Evidence map›Paper›PMID 36687793›Full record

ArticleFinancial innovation2023

Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market.

Cosmin Octavian Cepoi, Victor Dragotă, Ruxandra Trifan, Andreea Iordache

Open access · diamondAbstract read
In one paragraph

Article in Financial innovation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

0 citing papers in PubMed, 8 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 1 institution in 1 country.

Cosmin Octavian CepoiDepartment of Money and Banking and CEFIMO, Faculty of Finance and Banking, Bucharest University of Economic Studies, Bucharest, Romania.ORCID 0000-0002-5529-4339
Victor DragotăDepartment of Finance and CEFIMO, Faculty of Finance and Banking, Bucharest University of Economic Studies, Bucharest, Romania.ORCID 0000-0001-7284-5297
Ruxandra TrifanDoctoral School of Finance, Faculty of Finance and Banking, Bucharest University of Economic Studies, Bucharest, Romania.ORCID 0000-0002-6599-0055
Andreea IordacheDoctoral School of Finance, Faculty of Finance and Banking, Bucharest University of Economic Studies, Bucharest, Romania.ORCID 0000-0003-2243-4654
Bucharest University of Economic Studies · RO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using data from the Bucharest Stock Exchange, we examine the factors influencing the probability of informed trading (PIN) during February-October 2020, a COVID-19 pandemic period. Based on an unconditional quantile regression approach, we show that PIN exhibit asymmetric dependency with liquidity and trading costs. Furthermore, building a customized database that contains all insider transactions on the Bucharest Stock Exchange, we reveal that these types of orders monotonically increase the information asymmetry from the 50th to the 90th quantile throughout the PIN distribution. Finally, we bring strong empirical evidence associating the level of information asymmetry to the level of fake news related to the COVID-19 pandemic. This novel result suggests that during episodes when the level of PIN is medium to high (between 15 and 50%), any COVID-19 related news classified as misinformation released during the lockdown period, is discouraging informed traders to place buy or sell orders conditioned by their private information.

Indexed as

COVID-19Insider tradingMarket microstructurePIN

Identifiers

PMID36687793
PMCPMC9840563
OpenAlexW4316174799

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.