I am a PhD student in Finance and Economics at Warwick Business School, University of Warwick.
My research interests include Market Microstructure, Asset Pricing, FinTech, DeFi and Equity Research.
I am currently visiting the Haas School of Business, University of California, Berkeley. I was previously a visiting researcher at the Hong Kong Institute for Monetary and Financial Research, Hong Kong Monetary Authority.
Email: Jiawei.wang.2@warwick.ac.uk
(Draft available upon request)
Presentations: AsianFA (scheduled), UC Berkeley (scheduled), Durham Conference for Finance Job Market Papers (scheduled), FMARC Doctoral Consortium (scheduled), Hong Kong Monetary Authority, FMA Asia Doctoral Student Consortium 2025, the Gillmore Center Fintech PhD Workshop 2025, Warwick Business School
Abstract
Large language models (LLMs) are increasingly used as informational intermediaries in financial markets. This paper studies whether they improve market efficiency, whether these gains are monotone as adoption spreads, and whether they narrow the performance gap between institutional and retail investors. I develop a model in which LLM adoption improves the informational content of order flow but also increases correlated trading errors, generating a hump-shaped relation between adoption and market efficiency. Using exogenous ChatGPT outages as quasi-natural experiments, I show that variance ratios temporarily rise during outages, implying slower price discovery. I then use outage-based variation to construct firm-level ChatGPT exposure and show that more exposed firms experience larger post-launch efficiency gains, but that these gains are non-monotone and peak about 10.5 months after launch. Finally, retail trading profitability improves relative to institutional benchmarks, suggesting that LLMs reduce traditional informational asymmetries.
Working papers
(Draft available upon request)
Presentations: FMA Asia 2025, Warwick Business School
Abstract
We model investors’ decisions to use coarse thinking to process information and the effects of this on asset markets. Agents with weak priors may choose to pool together their signals across assets to mitigate the problem of overfitting. This model of coarse information processing helps explain features of return comovement in markets that are hard to explain with existing models based on rational expectations or limited attention. Theoretically and empirically, we demonstrate a link between return comovement and return predictability.
(Draft available upon request)
Presentations: Market Microstructure Summer School 2024, Warwick Business School
Abstract
This paper studies whether liquidity fragmentation across multiple liquidity pools within a blockchainbased decentralised exchange is welfare beneficial, and whether such fragmentation can emerge endogenously. Fragmentation does not arise in a frictionless decentralised exchange. The presence of front-running attacks drives fragmentation under a game-theoretical framework where liquidity providers compete in creating new liquidity pools for higher liquidity-provision payoff. Fragmentation benefits liquidity traders by allowing them to split trades across more pools, thereby decreasing exposure to front-running. Additionally, we characterise the level of stable fragmentation and transaction fees in equilibrium, in the sense that no liquidity provider has an incentive to deviate by creating additional pools (with any level of transaction fees).