I am a fifth-year Ph.D. student at MIT Sloan Finance, interested in institutions, behavior, and finance. Using blockchain data, I study crypto-equivalents of derivative market makers, money-market funds, bank runs, and bitcoin usages with Igor Makarov and Antoinette Schoar. Outside crypto, I looked into banks under hyperinflation, IPOs restrictions in China, and engineer-entrepreneurs on GitHub.

Before MIT, I studied statistics at the University of Chicago and mathematics at UCLA, and before that I tried to be an asteroid astronomer. Born and raised in Zhengzhou, China, I miss hulatang, a spicy breakfast soup.

Google Scholar: Jiageng Liu
Twitter: @JiagengLiu
Email: jiageng @ mit.edu

How to pronounce my name

Research

5. The Life Cycle of Prediction Markets, 2026

Work in progress.

4. Competition in Crypto Staking, 2025

with Igor Makarov and Antoinette Schoar. Working paper to come.

We study the economics of the Cardano staking market, which shares many similarities with traditional financial industries such as index and money market funds. Using detailed blockchain data, we present two main findings. First, we construct a measure of delegator mobility and document significant heterogeneity in mobility across delegators. Second, we show that validators strategically set their fees by considering delegator mobility and variations in scale economies. These findings provide direct evidence of the important role of capital mobility in shaping pricing strategies in equilibrium and offer important implications for the design of staking markets in decentralized finance.

Presented at CBER, LSE, MIT Sloan Finance Brownbag, SDA Bocconi, Warwick Business School, Innovations in Financial Intermediation Symposium, and Bank of Canada.

Distribution of capital mobility rates. The upper panel plots the cross-sectional capital mobility distribution of delegators (investors) at the beginning, middle, and end of the sample period. The bottom panel plots the distribution of the value-weighted average mobility rates on the validator (fund) level.

3. Anatomy of a Run: The Terra Luna Crash, 2023

with Igor Makarov and Antoinette Schoar.

Conditionally accepted, Journal of Financial Economics

We examine the dramatic collapse of the Terra blockchain in May 2022. Using granular blockchain data, we analyze the mechanisms underlying the run and draw parallels to the traditional financial system. We show that the early success of Terra’s algorithmic stablecoin, UST, was fueled by highly subsidized deposit rates, which attracted many investors but created a fragile system prone to runs. The blockchain allowed investors to observe Terra’s worsening fundamentals and monitor each other's exits. The presence of large investors, whose actions were observable and could impact prices, alleviated the need for coordination typical in canonical models of runs. Larger and sophisticated investors reacted faster to adverse signals and served as catalysts for the run. These findings challenge the idea that blockchain transparency levels the playing field and highlight how greater observability and concentration can amplify financial fragility. Our results contribute to the understanding of the limits of private money and the dynamics of runs in fully digital financial systems.

Paper, Twitter thread

Presented at LSE, Northwestern Kellogg, MIT Sloan, 2nd Annual DeFi conference, ICI–SNPI Conference, NYU Stern, McGill, MIT Digital Currency Initiative, LUISS, Bocconi, Chicago Fed, Chicago Booth, NBER Summer Institute 2023, 7th Annual Macroprudential Policy Conference, OSU, CFRI Conference, Federal Reserve Board, Tulane, Frankfurt, Banque de France, Vanderbilt, King's, LBS, Jackson Hole.

Withdrawals from Anchor from May 6 to May 14, 2022, broken down by the size of the deposit balance of addresses as of May 6, before the run.

2. Inflation and the Two Sides of Banks: How Disinflation Replaces Liquidity with Lending, 2025

with Tomás da Nóbrega. Working Paper.

We study how inflation differentially affects banks' two lines of business, liquidity management and loan making. After a large disinflation episode without fiscal adjustment or banking reforms, banks' deposit-side revenue decreased, interbank lending shrunk, and banks made more loans, as we show with high-frequency bank data. Deposit collection and liquidity management were highly rewarded during high inflation, and net interest margin decreased by two-thirds after the stabilization. Using a difference-in-differences (DiD) design, we show that banks more productive in loan making grew, and those more reliant on liquidity management shrunk or closed. Bank pricing became more responsive to the benchmark rate. Industries more dependent on external funding grew more after disinflation, which suggests that stable prices helps growth via the lending channel that can be otherwise crowded out by liquidity management. Large heterogeneity in banks' lending and deposit rates during high inflation suggests that the differential financial sophistication is a mechanism of monetary non-neutrality.

Inflation and bank profitability. The blue dashed line plots the inflation rate and the red solid line plots the net interest margin (NIM) of private commercial banks in Brazil around the 1994 monetary reform. Profits funded by nominal deposits disappeared and was replaced with loan income.

1. Delayed and Distorted Price Discovery: Post-IPO Stocks in China, 2020

Master's thesis. Resting.

Abstract: I document that regulatory changes in the Chinese IPO market in 2013-14 distorted the price of new stocks and delayed price discovery. The rules impose a low price-to-earnings cap on the IPO price and restrict the daily price changes after listing. Under the new regulations, prices stay at the +10% upper price limit with minimal trading for about two weeks after the listing date. Although the listing prices are suppressed, trading prices are, on average, 15% to 30% higher. This translates to a 240 billion CNY higher valuation among post-IPO stocks compared to that before the policy, though the overvaluation reverses to the market median level over the first year after listing. A shift in IPO industry composition from high-P/E to low-P/E after the rule change is consistent with market distortion.

Presented at MIT Golub Center for Finance and Policy (GCFP).

Price-to-Earnings (P/E) of IPOs in China, 2010-2020. Black points are the P/E of IPOs listed on the Shanghai or Shenzhen Stock Exchanges. The blue line is the median P/E of all stocks being actively traded on the market. The 2014 rule caps new listings at 23.

Past research in machine learning, optimization, etc.

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人能弘道，非道弘人。