on stylized facts with the proposed quantitative metrics. Traditionally, for the evaluation of artificial market simulations, the replication of stylized facts, a common phenomenon among financial markets and observed in empirical studies, is verified by humans. However, this prevents large-scale parameter tuning owing to the complexity of automation. Hence, this study utilizes a generative adversarial network (GAN) for this replacement because we assume that the GAN's learning architecture has a good fit for evaluating the distributional features of actual markets and can learn stylized facts implicitly. In the proposed parameter-tuning method, the simulated data are input into the critic of the GAN, and the outputs are employed as the objective value of the tuning. The parameter tuning results show that we successfully tuned the high-dimensional parameters of artificial market simulations and confirmed that the optimized parameter could replicate the stylized facts employed in traditional qualitative evaluation metrics.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network\",\"authors\":\"Masanori Hirano, K. Izumi\",\"doi\":\"10.1109/ICA55837.2022.00009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study focuses on parameter tuning of artificial market simulations and aims to replace the traditional qualitative evaluation metrics based on stylized facts with the proposed quantitative metrics. Traditionally, for the evaluation of artificial market simulations, the replication of stylized facts, a common phenomenon among financial markets and observed in empirical studies, is verified by humans. However, this prevents large-scale parameter tuning owing to the complexity of automation. Hence, this study utilizes a generative adversarial network (GAN) for this replacement because we assume that the GAN's learning architecture has a good fit for evaluating the distributional features of actual markets and can learn stylized facts implicitly. In the proposed parameter-tuning method, the simulated data are input into the critic of the GAN, and the outputs are employed as the objective value of the tuning. The parameter tuning results show that we successfully tuned the high-dimensional parameters of artificial market simulations and confirmed that the optimized parameter could replicate the stylized facts employed in traditional qualitative evaluation metrics.\",\"PeriodicalId\":150818,\"journal\":{\"name\":\"2022 IEEE International Conference on Agents (ICA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Agents (ICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICA55837.2022.00009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Agents (ICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICA55837.2022.00009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本研究的重点是人工市场模拟的参数调整,旨在用提出的定量指标取代传统的基于程式化事实的定性评估指标。传统上,对于人工市场模拟的评估,风格化事实的复制是由人类验证的,这是金融市场中常见的现象,并在实证研究中观察到。然而,由于自动化的复杂性,这阻止了大规模的参数调整。因此,本研究利用生成对抗网络(GAN)进行替代,因为我们假设GAN的学习架构非常适合评估实际市场的分布特征,并且可以隐式地学习风格化的事实。在所提出的参数整定方法中,将模拟数据输入到GAN的批评家中,并将输出作为整定的目标值。参数调整结果表明,我们成功地调整了人工市场模拟的高维参数,并证实了优化后的参数可以复制传统定性评估指标中采用的程式化事实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network
This study focuses on parameter tuning of artificial market simulations and aims to replace the traditional qualitative evaluation metrics based on stylized facts with the proposed quantitative metrics. Traditionally, for the evaluation of artificial market simulations, the replication of stylized facts, a common phenomenon among financial markets and observed in empirical studies, is verified by humans. However, this prevents large-scale parameter tuning owing to the complexity of automation. Hence, this study utilizes a generative adversarial network (GAN) for this replacement because we assume that the GAN's learning architecture has a good fit for evaluating the distributional features of actual markets and can learn stylized facts implicitly. In the proposed parameter-tuning method, the simulated data are input into the critic of the GAN, and the outputs are employed as the objective value of the tuning. The parameter tuning results show that we successfully tuned the high-dimensional parameters of artificial market simulations and confirmed that the optimized parameter could replicate the stylized facts employed in traditional qualitative evaluation metrics.
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:481959085
Book学术官方微信