Research
Working Papers
1. The Anatomy of a Blockchain Prediction Market: Polymarket in the 2024 U.S. Presidential Election
Available on SSRN, March 2026
Using Polymarket's settlement ledger, we study its 2024 U.S. presidential election markets. Share minting and burning make naive aggregation overstate turnover. We develop a transaction-level decomposition separating turnover, net inflow, and market activity. The overstatement spans 249 markets and is greatest in thin, young ones. Corrected price impact implies that moving the October Trump YES price by five percentage points cost \$9.1 million rather than \$15.6 million. Secondary-market flow carries greater price impact per dollar than primary-market flow. Capital entered on both sides of the race throughout October, consistent with heterogeneous beliefs rather than one-sided manipulation.
2. Political Shocks and Price Discovery in Prediction Markets: Evidence from the 2024 U.S. Presidential Election
Available on SSRN, February 2026
What do trading and prices each reveal when political news hits a prediction market? We answer using Polymarket's on-chain ledger around three shocks in the 2024 U.S. presidential election: the Biden--Trump debate, the assassination attempt on Trump, and Biden's withdrawal. Trading rises after every shock, mainly among incumbents with greater prior activity and with larger realized gains on pre-event portfolios. Price adjustment differs across shocks. The debate's price increase largely reverses, the assassination-attempt repricing persists, and Biden's withdrawal generates the heaviest trading with little change in the Trump price. The price response tracks what the news reveals about linked candidates and how much was already anticipated, not the amount of trading. Trading volume measures participation, while belief revision must be read from linked outcome prices and the surprise in the news.
3. Decision-Relevant Information in Partially Observed Production Networks
Available on SSRN, April 2020
A production network can remain largely unidentified even when the economic decision it supports is identified. We characterize sufficient measurements for exposure-based decisions and compute sharp maximum regret over networks consistent with released totals. Using earlier and later vintages of Japan's interregional input-output accounts, we select measurements from the 1995 table and evaluate the frozen design against the 2005 benchmark. At roughly half the statistics required for full disclosure, the resulting monitoring set loses only 0.07 percentage points of average exposure relative to the benchmark optimum, yet its sharp maximum regret across compatible networks is 4.83 points. In U.S. coal deliveries surrounding a 2005 Wyoming rail disruption, additional shipment measurements identify the optimal set of plants to monitor for inventory risk, even though four monitored plants' exposures to the affected coal supply remain unidentified. The results distinguish good benchmark performance from a decision guarantee and show that decisions can be identified before individual exposures. They suggest evaluating network data by the economic decisions they support.
Publications
6. Judicial Institution and Innovation: Evidence from China’s Intellectual Property Courts Reform
Published in Journal of Development Economics, February 2026 (with Liyang Wan, Qian Wan & Ying Zhao)
This paper examines the impact of intellectual property judicial institutions on innovation, focusing on the intellectual property courts (IPCs) reform in China. We find that IPCs reform leads to a significant 22.6 % increase in the number of invention patents at the city level, equating to an average rise of 215 annually. Notably, we rule out the possibility of inter-region and intra-conglomerate transfer of patents, indicating that the effect of the IPCs reform on innovation is not a zero-sum game among regions. Furthermore, we find that the IPCs reform alters the patent structure by shifting the focus from utility and design patents to invention patents; however, it does not appear to significantly improve invention patent quality. Mechanism analyses suggest that the IPCs reform increases social satisfaction with judicial protection of intellectual property, shorter case duration and higher plaintiff winning rates in intellectual property cases.
5. Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings
Published in Journal of Economic Dynamics and Control, November 2025 (with Kwok Ping Tsang)
Using FOMC votes and meeting transcripts from 1976–2018, we develop a deep learning model based on self-attention mechanism to quantify ''hidden dissent'' among members. Although explicit dissent is rare, we find that members often have reservations with the policy decision, and hidden dissent is mostly driven by current or predicted macroeconomic data. Additionally, hidden dissent strongly correlates with data from the Summary of Economic Projections and a measure of monetary policy sub-optimality, suggesting it reflects both divergent preferences and differing economic outlooks among members. Finally, financial markets show an immediate response to the hidden dissent disclosed through meeting minutes.
Pub Version WP Version Hidden Dissent Index Data
4. ESG Rating Disagreement and Corporate Total Factor Productivity: Inference and Prediction
Published in Finance Research Letters, May 2025 (with Zhanli Li)
This paper examines how ESG rating disagreement (Dis) affects corporate total factor productivity (TFP) in China based on data of A-share listed companies from 2015 to 2022. We find that Dis reduces TFP, especially in state-owned, non-capital-intensive, low-pollution and high-tech firms, green innovation strengthens the dampening effect of Dis on TFP, and that Dis lowers corporate TFP by increasing financing constraints and weakening human capital. Furthermore, XGBoost regression demonstrates that Dis plays a significant role in predicting TFP, with SHAP showing that the dampening effect of ESG rating disagreement on TFP is still pronounced in firms with large Dis values.
3. Do Connections Pay Off in the Bitcoin Market?
Published in Journal of Empirical Finance, February 2022 (with Kwok Ping Tsang)
This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.
2. The Market for Bitcoin Transactions
Published in Journal of Internantional Financial Market, Institution & Money, January 2021 (with Kwok Ping Tsang)
Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.
1. Price dispersion in bitcoin exchanges
Published in Economics Letters, September 2020 (with Kwok Ping Tsang)