Jerry D. Mathis
About me
Selected Abstracts
Unearthing Firm Value: The Effect of Mandatory Sustainability Disclosures on Firm Information Environments (Dissertation)
Using Dodd-Frank's conflict minerals disclosure mandate as a setting, I examine the information asymmetry and voluntary disclosure effects of mandatory sustainability disclosures. I find that firms’ conflict minerals disclosures, on average, resulted in decreased information asymmetry among investors and that this decrease was greatest for disclosures with more detailed information, better readability, and more publicized disclosures. By contrast, this decrease was mitigated for firms with greater institutional ownership but only for firms without previous supply-chain related sustainability concerns. These findings suggest that the decrease in information asymmetry arose from the overlap between the disclosure and the private information sets of sophisticated investors. I also find that managers respond to this decrease in information risk by reducing voluntary disclosure after the first mandatory filing. Together, these results demonstrate that mandatory sustainability disclosures change firms’ information environments and that managers may adapt to these changes by adjusting their voluntary disclosure behavior.
Do two wrongs make a right? Strategically forecasting EPS through inaccurate share forecasts (with Zachary Kaplan, Nathan Marshall and Ivy (Hanmeng) Wang
Despite the importance of EPS forecasts as benchmarks, little is known about their denominator: shares outstanding forecasts. Because investing clients have limited demand for accurate share forecasts, we predict that analysts develop share forecasts strategically to facilitate EPS incentives. We divide earnings forecasts by EPS forecasts to infer analysts’ share forecasts and evaluate their properties. Analysts’ forecasts of shares outstanding are significantly less accurate than simple time-series models; however, these same forecasts actually improve EPS forecast accuracy. Additional analysis explains why: analysts use share forecasts to herd EPS toward the consensus. That is, share forecast errors often have the same sign as street earnings forecast errors, moving EPS closer to the consensus and to actual EPS, and significantly reducing EPS dispersion. Analysts also appear to use share forecasts to cater to management. Specifically, bias in share forecasts facilitates firms’ ability to meet or beat (“MB”) EPS benchmarks and is consistent with manager preferences (i.e., deflating EPS forecasts at short horizons and inflating at longer horizons). Much of the MB effect arises because analysts fail to incorporate predictable variation in shares outstanding, such as past repurchases. Interviews with sell-side analysts confirm that clients have limited demand for share forecasts, consistent with the inattention and strategic use of forecasts documented in our study.
Giving away the ‘secret sauce’: Evidence on the consequences of actively managed ETF portfolio disclosures (with Todd Gormley, Zachary Kaplan and Jeremy Michels)
Actively managed ETFs must disclose their portfolio positions daily. We examine the information these daily disclosures reveal to the broader market. First, we document that ETF managers build positions over time, similar to mutual funds, with past trades predicting future trades. Analyzing quarterly position disclosures and daily trades suggests that ETFs build positions more slowly than traditional mutual funds, potentially allowing other investors to profitably front-run the ETF’s trades. We find actively managed ETFs trade in the direction of future earnings surprises, a result that is stronger when we filter out liquidity trade, consistent with the trades incorporating private information about future earnings. We also find that actively managed ETFs speed the assimilation of information into price, consistent with daily disclosure of portfolio information improving price efficiency. Given that trades are serially correlated and predict earnings, we expect these portfolio disclosures can be used to predict future returns. Consistent with the potential for front-running, we find a trading strategy based on actively managed ETFs’ daily portfolio disclosures earns a return of approximately 0.3% per week.
Uncovering fund trade: Evidence from fund returns around portfolio disclosures (with Todd Gormley and Zachary Kaplan)
To facilitate the flow of capital between savers and funds, the SEC requires mutual funds to disclose portfolios quarterly. Because savers use these disclosures as an input to their investing decisions, the obligation to disclose affects fund trade, but the timing and motivation of these disclosure-motivated trades has been difficult to ascertain. We use the association between daily fund returns and portfolio returns calculated assuming trade follows a specific pattern to conduct inference on the “average” time at which funds transition from beginning to closing positions. We find the average fund trades into their closing positions late in the quarter, particularly among funds that disclose quarterly as opposed to monthly. Additional tests along with evidence that funds that trade late in the quarter predict stronger future fund performance, suggests these trades reflect funds timing information discovery and trade to coincide with disclosure, rather than funds trading to conceal low skill.
Selection Bias in Management Forecasting: Evidence from 8-K Filing Choices (solo-authored)
This study examines the characteristics of management forecasts based on manager choice of dissemination medium. Recent research utilizes a variety of sources for managerial forecast information, implicitly relying on the assumption that the various data sources share similar properties. My results suggest systematic differences between forecasts from IBES Guidance (previously First Call) accompanied by a Form 8-K filing and those without. These biases present threats to studies utilizing EDGAR for textual analysis and voluntary disclosure measurement, as well as using IBES Guidance for research on forecasting behavior. I suggest sample selection criteria that help mitigate this bias and propose topical areas that may benefit from considering these differences further.
Now That You Mention It: Analyst Forecast Revisions and Firms' Macroeconomic Narratives (with Lindsey Gallo)
This study examines the presence and usefulness of macroeconomic narratives provided by managers in their annual earnings guidance. First, we document the presence of these “macro mentions” in 21% of the guidance in our sample with 38% of those mentions included in direct quotes by managers. We observe that these mentions are more frequent for firms with greater macro exposure, firms issuing bad news forecasts, firms issuing less precise forecasts, and firms facing less litigation risk. Next, we examine whether analysts glean information from these narratives and observe that analysts revise their earnings forecasts more in response to earnings guidance containing a macro mention, but only for firms with greater sensitivity to macroeconomic changes. Despite these increased revisions, we find no difference in analyst forecast accuracy after these revisions regardless of firms’ macroeconomic exposure. These findings suggest that analysts can effectively assess the influence of the macroeconomy on firm outcomes but gain no significant advantage over firms without these mentions or exposure.