Crowd panic in China's stock market boosts systemic risk through herding behavior.
problem Systemic risk in China's stock market due to herd behavior and contagion.
method Investigating networking stocks and herding behavior to reveal systemic risk.
result Herding behavior in China's stock market leads to too-connected-to-fail stocks, amplifying market crashes.
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 …
Study finds higher risk aversion leads to lower stock prices, supporting EIS > 1.
problem Reconciling theoretical asset pricing models with observed stock price declines during panic episodes.
method Comparing investor behavior during stock market panics with asset pricing models.
result Elasticity of intertemporal substitution (EIS) must be greater than one to match theory and observed stock price declines.
We study properties of the cross-sectional distribution of returns. A significant anti-correlation between dispersion and cross-sectional kurtosis is found such that dispersion is high but kurtosis is low in panic times, and the opposite in normal times. The co-movement of stock returns also increases in panic times. W…
Small stocks drive market crashes by suppressing resilience.
problem The single-security price limit exacerbates market panic during crashes.
method Simplified dynamic model on networks of investors and stocks, empirical verification.
result Unexpected linear association between price limit and critical market confidence.
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
problem Improving prediction accuracy of implied volatility surface.
method Constructed daily high-frequency sentiment data, used VAR method, deep learning (BERT, LSTM), FFT, EMD for sentiment decomposition.
result High-frequency sentiment correlates with ATM options' implied volatility, low-frequency with DOTM options.
Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat, or self-generated nervousness. Here we show that the recent economic crisis and earlier large single…
The relativistic quantum mechanic approach is used to develop a stock market dynamics. The relativistic is conceptional here as the meaning of big external volatility or volatility shock on a financial market. We used a differential geometry approach with the parallel transport of the prices to obtain a direct shift of…
Study reveals how investor flows impact stock prices, especially during herding episodes.
problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.
Stock market instability increased after 2018 February.
problem Understanding the recent increase in stock market instability.
method Applied a complex systems model to analyze stock price comovement and U parameter. result The U parameter value has decreased significantly since February 2018, indicating a new regime of market behavior. Modeling financial bubbles and crashes with a cubic momentum function.
problem Capturing the micro-level dynamics of investor behavior and panic selling.
method Introducing a cubic function of market momentum to model trend-following and sudden crashes.
result The model successfully replicates complex, nonlinear bubble dynamics.
The study analyzes aftershocks of stock market crashes using statistical methods.
problem Understanding the aftershocks of stock market crashes during crises.
method Structural break analysis and statistical methods applied to 1987 crash, 2008 financial crisis, and 2020 COVID-19 pandemic.
result The recovery of stock price during the COVID-19 pandemic may be faster than the financial crisis of 2008.
I sketch a program for a microeconomic theory of the main component of the business cycle as a recurring disequilibrium, driven by incompleteness of the financial market and by information asymmetries between borrowers and lenders. This proposal seeks to incorporate five distinct but connected processes that have been …
Cryptocurrencies are increasingly correlated with traditional financial markets.
problem Determining the independence of cryptocurrencies from traditional financial markets.
method High-frequency detrended cross-correlation analysis over various time scales and market periods.
result Cryptocurrencies have become more aligned with traditional financial markets, especially during bear phases.
Modeling bank panics and financial crises with contagion channels.
problem Understanding and predicting financial crises and contagion effects.
method Develops a comprehensive model for systemic risk that includes stock-flow consistency and Asset-Liability symmetry.
result Identifies and models the dangerous spillover effects that dominate future financial crises.
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.
We present a new volatility model, simple to implement, that includes a leverage effect whose return-volatility correlation function fits to empirical observations. This model is able to capture both the "retarded effect" induced by the specific risk, and the "panic effect", which occurs whenever systematic risk become…
Solves financial volatility clustering using Minkowski metric in GARCH(1,1) model.
problem Financial volatility clustering and long memory process.
method Minkowski metric applied to GARCH(1,1) model.
result Equivalent to dark volatility or hidden risk fear field.
In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…
Investors trade too much in experimental markets, harming their wealth.
problem Investors overtrade in artificial markets, leading to poorer outcomes.
method Experimental asset markets with explicit market impact.
result Excessive trading by subjects leads to poorer wealth outcomes.
Geography and distance impact financial dynamics in Chinese stock markets.
problem Investigate the impact of geography and distance on financial dynamics in Chinese stock markets.
method Daily data analysis of individual stocks in Shanghai and Shenzhen stock markets, focusing on geographical correlation and distance effect.
result Stock location impacts financial dynamics, except during financial crises. Short distance has higher probability than long distance, and correlation weakly decays with distance in Shanghai but remains stable in Shenzhen.
A characteristic feature of complex systems in general is a tight coupling between their constituent parts. In complex socio-economic systems this kind of behavior leads to self-organization, which may be both desirable (e.g. social cooperation) and undesirable (e.g. mass panic, financial "bubbles" or "crashes"). Abund…
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
problem Understanding the cause of the 2020 U.S. stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze four major U.S. stock market indexes.
result The 2020 U.S. stock market crash was endogenous, stemming from systemic instability, not COVID.
We study numeraire markets in open stock markets.
problem Understanding the numeraire portfolio in open stock markets.
method Constructed an example of a numeraire market that is asymptotically stable.
result Found an asymptotically stable numeraire market in open stock markets.
Study finds stock markets follow nonextensive statistical mechanics.
problem Understanding nonextensivity in stock market volatilities.
method Analysis of 34 major stock market indices over 10 years.
result Stock markets exhibit nonextensive behavior, distinguishing between developed and developing countries.
It seems to be very unlikely that all relevant information in the stock market could be fully encoded in a geometrical shape. Still,the present paper will reveal the geometry behind the stock market transactions. The prices of market index (DJIA) stock components are arranged in ascending order from the smallest one in…
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…
Deep Q-Network predicts global stock market returns from chart images.
problem Predicting global stock market returns using chart images.
method Deep Q-Network with CNN approximator, trained on US stock market, tested on 31 countries.
result Artificial intelligence can predict stock prices in small markets.
Survey on deep learning methods for stock market prediction.
problem Lack of comprehensive survey on deep learning methods for stock market prediction.
method Propose a novel taxonomy summarizing state-of-the-art models based on deep neural networks.
result Provide detailed statistics on datasets and evaluation metrics.
Study finds Indian stock market inefficient, suggesting undervalued securities offer returns.
problem Determining market efficiency and identifying undervalued securities.
method Analyzed five BSE indices, tested random walk model, and identified biased random time series.
result Indian stock market is inefficient, with undervalued securities offering higher returns.
Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.
problem Analyzing changes in investor expectations during the 2020 stock market crash and recovery.
method Surveying Vanguard clients at three points: before, during, and after the crash.
result Investor pessimism increased following the crash, with significant disagreement about future outcomes.
Study shows Chinese stock market returns are predictable over time, especially during market turbulence.
problem Predicting returns in the Chinese stock market is challenging due to market inefficiency.
method Used wild bootstrap automatic variance ratio test and generalized spectral test.
result Return predictability varies over time, with significant predictability during market turmoils.
The study examines network analysis for predicting stock market performance.
problem Understanding lead-lag relationships in the NYSE.
method Network analysis of the NYSE to identify lead-lag effects.
result Network analysis reveals valuable insights for investors and analysts.
Study shows stock market efficiency varies over time and can be networked.
problem Understanding the dynamic and collective aspects of stock market efficiency.
method Defined and calculated time-varying efficiency using permutation entropy of log-returns.
result Major world stock markets can be hierarchically classified into groups with similar efficiency profiles, but these rankings are unstable.
Study examines how economic policy uncertainty impacts stock markets.
problem Dynamic relationship between economic policy uncertainty and stock markets.
method Used symmetric thermal optimal path (TOPS) method.
result Different interaction patterns observed in emerging and developed markets.
We study the temporal evolution of the market efficiency in the stock markets using the complexity, entropy density, standard deviation, autocorrelation function, and probability distribution of the log return for Standard and Poor's 500 (S&P 500), Nikkei stock average index, and Korean composition stock price index (K…
The paper reports the construction of artificial stock market that emerges the similar statistical facts with real data in Indonesian stock market. We use the individual but dominant data, i.e.: PT TELKOM in hourly interval. The artificial stock market shows standard statistical facts, e.g.: volatility clustering, the …
Stock market price fluctuations follow Lévy's stable distribution over long term.
problem Understanding the stability of stock market price fluctuations over different time scales.
method Estimated Lévy's stable parameters from four stock markets over long and short term.
result Stable parameters from different stock markets showed a unique value over long term, but fluctuated with correlation in short term.
Study finds long memory in some emerging Asian stocks but not in developed markets.
problem Evaluating stock market efficiency in emerging vs developed markets.
method Improved wavelet estimator of long range dependence.
result Emerging Asian markets show more long memory in stock returns than developed markets.
Deep learning predicts stock market trends using candlestick charts.
problem Predicting stock market prices with multiple influencing factors.
method Used Deep Convolutional Neural Networks and candlestick charts.
result 92.2% and 92.1% accuracy for Taiwan and Indonesian stock markets.
This paper surveys NLP techniques for predicting stock market movements.
problem Predicting stock market movements with volatility, seasonality, and time dependence.
method Survey of recent literature in NLP and ML for stock market prediction.
result Illustrates recent trends in stock market prediction research.
A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcros…
Model shows stock markets can be inefficiently mispriced.
problem Limits of informationally efficient stock markets.
method Chartist-fundamentalist model with chartists and fundamentalists trading conditions.
result Stock markets can exhibit constant or oscillatory mispricing.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
The paper analyzes the crash of stock and commodity markets during COVID-19 using Topological Data Analysis.
problem Identifying and understanding the dynamics and interdependence of stock and commodity markets during the COVID-19 crash.
method Topological Data Analysis (TDA) and Wasserstein Distance (WD) to identify crashes and compare market dynamics.
result Significant topological differences and interdependence between stock and commodity markets during the crash period.
Study confirms Indian stock market is weak form inefficient.
problem Impact of stock market efficiency on investment returns.
method Runs test, Autocorrelation test, Autoregression test on daily stock indices.
result Indian stock market is weak form inefficient and can be outperformed.
Artificial Neural Networks predict stock returns, finding larger stocks less predictable.
problem Evaluating the validity of the Efficient Market Hypothesis.
method Backpropagation Artificial Neural Network analysis of Brazilian stock market.
result Predictability of stock returns is related to market capitalization, with larger stocks less predictable.
Study uses Kalman-Filter to assess market efficiency in major stock markets.
problem Assessing market efficiency in major stock markets.
method Utilizes Kalman-Filter in two stages, assuming a trendline representing true market value.
result Significant portfolio returns in emerging and developed markets.