I am Qihong Ruan (QR33@CORNELL.EDU) from the Department of Economics at Cornell University. I study market microstructure in various financial markets, with a focus on stock markets, cryptocurrencies, and derivatives, while exploring other areas like prediction markets and currency markets. I'm enthusiastic about leveraging econometrics and data science tools to extract critical insights into markets from various big data sources. My research explores the underlying logic behind asset price dynamics and examines economic issues related to emerging technologies like blockchain and AI.
I am currently completing my PhD dissertation on retail trading networks in US equity markets. My research examines network structures across stocks and ETFs, including leveraged products and other retail-accessible securities. I analyze how network peers influence price dynamics and market microstructure. I use data from Nasdaq daily retail trading activitiy tracker to measure general retail trading, and use latency-based method in BMO2024 to estimate the fractional shares from Robinhood and Drivewealth using Trades and Quotes data from NYSE. And I extend BMO2024's method to the end of 2024 and identify the change of latency distribution to effectively use this method. I discover the network profile of each security, their followers and followings-just like the profile of a social network. Knowing the network structure of retail trades help predict market dynmamics using peers' information. I study both the cross-sectional and time-series effects of retail trading network. My findings are useful for asset managemnent and electronic market making, especially for new asset class like Bitcoin ETF which does not fall in traditional industry categories and retail trades data can help find their peers. (Draft Coming Soon!)
Using Vision Large Models to Understand Asset Returns
The War of Hundreds of Large Models 百模大战
General Intelligence and Social Welfare
The Demand for Data in Healthcare AI
Perpetual Futures Contracts and Cryptocurrency Market Quality
Inflation Expectation and Cryptocurrency Investment
Systemic Risks in Financial Networks Under Strategic Attacks: Lessons from the Terra-Luna Crash
Community Governance and Value in Cryptocurrency Ecosystems: Initial Evidence from Tron Network
What Drives Stablecoin Interest Rates?
A Snapshot of the DeFi Landscape in February 2022
Amihud Premium: The Compensation for Illiquidity or Mispricing?
Presentation: Amihud Premium: The Compensation for Illiquidity or Mispricing?
Amihud溢价：非流动性补偿还是错误定价? (Amihud Premium: Illiquidity Compensation or Mispricing?)
差异化红利税能抑制投机吗？(Does Differentiated Dividend Tax Effectively Curb Speculation?)
期权时间价值日内模式与日内定价效率 (Intraday Pattern of Option Time Value and Pricing Efficiency)
What Are We Learning From Fama-MacBeth, Fixed-Effects, and OLS Regressions? - I discern the differences of estiamtes in Fama-MacBeth, Fixed Effects, and OLS regressions by math derivations and simulations with carefully designed data generating process. The findings dispell the misconceptions that Fama-MacBeth regressions can be replicated by fixed effects model with clustering errors. Fama-MacBeth regressions is an average of the estimates of a number of cross-sectional regressions and captures the cross-sectional variables. I give an example that Fama-MacBeth, Fixed-effects, and OLS regressions give three different results, and find that changing the order of adding individual fixed effects and time fixed effects can alter the sign of the estimates of fixed-effects models.
What Drives Perpeutals Funding Rates? I study the drivers of crypto perps funding rate, examine candicates from market microstructure measures from crypto markets, and external factors including dollar index, AAA, BAA, T-bill interst rate, exchange rate, LIBOR and more.
Perpetual Contracts, Funding Rates, Currency, and Bond Markets I investigate the association between crypto perp funding rates and the foreign exchanges and bond markets.
Prediction Markets On Chain - Study exploring the implementation and efficiency of blockchain-based prediction markets compared to traditional prediction market mechanisms. The focus is at the markets regarding whether Bitcoin or other main cryptocurrencies will hit a price threshold. Leveraging the feature of prediction market with a clear ending date, I can examine the effectiveness of various asset pricing and market microstructure indicators.
The Impact of Forkability on Decentralized Exchange Liquidity - Research investigating how the possibility of blockchain forks powered by the open-source nature of blockchain affects liquidity provision and market efficiency in decentralized exchanges.
Convergence and Disruption: Navigating the Boundaries Between TradFi, CeFi, and DeFi in an Era of Financial Technology and Regulatory Change - A comprehensive analysis of the evolving landscape across traditional finance, centralized crypto finance, and decentralized finance.
GitHub Repository: Chinese TAQ Code - SAS code for handling the Chinese TAQ database from CSMAR
Amihud Premium: The Compensation for Illiquidity or Mispricing? - Research paper using the Chinese TAQ data to compute liquidity measures
Indices of Chinese Financial Market Liquidity: 2007-2017 - Research paper using the Chinese TAQ data to construct liquidity indices
期权时间价值日内模式与日内定价效率 (Intraday Pattern of Option Time Value and Pricing Efficiency) - Research paper using the Chinese TAQ in Options markets
维维健康科技有限公司商业计划书 (Weiwei Health Technology Co., Ltd. Business Plan) - Comprehensive business plan for a health technology enterprise focusing on innovative healthcare solutions.
AI Agents and Blockchain - AI and blockchains both excel at handling digital content; AI greatly improves productivity at creating digital contents, while blockchains create digital scarcity. This unique complementarity of AI and Blockchain enables the potential of Decentralized Digital Hub. AI agents running on open-source blockchain networks can dramatically reduce economic rents extracted by centralized platforms, creating community-governed models where content creators truly own their digital assets and consumers gain transparency about the AI systems they use. Key applications include AI creating digital content verified and monetized by blockchain, AI agents navigating users through DeFi protocols to build customized financial strategies, and AI agents connecting content creators with consumers in new economic models. Drawing parallels to deep learning's scaling laws, blockchain adoption will accelerate as younger generations adopt solutions with self-custody of digital assets, creating economic network effects that reshape the digital economy. I acknowledge challenges including blockchain congestion from flooding digital content and the risk of AI agents producing harmful content alongside public goods, just like the murdering smart contract depicted in The Oracles by Ari Juels. A solution is community governance and ethical development. - Ripple University Blockchain Research Initiative (UBRI) 2024