Market Crash Research Articles

A study of the impact of short selling mechanism on the stock price collapse risk

Drawing from an extensive review of existing literature, this study investigates the influence of the short selling mechanism (SSM) on x through both theoretical and empirical lenses. It further assesses whether this mechanism can effectively mitigate stock price collapse risk (SPCR). The findings reveal that the SSM has a significant moderating effect on stock price declines, with stability tests for alternative factors affirming the robustness of these results. The study then narrows its focus to listed companies in China, examining the role of the SSM in this specific context. Results indicate that the SSM enhances the information content of stocks, thereby reducing the risk of stock market crashes. Using regression analysis and a mediating role model, the paper empirically explores the relationship between the SSM and SPCR. The conclusions suggest that the SSM can partially reduce the risk of stock market crashes by increasing the information content of stock prices.

Relationship Between IPO and Company Development Based on the Case of Poten Enviro

Abstract: The stock market crash in early 2024 in China has sparked criticism from investors regarding the current Chinese IPO system. There has been extensive debate on whether IPOs should serve as a venue for financing for companies facing internal and external development difficulties and questionable profitability, and whether the interests of shareholders should receive better protection. To delve deeper into the issues surrounding the Chinese IPO system, a listed companyPoten Envirohas been selected, and a comprehensive review has been conducted on its development history, internal and external challenges, IPO process, financial irregularities, and other related behaviors. The company should proper analyze the external environmental changes, and the IPO should not be pursued blindly. Also, the regulation institution should strengthen supervision of listed companies to avoid losses for the public. This study will be beneficial in providing insights for the reform of the Chinese stock market and offering guidance for strategic decision-making by enterprises.

Idiosyncratic risk and stock price crash risk: The moderating role of discretionary income smoothing

Given the growing significance of the capital market, investors tend to steer clear of stock price crashes. This study aims to examine how idiosyncratic risk affects the likelihood of a stock price crash and how discretionary income smoothing affects the relationship between them. This study uses a data panel to empirically examine the hypothesis. This study uses a data panel to empirically examine the hypothesis, using 1,203 firm-year observations from non-financial companies publicly traded on the Indonesia Stock Exchange from 2019 to 2021. The results show that firms with greater idiosyncratic risk do not significantly generate higher stock price crash risk. Nevertheless, this study also discovered that managing discretionary income smoothing is essential to increasing the risk of crashes. The test shows that the coefficient of discretionary income smoothing is 0.153 and significant with a t-value of 2.104. Moreover, the investigations also indicate that greater use of discretionary income smoothing can amplify the impact of idiosyncratic risk on the likelihood of stock price crashes. This is shown from the results where the moderation of the two variables has a positive coefficient of 0.087 and is significant at 10% with a t-value of 1.446. Based on the findings, this study concludes that the presence of idiosyncratic risk by itself may not substantially impact the probability of stock market crashes. However, combined with discretionary income smoothing, it can worsen the potential negative consequences. It implies that how a firm reports its income can affect its susceptibility to stock price crashes.

Functional Hypergraphs of Stock Markets.

In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved around the assumption that correlations are mediated in a pairwise manner, whereas, in a system as intricate as this, the interactions need not be limited to pairwise only. Here, we introduce a general methodology using information-theoretic tools to construct a higher-order representation of the stock market data, which we call functional hypergraphs. This framework enables us to examine stock market events by analyzing the following functional hypergraph quantities: Forman-Ricci curvature, von Neumann entropy, and eigenvector centrality. We compare the corresponding quantities of networks and hypergraphs to analyze the evolution of both structures and observe features like robustness towards events like crashes during the course of a time period.

Media pressure, corporate social responsibility, and the risk of share price crash

The investigation employs a compilation of data from 5085 listed entities over the period from 2013 to 2022 to explore the dynamics between media coverage, corporate social responsibility initiatives (CSR), and the risk of stock market crashes. The results suggest that greater media focus can elevate the chances of stock value plummeting, whereas robust CSR dedication tends to mitigate such risks. The extent of overall debt funding plays a significant role as an intermediary in the connection between CSR efforts and the threat of equity market meltdowns. Moreover, the influence of the audit services provided by the Big Four firms greatly alters the correlation between CSR endeavors and the probability of market downturns. Additionally, the influence of CSR on the risk of crashes varies based on the profitability of the company. The study proposes that enhancing CSR execution, maintaining moderate levels of financial leverage, and optimizing approaches to media relations can all contribute to reducing the likelihood of stock market crashes.

Near or far? An investigation into the relationship between spatial proximity to CSRC district offices and stock crash risk

ABSTRACT Our analysis reveals that companies with headquarters in further proximity to China Securities Regulatory Commission (CSRC) district offices are more susceptible to stock crash risk. Furthermore, this relationship is notably stronger for firms whose CEOs lack political connections, are non-state-owned, and are not audited by the Big Four audit firms. Our findings imply that regulatory remoteness plays a crucial role in shaping stock market dynamics and risk.

Leveraging corporate governance characteristics for stock crash risk assessment

Causing considerable losses for individual investors, stock crashes are also potential triggers for wider financial crises. In contrast to existing research that primarily focuses on specific corporate governance characteristics in relation to stock crash risk, in this study, we construct a set of corporate governance attributes and employ machine learning techniques for a comprehensive assessment of stock crash risk. Through empirical analysis in the Chinese stock market, we find that our corporate governance attributes are effective indicators of stock crash risk, yielding statistically significant and economically meaningful crash risk assessment. Additionally, our heterogeneity tests suggest that firms with less concentrated shareholding result in superior assessment accuracy. This observation is consistent with prior findings that increased retail investor participation enhances corporate governance transparency, thereby contributing to better assessment performance. Industry-wise, the performance of crash risk assessment for real estate companies is higher than that for cultural, sports, and entertainment sectors. We speculate that these variations stem from industry-specific factors, such as business logic, operational models, and profit mechanisms. Our results also suggest that characteristics related to shareholder meetings are pivotal in assessing crash risks for manufacturing companies, while board-related features are essential for real estate firms. Overall, this study contributes to current literature by validating the feasibility of employing corporate governance characteristics to assess crash risk and by exploring the economic value of crash risk assessment. Furthermore, we discuss several valuable insights into the Chinese stock market through heterogeneity analysis.

Systemic Contagion

This article unifies existing systemic risk measures to present a novel intersecting notion of systemic contagion which captures institutional risk transmission amplified by a distressed financial system. The article constructs a test statistic for systemic contagion which demonstrates its systemic capacity in testing contagion under a series of numerical experiments. Using the new test the article constructs a forward-looking directed network to empirically measure the scale of systemic contagion between leading U.S. financial institutions. The proposed systemic contagion measure stands out as it provides timely warning signals of market crashes including that driven by the recent health-induced financial crisis.

Hedge funds network and stock price crash risk

Utilizing a dataset from 2013 to 2022 on China’s listed companies, we explored whether a hedge fund network could help explain the occurrence of Chinese stock crash. First, this study constructs a hedge fund network based on common holdings. Then, from the perspective of network centrality, we explore the impact of hedge fund network on stock crash risk and its mechanisms. Our findings suggest that companies with greater network centrality experience lower stock crash risk. Such results remain valid after alternating measures, using the propensity score matching method, and excluding other network effects. We further document that the centrality of hedge fund network reduces crash risk through two channels: information asymmetry and governance monitoring. In addition, the negative impact of hedge fund network centrality on stock crash risk is more pronounced for non-SOEs firms. In summary, our research shed light on the important role of hedge fund information network in curbing stock crash.

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (ρD), average path length (l̄), and average clustering coefficient (cc¯) are estimated from these networks. We find that both ρD and cc¯ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ρD and cc¯ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get l̄ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ρD, cc¯, and l̄ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

The end of ESG? Return spillover between ESG and non-ESG portfolios

We investigate the return spillover between firms with environmental, social and governance (ESG) and non-ESG disclosure. Using a sample of Chinese firms from 2010 to 2022, we find that the ESG portfolio return has a stronger spillover effect on the non-ESG portfolio return. Moreover, the spillover effect between the two portfolios has decreased significantly in recent years, indicating that investors recognise ESG's unique advantage. Important global events, such as stock market crashes, the Sino–US trade wars and epidemics, increase ESG spillover into non-ESG portfolios.

Exploring the Dynamic Behavior of Crude Oil Prices in Times of Crisis: Quantifying the Aftershock Sequence of the COVID-19 Pandemic

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law’s exponent value increases with the threshold value’s magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives.

Herding and investor sentiment after the cryptocurrency crash: evidence from Twitter and natural language processing

Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, “wagmi” (We’re all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts’ tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community (“wagmi”). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts.

MRRFGNN: Multi-relation reconstruction and fusion graph neural network for stock crash prediction

Stock crash risk often propagates through various interconnected relationships between firms, amplifying its impact across financial markets. Few studies predicted the crash risk of one firm in terms of its relevant firms. A common strategy is to adopt graph neural networks (GNNs) with some predefined firm relations. However, many relations remain undetected or evolve over time. Restricting to several predefined relations inevitably makes noise and thus misleads stock crash predictions. In addition, these relationships are not independent during the process of propagating information and interacting with each other. This study proposes the multi-relation reconstruction and fusion graph neural network (MRRFGNN) to predict stock crash risk by capturing complex relations among listed companies. First, the model employs self-supervised learning and contrastive learning to reconstruct and infer implicit relationships between companies. Second, the model incorporates a relation self-attention mechanism to integrate various types of relationships, enabling a more nuanced understanding of the multiple spillover effects. Empirical evidence from a series of experiments demonstrates the superiority of the proposed method, which achieves the best performance with improvements of at least 2.14% in area under the curve (AUC) and 2.64% in Matthews correlation coefficient (MCC), highlighting its potential for practical application in financial markets.

CEO GENDER AND FIRM PERFORMANCE: EVIDENCE FROM THE COVID-19 PANDEMIC

The COVID 19 pandemic precipitated an unprecedented deceleration of economic activities and a stock market crash. The unparalleled shock and the altered risk attitudes present a distinctive opportunity to examine whether the well-established concept of the "glass ceiling" is indicative of latent gender differentials in company performance. Utilizing US financial data, the study employs a range of methodologies to examine whether firms led by female CEOs exhibited the same performance as firms led by male CEOs during 2020-2021. Our empirical results confirm previous findings from the finance literature, as we neither find a systematic difference in returns to holding stock in female-led firms, nor a difference in accounting returns between female-led and male-headed firms.

Predicting stock market crashes with machine learning: A review and methodological proposal

This review paper examines the utilisation of machine learning techniques for predicting stock market crashes. It surveys existing methodologies, identifies common trends, and analyses strengths and weaknesses. A novel methodological framework is proposed, integrating ensemble learning, alternative data sources, and model interpretability to address limitations in current approaches. The proposed framework aims to enhance predictive accuracy, transparency, and actionable insights in financial forecasting. Future research directions include empirical validation, interdisciplinary collaboration, and the integration of emerging technologies. Continued research in leveraging machine learning for financial forecasting is vital for advancing risk management practices and fostering resilient financial systems.

Predicting stock market crashes in MENA regions: study based on the irrationality of investor behavior and the NARX model

PurposeThis study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs (NARX) model with two measures of investor sentiment: the ARMS indicator and Google Trends' search volume of positive and negative words.Design/methodology/approachEmploying a novel approach, this study utilizes the NARX model with ten neurons in the hidden layer and the Levenberg–Marquardt training algorithm. It evaluates model performance through learning, validation and test errors, as well as correlation analysis between predicted and actual crises.FindingsThe NARX model, incorporating investor sentiment, has proven to be a reliable tool for forecasting crises, helping market participants understand data complexity and avoid crisis consequences. The divergence in how investors interpret market news, with some focusing solely on negative developments and others valuing positive outcomes, highlights the predictive nature of the optimistic and pessimistic sentiments captured by the model.Research limitations/implicationsThis study advocates for integrating behavioral approaches into stock market crisis prediction, highlighting the significance of investor sentiment and deep learning. It advances crisis mechanism understanding and opens avenues in behavioral finance. Integration of these findings into finance and economics education could enhance students' risk understanding and mitigation strategies.Practical implicationsThe adoption of NARX models, incorporating investor sentiment, empowers market participants to proactively manage crises, adjust strategies, enhance asset protection and make informed decisions. These models enable them to minimize losses, maximize returns and diversify portfolios effectively in response to market fluctuations. These insights also guide policymakers such as governments, regulatory institutions and financial organizations in formulating crisis prevention and mitigation policies, bolstering economic and financial stability.Social implicationsThis research reduces economic uncertainty, safeguards individuals' savings and investments and promotes a stable financial climate.Originality/valueThis study is one of the first attempts to demonstrate the detection and prediction of stock market crises, specifically in the MENA stock market, using the NARX model. It offers a robust forecasting model using machine learning and investor sentiment, providing decision-making support for investment strategies and policy development aimed at enhancing financial and economic stability.

A Markov‐switching spatio‐temporal ARCH model

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

COVID‑19 and the Stock Market Crash: Evidence from Indonesia

COVID‑19 and the Stock Market Crash: Evidence from Indonesia

Retail traders and co-movement: Evidence from Robinhood trading activity

We find evidence that retail trading activity by Robinhood traders is associated with lower levels of return co-movement and liquidity co-movement. We demonstrate the significance of these relationships primarily employing a two-way fixed effects panel data regression model covering cross-sectional and time-series observations of US stock trades from May 2018 to July 2020. The findings hold true across various cross-sectional subsamples and across subperiods before and after March 2020, coinciding with the stock market crash induced by heightened risks of the COVID-19 pandemic. With regard to returns, our analysis identifies three co-movement channels: 1) increased correlated trading by retail traders, 2) extended delay in incorporating market-wide information, and 3) a direct effect influenced by the integration of firm-specific information into prices. In contrast, for liquidity co-movement, we fail to identify evidence of plausible channels relating to either volatility or correlated trading. However, it appears that the significant direct effects that we document stem from a broadened array of trading strategies when retail traders contribute to the market. Collectively, these findings have important implications as return co-movement limits investors' ability to diversify investments and manage risks, while liquidity co-movement heightens market vulnerability to crashes.