Sean Wang
I am an Assistant Professor of Accounting at the Edwin L. Cox School of Business at Southern Methodist University. My research focuses primarily on two streams of literature: (1) forces that affect the efficacy of information acquisition, transmission and price discovery in equity markets, and (2) economic and psychological drivers of executive decision-making.
I have published research in several leading academic journals such as the Journal of Financial Economics, Journal of Accounting and Economics, Journal of Accounting Research, The Accounting Review, and Management Science My work has also been featured in the media by various outlets, such as The Economist, Wall Street Journal, Harvard Business Review, The Financial Times, New York Times, ABC News, USA Today and The Washington Post.
Prior to my career as an academic, I earned a PhD from Cornell University (Johnson) in accounting, an MBA in finance from NYU (Stern), an M.A. in chemistry from University of South Florida, and a B.A. in chemistry from Duke University.
Intensive efforts to acquire and process firm-specific information can delay the incorporation of macroeconomic information into stock prices.
Extreme earnings surprises lead to the neglect of macro news embedded in the aggregate return, resulting in predictable returns over the subsequent 3-day [t+1, t+3] period after the earnings announcement (EA) date when conditioned on the magnitude of the aggregate return on the EA date.
Macro news delays and return predictability are larger when:
Firm-specific information acquisition efforts are highest (SEC Edgar downloads)
Investors have limited resources (retail trade intensity)
The firm's economics are more strongly tied to the macroeconomy (beta)
While prior research shows that information acquisition aids price discovery, our study finds that limited investor attention and a focus on firm-specific news can delay the processing of macroeconomic information.
Analysts who bundle their EPS revisions with recommendation and target price revisions of consistent sign convey private information to capital markets about future EPS without directly revising their EPS forecasts.
Analysts signal information via bundled EPS reports more frequently when the cost of being bold and incorrect is high, e.g. during periods of high macroeconomic uncertainty (e.g. during COVID-19 and Great Financial Crisis).
Aggregating bundled EPS reports at the firm-quarter level creates a consensus measure of implicitly signaled information, BF_Score, and reveals a robust predictor of quarterly analyst-based EPS surprise, Analyst_SUE.
The correlation between (BF_Score_Rank and Analyst_SUE) = 0.70.
A 1 SD increase in BF_Score ≈ $0.03 cents of positive EPS surprise.
Analysts also bundle (positive) private EPS news, effectively suppressing their EPS targets to help firms more easily meet/beat their expectations:
Accounting for the information embedded in BF_Score improves the distributional properties of Analyst_SUE by 55.9% (towards a normal distribution). These improvements come from a reduction in abnormal levels of:
Kurtosis (48% improvement), negative skewness (95% improvement), kink asymmetry around zero EPS surprise (66% improvement).
Our study suggests that implicit signals relayed through analysts’ bundling intensity should be accounted for in research involving the consensus EPS forecast & Analyst_SUE.
Accepted at Journal of Accounting and Economics
Bad news has a 57% larger adverse impact on analysts' valuations when the CEO is Non-White, resulting in more pessimistic valuations compared to White CEOs.
Non-White CEO firms are more likely to surpass analysts' valuation targets in the subsequent 12 months, suggesting this racial gap lacks economic justification.
Evidence of racial bias is provided through:
A controlled experiment corroborating the empirical findings
Analysts' valuation disparities towards Non-White CEOs becoming larger when race relations are worse
Increases in racial awareness and CEO familiarity attenuate these disparities, suggesting the bias is subconscious.
Findings suggest resources allocated towards Diversity, Equity and Inclusion (DEI) regarding racial stereotypes may promote equality within capital markets.
Forthcoming at Management Science
Existing accounting standards expense R&D, advertising and SG&A.
These expenditures build intangible capital, but are missing from a firm's balance sheet, resulting in a downward bias of reported assets.
To characterize off-balance sheet intangible assets, we use transaction prices to estimate this missing intangible capital.
Our new measure of intangible capital is 10% smaller than prior estimates, while varying more by industry.
Our estimates better explain market values, increase HML portfolio returns, act as a better proxy for human capital and brand rankings, and exhibit a strong association with patent values.
We are providing data and code to all academic researchers looking to use either our capitalization parameters or the capitalized stocks from our model.
Best Paper (Outstanding Manuscript) award at the 2023 American Accounting Association's ABO conference.
We find evidence that narcissistic executives can positively influence external stakeholder perceptions of the firm.
In the laboratory, we measure the narcissism of experimental participants using well-established psychometric tests (NPI-16), and find that individuals with high narcissism are more likely to engage in persuasive tactics to elicit higher firm valuation from financial analysts.
Analyzing historical data from analyst valuation reports, we find that CFO narcissism is associated with overly optimistic target prices.
These valuations are less sensitive to earnings news, particularly when the earnings news is bad.
Analyzing conference call transcript data, we find evidence that narcissistic CFOs engage in persuasive tactics when speaking with analysts.
Narcissistic CFOs exhibit greater levels of engagement with analysts, speak more optimistically and are more likely to use argumentative prose and corporate euphemisms.
Contact Me
E-mail: seanwang at smu dot edu
Tel: (214) 768-2547