nep-mst New Economics Papers
on Market Microstructure
Issue of 2026–03–16
four papers chosen by
Thanos Verousis, Vlerick Business School


  1. TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure By Maxime Kawawa-Beaudan; Srijan Sood; Kassiani Papasotiriou; Daniel Borrajo; Manuela Veloso
  2. Pricing and hedging for liquidity provision in Constant Function Market Making By Jimmy Risk; Shen-Ning Tung; Tai-Ho Wang
  3. Social Network and Sentiment Contagion: Evidence from the Bitcoin Market By Bing Han; Haoyang Liu; Pengfei Sui
  4. When David becomes Goliath: Repo dealer-driven bond mispricing By Carlos Canon; Eddie Gerba; Jozef Barunik

  1. By: Maxime Kawawa-Beaudan; Srijan Sood; Kassiani Papasotiriou; Daniel Borrajo; Manuela Veloso
    Abstract: Foundation models have transformed domains from language to genomics by learning general-purpose representations from large-scale, heterogeneous data. We introduce TradeFM, a 524M-parameter generative Transformer that brings this paradigm to market microstructure, learning directly from billions of trade events across >9K equities. To enable cross-asset generalization, we develop scale-invariant features and a universal tokenization scheme that map the heterogeneous, multi-modal event stream of order flow into a unified discrete sequence -- eliminating asset-specific calibration. Integrated with a deterministic market simulator, TradeFM-generated rollouts reproduce key stylized facts of financial returns, including heavy tails, volatility clustering, and absence of return autocorrelation. Quantitatively, TradeFM achieves 2-3x lower distributional error than Compound Hawkes baselines and generalizes zero-shot to geographically out-of-distribution APAC markets with moderate perplexity degradation. Together, these results suggest that scale-invariant trade representations capture transferable structure in market microstructure, opening a path toward synthetic data generation, stress testing, and learning-based trading agents.
    Date: 2026–02
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2602.23784
  2. By: Jimmy Risk; Shen-Ning Tung; Tai-Ho Wang
    Abstract: This paper develops a robust mathematical framework for Constant Function Market Makers (CFMMs) by transitioning from traditional token reserve analyses to a coordinate system defined by price and intrinsic liquidity. We establish a canonical parametrization of the bonding curve that ensures dimensional consistency across diverse trading functions, such as those employed by Uniswap and Balancer, and demonstrate that asset reserves and value functions exhibit a linear dependence on this intrinsic liquidity. This linear structure facilitates a streamlined approach to arbitrage-free pricing, delta hedging, and systematic risk management. By leveraging the Carr-Madan spanning formula, we characterize Impermanent Loss (IL) as a weighted strip of vanilla options, thereby defining a fine-grained implied volatility structure for liquidity profiles. Furthermore, we provide a path-dependent analysis of IL using the last-passage time. Empirical results from Uniswap v3 ETH/USDC pools and Deribit option markets confirm a volatility smile consistent with crypto-asset dynamics, validating the framework's utility in characterizing the risk-neutral fair value of liquidity provision.
    Date: 2026–03
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2603.01344
  3. By: Bing Han; Haoyang Liu; Pengfei Sui
    Abstract: Using new data on social interactions and individual trading records in the Bitcoin market, we show that investor sentiment spreads across social connections. Investors systematically revise their beliefs about Bitcoin prices in the direction of average peer sentiment—even though that sentiment does not predict future prices. We document specific patterns in the diffusion of beliefs across networks, including evidence consistent with confirmation bias. Moreover, this social-sentiment contagion influences both individual trading decisions and overall market dynamics. Our novel measure of contagion intensity significantly forecasts Bitcoin volatility, trading volume and market crashes.
    Keywords: social interactions; belief updating; sentiment contagion; bitcoin; bubbles
    JEL: G11 G12 G41 G53
    Date: 2026–03–02
    URL: https://d.repec.org/n?u=RePEc:fip:feddwp:102864
  4. By: Carlos Canon; Eddie Gerba; Jozef Barunik
    Abstract: This paper studies the impact of funding market frictions on bond prices and market-wide liquidity. Using proprietary transaction-level data on all gilt-backed repo and reverse-repo trades, we demonstrate how the market power of individual dealers and their linkages generate frictions. Specifically, we show that frictions related to market power account for between 0.5 and 1.3 percentage points of bond yield deviation, while the transmission of heterogeneously persistent shocks between dealers accounts for between 2 and 4 percentage points of yield deviation.
    Date: 2026–03
    URL: https://d.repec.org/n?u=RePEc:arx:papers:2603.10690

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