Ran Shi
Assistant Professor of Finance, University of Colorado Boulder
Email: ran.shi@colorado.edu
Research Interest: Asset Pricing, International Finance, Financial Economics
Working Papers
Convexity Bounds for the
Stochastic Discount Factor: Theory and Evidence (with Ian Martin)
We derive new entropy and
moment bounds for the stochastic discount factor (SDF). Our results
generalize existing bounds which exploit risk-adjusted measures of
investment opportunities—such as Sharpe ratios or expected log
returns—that are maximized in the cross-section, across assets. By
contrast, we can fix a single asset and optimally exploit information in
its true and risk-neutral return distributions. Applying the framework
to the S&P 500 index, we find that the \(\theta\)th SDF moment grows extremely
rapidly when \(\theta > 1\), and
appears to diverge to infinity before \(\theta=2\). But entropy measures and the
\(\theta\)th moments with \(\theta \in (0,1)\) are well-behaved
theoretically and empirically, and can be related to measures of market
risk aversion and of the attractiveness of investment opportunities.
Conditional
Asset Pricing with Text-Managed Portfolios (with Jian Feng, Jiantao Huang and
Shiyang Huang)
We construct managed
portfolios that exploit information extracted from firms’ earnings call
transcripts and examine their asset pricing implications. Returns on
these text-managed portfolios correlate with investor sentiment and
predict macroeconomic outcomes. Individual stocks’ exposures to the
text-managed portfolios explain as much return variation as those to the
characteristics-sorted portfolios. Adding earnings call information to
firm characteristics increases mean-variance efficiency, though it does
not improve stock-level return predictability. Consistent with the
insights from Kozak and Nagel (2024) on mean-variance spanning, our
results suggest that earnings calls provide information about return
covariances beyond what is captured by firm characteristics alone.
Forecasting Crashes with a
Smile (with Ian Martin)
Jack Treynor Prize
We use option prices to derive
bounds on the probability of a crash in an individual stock, and argue
that the lower bound should be close to the truth. Empirically, we find
that the lower bound is a highly successful predictor of crashes, both
in and out of sample; on its own, it outperforms 15 stock
characteristics proposed by the prior literature combined. In a
multivariate regression, a one standard deviation increase in the bound
raises the predicted crash probability by 3 percentage points, whereas a
one standard deviation increase in the next most important predictor (a
measure of short interest) raises the predicted probability by only 0.3
percentage points.
A Quantitative Model of
Limited Arbitrage in Currency Markets: Theory and
Estimation
I develop and estimate a
limits-to-arbitrage model to quantify the effects of financial
constraints, arbitrage capital, and hedging demands on asset prices and
their deviations from frictionless benchmarks. Using foreign exchange
derivatives price and quantity data, I find that varying financial
constraints and hedging demands contribute to 46 and 35 percent
variation in the deviations from covered interest parity of one-year
maturities. While arbitrage capital fluctuation explains the remaining
19 percent of variation on average, it periodically stabilizes prices
when the other two forces exert disproportionately large impacts. The
model features a general form of financial constraints and produces a
nonparametric arbitrage profit function. I unveil the shapes and
dynamics of financial constraints from estimates of this function.
Publication
Model
Uncertainty in the Cross Section of Stock Returns (with Jiantao Huang)
Journal of Econometrics, accepted
The
Spread of COVID-19 in London: Network Effects and Optimal
Lockdowns (with Christian Julliard and Kathy Yuan) Journal of
Econometrics (2023), 235:2:2125-2154
Teaching
FNCE 3030 - Investment and Portfolio Management (Spring 2023, Fall 2023, 2024) FNCE 7020 Research Topics (Empirical Asset Pricing) (Spring 2024)