Monetary policy on twitter and asset prices: Evidence from computational text analysis

Lüdering, Jochen; Tillmann, Peter

doi:10.1016/j.najef.2018.11.004

Cited by 14 publications

(15 citation statements)

References 18 publications

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“…Meinusch and Tillmann (2017) and Stiefel and Vivès (2019) exploit tweets to identify beliefs about monetary policy (in the former case about the timing of the exit from the Fed's quantitative easing, in the latter case about the likelihood of an ECB intervention following former ECB president Draghi's 2012 "Whatever it takes" statement), and show that these beliefs are mirrored in financial market developments. Similarly, Lüdering and Tillmann (2020) find that the discussion on Twitter around the "taper tantrum" episode in 2013 contains relevant information for market pricing. Furthermore, Azar and Lo (2016) provide evidence that the content of tweets referencing the Federal Reserve around FOMC meetings can be used to predict future returns, even after controlling for common asset pricing factors.…”

Section: Related Literaturementioning

confidence: 89%

Central bank communication with non-experts – A road to nowhere?

Ehrmann

Wabitsch

2022

Journal of Monetary Economics

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Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use:Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an OpenContent Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

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Section: Related Literaturementioning

confidence: 89%

Central bank communication with non-experts – A road to nowhere?

Ehrmann

Wabitsch

2022

Journal of Monetary Economics

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“…Many studies have been conducted to identify and remove spam and spam accounts [3,15,16,18,19,24,25]. The best precaution to be taken against these threats, which use social media extensively, is to know the ways in which spammers threaten users and to take personal precautions against them [26,27]. Facebook, Twitter, LinkedIn, WhatsApp and Instagram are the most common social networks on the Internet that are used for different purposes [2,4,28,29].…”

Section: Related Studies In Literaturementioning

confidence: 99%

A Hybrid Spam Detection Framework for Social Networks

Çıtlak¹,

Dörterler²,

Doğru³

2023

Politeknik Dergisi

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The widespread use of social networks has caused these platforms to become the target of malicious people. Although social networks have their own spam detection systems, these systems sometimes may not prevent spams in their social networks. Spam contents and messages threaten the security and performance of users of these networks. A spam account detection framework based on three components is proposed in this study. Short link analysis, machine learning and text analysis are the components used together in the proposed framework. First, a dataset was created for this purpose and the attributes of spam accounts were determined. Later, the hyperlinks in the messages in this dataset were analyzed through link analysis component. The machine learning component was modelled through attributes. Moreover, the messages of the social network users were analyzed through text analysis method. A web-based application of the proposed model was put into practice. As a result of the experimental studies carried out thanks to the framework, it was determined that the proposed framework showed a performance of 95.69 %. The success of this article was calculated according to the F-measure and precision evaluation metrics under the influence of sensitive content rate. It is aimed to detect spam accounts on social network and the spam detection policy of these networks is intended to support.

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“…Massive abstracts of papers on neighborhood sustainability are studied and discussed by clustering methods at both geographical and temporal levels [ 26 ]. Moreover, social media texts such as comments and posts can be used as materials for policy analysis, e.g., the Twitter debate about monetary policies of the U.S. is analyzed via combined computational text analysis [ 27 ]. Topics and sentiments are identified from Twitter data to explore public attitudes and factors to a transit network [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary analysis of new energy and industry policy tools in China based on large-scale policy topic modeling

Wang

2021

PLoS ONE

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This study investigates the evolution of provincial new energy policies and industries of China using a topic modeling approach. To this end, six out of 31 provinces in China are first selected as research samples, central and provincial new energy policies in the period of 2010 to 2019 are collected to establish a text corpus with 23, 674 documents. Then, the policy corpus is fed to two different topic models, one is the Latent Dirichlet Allocation for modeling static policy topics, another is the Dynamic Topic Model for extracting topics over time. Finally, the obtained topics are mapped into policy tools for comparisons. The dynamic policy topics are further analyzed with the panel data from provincial new energy industries. The results show that the provincial new energy policies moved to different tracks after about 2014 due to the regional conditions such as the economy and CO2 emission intensity. Underdeveloped provinces tend to use environment-oriented tools to regulate and control CO2 emissions, while developed regions employ the more balanced policy mix for improving new energy vehicles and other industries. Widespread hysteretic effects are revealed during the correlation analysis of the policy topics and new energy capacity.

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Monetary policy on twitter and asset prices: Evidence from computational text analysis

Cited by 14 publications

References 18 publications

Central bank communication with non-experts – A road to nowhere?

Central bank communication with non-experts – A road to nowhere?

A Hybrid Spam Detection Framework for Social Networks

An evolutionary analysis of new energy and industry policy tools in China based on large-scale policy topic modeling

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