Twitter-based uncertainty and stock market returns: Evidence from G7 countries

IF 2.8 3区 经济学 Q2 BUSINESS, FINANCE
Merve Coskun, Nigar Taspinar
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引用次数: 0

Abstract

The aim of this study is to investigate the impact of Twitter-based economic uncertainty (TEU) and Twitter-based market uncertainty (TMU) on G7 stock returns in the challenging year in which the COVID-19 pandemic began (2020) under different stock market conditions (bearish, normal, and bullish). To this aim, this study applies novel quantile-based approaches, namely Quantile autoregression unit root test, Quantile-on-quantile approach, and Quantile Granger-causality test covering the period from 01 January 2020 to 15 September 2020. Main findings of the study are (1) G7 stock return series are stationary for all quantiles of the conditional distributions with minor exceptions meaning that shocks have temporary effects on stock returns of G7 markets. (2) The impact of Twitter-based uncertainty strongly depends on the market condition, whether it is bullish or bearish for all G7 markets. A heterogeneous association exists between variables caused by different market conditions. (3) A bi-directional causal association exists between stock returns-TEU and stock returns-TMU. This result confirms the existence of feedback hypothesis between G7 stock returns and TEU, TMU, respectively. This study provides important policy implications and recommendations for policy makers and investors on the nexus between Twitter-based uncertainties and stock returns.

基于 Twitter 的不确定性与股市回报:来自 G7 国家的证据
本研究旨在探讨在不同的股市条件下(看跌、正常和看涨),基于 Twitter 的经济不确定性(TEU)和基于 Twitter 的市场不确定性(TMU)对 COVID-19 大流行开始的那一年(2020 年)七国集团股票回报率的影响。为此,本研究采用了基于量值的新方法,即量值自回归单位根检验、量值对量值方法和量值格兰杰因果关系检验,时间跨度为 2020 年 1 月 1 日至 2020 年 9 月 15 日。研究的主要发现有:(1)G7 股票收益率序列在条件分布的所有量级上都是静态的,只有少数例外,这意味着冲击对 G7 市场股票收益率的影响是暂时的。(2) 基于 Twitter 的不确定性的影响在很大程度上取决于市场状况,即对所有 G7 市场是看涨还是看跌。不同市场条件导致的变量之间存在异质性关联。(3) 股票收益率-TEU 和股票收益率-TMU 之间存在双向因果关系。这一结果证实了 G7 股票收益率分别与 TEU、TMU 之间存在反馈假说。本研究就基于 Twitter 的不确定性与股票收益之间的关系为政策制定者和投资者提供了重要的政策启示和建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
143
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