We analyse all Mini Flash Crashes (or Flash Equity Failures) in the US equity markets in the four most volatile months during 2006-2011. In contrast to previous studies, we find that Mini Flash Crashes are the result of regulation framework and market fragmentation, in particular due to the aggressive use of Intermarke…
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48 results for “mini flash crashes”
Model captures mini-flash crashes caused by liquidity, feedback loops, and market fragmentation.
problem Understanding and predicting mini-flash crashes in financial markets.
method Develops a mathematical model borrowing from optimal execution literature.
result Mini-flash crashes can occur even when participants are uncertain of their impact.
Agent-based model simulates financial market crashes and identifies key factors.
problem Analyzing and understanding flash crashes in financial markets.
method Agent-based modelling approach with calibrated high-frequency financial simulator.
result Model accurately reproduces historical flash crash events and identifies key factors.
Study of stock market mini flash crashes, revealing large market orders as primary cause.
problem Understanding the causes and recovery of mini flash crashes in stock markets.
method Empirical analysis of stock market data, focusing on Ultrafast Extreme Events.
result Large market orders are the primary cause of most mini flash crashes, not high frequency trading.
Study finds a phase transition in flash crashes involving large and liquid stocks.
problem Systemic risk and propagation of shocks in high frequency trading.
method In-depth investigation of co-crashes in high frequency trading.
result Large co-crashes involve mostly illiquid stocks, while small crashes involve a mix of liquid and illiquid stocks.
New method uses topological data analysis to study stock market crashes.
problem Characterizing and predicting stock market crashes.
method Topological data analysis, persistence landscape, dynamic time series analysis.
result Demonstrates effectiveness of new method for Flash Crash characterization and prediction.
Study shows flash crashes in finance are self-organized criticality events.
problem Understanding and predicting anomalous price events in high-frequency finance.
method Investigated volume distributions during flash crashes and linked them to self-organized criticality.
result Volume distributions during flash crashes indicate a diverging second moment, suggesting self-organized criticality.
Hybrid model assesses flash crash contagion and systemic risk.
problem Understanding conditions for flash crash contagion and systemic risk.
method Micro-macro agent-based model with endogenous price impact.
result Systemic risk depends on algorithmic trader behavior, leverage, and network topology.
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
Flash crash on Ethereum shows social coordination can destabilize blockchain systems.
problem Interconnected socio-technical systems can be fragile due to social coordination.
method Analyzed the 2017 Ethereum flash-crash to illustrate the fragility of blockchain systems.
result Emergent centralization can lead to catastrophic cascades in interconnected systems.
Zero-Liquidation loans protect ETH borrowers from liquidation risks.
problem Risk of liquidation in DeFi lending protocols.
method Allows borrowers to repay in either USDC or pledged ETH, compensating liquidity providers with higher yield.
result More robust and less contagion-prone lending compared to traditional protocols.
A Kyle-inspired model with adaptive agents explains excess volatility and volatility clustering.
problem Reconciling asymmetrically informed traders with adaptive market hypothesis.
method Proposes a model with adaptive agents using inductive reasoning, reconciling Kyle model with Adaptive Market Hypothesis.
result Microfoundations for GARCH models and volatility clustering explained.
Model predicts bid and ask price dynamics with spread-dependent intensities.
problem Predicting bid and ask price dynamics in high-frequency stock markets.
method Extended Hawkes process with zero intensities, spread-dependent intensities, and negative excitement.
result Spread-narrowing tendency, excitations caused by previous events, impact of flash crashes, and different market participant features.
We introduce a new measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes are due to endogenous feedback processes, as opposed to exogenous news. For this, we calibrate the self-excited conditional Poisson Hawkes model, whi…
Study uses RL to simulate realistic market behavior.
problem Traditional market simulators lack realistic dynamic behavior.
method Agent-based simulation with reinforcement learning agents.
result RL agents simulate realistic stylized facts and market behavior.
New measures detect asymmetries, non-linearity in stock returns.
problem Detecting asymmetries and non-linearity in stock returns.
method Proposed non-linear, local, invariant dependence measures; nonparametric estimator proven.
result Measures show tail asymmetry, non-linearity, risk buildup during market distress.
We consider a self-exciting counting process, the parameters of which depend on a hidden finite-state Markov chain. We derive the optimal filter and smoother for the hidden chain based on observation of the jump process. This filter is in closed form and is finite dimensional. We demonstrate the performance of this fil…
Cryptocurrency patterns stable across market caps, validated by microstructure theory.
problem Stable patterns in cryptocurrency microstructure across different market caps.
method Unified CatBoost modeling pipeline with time-series cross validation, validated by backtests.
result Feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility.
Model shows triangular arbitrage key to cross-currency correlations in forex markets.
problem Understanding cross-currency correlations in forex markets.
method Agent-based model of market interactions.
result Triangular arbitrage is primary driver of cross-currency correlations.
Two price regimes identified in limit order books: close and far from quotes.
problem Understanding the distribution and behavior of limit orders in limit order books.
method Analysis of limit order book data in dimensions of price, time, lifetime, and volume.
result Identification of two distinct regimes in the limit order book: close and far from quotes.
We introduce a mathematical model on the dynamics of demand and supply incorporating collectability and saturation factors. Our analysis shows that when the fluctuation of the determinants of demand and supply is strong enough, there is chaos in the demand-supply dynamics. Our numerical simulation shows that such a cha…
The substantial turmoil created by both 2000 dot-com crash and 2008 subprime crisis has fueled the belief that the two classical paradigms of economics, which are the invisible hand and the rational agent, are not appropriate to describe market dynamics and should be abandoned at the benefit of alternative new theoreti…
Flashot visualizes Flash Loan attacks in DeFi systems.
problem Lack of standard tools to study Flash Loan attacks in DeFi.
method Proposes Flashot, a prototype to transparently illustrate asset flows.
result Demonstrates the effectiveness of Flashot in studying Flash Loan attacks.
New method uses statistical physics to detect financial market manipulation.
problem Detecting financial market manipulation activities like spoofing and layering.
method Modeling order book dynamics as particle motion and using momentum measure.
result Method outperforms conventional Z-score-based anomaly detection.
Study shows no sure profits via flash strategies if asset prices don't have predictable jumps.
problem Existence of sure profits via flash strategies in financial markets.
method Introduced and studied the notion of sure profit via flash strategy, proving the existence of such profits under specific conditions.
result No sure profits via flash strategies if and only if asset prices do not exhibit predictable jumps.
Society's drive toward ever faster socio-technical systems, means that there is an urgent need to understand the threat from 'black swan' extreme events that might emerge. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unp…
Study on order book dynamics with uniform catastrophes, explaining volatility and trends.
problem Understanding volatility and trends in financial markets with different types of liquidity.
method Stochastic models and population processes with uniform catastrophes.
result Law of large numbers, central limit theorem, and large deviations proved for the model.
New method calibrates financial market simulators using neural networks.
problem Calibrating market simulators to specific trading periods.
method Neural density estimators and embedding networks.
result Approach accurately identifies high-probability parameter sets.
Model predicts Chinese stock market liquidity and customer order behavior.
problem Understanding market liquidity and customer order behavior in the Chinese stock market.
method Dual state-space model using Fourier transform to connect volume-at-price buckets to correlations.
result Customer orders are correlated with market sentiment and stock returns, not with bond returns.
In this article, we present a discrete time modeling framework, in which the shape and dynamics of a Limit Order Book (LOB) arise endogenously from an equilibrium between multiple market participants (agents). We use the proposed modeling framework to analyze the effects of trading frequency on market liquidity in a ve…
This study analyzes cryptocurrency market crashes using complex network analysis.
problem Identifying and understanding dynamics of cryptocurrency market crashes.
method Complex network analysis of cryptocurrency market during pre-crash, crash, and post-crash periods.
result Network density and clustering coefficient spike during crashes, indicating uninformed panic sell-off.
The paper analyzes algorithmic trading in cryptocurrency exchanges, finding a profitable strategy involving indirect conversions.
problem Lack of empirical analysis of algorithmic trading in cryptocurrency exchanges.
method Empirical analysis using historical trades data from Binance.
result A triangular arbitrage strategy yields a 14.4 basis point advantage over direct exchanges.
FLASH-MAX predicts electromagnetic fields from sparse data in seconds.
problem Predicting homogeneous electromagnetic fields from sparse pointwise observations.
method Exact-by-construction neural network architecture that satisfies Maxwell's equations symbolically.
result FLASH-MAX achieves sub-1% relative validation error from 1K sparse observations in seconds.
Cross-sectional signatures of market panic were recently discussed on daily time scales in [1], extended here to a study of cross-sectional properties of stocks on intra-day time scales. We confirm specific intra-day patterns of dispersion and kurtosis, and find that the correlation across stocks increases in times of …
A simple and elegant arrangement of stock components of a portfolio (market index-DJIA) in a recent paper [1], has led to the construction of crossing of stocks diagram. The crossing stocks method revealed hidden remarkable algebraic and geometrical aspects of stock market. The present paper continues to uncover new ma…
Study shows economic policy uncertainty increases stock market crash risk during pandemic.
problem Impact of economic policy uncertainty on stock market crashes during the pandemic.
method Used GARCH-S model to estimate daily skewness as a proxy for crash risk, analyzed data from US stock market.
result Significantly negative correlation between economic policy uncertainty and stock market crash risk, stronger during pandemic.
This study uses ARM to analyze pedestrian crashes under different lighting conditions.
problem Identifying crash risk factors under varying lighting conditions.
method Applied Association Rules Mining to Louisiana pedestrian crash data.
result Daylight crashes are associated with children, seniors, and older drivers.
New SigSwap model for path-dependent financial risk.
problem Managing complex, path-dependent financial risks.
method Geometry-based approach using path-signature and Signature Expected Shortfall.
result Path-dependent risks can be converted into transparent risk factors.
Predicts stock market crashes using rational bubble model.
problem Financial market crashes prediction.
method White box model based on rational bubble theory.
result Successfully predicts major crashes in Dow Jones and Bitcoin markets.
Study examines financial market structure changes during the COVID-19 crash using a novel MI approach.
problem Analyzing nonlinear dependencies among major stocks during market crashes.
method Conditional p-threshold mutual information (MI) and Minimum Spanning Tree (MST) framework.
result Financial networks become more integrated during crashes, with increased periphery vulnerability.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
problem Estimating the heterogeneous causal effects of crashes on highway traffic.
method Neyman-Rubin Causal Model, Conditional Shapley Value Index, Structural Causal Model, Doubly Robust Learning.
result Different types of crashes have varying impacts on traffic, with rear-end crashes causing the most severe congestion.
This study identifies RwD crash patterns on rural two-lane highways under different lighting conditions.
problem Insufficient investigation of RwD crashes under varying lighting conditions.
method Data mining using association rules mining (ARM) on crash database.
result Interesting crash patterns and risk factors identified under different lighting conditions.
Study reveals 2020 stock crashes were mostly endogenous, not exogenous.
problem Identifying the cause of the 2020 global stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze stock market indexes.
result The 2020 stock market crashes were mostly endogenous, driven by systemic instability.
The paper models market crashes as phase transitions, finding dynamic transitions offer better predictions.
problem Understanding and predicting extreme financial events like market crashes.
method Employing phase transition theory, focusing on endogenous crashes, and comparing DPT, CPT, and SPT.
result Dynamic phase transitions provide more accurate predictions of market crashes compared to critical and stochastic models.
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.
Study proposes a machine learning method to predict stock price crashes based on investor sentiment.
problem Predicting stock price crashes due to investor sentiment.
method Minimum covariance determinant methodology and cross-sectional regression analysis.
result The proposed method effectively captures stock price crash risk and is robust across different firm sizes.
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
problem Forecasting crash rates in areas with irregular traffic patterns and exogenous events.
method Adopted a stochastic volatility model to capture heterogeneity and temporal instability.
result The stochastic volatility model outperforms conventional models in forecasting crash rates in Washington, D.C.
Stock market crashes are outlierscond-mat.stat-mech
We call attention against what seems to a widely held misconception according to which large crashes are the largest events of distributions of price variations with fat tails. We demonstrate on the Dow Jones Industrial index that with high probability the three largest crashes in this century are outliers. This result…