Does it payoff to be overconfident? Evidence from an emerging market – a quantile regression approach

Toma, Filip-Mihai; Cepoi, Cosmin-Octavian; Négréa, Bogdan

doi:10.1016/j.frl.2020.101480

Cited by 2 publications

(2 citation statements)

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“…We apply this procedure to the case of the Romanian capital market. Compared to other studies that use classical regression, some previous studies investigating investor behaviour on the Bucharest Stock Exchange have provided some interesting features under a quantile regression framework (Pochea et al 2017 ; Toma et al 2021 ). The overconfidence among Romanian investors has a greater positive effect on higher returns for shorter investment horizons—lower than one year (Toma et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other studies that use classical regression, some previous studies investigating investor behaviour on the Bucharest Stock Exchange have provided some interesting features under a quantile regression framework (Pochea et al 2017 ; Toma et al 2021 ). The overconfidence among Romanian investors has a greater positive effect on higher returns for shorter investment horizons—lower than one year (Toma et al 2021 ). Therefore, considering that our paper deals with investors’ behaviour, during a period characterized by fear and low overconfidence (COVID-19 pandemic), the quantile regression framework may be the best approach for conducting estimates.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%