Evidence map›Paper›PMID 36281469›Full record

ArticleThe Quarterly review of economics and finance : journal of the Midwest Economics Association2023

The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19.

Nikolaos Kyriazis, Stephanos Papadamou, Panayiotis Tzeremes, Shaen Corbet

Abstract read
In one paragraph

Article in The Quarterly review of economics and finance : journal of the Midwest Economics Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nikolaos KyriazisUniversity of Thessaly, Filellinon, Volos 382 21, Greece.
Stephanos PapadamouUniversity of Thessaly, Filellinon, Volos 382 21, Greece.
Panayiotis TzeremesUniversity of Thessaly, Filellinon, Volos 382 21, Greece.
Shaen CorbetDCU Business School, Dublin City University, Dublin 9, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.

Indexed as

Black SwansCOVID-19CryptocurrenciesPandemicsSentimentUncertainty

Identifiers

PMID36281469
PMCPMC9581699

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.