Research
Working Papers
My research interests include AI in finance, textual analysis,
institutional investors, and investments.
What Makes a Star Analyst? Evidence on Industry Expertise from
1.2 Million Analyst Reports
with Alejandro Lopez-Lira, Yuehua Tang, and Yuan Wang
We develop novel text-based measures of sell-side analyst industry
knowledge and report quality by applying a large language model
evaluation pipeline to 1.2 million industry reports from 40 major
brokerage houses between 2012 and 2024. Each report is evaluated
across 18 dimensions capturing general research quality and
specific forms of industry expertise.
Our measures are substantially more predictive of Institutional
Investor All-Star status than traditional forecast-accuracy
metrics. Analysts with higher knowledge and quality scores also
produce more accurate earnings forecasts, and their forecast
revisions and recommendation changes elicit stronger market
reactions. At the firm level, coverage by more knowledgeable
analysts is associated with lower information asymmetry and a
lower implied cost of capital.
Presented at:
2026 FMA Annual Meeting,
2026 NFA Annual Meeting.
The Memorization Problem: Can We Trust LLMs’ Economic Forecasts?
with Alejandro Lopez-Lira and Yuehua Tang
Large language models cannot be trusted for economic forecasts
during periods covered by their training data. Under black-box
access, counterfactual forecasting ability is not identified when
a model has already seen the realized values: any observed output
is consistent with both genuine forecasting skill and memorization.
We show that LLMs have memorized economic and financial data,
including exact values from before their knowledge cutoffs.
Instructions to respect historical information boundaries do not
prevent recall-level accuracy, while masking fails because models
can reconstruct entities and dates from limited contextual
information. We observe no comparable recall after the models’
knowledge cutoffs. Memorization also extends to embeddings.
Presented at:
2026 EFA Annual Meeting,
2026 Wolfe Research 8th Annual AI in Finance Conference,
2026 AFA Meeting,
2025 GSU–MS AI & FinTech Conference,
2025 Journal of Accounting, Auditing and Finance Symposium,
Applied Machine Learning, Economics, and Data Science Webinar.
Reaching for Synthetic Yield
with Wei Jiang, Yuehua Tang, and Yanbin Wu
We examine “reaching for synthetic yield” through covered-call
option writing by investment companies. Using return and holdings
data, including short positions, from a comprehensive sample of
U.S. options-writing funds, we find that funds become more
aggressive in synthesizing income when interest rates, term
premiums, and default premiums are low.
Investors reward high-yield funds with inflows, conditional on
total performance, without fully distinguishing between
asset-generated yield and yield manufactured through option
writing. However, yield-manufacturing funds experience greater
total-return losses, particularly during bull markets. Their
aggregate option-writing activity also suppresses option prices
and short-term implied volatility.
Presented at:
2026 ICI Summer Research Workshop,
2025 FMA Meeting,
2023 University of Florida Warrington Seminar.
Technology Adoption in Mutual Funds
with Beatrice Chang
We study technology adoption by mutual fund families using the
hiring of employees in data- and engineering-related roles. Fund
families with larger average fund sizes are more likely to hire
for these positions, suggesting that scale facilitates investments
in technology.
Families that hire data and engineering employees subsequently
have lower expense ratios, are more likely to launch new funds,
and experience higher overall flows. Textual analysis of job
descriptions and responsibilities supports the interpretation that
these positions contribute to cost reduction, product development,
and investor acquisition.
Presented at:
2024 University of Florida Warrington Seminar.