Market Crash Research Articles

Disclosure Quality and Risk of Share Price Collapse

In recent years, the stock crash has become a serious financial crisis, which can seriously undermine the personal gains of investors and the stability of society, as well as bring about long-term negative impacts on society. Therefore, it is necessary to strengthen the regulation in order to prevent the emergence of such crisis. Through empirical analysis, we can see that when companies strengthen internal control and improve information disclosure, it will help to effectively reduce the possibility of future stock crash. Through this study, we can see that the corporate internal control disclosure system implemented in China in recent years has been able to help companies avoid the crisis of stock plunge as well as provide strong support for the sustainable growth of China's capital market. This study adopts a state-of-the-art methodology, utilizing a number of data about internal information control of listed companies and its related data, as well as related indicators about stock crashes, thus greatly expanding out the development trend in recent years. This provides theoretical references on how companies can avoid stock price crashes and whether the level of internal control disclosure needs to change.

Relationship between Real Earning Management, Corporate Governance and Stock Crash Risk (Dynamic Panel Data)

Relationship between Real Earning Management, Corporate Governance and Stock Crash Risk (Dynamic Panel Data)

Dynamics of Herding Behavior in Cryptocurrency Markets Amid Market Crashes

Dynamics of Herding Behavior in Cryptocurrency Markets Amid Market Crashes

Returns behavior of ESG ETFs in the COVID‐19 market crash: Are green funds more resilient?

AbstractThis study explores the importance of environmental, social, and governance (ESG) performance in explaining the returns and volatility of returns of exchange‐traded funds (ETFs) during the market crash induced by the COVID‐19 pandemic. The study uses an unbalanced panel of monthly data during the period 2005–2021 for a diversified sample of 160 iShares Morgan Stanley Capital International conventional and ESG/socially responsible investment (SRI) equity funds. To obtain robust results, a variety of analyses are conducted, including multiple panel regressions based on one‐way clustering, standard error estimates obtained from different covariance matrix estimators that are heteroskedasticity and autocorrelation consistent, simultaneous quantile and inter‐quantile regressions, weighted least square regressions with alternative weights for the regression coefficients, and tests of coefficient equality with bootstrapped standard error of difference in subsample regressions. All the sensitivity analyses give similar results, confirming their validity. The results provide strong evidence for the resilience of sustainable funds during the crash, with ETF returns showing persistence during the market downturn. This study suggests that ESG/SRI investments may help ETFs to outperform their counterparts, indicates that sustainability investments matter in times of crisis, and implies the adoption of strategies that promote sustainable investment development.

Predicting stock market crashes on the African stock markets: evidence from log-periodic power law model

PurposeThis study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.Design/methodology/approachOver the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.FindingsBy minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.Practical implicationsThe adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.Originality/valueThis study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

The economic effects of perceptions of the Russia-Ukraine war in Ecuador

Background Using an online questionnaire capturing the immediate economic and social effects of the Russia-Ukraine war. The study assesses the topics of more profound concern for university students and the variation of economic attitudes related to their socio-demographic variables. Methods Three hundred eighty-five participants, between 18 and 22 years of age, 49% female, leads us to identify significant differences by sex and economic status related to the stock crash, inflation, corruption, and poverty perceptions. However, the effect size and sampling could be improved. Results Kruskal-Wallis test confirms that the below-average economic status group feels more worried about higher inflation, while females tend to be more concerned about inflation, corruption, and poverty because of the conflict. Ordered logistic regression reveals that participants who express higher levels of concern regarding the impact of increased energy prices and poverty tend to exhibit greater overall worry. Conclusions Even though convenience sampling imposes constraints to extrapolate the results broadly, the research constitutes a benchmark for similar studies among Latin American and Caribbean countries since economic expectations and economic knowledge from citizens, applied in their decisions, play an essential role in national development.

Is This Time Different? Comparative Analysis of Supply Shocks in the 1970s and 2020s

Based on the supply shocks in the 1970s and 2020s, this study discusses the similarities and differences between the two periods qualitatively. While both periods experienced price increases due to reliance on imported oil and stock market crashes, there are distinct factors that make a repeat of stagflation unlikely. The current labor market remains strong, and inflation is showing signs of slowing down. In addition, the bargaining power is weaker now than in the 1970s; higher productivity resulting from innovative technology and the trend of a more integrated global economy, which help to mitigate the impact of inflation. Policy solutions are proposed to address the challenges of easing supply pressure, promoting industrial development, and ensuring employment. The three key policy suggestions are as follows: energy transition incentives, research and development grants, skills development initiatives. By adopting these policies, it is believed that governments will address supply pressure, foster industrial development and promote employment.

How ESG Disclosure Affects the Stock Price Crash Risk

This study delves into the relationship between ESG (environmental, social, and governance) factors and the possibility of a stock market collapse. While ESG factors have become a pivotal consideration in assessing a company's sustainable and ethical business practices, the dynamics between ESG scores and the stock market collapse risk remain underexplored. Drawing upon a comprehensive dataset from 2009-2020, our research offers an enriched understanding of these factors' interplay. Initial findings suggest a correlation between higher ESG scores and reduced stock price collapse risk. The paper contributes to the broader discourse in three keyways. Firstly, it expands the understanding of ESG's economic consequences, emphasizing its potential protective role against significant stock price downturns. Secondly, by incorporating the perspective of sustainable business practices, the research contributes to the existing literature on the factors that determine of stock price collapse risk. Finally, it underscores the practical implications of promoting ESG in mitigating financial risks, informing both corporate strategies and regulatory frameworks.

Analysing implied volatility smirk to predict the US stock market crash during the global financial crisis

This study analyses the presence of implied volatility smirk (IVS) and its predictability of the US stock market crash during the Global Financial Crisis (GFC) through the in-sample and out-of-sample tests. The in-sample investigation was conducted for 18 cases (cases 1–18) to confirm the development of IVS during GFC (2007–2009). It also examined another 18 cases (cases 19–36) to ensure the absence of the IVS in the post-GFC period (2010–2011). Finally, an out-of-sample test analysed 63 cases (cases 37–99) to assess the predictability of IVS for the stock market crash during the GFC. The study reveals several critical findings. The in-sample test results show the negative return from simultaneous trading of out-of-the-money put options (OTMP) and at-the-money call options (ATMC) due to out-of-the-money put options implied volatility (OTMPIV) is higher than at-the-money call options implied volatility (ATMCIV) for all 18 cases (cases 1–18), confirming the development of IVS during GFC. However, findings of the in-sample test for cases 19–36 reveal the positive return from simultaneous trading of OTMP and ATMC because of the lower value of OTMPIV compared to ATMCIV, ensuring no occurrence of IVS in the post-GFC period. Finally, only 13 out of 63 cases (from cases 37–99) under the out-of-sample test show the IVS can forecast negative returns in an abnormal stock market. Further, the predictability of IVS for the US stock market crash depends on the maturity of options, forecast horizon, and options trading period

Professional financial advice and investor behavior during the COVID‐19 pandemic

AbstractEmployees are commonly defaulted into either a homogeneous passive investment such as a target‐date fund, or into a managed account that offers personalization and access to an advisor. Higher performance among investors in target‐date funds suggests a possible behavioral benefit of a passive default that requires no active monitoring by investors. Access to a human advisor and an asset allocation matched to employee risk preferences can also help investors avoid trading after a market decline. This paper explores the relationship between trading activity and advice‐seeking behavior among 5 million US DC participants during the COVID‐19 pandemic, comparing self‐directors and target‐date fund investors to those using managed accounts service. We find participants who use target‐date funds are less likely to phone during a market crash. Participants in managed accounts are the least likely to trade after seeking advice. These findings are consistent with the hypothesis that passive default can improve outcomes where active choice can harm outcomes, and personalized defaults with access to advice can help participants avoid investment mistakes.

Investigation of herding behavior using machine learning models

PurposeThe study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.Design/methodology/approachFor empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.FindingsThe results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.Originality/valueIn the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

News and Markets in the Time of COVID-19

Abstract The onset of COVID-19 was characterized by voluminous, negative news. Higher narrativity news topics (measured by textual proximity to articles describing the 1987 stock market crash and textual distance from Federal Reserve communications) were systematically associated with contemporaneous market responses, which were larger on high volatility days (hypersensitivity), and with markets–news feedback. Hypersensitive news topic-market pairs were associated with next-day reversals. A test using the news–markets relationship identifies a mid-March 2020 structural break, which was knowable by the end of April. Post break, markets and news became considerably less coupled, and hypersensitivity and reversals abated.

Target Date Fund Flows Amidst the COVID-19 Pandemic

The popularity of target date funds (TDFs) has recently increased among defined contribution pension plans. Despite their growing importance, how TDF flows and flow-performance relationships have changed in response to the COVID-19 pandemic is yet to be examined, particularly during the March 2020 market turmoil, when bond funds witnessed unprecedented outflows. We find that short-horizon TDFs experienced sizable outflows of over $13.5 billion between February and April 2020, whereas mid- and long-horizon TDFs received inflows over the same period. Given that short-horizon TDFs hold a much larger fraction of their portfolios on fixed-income securities, the outflows from these TDFs, whose fund-of-funds investment structure has been identified as a source of intra-family liquidity provision in previous studies, may have compromised the effectiveness of this liquidity provision mechanism during the COVID-19 market crash. We find that in the aftermath of the pandemic, the flow–performance relationship of short-horizon TDFs becomes markedly more concave, which hints at investors’ greater awareness of the downside risks associated with large outflows following their experience during the COVID-19 turmoil.

Social trust and risky financial market participation: Evidence from China

ABSTRACT The Chinese government has promoted the development of risky financial markets under the market-oriented reform period. Meanwhile, social trust has changed with institutional transitions. This study investigates the influence of social trust on risky financial market participation in China based on the national longitudinal data of 2014–2018. It also estimates the effects of social trust by education, age, urban/rural hukou resident groups, and periods. The empirical results indicate that social trust positively affects the probability of holding risky financial assets and their shares. However, these effects turn insignificant when accounting for unobservable individual heterogeneity. The positive effect of social trust is more pronounced among the younger generation, highly educated groups, women, and urban hukou residents compared to their counterparts. Additionally, the positive impact of social trust is observed to be stronger in the period following the 2015 stock market crash shock than in the period before 2015.

Using threshold regression to analyze the impact of external earnings pressure on stock price crash risk based on management tone in China

ABSTRACT Each stock market crash, from the 2015 China crash to the 2020 new coronavirus crash, has left investors terrified and hit China’s economy hard. Companies that fail to meet financial analysts’ surplus forecasts can trigger a negative market reaction. This reaction directly affects management and investor decisions and increases the risk of corporate share price collapse. Corporate management will therefore attempt to restrain excessive investor expectations by releasing a negative management tone designed to induce investors to invest scientifically and rationally, thereby reducing the risk of share price collapse. This study empirically examines the impact of external earnings pressure on stock price crash risk. It determines whether management tone has a threshold effect using, data on A-share listed companies in Shenzhen and Shanghai, China, covering 2014 to 2020. The results show that external earnings pressure significantly increases the risk of the stock price collapse; management tone has a single threshold effect on external earnings pressure and stock price collapse risk; a significantly negative correlation is observed between external earnings pressure and stock price collapse risk when management tone is below the threshold. This study provides sound advice for analysts, management and investors to better prevent and control risk.

Looking into the Market Behaviors through the Lens of Correlations and Eigenvalues: An Investigation on the Chinese and US Markets Using RMT.

This research systematically analyzes the behaviors of correlations among stock prices and the eigenvalues for correlation matrices by utilizing random matrix theory (RMT) for Chinese and US stock markets. Results suggest that most eigenvalues of both markets fall within the predicted distribution intervals by RMT, whereas some larger eigenvalues fall beyond the noises and carry market information. The largest eigenvalue represents the market and is a good indicator for averaged correlations. Further, the average largest eigenvalue shows similar movement with the index for both markets. The analysis demonstrates the fraction of eigenvalues falling beyond the predicted interval, pinpointing major market switching points. It has identified that the average of eigenvector components corresponds to the largest eigenvalue switch with the market itself. The investigation on the second largest eigenvalue and its eigenvector suggests that the Chinese market is dominated by four industries whereas the US market contains three leading industries. The study later investigates how it changes before and after a market crash, revealing that the two markets behave differently, and a major market structure change is observed in the Chinese market but not in the US market. The results shed new light on mining hidden information from stock market data.

Market inefficiency spillover network across different regimes

This study examines risk transmission among 34 stock markets from the perspective of market inefficiency spillover effects. We suggest the use of the hidden Markov model along with the multifractal detrended fluctuation analysis to measure the degree of market efficiency for both bull and bear regimes. Diebold–Yilmaz spillover indices are used to document the asymmetric characteristics of market inefficiency spillovers in bull and bear regimes. When the market crashes or bubbles and the external economic and financial environment worsens, inefficiency spillovers increase significantly.

Risk-return tradeoff and serial correlation in the Chinese stock market: A bailout-driven crash feedback hypothesis

This paper explores two puzzling phenomena about aggregate stock market behavior: the weak mean–variance (risk-return) relation and return autocorrelation. Existing literature typically analyzes these two phenomena in isolation, with few attempts to understand their coexistence. To bridge this gap, we integrate two key factors, market-wide bailout guarantees and feedback trading, within the context of the Chinese stock market. We develop joint pricing formulas to theoretically show that feedback traders’ speculation on bailouts can simultaneously produce these anomalies by tying them to features typical of market crashes. Our empirical analysis, based on Chinese market data from 1997 to 2020 and a market crash index, provides consistent support for these theoretical derivations. The results highlight the significant role of speculation driven by bailout expectations in influencing aggregate stock market dynamics. Accordingly, this also illuminates the necessity for further research on the extensive pricing implications of both government intervention and speculative trading.

An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

Analisis Faktor-Faktor yang Berpengaruh terhadap Sikap Pengguna Aset Digital Non-Fungible Token Berbasis Blockchain pada Komunitas NFT Indonesia

Blockchain is an inspiring new technology that requires a lot of attention from various researchers and companies. This technology offers various benefits, where the newest derivative of this technology is Non-Fungible Tokens (NFT). In NFT, various digital assets such as images, videos, music or virtual creations are traded. The relatively new NFT creates a lack of user knowledge to be able to utilize this technology as a digital asset for trading. On the one hand, users welcome the use of NFTs as a tool to gain financial gain, but on the other hand, there are users who doubt the long-term success of NFTs as a form of investment and see them as a temporary trend that may be detrimental in the event of a market crash. The purpose of this research is to explore various factors that can impact the attitudes of NFT users. The research used a quantitative method which was carried out at the Indonesian NFT Community, involving 377 respondents as a sample. Questionnaires were distributed online using the Google form on the Telegram platform. The data analysis used in this research is by using Structural Equation Modeling (SEM). The results of the study show that there are five factors that significantly influence the attitude of NFT users. Perceived ease of use, perceived usefulness, perceived trust, and personal innovativeness have a positive and significant effect on user attitudes, while perceived risk has a negative effect and is one of the factors that has the most impact on user attitudes than other factors, followed by perceived trust in the second position as the most dominant variable. So like any new technology, NFTs have risks and trust issues that most users consider.