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on Market Microstructure |
| By: | Bixing Qiao; Weixuan Xia |
| Abstract: | The present paper investigates how insiders strategically navigate ongoing legal risk while leveraging stealth trading within a continuous-time Kyle-type framework. Legal enforcement operates concurrently with trading, which dynamic can be adversely obscured by a large surrounding population of noise traders. While surveillance intensity responds directly to the insider's trading intensity, triggering a random prosecution time, the resulting legal sanctions encompass both strategy-focused criminal penalties and profit-dependent civil penalties. Employing a new impact-neutral measure change, equilibrium analysis shows that even after achieving stealth, the insider internalizes regulatory exposure, and enforcement can significantly shape equilibrium trading strategies. The associated limiting equilibria yield a rich set of outcomes, with three key insights for regulatory impact: (i) under committed regulatory scrutiny, the insider trades a time-varying function of the discrepancy between the asset's fundamental value and its market price, and trading may intensify indefinitely near the end of the trading horizon as legal risk recedes; (ii) merely raising penalties as an advantageous selection cost proves ineffective in offsetting declines in regulatory diligence; (iii) criminal penalties remain essential for deterring aggressive insider trading, as they impose critical temporal constraints on trading intensity not achievable through civil penalties alone. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.27684 |
| By: | Nag, Arindam |
| Abstract: | This paper investigates whether artificial intelligence amplifies systemic risk in equity markets using daily data spanning February 2023 to December 2025, comprising 721 observations across the CBOE Volatility Index, S&P 500 and NASDAQ Composite returns, abnormal trading volume, and the Amihud illiquidity ratio. Employing descriptive statistical analysis, an event study framework, OLS regression with Newey-West HAC-corrected standard errors, and a six-lag Vector Autoregression, the results provide evidence broadly consistent with systemic risk amplification through the liquidity withdrawal channel. The regression results indicate that market illiquidity, as measured by the Amihud ratio, is a statistically significant predictor of volatility (coefficient = 1, 144, 957; p |
| Keywords: | Artificial Intelligence, Algorithmic Trading, Systemic Risk, Market Volatility, Financial Stability, Liquidity Risk |
| JEL: | G0 G10 G14 G18 G3 G33 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128853 |
| By: | Edwards, Geoff |
| Abstract: | This article surveys research on how geopolitical/ political and climate risks affect corporate behavior and market outcomes through financial constraints, information frictions, and market design. It integrates recent evidence on geopolitical risk and corporate tax avoidance under constraints; climate risk and asymmetric tail spillovers in international energy markets; transparency/anonymity reforms, broker identity disclosure, latency reduction, and venue switching as determinants of market quality; corporate events (M&A, bank lending, bankruptcies) as information shocks influencing liquidity and informed trading; and systemic risk/ratings mechanisms, including connectedness, CoVaR, capital shortfall, SRISK, and sovereign rating ceiling effects. The survey highlights common empirical architectures: text- based risk indices, event studies, high-frequency microstructure metrics (spreads, price impact, Kyle’s lambda), and time-frequency connectedness methods. A unifying “risk–friction– liquidity” framework is proposed with testable implications and a research agenda focused on identification, robustness, and cross-asset tail dynamics. |
| Keywords: | Geopolitical risk Policical risk Corporate behaviour Market design |
| JEL: | G1 G10 G19 G28 |
| Date: | 2026–02–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128690 |
| By: | Ilija I Zovko |
| Abstract: | An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.13419 |