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), Durham Conference for Finance Job Market Papers (scheduled), FMARC Doctoral Consortium (scheduled), CES (scheduled), UC Berkeley, 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: Market Microstructure Summer School 2025, 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 develops a game-theoretic model of endogenous liquidity fragmentation in decentralized exchanges with automated market makers. Liquidity providers strategically create competing pools, while traders optimally split orders across pools by balancing price impact and gas fees. When gas fees are sufficiently small, no pure-strategy equilibrium exists because entry by additional pools is always profitable. For intermediate gas fees, multiple fragmented equilibria may coexist. For sufficiently large gas fees, fragmentation collapses and the market converges to a unique one-pool equilibrium. We then show that front-running creates an additional motive for fragmentation: by splitting orders across pools, traders reduce rents extractable through sandwich attacks. Thus fragmentation can arise as a defensive response to predatory transaction ordering. The equilibrium degree of fragmentation is endogenously determined by the interaction of gas fees, entry incentives, and front-running risk.