Increased trading in dark venues around earnings announcements offers advantages to businesses and markets that can allay regulators’ concerns
- B.Tech (DAIICT)
- M.Res (London Business School)
- PhD (London Business School)
Peeyush joined HKU in 2020 as an Assistant Professor of Accounting after receiving his Ph.D. in Accounting from London Business School. His research focuses on how disclosures and information intermediaries influence capital market outcomes.
- Introduction to Financial Accounting (ACCT1101)
- Information intermediaries
- Fundamental analysis
- Karthik Balakrishnan, Xanthi Gkougkousi, Wayne R. Landsman, and Peeyush Taori (2022), “Dark Market Share around Earnings Announcements and Speed of Resolution of Investor Disagreement”, The Accounting Review, 97(5), pp. 1-28.
- Karthik Balakrishnan, Lakshmanan Shivakumar, and Peeyush Taori (2021), “Analysts’ Estimates of the Cost of Equity Capital”, Journal of Accounting and Economics, 71(2-3), 101367.
This study examines how the market share of dark venues changes at earnings announcements. Our analysis shows a statistically significant increase in dark market share in the weeks prior to, during, and following the earnings announcement. We also predict and find evidence that increases in dark market share around earnings announcements are higher for firms with high quality accounting information. In addition, we find a positive relation between the change in dark market share and the speed of resolution of investor disagreement-a key dimension of informational efficiency, which suggests that dark trading is associated with an improvement in market quality. How market fragmentation changes around news events, the role accounting information plays in market fragmentation, and how changes in market fragmentation relate to market quality can help provide insights to securities regulators.
We explore a large sample of analysts' estimates of the cost of equity capital (CoE) to evaluate their usefulness as expected return proxies (ERP). We find that the CoE estimates are significantly related to a firm's beta, size, book-to-market ratio, leverage, and idiosyncratic volatility but not other risk proxies. Even after controlling for the popular return predictors, the CoE estimates incrementally predict future stock returns. This predictive ability is better explained as the CoE estimates containing ERP information rather than reflecting stock mispricing. When evaluated against traditional ERPs, including the implied costs of capital, the CoE estimates are found to be the least noisy. Finally, we document CoE responses around earnings announcements, demonstrating their usefulness to study discount-rate reactions of market participants. We conclude that analysts' CoE estimates are meaningful ERPs that can be fruitfully employed in a variety of asset pricing contexts.