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.
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.
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
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.
Study shows COVID-19 increases stock market crash risk in China.
problem Impact of COVID-19 on stock market crash risk in China.
method Estimated conditional skewness using GARCH-S model and constructed fear index from Baidu Index data.
result Conditional skewness reacts negatively to daily growth in total confirmed cases, indicating increased crash risk.
SRR detects early signs of financial crises using multi-layer graphs.
problem Predicting systemic financial transitions from evolving market interactions.
method Systemic Risk Radar (SRR) models financial markets as multi-layer graphs.
result Graph-derived features provide useful early-warning signals compared to feature-based models.
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.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
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.
Enhances systemic risk analysis by incorporating debt valuation factors.
problem Systemic risk in financial networks due to bank failures.
method Incorporates debt valuation factors into existing risk analysis frameworks.
result Additional debt valuation factors substantially influence risk assessment outcomes.
Digitwashing gap boosts stock crash risk, study finds.
problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.
Hybrid ML ensemble predicts market risk and generates alpha.
problem Forecasting short-horizon market risk and generating alpha.
method Neural networks and tree-based voting models integrated for trading signal.
result Sharpe ratio of 2.51 and annualized CAPM alpha of +0.28.
This study shows ESG ratings reduce equity crash risk during market downturns.
problem Decoupling of alpha from tail risk resilience in traditional models.
method Double Machine Learning for structural deconfounding, state-dependent analysis.
result High ESG ratings reduce crash incidence during systemic drawdowns.
Study reveals how illiquidity network signals Chinese stock market crashes.
problem Understanding and predicting Chinese stock market crashes.
method Established an illiquidity network to model market dynamics.
result Market crashes are preceded by a more densely connected illiquidity network.
The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt cras…
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.
We discuss the statistical properties of index returns in a financial market just after a major market crash. The observed non-stationary behavior of index returns is characterized in terms of the exceedances over a given threshold. This characterization is analogous to the Omori law originally observed in geophysics. …
Improved forecasting of financial risk using Diffusion-Copula framework.
problem Capturing complex, asymmetric dependence structures in financial markets.
method Explicitly decouples marginal distribution learning from dependence structure using Mixture Density Networks and Classification-Diffusion Copula.
result Superior performance in forecasting systemic extremes of marginal and joint events.
Crashes have fascinated and baffled many canny observers of financial markets. In the strict orthodoxy of the efficient market theory, crashes must be due to sudden changes of the fundamental valuation of assets. However, detailed empirical studies suggest that large price jumps cannot be explained by news and are the …
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.
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.
Paper optimizes a big data and ML risk monitoring system for financial markets.
problem Traditional risk monitoring methods are inadequate for modern financial markets due to data complexity and volume.
method Four-layer architecture integrating big data and advanced ML algorithms (LSTM, RF, GB).
result Significantly enhances efficiency and accuracy in risk management, especially in market crash risk detection.
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.
Cryptocurrency markets exhibit violent, synchronised drawdowns, challenging diversification claims.
problem Cryptocurrency markets' violent drawdowns challenge diversification claims.
method Dynamic conditional tail dependence analysis
result Near-complete and stable lower-tail graph, upper tail that thins over time, dissolution of token categories into a core.
This paper develops a CVaR framework for managing tail risks using puts and trend-following strategies.
problem Managing tail risks, especially crashes and drawdowns, requires different forms of protection.
method Develops a continuous-time CVaR framework that integrates long out-of-the-money put options and systematic trend-following overlays.
result Shows how convex crash protection and drawdown protection can be optimally combined in a mandate.
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.
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.
problem Improving risk assessment for financial portfolios using asymmetric data.
method Generalized Tsallis relative entropy (ATRE) for asymmetric distributions of returns.
result ATRE shows better risk-return profiles, especially during market crashes.
Paper presents LSTMMDN for hourly bike flow estimation in Copenhagen.
problem Sparse or unavailable hourly bike flow data for safety analysis.
method Hybrid LSTM MDN model for hourly bike flow estimation.
result 66-77% more accurate bike flow estimates compared to calibration factors.
Using a recently introduced rational expectation model of bubbles, based on the interplay between stochasticity and positive feedbacks of prices on returns and volatility, we develop a new methodology to test how this model classifies 9 time series that have been previously considered as bubbles ending in crashes. The …
The purpose of this paper is to advance the understanding of the conditions that give rise to flash crash contagion, particularly with respect to overlapping asset portfolio crowding. To this end, we designed, implemented, and assessed a hybrid micro-macro agent-based model, where price impact arises endogenously throu…
IVMs help identify dangerous traffic conditions in real-time.
problem Real-time crash risk analysis in urban traffic systems.
method Import Vector Machines (IVMs) applied to historical crash and traffic data.
result IVMs successfully identify dangerous traffic conditions with computational advantage.
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.
The paper introduces a US crime index to assess financial losses from property and cyber crimes.
problem Lack of indices evaluating crime's financial impact on investments.
method Developed an index-based insurance portfolio using FBI financial losses data.
result Real estate, ransomware, and government impersonation are major risk contributors.
Study arbitrage theory without numéraire, generalizing NUPBR.
problem Arbitrage theory in markets without numéraire.
method Disintegration of probability space into crash times.
result Generalization of NUPBR to no unbounded profits with bounded risk.
Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events i…
In complex systems like financial market, risk tolerance of individuals is crucial for system resilience.The single-security price limit, designed as risk tolerance to protect investors by avoiding sharp price fluctuation, is blamed for feeding market panic in times of crash.The relationship between the critical market…
We clarify the status of log-periodicity associated with speculative bubbles preceding financial crashes. In particular, we address Feigenbaum's [2001] criticism and show how it can be rebuked. Feigenbaum's main result is as follows: ``the hypothesis that the log-periodic component is present in the data cannot be reje…
A taxonomy of large financial crashes proposed in the literature locates the burst of speculative bubbles due to endogenous causes in the framework of extreme stock market crashes, defined as falls of market prices that are outlier with respect to the bulk of drawdown price movement distribution. This paper goes on dee…
Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision, fundamental value shocks, and market fragmentation. We develop a mathematical model which captures aspects of the first three explanations. Empirical features of recent mini-flash crashes ar…
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.
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.
Study models stock price recovery during COVID-19, distinguishing V and L-shape recoveries.
problem Analyzing stock price recovery during the COVID-19 pandemic.
method Developed a stock price model based on net-fund-flow and financial antifragility.
result Quality stocks with higher financial antifragility show V-shape recovery, while those with lower antifragility show L-shape recovery.
Model explains stock price bubbles through debt crises and financial crashes.
problem Analyzing financial fragility and stock price bubbles.
method Stock-flow consistent model integrating macroeconomic and financial market dynamics.
result Model demonstrates how credit expansion and crash risk lead to recurrent boom-bust cycles.
The paper introduces isotropy as a regularizer to enhance portfolio stability.
problem Model uncertainty and estimation errors in diversification strategies.
method Integrates isotropy as a geometric regularizer into mean-variance optimization.
result Isotropy constraint systematically induces negative average-signal exposure, providing a robust crash hedge.
We present a simple agent-based model to study the development of a bubble and the consequential crash and investigate how their proximate triggering factor might relate to their fundamental mechanism, and vice versa. Our agents invest according to their opinion on future price movements, which is based on three source…
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.
Margin trading in which investors purchase shares with money borrowed from brokers is blamed to be a major cause of the 2015 Chinese stock market crash. We propose a cascading failure model and examine how an increase in margin trading increases share price vulnerability. The model is based on a bipartite graph of inve…