{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:51:12Z","timestamp":1791651072845,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Instability in electronic markets is usually attributed to a single local cause: a news event, a speed advantage, a liquidity shortage or an operational outage. In a limit-order-book market populated by interacting heterogeneous traders, however, the response to any of these disturbances is a collective property that depends jointly on the composition of the population, on the timing of information and execution, and on the state of the book. We study this collective response in a single continuous double-auction agent-based model with noise traders, fundamentalists, trend-following chartists with Lux\u2013Marchesi-type opinion dynamics, strategy-switching agents and an inventory-constrained market maker. The same engine is subjected to eight distinct frictions and shocks: an execution-speed advantage for a share \u03c6 of chartists, event-time versus calendar-time measurement, delayed observation of the book combined with thin or deep initial liquidity, server-side execution latency, temporary loss of participation, belief (news) shocks, shifts of the fundamental value, and changes in trader aggressiveness. The campaign comprises 11 250 Monte Carlo runs plus 660 runs at larger population sizes. All comparisons are reported as effect sizes with bootstrap confidence intervals and Monte Carlo precision diagnostics, and the baseline dynamics are checked against the empirical stylised facts. The model reproduces heavy-tailed returns and positive dependence of volatility proxies; a pure power-law tail is not supported by a Clauset\u2013Shalizi\u2013Newman test, and the tail index lies above the empirical cubic law, which we relate to the finite population and to the bounded order-size rules. The joint effect of stale information and thin liquidity on post-shock drawdown, the volatility increase under recurrent news, and the lengthening of recovery after large fundamental shifts are large and stable; the event-time and latency contrasts are not resolved at the planned sample sizes. The execution-speed response is nonlinear in \u03c6 and its threshold location moves from \u03c6\u2217 = 0.2 at N = 26 to 0.5 at N = 51 and 0.6 at N = 76, while the fluctuation peak does not track the threshold and the response does not sharpen with size. We therefore read the speed response as a size-dependent nonlinear crossover rather than as evidence of a critical point, and we spell out the finite-size scaling programme that would be required to decide the question. Market resilience emerges as mechanism-specific and must be assessed jointly across price, liquidity, tail, recovery and size-scaling measures. \u200b<\/jats:p>","DOI":"10.2139\/ssrn.7593492","type":"posted-content","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:17:01Z","timestamp":1791649021000},"source":"Crossref","is-referenced-by-count":0,"title":["Mechanism-specific market resilience and size-dependent nonlinear response in a unified limit-order-book agent-based model 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