Research
Research Interests:
Financial accounting; capital markets; equity analysts; bond analysts; market microstructure; order book;
earnings announcements.
Dissertation:
“Informativeness of Order Book Activity at Earnings Announcements”
Abstract: Using Nasdaq millisecond order book data, this study examines the informativeness of the order book structure at earnings announcements. The evidence illustrates that before market-hours order book imbalances in the day leading up to the earnings announcement predict returns at the moment of the earnings announcement up until at least two weeks following the event without evidence of reversals. These imbalances are extremely informative as they correlate with reported quarterly surprises and they anticipate returns better than having perfect foresight of the reporting surprises themselves. The results are more pronounced for imbalances generated by large investors (whales) and for larger stocks.
Committee: Alastair Lawrence (chair), Lakshmanan Shivakumar, Roberto Gomez Cram.
Publications & Working Papers:
Computing corporate bond returns: a word (or two) of caution. Co-authored with Dr. Diogo Palhares and Professor Scott Richardson. Published at the Review of Accounting Studies (2023).
Abstract: We offer several suggestions for researchers using corporate bond return data. First, despite clear instructions from older papers (e.g., Bessembinder et al. 2009) about the correct way to compute credit excess returns, a lot of recent research simply subtracts a Treasury-bill return. We show that this imprecision is likely to contaminate inferences as the rate component of returns is negatively correlated to the spread component. This is a problem for all research looking at corporate bonds returns, especially time series analysis and safer corporate bonds (e.g., Investment Grade). Second, we note significant differences in coverage of corporate bonds across the Trade Reporting and Compliance Engine (TRACE) platform and typical corporate bond indices. We provide some simple rules for researchers using TRACE to select a subset of bonds closest to those contained inside corporate bond indices used by institutional investors. Third, we note differential quality in the prices and hence returns between TRACE and typical corporate bond indices. Corporate bond returns provided by corporate bond indices (i) correctly estimate credit excess returns, (ii) are synchronous for the entire set of bonds allowing for consistent cross-sectional comparability, and (iii) suffer less from stale pricing issues. Where possible researchers should try to source return data from multiple sources to ensure the robustness of their results due to these coverage and data quality issues.
Are CEOs rewarded for luck? Evidence from corporate tax windfalls. Co-authored with Professor Atif Ellahie and Professor Lakshmanan Shivakumar. Journal of Finance, forthcoming.
Abstract: We take advantage of a 2017 change in tax rules in the U.S. to re-examine whether CEOs are rewarded for luck. We examine the effect of one-off tax gains and losses associated with deferred tax assets and liabilities on CEO compensation around the Tax Cuts and Jobs Act (TCJA) of 2017. Relative to other years, we find that less visible firms compensated their CEOs more for the one-time tax windfall gains during the TCJA-transition period. Further, we find evidence in support of pay asymmetry; CEOs of less visible firms were compensated more for tax windfall gains but were not compensated less for tax windfall losses. The CEO pay associated with the tax windfalls cannot be explained as firms sharing these tax gains with all employees. These results are consistent with rent-extraction by CEOs of less visible firms.
MEDIA MENTIONS
The Value of Bond Analysts’ Reports. Co-authored with Professor Elsa Maria Juliani and Florin Vasvari. Working Paper. 2023.
Abstract: We document that the issuance of sell-side bond analyst research reports follows both public information events, such as borrower earnings surprises, credit rating changes, or bond issuances, and private information releases that occur around bank loan issues or loan trading in the secondary market. We also find that bond analysts at brokerages with underwriting roles in the loan market are more likely to provide a report, consistent with the interpretation that they incorporate private information from the loan market. The results are stronger if the brokerage previously released a bond report covering the firm. We further document that investment recommendations in bond, but not equity, reports are associated with subsequent abnormal bond returns, highlighting the investment value of bond analysts’ reports. Recommendations in bond reports predict bond returns especially when they interpret negative public news, or they are likely to reflect private information from the loan market. Overall, our evidence suggests that bond analysts play an important role in the bond market, incorporating both public and private credit relevant information in their reports.
The Accuracy of Automated Financial Analysts. Solo-authored. Working Paper. 2022.
Abstract: This study analyses the accuracy of automated financial analysts—companies that implement Machine Learning (ML) algorithms to “automate” sell-side analyst research activities. The paper illustrates that over the past decade the number of automated analyst reports in the U.S. have roughly doubled and now constitute 16% of all analyst reports issued each year. Relative to traditional (i.e. human) financial analysts, the results show that automated analysts have up to 1.9 percent more accurate target prices forecasts, yet 6% and 3% less accurate revenues and EPS forecasts, respectively. Moreover, automated analysts appear to have less positively biased ratings and somewhat more profitable target prices forecasts. These findings raise a potential conundrum as to why automated analysts’ target prices outperform those of traditional analysts, but their financial estimates (i.e., revenue and EPS forecasts) significantly underperform.