Good (Bad) News and the Probability of Informed Trading: Evidence from Illegal Insider Trading

ABSTRACT

We present a closed-form solution connecting the probability of informed trading ($\mathit{PIN}$) to the overlooked parameter that signaling private information is good or bad. Estimating $\mathit{PIN}$ using illegal insider trading data, we find it sensitive to the certainty of positive private information in addition to the existed explanations, offering a new explanation for $\mathit{PIN}$‘s limitations in prior literature.

KEYWORDS:

• Probability of informed trading
• liquidity
• information asymmetry
• insider trading

JEL:

• G12
• G14

Acknowledgments

We thank the editor Paresh Narayan, a subject editor and two reviewers for their comments. We also thank Tao Bing and Nianling Wang from Capital University of Economics and Business, as well as Cheng Yan from the University of Essex, for helpful discussions. We are grateful to Jaideep Oberoi from SOAS, University of London for providing the code estimating PIN with Bayesian approach. We are also indebted to the FinTech Lab of the School of Finance at Capital University of Economics and Business for providing High-Performance Computing (HPC) resources for large dataset estimation. This research is funded by Capital University of Economics and Business (Grant ID: QNTD202301). All errors are our own responsibility.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Capital University of Economics and Business [QNTD202301].