Hello, I am Xiao Li. I am a Ph.D in finance candidate at Eller School of Management, University of Arizona. You can find my cv here
My research interests include, empirical asset pricing, portfolio allocation, machine learning, anomalies, and market efficiency. I will be on the job market in the 2018-2019 academic year.
I attempt to address an important issue of the portfolio allocation literature -- none of the allocation rules developed in prior literature seems to consistently deliver good performance across different asset samples. For this purpose, I develop an approach that aggregates information from multiple sources for optimal portfolio weights. In particular, my approach uses the weights implied by extant allocation rules as instruments and decides the relative contribution from each rule through Elastic Net, a machine learning technique. Out-of-sample tests suggest that, by aggregating information from twelve allocation rules, my approach consistently achieves good performance across a variety of asset samples whereas none of the twelve rules can match the consistency. My paper also emphasizes the relevancy of the mean-variance framework and the allocation rules developed in prior studies. Even though these rules might not deliver satisfactory performance individually, their weights still contain valuable information and serve well as instruments.
Peyton Finance Seminar, University of Arizona 2018
Financial Management Association Annual Meeting 2018
American Financial Association Annual Meeting Ph.D. Poster Session 2019
Stein (2009) suggests that too much arbitrage capital exploiting underreaction can lead to overreaction, pushing price further away from fundamental value. I test this hypothesis by investigating the relation between changes in short interest ratio around earning announcement and the subsequent drift return. There are two main findings in this paper. First, my results suggest that too much arbitrage capital does contribute to overreaction (with a t-statistics around 4 on average). These findings are robust to alternative sample periods or length of the window for drift calculation. Second, contrary to the findings in prior literature that show that short sellers mitigate the magnitude of drift, my results show that almost all of this effect are actually contributed by the observations that are more likely to represent overreaction
Peyton Finance Seminar, University of Arizona 2016
Financial Management Association Annual Meeting 2017
Southern Financial Association Annual Meeting 2018
1. Performance of Allocation Rules Revisited -- Big Data Perspective (with Scott Cederburg)
2.Stock Portfolio and the Multitude of Firm Characteristics – Impact from Nonlinearity and Inter-Characteristic Arbitrage (with Danqiao Guo)
3. Deep Neural Network and Pricing Factors (with Zhaobin Kuang)
Work in Progress
"The Adaptation of the S&P 500 Index Effect" Journal of Index Investing, Summer 2017, 8 (1) 29-36
with Timothy T. Perry and Chan W. Kim
Invited Interview: I.I. Journals Practical Application, with Howard Moore, Aug 7th, 2017
Markowitz Meets Talmud 2.0: A Two Stage Approach
Tu and Zhou (2011) study the combinations of equally-weighted portfolio with four allocation rules on a theoretical ground. In this paper, I develop a two stage approach that optimally combines the equally-weighted portfolio with any particular allocation rule developed in prior literature. There are two important features of my approach. First, the combination of equallyweighted portfolio and a particular allocation rule can effectively improve the occasional poor performance of that allocation rule. Second, when the allocation rule has already performed well, the optimal combination can largely maintain or even improve its original performance.
Peyton Finance Seminar, University of Arizona 2017
Principle of Financial Management
Corporate Financial Problems
Calculus I (U of MN)
Calculus III lab (U of MN)
Principle of Financial Management
Summer 2015 (4.3/5.0)
Summer 2016 (4.2/5.0)
Summer 2018 (4.4/5.0)
Fall, Spring 2013 (4.8/5.0)
Calculus III lab
Fall, Spring 2014 (4.9/5.0)
"Fun, thy name is teaching."
-------- Li, Xiao
Two things here.
One, teaching is fun.
Second, we need fun in teaching.
I guess one needs some passion to become an effective teacher.
I also believe it is a mission for a teacher to engage the students and the best way to do it is make it fun.
Thank God! I got both!
"Li was always very prepared and tried to keep the class engaged. I could tell he really cared about us understanding everything. He also kept the class interesting by including real world examples to things we were learning about."
"Awesome teacher, was fun and helpful."
"He was very positive and made me feel comfortable asking questions. Whatever I didn't understand in class I could go to office hours and have everything answered for me."
"I especially enjoyed the instructor's historical references."
"I especially liked the professor's enthusiasm and wisdom. I am sure that I want to pursue this industry in my career after school because this class material is so interesting."
"Great insight about finance and how it relates to the real world."
"I liked the pace of the class. Didn’t feel like we were cramming information ever, yet I feel like I learned a lot in a short period of time."
"Li is very passionate about teaching the subject, and tried very hard to ensure that each student understood confusing concepts."
"Li is a great teacher and a funny guy."
"Class was organized in a manner that allows us to build upon previous, more basic concepts."
"Learning is never a sprint, it is a marathon."
-------- Li, Xiao
Once I complain to a smart friend of mine.
Not average smart.
I say, "dude, I wish I could be as smart as you."
You know what he said?
He said "I don't call it smart, I just oftentimes rehearse what I learnt before"
So my students, you should do the same thing.
Probably more often than he does.
I enjoy reading history, singing (folk and rap), basketball, body building and cooking.
This picture was taken Nov. 2017 at a singing competition.