Research
The Luxury of Rounding: Evidence from FinTech Consumer Lending
Presented at: SFA(2022), AFA (2022 Poster), Syracuse University (2021), Southern Methodist University (2021), Iowa State University (2021), Washington State University (2021), FMA (2021), CU Boulder - Finance Seminar (2021), CU Boulder CFDM Lab Meetings (2021)
This paper investigates the hidden private information associated with deviations from
strong cognitive heuristics and behavioral biases. Focusing on the consumer credit market,
I examine whether deviations from prevalent round number biases can serve as indicators
of borrowers facing heightened financial constraints and demonstrate both significant statistical
and economic implications for the industry. Leveraging a unique, comprehensive
dataset from the world’s largest peer-to-peer FinTech consumer lending platform, I develop
and empirically test hypotheses in a real-world setting, analyzing the relationship between
choosing non-round loan amounts and financial constraints. Specifically, the future FICO
scores of these non-round number borrowers fall short by an average of 2.7 points compared
to their round-number peers, and they also display a markedly reduced propensity
for early payoff. Controlling for all borrower and loan characteristics, I robustly find that
individuals who opt for non-round-number loans are 10% more likely to default than their
round-number counterparts, equivalent to a significant difference of 2 percentage points in
default rate. Moreover, when segmenting lenders by their nature, the findings further indicate
that institutional lenders largely offset this elevated default risk through their screening
processes, while the burden of mispricing disproportionately affects retail investors.
Co-author with Diego García and Maximilian Rohrer
Journal of Financial Economics
The Best FinTech Paper Award at 2020 Toronto FinTech Conference
Featured in Wall Street Portal, Video version – www.tinyurl.com/cofw-video, Podcast version – www.tinyurl.com/cofw-podcast
Presented at: FMA (2021), SFS Cavalcade North America Conference (2021)*, 3rd Toronto FinTech Conference (2020)*, European Finance Association meetings (2020)*, FutFinInfo webinar (2020)*, Indiana University (2020)*, NHH Finance Brown Bag (2020)*, SWFA Conference (2020), CU Boulder CS-NLP Lab (2020), Michigan State University Finance Conference (2019)*, Asia-Pacific Financial Markets conference (2019)*, CU Boulder - Finance Seminar (2019)
Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.
Get Out While the Getting’s Good? A Test of The First-Mover Hypothesis in Bond Mutual Funds
Co-author with Emily A. Gallagher and Sean Collins
Presented at: FMA (2021), CICF (2021), Federal Reserve Bank of Boston (2021)*, CU Boulder - Finance Seminar (2020)*
This paper develops a testable model to evaluate the concern that illiquidity in corporate bonds may generate a first-mover advantage among bond fund investors. A key insight of the model is that first-movers, if present in bond funds, must impose non-trivial costs on non-redeeming investors. We use estimates from a structural VAR applied to daily data spanning five periods of turbulent bond markets to calculate the “cost of remaining invested” (CRI), in terms of reduced returns, on days when the market drops. We find little evidence of an economically meaningful incentive for first-movers to redeem. In aggregate, CRI is typically very small, less than 4 basis points on an annualized basis, even for funds that hold less liquid bonds. Our hypothesis test for the presence of first-movers in individual funds signals that, for a subset of high yield bond funds, the elasticity of fund flows to market returns is higher than would be expected by chance alone. However, the assets in these funds are too small to impose meaningful price pressure on the high yield bond market.
Helping or Distorting? The Role of Designated Market Makers in the ETF Ecosystem
Co-author with Yizhao Huang, Hongfei Tang, Tianyang Wang, and Ying Yuan
Presented at: NFA(2023), Southern Methodist University - brownbag (2023), FMA (2023)*
Motivated by the contentious debate prior to their implementation, this paper examines the controversial role of Exchange-Traded Funds’ (ETFs) Designated Market Makers (DMMs), offering the first systematic perspectives to analyze DMMs’ behaviors and their impacts on ETFs. A theoretical market microstructure framework is proposed to analyze the behavior of ETF DMMs and the impact of their trading on the ETF market. Using a unique dataset that includes the exact DMM inauguration events and high-frequency trading data, we provide new empirical evidence that is coherent with the theoretical predictions and strongly supports the valuable role of ETF DMMs. Specifically, the introduction of DMMs led to a reduction of over 50% in ETFs’ bid-ask spreads and significantly enhanced ETF price efficiency, especially for illiquid and positive-premium ETFs. Furthermore, DMMs contribute to the survival and success of ETFs and offer benefits to market participants. However, DMMs are found to be reluctant to provide liquidity during extreme market distress. These results underscore the importance of DMMs for ETFs and suggest directions for regulatory enhancement during extreme market environments, providing valuable insights for policy implications.
*co-author presented