Sentiment-driven speculation in financial markets with heterogeneous beliefs: A machine learning approach

IF 1.9 3区 经济学 Q2 ECONOMICS
Tommaso Di Francesco , Cars Hommes
{"title":"Sentiment-driven speculation in financial markets with heterogeneous beliefs: A machine learning approach","authors":"Tommaso Di Francesco ,&nbsp;Cars Hommes","doi":"10.1016/j.jedc.2025.105092","DOIUrl":null,"url":null,"abstract":"<div><div>We study an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, we study the effect of two types of speculation: one based on fundamentalist and the other on rational expectations. While the first is only based on knowledge of the asset underlying dynamics, the second takes also into account the behavior of other investors. We bring the model to data by estimating it on the Bitcoin Market with two contributions, relying on methods from Machine Learning. First, we construct the Bitcoin Twitter Sentiment Index (BiTSI) to proxy a time varying bias. Second, we propose a new method based on a Neural Network, for the estimation of the resulting heterogeneous agent model with rational speculators. We show that the switching finds support in the data and that while fundamentalist speculation amplifies volatility, rational speculation has a stabilizing effect on the market.</div></div>","PeriodicalId":48314,"journal":{"name":"Journal of Economic Dynamics & Control","volume":"175 ","pages":"Article 105092"},"PeriodicalIF":1.9000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic Dynamics & Control","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165188925000582","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

We study an heterogenous asset pricing model in which different classes of investors coexist and evolve, switching among strategies over time according to a fitness measure. In the presence of boundedly rational agents, with biased forecasts and trend following rules, we study the effect of two types of speculation: one based on fundamentalist and the other on rational expectations. While the first is only based on knowledge of the asset underlying dynamics, the second takes also into account the behavior of other investors. We bring the model to data by estimating it on the Bitcoin Market with two contributions, relying on methods from Machine Learning. First, we construct the Bitcoin Twitter Sentiment Index (BiTSI) to proxy a time varying bias. Second, we propose a new method based on a Neural Network, for the estimation of the resulting heterogeneous agent model with rational speculators. We show that the switching finds support in the data and that while fundamentalist speculation amplifies volatility, rational speculation has a stabilizing effect on the market.
具有异质信念的金融市场中情绪驱动的投机:一种机器学习方法
我们研究了一个异质资产定价模型,其中不同类别的投资者共存并演变,根据适应度度量随时间在策略之间切换。在有界理性主体存在的情况下,我们研究了两种投机行为的影响:一种是基于基本预期的投机行为,另一种是基于理性预期的投机行为。前者仅基于对资产潜在动态的了解,后者还考虑了其他投资者的行为。我们依靠机器学习的方法,通过对比特币市场的两个贡献进行估计,将模型带入数据。首先,我们构建了比特币推特情绪指数(BiTSI)来代表时变偏差。其次,我们提出了一种基于神经网络的新方法,用于估计具有理性投机者的异构智能体模型。我们表明,这种转换在数据中得到了支持,而原教旨主义投机放大了波动性,理性投机对市场具有稳定作用。
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
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
×
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