The aim of my research is to understand how firms alter their debt structure and accounting policy in response to strengthening some creditors' rights. To this end, I examine the impact of the Anti-recharacterization law, which enhances the rights of securitization creditors, on firms' debt concentration and financial reporting. By considering the conflict of interest between creditors during bankruptcy, I find that the legal reform leads firms to concentrate their debt structure (e.g., by using fewer types of debt or contracting with fewer creditors) in order to mitigate creditors' coordination costs in bankruptcy. This effect is more pronounced for firms experiencing financial distress, with low re-deployable assets, poor disclosure quality, and high accounts receivable. Additionally, firms' financial reporting becomes more conservative after the reform in an effort to compensate other creditors and improve their chances of recovering their claims to prevent coordination failure in bankruptcy. This effect is more pronounced for firms in financial distress, with high re-deployable assets, and high accounts receivable. Overall, my results highlight how firms attempt to mitigate the risk of creditors' coordination failure in bankruptcy.

The purpose of this dissertation was to study why corporate fraud detection models are often met with skepticism by industry practitioners despite a vast literature supporting their use. This dissertation examined the parsimonious standards in the academic literature for corporate fraud detection and included the latest studies that introduced ideas from Benford’s Law and machine learning algorithms. The study of corporate fraud detection models is important because academic literature is relied upon by industry practitioners and government regulators including the Securities and Exchange Commission. This paper starts with a critique that was recently published in Econ Journal Watch. This critique examined the results of a paper recently published in the Journal of Accounting Research applying machine learning to the detection of accounting fraud. Afterwards, I applied the most popular ensemble boosting algorithm in machine learning known as XGBoost to a comprehensive sample of financial ratios and variables. In addition to this model, I ran a horserace with the other models from the extant literature. Results showed that the F-Score (Dechow, et al. 2011) stood up quite well against the machine learning models. Interestingly, a univariate screen on sales growth performed about as well as more complicated methodologies at the top of the probability distribution. Finally, I provided a discussion based on a Bayesian analysis that illustrated why practitioners find fraud detection difficult.

Requiring short sellers to disclose their positions may discourage them from participating in the market, thus harming price efficiency (“constraint effect”). However, it may also provide a new source of value-relevant information to the market, thus improving price efficiency (“information effect”). The two effects counteract each other, rendering it difficult to empirically capture each effect distinctly. While prior literature has provided evidence of the constraint effect, it has left the information effect relatively unexplored. I identify a setting during which the information effect dominates, namely the earnings announcement setting, and document the implications of the information effect: information asymmetry and uncertainty decline around the announcement, pre-announcement information acquisition is greater, and earnings news is impounded more efficiently following its release. I study EU regulatory changes in a staggered difference-in-differences design. The empirical findings in this paper provide additional insights for the ongoing debate on short disclosure regulation in the US, providing credence to the argument of its advocates that mandatory short disclosures would provide valuable information.

In the first chapter titled “Identifying the Effect of Stock Indexing: Impetus or Impediment to Arbitrage and Price Discovery?”, with Panos N. Patatoukas, we validate Russell reconstitution as a quasi-natural experimental setting and explore securities lending market dynamics and price discovery process at both the upper and lower cutoffs.

The rise of stock indexing has raised concerns that index investing impedes arbitrage and degrades price discovery. This paper uses Russell’s reconstitution to identify the causal effect of index investing on information arbitrage and price discovery. While index investing has no discernible effect on the ability of arbitrageurs to trade and impound news into the prices of large- and mid-cap stocks, we find that index investing increases the speed of price adjustment to news for micro-cap stocks. Our causal evidence identifies the relaxation of arbitrage constraints as a mechanism through which indexing facilitates informed trading for more arbitrage-constrained micro-cap stocks.

In the second chapter titled “Does Index Membership Affect the Quality of Mandatory Financial Report? Evidence from Index Deletions”, I examine the effect of stock indexing on mandatory disclosure quality.

This paper examines whether stock indexing affects the quality of mandatory financial reports. Using a fuzzy regression discontinuity design at the lower cutoff of the Russell 2000 Index, I find that small-cap firms moving out of the Russell 2000 exhibit more errors in annual and quarterly financial statement numbers, reduce the amount of textual disclosure, and increase the ambiguous tone in 10-K filings. On the contrary, mandatory reporting quality does not change following the addition to the Russell 2000. The evidence suggests that index deletion poses a higher informational risk to investors and highlights the positive effect of index membership in ensuring disclosure quality for small-cap firms in a limited information environment.