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on Market Microstructure |
| By: | Cetin, Umut; Danilova, Albina |
| Abstract: | Does retail order internalization benefit (via price improvement) or harm (via reduced liquidity) retail traders? To answer this question, we compare two market designs that differ in their mode of liquidity provision: In the setting capturing retail order internalization, liquidity is provided by market makers (wholesalers) competing for the retail order flow in a Bertrand fashion. Instead, in the open exchange setting, price-taking competitive agents act as liquidity providers. We discover that, when liquidity providers are risk averse, routing of marketable orders to wholesalers is preferred by all retail traders: informed, uninformed, and noise. Furthermore, most measures of liquidity are unaffected by the market design. We also identify a universal parameter that allows comparison of market liquidity, profit and value of information across different markets. |
| JEL: | C1 |
| Date: | 2025–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129686 |
| By: | Russ, David |
| Abstract: | This paper shows that, in a setup 'a la Kyle (1985), correlated retail trading opens up new profit opportunities for professional investors at the expense of retail investors. Additionally, it demonstrates that market quality can benefit through higher market liquidity and higher price efficiency. Our results lend support to concerns that social trading via Finfluencers and stock message boards harms rather than benefits retail investors. |
| Keywords: | social trading, noise trading, non-fundamental information, strategic trading |
| JEL: | G12 G14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:330308 |
| By: | M. I. Balakaeva; A. Yu. Veretennikov |
| Abstract: | An approximation method for construction of optimal strategies in the bid \& ask limit order book in the high-frequency trading (HFT) is studied. The basis is the article by M. Avellaneda \& S. Stoikov 2008, in which certain seemingly serious gaps have been found; in the present paper they are carefully corrected. However, a bit surprisingly, our corrections do not change the main answer in the cited paper, so that, in fact, the gaps turn out to be unimportant. An explanation of this effect is offered. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15988 |
| By: | Luigi Foscari; Emanuele Guidotti; Nicol\`o Cesa-Bianchi; Tatjana Chavdarova; Alfio Ferrara |
| Abstract: | We study the emergence of tacit collusion between adaptive trading agents in a stochastic market with endogenous price formation. Using a two-player repeated game between a market maker and a market taker, we characterize feasible and collusive strategy profiles that raise prices beyond competitive levels. We show that, when agents follow simple learning algorithms (e.g., gradient ascent) to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles, even in highly liquid markets with small trade sizes. By highlighting how simple learning strategies naturally lead to tacit collusion, our results offer new insights into the dynamics of AI-driven markets. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15995 |
| By: | Federico Gatta; Fabrizio Lillo; Piero Mazzarisi |
| Abstract: | We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, which displays some limitations in describing empirical data. More specifically, as the RQ, the RRM method transforms intra-day returns in intrinsic time using a subordinator process, in order to capture the inhomogeneity of trading activity and/or volatility clustering. Then, microstructural effects resulting in non-zero autocorrelation are filtered out using a suitable moving average process. Finally, a fat-tailed distribution is fitted on the cleaned intra-day returns. The return distribution at low frequency (daily) is then extrapolated via either a characteristic function approach or Monte Carlo simulations. VaR and ES are estimated as the quantile and the tail mean of the distribution, respectively. The proposed approach is benchmarked against the RQ through several experiments. Extensive numerical simulations and an empirical study on 18 US stocks show the outperformance of our method, both in terms of the in-sample estimated risk measures and in the out-of-sample risk forecasting |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16526 |