\section{Related literature} \label{sec:literature} % All 110 citations verified at DOI level via OpenAlex/Crossref on 2026-07-13; % see docs/LITERATURE_MAP.md. Novelty statements are qualified by the documented % search of the same date. This paper measures how quickly, how broadly, how persistently, and how asymmetrically idiosyncratic intraday shocks travel across the U.S.\ equity cross-section, and how that transmission evolved between 1992 and 2026. The question sits at the intersection of several literatures: theories of cross-asset shock transmission; empirical work on fire sales, flows, and ownership linkages; the measurement of comovement, commonality, and liquidity dynamics; lead--lag effects and information diffusion; network approaches to contagion and systemic risk; the transformation of U.S.\ market structure and the high-frequency trading debate; the econometrics of jumps, co-jumps, and scheduled announcements; and the measurement toolkit for high-frequency and OHLCV data. We take each in turn and close with the paper's positioning. \subsection{Theories of cross-asset shock transmission} Theory identifies two broad families of channels through which a shock to one security moves others. The first is informational. \citet{king1990transmission} model rational agents who infer information from \emph{other} markets' price changes, so that an idiosyncratic ``mistake'' in one market transmits broadly--- a mechanism designed to explain why nearly all markets crashed together in October 1987. \citet{kodres2002rational} show that cross-market portfolio rebalancing of shared macro risk exposures transmits idiosyncratic shocks even without common fundamentals, with severity increasing in information asymmetry. The second family operates through balance sheets and funding. \citet{kyle2001contagion} generate contagion as a wealth effect: convergence traders who lose money in one market liquidate positions in others, raising volatility and correlations exactly when capital is scarce. \citet{yuan2005asymmetric} shows that borrowing constraints make crises, contagion, and asymmetric price movements likelier after intermediate losses. \citet{brunnermeier2009market} link market liquidity to funding liquidity in mutually reinforcing margin spirals that explain sudden, correlated liquidity dry-ups and flight to quality. Most directly related to our intraday setting, \citet{cespa2014illiquidity} show that when liquidity providers learn from \emph{other securities' prices}, illiquidity becomes contagious and the market admits liquidity-crash equilibria---a mechanism they connect explicitly to May~6, 2010. These theories jointly predict that transmission should be state-dependent (stronger when volatility is high and capital constrained), asymmetric (stronger for negative shocks), and increasing in the degree to which pricing and liquidity provision are cross-security activities. Our measures of propagation speed, breadth, asymmetry, and market-state dependence are designed to test exactly these comparative statics at the minute scale. \subsection{Fire sales, flows, and ownership networks} A complementary empirical literature documents non-fundamental transmission through investor portfolios. Mutual funds facing outflows engage in forced sales concentrated in their holdings, generating price pressure and reversals \citep{coval2007asset}; predictable flows generate predictable price pressure \citep{lou2012flow}; and uninformative trading imbalances in one stock spill over to correlated stocks through risk-averse liquidity providers \citep{andrade2008trading}. Ownership structure itself predicts fragility: stocks with concentrated or correlated-flow ownership have higher volatility and comovement \citep{greenwood2011stock}, and common active ownership forecasts cross-sectional correlation beyond fundamentals-based similarity \citep{anton2014connected}. At the institutional level, overlapping portfolios create fire-sale spillovers that aggregate into measurable systemic vulnerability \citep{greenwood2015vulnerable,duarte2021fire}. This work establishes that idiosyncratic demand shocks \emph{do} propagate, but its resolution is coarse---monthly or quarterly holdings and flows. Whether the same propagation operates within the hour, and whether its speed has changed as intermediation became algorithmic, is the margin our data address. \subsection{Comovement, commonality, and liquidity dynamics} The commonality literature establishes that liquidity has a systematic component: spreads and depths co-move with market- and industry-wide liquidity \citep{chordia2000commonality}, common factors in order flow explain much of the commonality in returns \citep{hasbrouck2001common}, and commonality is supplied in part by common intermediaries---stocks sharing a NYSE specialist firm co-move in liquidity beyond the market factor \citep{coughenour2004common}. Time variation is systematic as well: liquidity falls after market declines, especially when funding is tight \citep{hameed2010stock}; specialist inventories and trading losses forecast spread widening \citep{comertonforde2010time}; and returns to liquidity provision spike with the VIX as liquidity ``evaporates'' in turmoil \citep{nagel2012evaporating}. Aggregate liquidity risk is priced \citep{pastor2003liquidity,acharya2005asset}, and commonality strengthens in downturns across forty markets \citep{karolyi2012understanding}. Relative to this literature, we do not measure liquidity \emph{levels}; we measure the \emph{transmission of extreme events} and use liquidity states---constructed from OHLCV proxies---as conditioning variables. At very short horizons, measured comovement is attenuated mechanically by asynchronous trading \citep{epps1979comovements}, a bias any minute-level design must engineer around; our breadth measures therefore condition on trading eligibility rather than assuming synchronous observation. \subsection{Lead--lag effects and gradual information diffusion} Cross-autocorrelation in returns is among the oldest facts in empirical asset pricing: large stocks lead small stocks \citep{lo1990contrarian}, predominantly within industries, consistent with gradual information diffusion \citep{hou2007industry}. Economic links sharpen the pattern: returns of principal customers predict suppliers' returns \citep{cohen2008economic}, and cross-predictability follows supplier--customer industry links broadly, declining with analyst coverage and institutional ownership \citep{menzly2010market}. At high frequency, tick-data studies document that liquid assets lead illiquid ones with intraday-seasonal lead--lag structure \citep{huth2014high}, and \citet{chinco2019sparse} show that one-minute-ahead cross-stock predictability exists but is sparse, short-lived, and news-related. These studies measure \emph{unconditional} predictive structure. Our object is different: the conditional response of the cross-section to an identified extreme idiosyncratic event, its horizon profile, and the secular evolution of both. The lead--lag literature nonetheless supplies one of our falsification benchmarks: propagation estimated through lagged-return networks should reproduce known liquidity-ordered asymmetries if it is measuring genuine transmission. \subsection{Networks: real linkages, financial linkages, and measured connectedness} Network theory shows when idiosyncratic shocks fail to diversify away: sufficiently asymmetric input--output structures let micro shocks aggregate \citep{acemoglu2012network}, and intermediate integration and diversification maximize cascade susceptibility in financial cross-holdings \citep{elliott2014financial}; \citet{glasserman2016contagion} survey the contagion-network literature. Real-economy links propagate firm-level shocks measurably---natural-disaster shocks to suppliers impose large value losses on customers \citep{barrot2016input}---and network positions carry asset-pricing implications \citep{herskovic2018networks}. On the measurement side, connectedness is estimated from variance decompositions \citep{diebold2012better,diebold2014network}, Granger-causality graphs among financial institutions \citep{billio2012econometric}, sparse VARs \citep{barigozzi2019nets}, and tail-risk networks \citep{hautsch2015financial}. The contagion-measurement tradition warns that unconditional correlation comparisons are biased in volatile regimes \citep{forbes2002no}; defining contagion as comovement beyond fundamentals, \citet{bekaert2014global} find ``wake-up call'' transmission tied to weak fundamentals. Classic volatility-spillover evidence---meteor showers in FX \citep{engle1990meteor}, international open/close spillovers \citep{hamao1990correlations}, and cross-market volatility transmission \citep{king1990transmission}---anticipates our design at coarser resolution and for a handful of markets. Across all of this work the networks are estimated at daily or lower frequency on small panels. We estimate pre-shock networks on minute data for thousands of securities and use them as lagged conditioning information---asking whether network position predicts transmitters, receivers, amplifiers, and absorbers---with permuted-network placebos in the spirit of the falsification tradition. \subsection{Market-structure evolution and the high-frequency trading debate} Our sample spans essentially the entire modern transformation of U.S.\ equity trading, and the literature documents each step. Decimalization in 2001 cut spreads, particularly for active stocks \citep{bessembinder2003trade}; liquidity itself promotes price efficiency \citep{chordia2008liquidity}; turnover exploded in the 2000s alongside algorithmic trading \citep{chordia2011recent}; the NYSE's 2006--07 Hybrid automation made prices more efficient while raising the cost of immediacy \citep{hendershott2011automation}; and fragmentation across venues did not measurably harm quality \citep{ohara2011fragmentation}. On algorithmic and high-frequency trading, the normal-times evidence is broadly positive: algorithmic trading narrows spreads and improves price discovery \citep{hendershott2011algorithmic,brogaard2014high}, modern HFT market makers halve spreads on entry \citep{menkveld2013high}, low-latency activity improves short-horizon quality \citep{hasbrouck2013low}, machines improve FX efficiency \citep{chaboud2014rise}, and colocation-based evidence replicates internationally, with higher short-term volatility as the caveat \citep{boehmer2021algorithmic}; \citet{menkveld2016economics} surveys the channel-by-channel evidence. Theory tempers the optimism: speed investment can be socially excessive \citep{biais2015equilibrium}, fast news trading concentrates volume and short-run price correlation \citep{foucault2016news}, toxic arbitrage degrades liquidity \citep{foucault2017toxic}, the continuous limit order book hard-wires a latency arms race \citep{budish2015high}---measured directly in message data as races won in microseconds, a fifth of volume \citep{aquilina2022quantifying}---and speed can crowd out information acquisition \citep{weller2018does,baldauf2020high}, with HFTs eventually trading \emph{with} informed institutional orders \citep{vankervel2019high}. \citet{ohara2015high} argues the machine age changes what microstructure concepts mean. This literature motivates our central hypothesis---normal-times absorption improving while tail transmission accelerates---but it measures liquidity levels, price discovery, or single events. We contribute the missing time series: propagation speed and breadth of extreme shocks, measured uniformly across three decades of structural change, with flexible year effects rather than imposed trend breaks. \subsection{Fragility and flash events} Episodic evidence shows modern liquidity can vanish abruptly. On May 6, 2010, high-frequency traders did not trigger the crash but amplified selling once it began \citep{kirilenko2017flash}; cross-market arbitrage broke down, leaving the seller dependent on local liquidity \citep{menkveld2019flash}; and fragmentation shaped which ETFs were hit \citep{madhavan2012exchange}. More systematically, HFTs supply liquidity in single-stock extreme moves but their provision weakens when \emph{many} stocks move together \citep{brogaard2018high}---precisely the margin our breadth measure tracks. Proposed early-warning metrics remain contested: VPIN signaled deteriorating conditions before the Flash Crash \citep{easley2012flow}, but its power appears mechanically tied to volatility and trading intensity \citep{andersen2014vpin}. This debate cautions against fragility indicators that repackage volatility; our propagation measures are therefore defined relative to lagged conditional distributions and validated against matched placebo times. \subsection{ETFs, indexing, and passive intermediation} A structural change specific to the later sample is the growth of index-linked intermediation. ETF ownership raises non-fundamental volatility of underlying stocks through the arbitrage channel \citep{bendavid2018etfs} and contributes to return comovement, especially in small and illiquid names \citep{da2018exchange}; index-product arbitrage flipped index return serial dependence from positive to negative across twenty markets \citep{baltussen2019indexing}. Effects on efficiency are contested: ETF ownership is associated with weaker firm-specific information environments \citep{israeli2017dark}, yet ETF activity speeds the incorporation of systematic earnings information where information environments are weak \citep{glosten2021etf}, and Russell-reconstitution evidence suggests price efficiency survives passive growth because the remaining informed investors adjust \citep{coles2022index}. For us, the ETF layer is both a candidate mechanism for the \emph{widening} of propagation (basket arbitrage converts idiosyncratic shocks into correlated flows) and a required robustness screen: no headline result may be driven by ETFs themselves. \subsection{Jumps, co-jumps, and scheduled announcements} Our shock measurement builds directly on high-frequency econometrics. Intraday volatility periodicity must be filtered before anything else \citep{andersen1997intraday}; bipower variation separates jumps from diffusion \citep{barndorffnielsen2004power}; formal jump tests exist at the observation level \citep{lee2008jumps,aitsahalia2009testing}. Two cautions discipline our thresholds: at ultra-high frequency much of what coarser methods call jumps is friction or ``drift bursts'' \citep{christensen2014fact}, and multiple-testing corrections eliminate a large share of spurious detections, with true jumps rarer and news-related \citep{bajgrowicz2016jumps}. The co-jump literature is the nearest empirical neighbor to our propagation object: index-level jumps are systematic co-jumps linked to macro news while idiosyncratic jumps diversify away \citep{bollerslev2008risk}; simultaneous multivariate jumps are frequent, associated with liquidity dry-ups, and priced \citep{caporin2017systemic}; jumps coincide with liquidity-demand shocks and news \citep{boudt2014intraday}; and simultaneous jumps across countries carry portfolio consequences \citep{das2004systemic}. Self- and cross-excitation of jumps has a canonical language in Hawkes processes \citep{hawkes1971spectra,bacry2015hawkes}, estimated on international indices by \citet{aitsahalia2015modeling}. Scheduled macro announcements provide the identified common shocks against which idiosyncratic propagation must be benchmarked: announcement surprises produce jump-like conditional-mean responses in FX and global markets \citep{andersen2003micro,andersen2007real}, generate the cojumps documented by \citet{lahaye2011jumps}, and structure expected returns around announcement times \citep{savor2013how,lucca2015pre}. Accordingly, our baseline excludes announcement windows and treats announcement-window propagation as a separate, labeled object. Where the co-jump literature tests for \emph{simultaneity}, our measures trace the \emph{sequence}---breadth and intensity at one through 120 minutes---and its secular change. \subsection{High-frequency measurement: factor structure, OHLCV proxies, and machine learning} Separating idiosyncratic from systematic movement at the minute scale requires a factor structure: common factors exist in high-frequency returns and order flow \citep{hasbrouck2001common}, high-frequency PCA is consistent with low-frequency covariance structure \citep{aitsahalia2019principal}, latent continuous and jump factors are estimable and economically interpretable \citep{pelger2020understanding}, and betas decompose into continuous, jump, and overnight components with distinct pricing \citep{bollerslev2016roughing}; signed semicovariances refine the decomposition \citep{bollerslev2020realized}. Jump-tail risk itself commands a large, time-varying premium \citep{bollerslev2011tails}, which is why our propagation outcomes separate tail responses from ordinary ones. Because our data are bars rather than quotes, liquidity enters only through proxies with a long pedigree: spread estimators from return autocovariance \citep{roll1984simple} and high--low ranges \citep{corwin2012simple}, range-based volatility \citep{parkinson1980extreme,garman1980estimation}, price-impact ratios \citep{amihud2002illiquidity}, and trade-based information measures \citep{hasbrouck1991measuring}; \citet{goyenko2009liquidity} validate such proxies against TAQ benchmarks. For estimation we use local projections \citep{jorda2005estimation} for horizon-specific responses, and the machine-learning extension follows the out-of-sample discipline of \citet{gu2020empirical}, informed by evidence that microstructure features carry universal predictive content \citep{sirignano2019universal} and remain informative in the machine age \citep{easley2021microstructure}. \subsection{Positioning and contribution} Each literature above contributes a piece: theory predicts state-dependent, asymmetric transmission; the flows literature demonstrates non-fundamental propagation at monthly resolution; the network literature measures connectedness on small panels at daily frequency; the market-structure literature documents the transformation of trading but measures liquidity levels and single events; the co-jump literature tests simultaneity at high frequency over short samples. To our knowledge---based on a documented search dated 2026-07-13 (replication package)---no prior study combines (i) minute-level resolution, (ii) the full U.S.\ equity cross-section including delisted securities, and (iii) a 1992--2026 sample spanning floor trading, decimalization, Reg~NMS fragmentation, and ETF-intermediated, algorithmic markets. The joint analysis of propagation speed, breadth, persistence, asymmetry, secular evolution, and network conditioning at that resolution and length---with placebo-timing, permuted-network, and announcement-exclusion falsifications---is the contribution.