arXiv subjects

Yinhong Zhao

Publications and source records attributed to Yinhong Zhao.

3 recordsLinked to original sources

arXiv2026

Preying on Leveraged ETFs

We argue that speculators preying on the closing rebalances of leveraged exchange-traded funds (LETFs) contributed to the Korean market's extreme volatility in 2026. An LETF's mandated daily rebalance is sized by the day's return, which generates an upward-sloping demand at market close. In response, rational speculators pre-position, enlarge the fund's order, and liquidate into the demand they have induced. Consistent with this mechanism, Korean stocks tracked by LETFs reverse about 75% of their first-day response to pre-open U.S. news by the next close and oscillate for several days thereafter, a pattern absent in every control group. Our quantification implies that self-reinforcing rebalance raised SK Hynix's annualized volatility from 100.0% to 136.7% over nine weeks and cost its products' predominantly retail holders 17.6% of their initial investment. Dispersing the rebalance across the trading day may backfire, whereas a flexible leverage multiple could help.

econ.GN↗

arXiv2023

Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.

econ.GN↗

arXiv2022

Deciphering Bitcoin Blockchain Data by Cohort Analysis

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

econ.GN↗