• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

运用因果影响贝叶斯分析对“特朗普效应”对美国金融市场的影响进行实证研究。

An empirical approach to the "Trump Effect" on US financial markets with causal-impact Bayesian analysis.

作者信息

Martín Cervantes Pedro Antonio, Cruz Rambaud Salvador

机构信息

Departamento de Economía y Empresa, Universidad de Almería, Spain.

出版信息

Heliyon. 2020 Aug 26;6(8):e04760. doi: 10.1016/j.heliyon.2020.e04760. eCollection 2020 Aug.

DOI:10.1016/j.heliyon.2020.e04760
PMID:32923716
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7475121/
Abstract

In this paper, we have tested the existence of a causal relationship between the arrival of the 45th presidency of United States and the performance of American stock markets by using a relatively novel methodology, namely the causal-impact Bayesian approach. In effect, we have found strong causal relationships which, in addition to satisfying the classical Granger Causality linear test, have been quantified in absolute and relative terms. Our findings should be included in the context of one of the main markets anomalies, the so-called "calendar effects". More specifically, when distinguishing between the subperiods of pre- and post-intervention, data confirm that the "US presidential cycle" represents a process of high uncertainty and volatility in which the behavior of the prices of financial assets refutes the Efficient-Market Hypothesis.

摘要

在本文中,我们运用一种相对新颖的方法,即因果影响贝叶斯方法,检验了美国第45任总统上任与美国股票市场表现之间因果关系的存在性。实际上,我们发现了强有力的因果关系,这些关系除了满足经典的格兰杰因果线性检验外,还从绝对和相对角度进行了量化。我们的研究结果应置于主要的市场异常现象之一,即所谓“日历效应”的背景下来考量。更具体地说,当区分干预前和干预后的子时期时,数据证实“美国总统周期”代表了一个高度不确定性和波动性的过程,其中金融资产价格的行为驳斥了有效市场假说。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/ad8dda1ca6f2/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/5bd0c1247e78/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/96329692663b/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/ce1ca6f1d053/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/799457f2e696/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/ad8dda1ca6f2/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/5bd0c1247e78/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/96329692663b/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/ce1ca6f1d053/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/799457f2e696/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f39a/7475121/ad8dda1ca6f2/gr005.jpg

相似文献

1
An empirical approach to the "Trump Effect" on US financial markets with causal-impact Bayesian analysis.运用因果影响贝叶斯分析对“特朗普效应”对美国金融市场的影响进行实证研究。
Heliyon. 2020 Aug 26;6(8):e04760. doi: 10.1016/j.heliyon.2020.e04760. eCollection 2020 Aug.
2
Econometrics as evidence? Examining the 'causal' connections between financial speculation and commodities prices.计量经济学能作为证据吗?审视金融投机与大宗商品价格之间的“因果”联系。
Soc Stud Sci. 2016 Oct;46(5):701-724. doi: 10.1177/0306312716658980. Epub 2016 Aug 20.
3
Keynes, population, and equity prices.凯恩斯、人口与股票价格。
J Post Keynes Econ. 1985 Spring;7(3):303-10. doi: 10.1080/01603477.1985.11489504.
4
Time-varying economic dominance in financial markets: A bistable dynamics approach.金融市场中随时间变化的经济主导地位:一种双稳态动力学方法。
Chaos. 2018 May;28(5):055903. doi: 10.1063/1.5021141.
5
Impact of Environmental Fluctuations on Stock Markets: Empirical Evidence from South Asia.环境波动对股票市场的影响:来自南亚的经验证据。
J Environ Public Health. 2022 Jul 14;2022:7692086. doi: 10.1155/2022/7692086. eCollection 2022.
6
Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.基于动态相关的时变系数向量自回归模型及其在原油和股票市场的应用。
Environ Res. 2017 Jan;152:351-359. doi: 10.1016/j.envres.2016.07.015. Epub 2016 Aug 5.
7
Relationship between uncertainty in the oil and stock markets before and after the shale gas revolution: Evidence from the OVX, VIX, and VKOSPI volatility indices.页岩气革命前后石油和股票市场的不确定性关系:来自 OVX、VIX 和 VKOSPI 波动率指数的证据。
PLoS One. 2020 May 5;15(5):e0232508. doi: 10.1371/journal.pone.0232508. eCollection 2020.
8
Shift contagion and minimum causal intensity portfolio during the COVID-19 and the ongoing Russia-Ukraine conflict.新冠疫情及俄乌冲突期间的转移传染与最小因果强度投资组合
Financ Res Lett. 2023 Jul;55:103853. doi: 10.1016/j.frl.2023.103853. Epub 2023 Apr 7.
9
Herding intensity and volatility in cryptocurrency markets during the COVID-19.新冠疫情期间加密货币市场的羊群效应强度与波动性。
Financ Res Lett. 2022 May;46:102382. doi: 10.1016/j.frl.2021.102382. Epub 2021 Aug 17.
10
The impact of COVID-19 on stock market performance in Africa: A Bayesian structural time series approach.新冠疫情对非洲股票市场表现的影响:一种贝叶斯结构时间序列方法。
J Econ Bus. 2021 May-Jun;115:105968. doi: 10.1016/j.jeconbus.2020.105968. Epub 2020 Dec 8.

引用本文的文献

1
Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis.近期堕胎立法对 Twitter 用户参与度、情绪以及对临床医生的信任和健康信息隐私的表达的影响:内容分析。
J Med Internet Res. 2023 May 12;25:e46655. doi: 10.2196/46655.

本文引用的文献

1
Under his thumb the effect of president Donald Trump's Twitter messages on the US stock market.唐纳德·特朗普总统在 Twitter 上发布的消息对美国股市的影响。
PLoS One. 2020 Mar 11;15(3):e0229931. doi: 10.1371/journal.pone.0229931. eCollection 2020.
2
Three Insights from a Bayesian Interpretation of the One-Sided Value.对单边值的贝叶斯解释的三点见解。
Educ Psychol Meas. 2017 Jun;77(3):529-539. doi: 10.1177/0013164416669201. Epub 2016 Oct 5.
3
How to write the methods section of a research paper.如何撰写研究论文的方法部分。
Respir Care. 2004 Oct;49(10):1229-32.