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Yan Liu

Assistant Professor of Finance, Purdue University

mailing address: Krannert Building, 403 W. State Street, West Lafayette, IN 47907-2056, USA
phone: (919) 428-1118  

bio.   I received my Ph.D. in finance from Duke University in 2014. My Ph.D. thesis consists of two parts: the first part studies asset market anomalies and proposes new methods to test asset pricing models; the second part develops the concept of generalized entropy and uses it to diagnose macro finance models. Before going to Duke, I obtained my M.A. in statistics (I was a Ph.D. candidate initially) from the University of Minnesota, Twin Cities, and my B.S. in mathematics (with distinction) from Tsinghua University, Beijing. I was at Texas A&M University from 2014 to 2019. I moved to Purdue finance in June 2019. See my CV for more details.

research.   My current research interests include empirical and theoretical asset pricing, financial econometrics, macro finance, mutual funds and hedge funds, financial reporting, and financial institutions.

teaching.   I teach Options and Futures and Financial Risk Management at Purdue University. Previously, I taught Investment Analysis (undergraduate level) at Texas A&M University. See my CV for student evaluations for my class.

  Reasearch Papers (see my CV for more publications and bibliographic details)

  Working Papers
  • Luck versus Skill in the Cross-Section of Mutual Fund Returns: Reexamining the Evidence, with Campbell R. Harvey, 2020. (SSRN) (Conditionally accepted, Journal of Finance )

  • [Abs: Do there exist outperforming mutual funds or not? Prominent academic studies arrive at conflicting conclusions. We dissect the popular bootstrapping methods used by Kosowski et al. (2006) and Fama and French (2010). We show Fama and French (2010) suffer from an undersampling issue that results in a lack of power to detect outperforming funds, whereas Kosowski et al. (2006) is over sized and tends to reject the null hypothesis too often. We provide guidance on which method to use for future research.]

  • Distorting Arrow-Debreu Securities: New Entropy Restrictions Implied by the Option Cross Section, with Fousseni Chabi-Yo, 2020. (SSRN)

  • [Abs: Just like how the equity risk premium helps bound the entropy of the pricing kernel, we show a spectrum of distances between physical and risk-neutral moments all provide information on the entropy (or generalized entropies). Utilizing information from the option cross section, we quantify the degree of deficiency of a collection of macro-finance models in satisfying our newly defined entropy constraints.]

    - Conference presentations: EFA (2020); 7th SAFE Asset Pricing Workshop

  • Diversifying Private Equity, with Oleg Gredil and Berk Sensoy, 2020. (SSRN)

  • [Abs: What prevents educated individuals and institutional investors from investing heavily in hedge funds and private equity (maybe less so for mutual funds), despite the supposedly good performance of the average fund? We show performance uncertainty and diversification constraints substantially attenuate the profits from investing in alternative assets.]

    - Conference presentations: Virtual Asset Management Seminar Series (VAMSS), 12th Annual Symposium on Private Equity Research (2020, scheduled)

  • Crowding: Evidence from Fund Managerial Structure, with Campbell R. Harvey, Eric Tan, and Min Zhu, 2020. (SSRN)

  • [Abs: What explains the striking evoluation in fund management structure, with team-managed funds growing from 30% of funds to over 70% today? We show team-managed funds are much better than solo-managed funds at absorbing new capital without sacrificing performance. Hence, team management helps mitigate the crowding out effect of investment ideas.]

    - Conference presentations: AFA (2021, scheduled)

  • Optimal Cross-Sectional Regression , with Zhipeng Liao, 2020. (SSRN)

  • [Abs: Two-pass regression is equivalent to GMM! We show, in the classical linear-beta pricing framework, the asymptotic variance-covariance matrix for the optimal GMM estimator is the same as the one for a new class of two-pass estimators. Hence, contrary to popular belief, we show there need not be information loss when we go from the methodical GMM approach to the simple-to-implement two-pass regressions. ]

  • Maxing Out Entropy: A Conditioning Approach, with Fousseni Chabi-Yo, 2020. (SSRN)

  • [Abs: We show how conditioning information can be optimally incorporated to bound the entropy of the pricing kernel, complementing the well-known Sharpe ratio bound for the $L^2$-space. Similar to Sharpe ratio, our solution is interpretable as generalized Sharpe ratios in the entropy space and strikes a balance in exploiting physical return predictability and hedging risk-neutral higher order moments. We apply our approach to recently proposed return predictors and document the unique information provided by the variance risk premium (VRP) and VIX options. ]

  • A census of the factor zoo, with Campbell R. Harvey, 2019. (SSRN)

  • [Abs: Check out the 500+ anomalies that extend the database in Harvey, Liu, and Zhu (2016)!]

  • Distorted Risk Incentives from Size Threshold-Based Regulations, with Shane A. Johnson, 2018. (SSRN)

  • [Abs: When regulation is based on size thresholds, risk incentives for firms around the size threshold are distorted, oftentimes contrary to the intended consequence of the regulation. We show this in the important context of the Dodd-Frank regulation.]

    - Conference presentations: CICF 2017, FMA 2017

  • Asset Pricing with Prior Anchors: Evidence Based on Disclosed Risk Factors, with Sophia Hu and Shane A. Johnson, 2017. (SSRN)

  • [Abs: When firms disclose exposures to risks (through Item 1a in 10K) that conflict with historical risk estimates, cost of capital rises. We interpret this as an uncertainty effect: uncertainty in beta estimate raises cost of capital. ]

  Selected Publications
  • Reconstructing the Yield Curve, with Jing Cynthia Wu, 2020. Forthcoming, Journal of Financial Economics . (Link)

  • [Abs: We reconstruct the zero-coupon Treasury yield curve data and show its impact on studying economically important questions. Download our data at Yield Data]

  • Index Option Returns and Generalized Entropy Bounds, 2020. Forthcoming, Journal of Financial Economics . (Link)

  • [Abs: I recast the entropy constraint into an economic optimization problem, where economic agents seek to maximize their utilities subject to the Euler equation constraint prescribed by a given pricing kernel. Agents' optimized utilities naturally impose restrictions on moments of the pricing kernel, with the entropy constraint corresponding to the special case of logarithmic utility. I thereby generalize the entropy constraint and use the generalizations to study leading macro-finance models.]

  • False (and Missed) Discoveries in Financial Economics, with Campbell R. Harvey, 2020. Forthcoming, Journal of Finance. (Link)

  • [Abs: In a multiple testing context, how do we gauge the Type I versus Type II error rate, or a weighted average of both? We propose a double bootstrapping framework that allows one to quantify the Type I versus Type II error tradeoff with the presense of multiple tests. ]

  • Lucky Factors? , with Campbell R. Harvey, 2020. Forthcoming, Journal of Financial Economics . (Link)

  • [Abs: How can we sift through hundreds of published risk factors (or potential risk factors)? We propose a testing framework that controls for data mining, allows the use of individual stocks as test assets, and accommodates economically meaningful test statistics such as the value-weighted reduction in alpha. ]

  • Cross-Sectional Alpha Dispersion and Performance Evaluation, with Campbell R. Harvey, 2018. Journal of Financial Economics, 134, 273-296. (Link)

  • [Abs: Investors face lots of uncertainty when selecting fund managers. We show one source of such uncertainty is the cross-sectional dispersion in alpha. Similar to a fund's idiosyncratic risk, investors (rationally) discount the positive alphas of outperforming funds when cross-sectional alpha dispersion is high, leading to reduced flows to these funds. ]

  • Detecting Repeatable Performance, with Campbell R. Harvey, 2018. Review of Financial Studies, 29, 5-72. (Link) (NBER)

  • [Abs: Performance evaluation centers around the cross-sectional alpha distribution, which can follow an arbitrary distribution. We approximate this distribution with a normal-mixture model and propose an efficient algorithm to estimate our model. We shed light on the shape of the cross-sectional alpha distribution as well as its implication on performance persistence. ]

  • Dots and the Cross-section of Expected Returns, with Campbell R. Harvey and Heqing Zhu, 2016. Review of Financial Studies, 29, 5-72. (Link) (NBER)

  • [Abs: We document and categorize more than 300 anomalies proposed by the finance and accounting literature. We argue that data mining may explain the bulk of these anomalies. Using a variety of multiple testing techniques (including a new model that takes publication bias into account), we adjust the statistical significance of published anomalies. ]

    - Lead article
    - NASDAQ OMX Award, 2014, for the best paper in asset pricing at the Western Finance Association Meetings (WFA, 2014)
    - Best Paper Award, 2014, INQUIRE-Europe-UK