Market Crash Research Articles

Entrepreneurial Wealth and Employment: Tracing Out the Effects of a Stock Market Crash

ABSTRACTUsing the dispersion in stock returns during the financial crisis as a source of exogenous variation in the wealth of Norwegian entrepreneurs who held listed stocks, I show that adverse shocks to the wealth of business owners had large effects on their firms' financing, employment, and investment. The effects on investment and employment are driven by young firms, that obtain differentially less bank financing following an owner wealth shock. The effects on employment operate primarily through reduced hiring. My findings highlight that equity‐financing frictions and the procyclicality of entrepreneurial wealth are important channels that can amplify economic shocks.

Volatility Connectedness of Chinese Financial Institutions: Evidence from a Frequency Dynamics Perspective

Accurately measuring systemic financial risk and analyzing its sources are important issues. This study focuses on the frequency dynamics of volatility connectedness in Chinese financial institutions using a spectral representation framework of generalized forecast error variance decomposition with the least absolute shrinkage and selection operator vector autoregression. It assesses the volatility connectedness network using complex network analysis techniques. The data are derived from 31 publicly traded Chinese financial institutions between 4 January 2011 and 31 August 2023, encompassing the Chinese stock market crash in 2015 and the COVID-19 pandemic. The frequency dynamics of the volatility connectedness results indicate that long-term connectedness peaks and cross-sectoral connectedness rises during periods of financial instability, especially in the recent bull market (2014–2015) and the 2015 Chinese stock market crash. The volatility connectedness of Chinese financial institutions declined during the COVID-19 pandemic but rose during the post-COVID-19 pandemic period. Network estimation results show that securities triggered the 2015 bull market, whereas banks were the main risk transmitters during the 2015 market crash. These results have important practical implications for supervisory authorities.

Judge me on my losers: Do robo‐advisors outperform human investors during the COVID‐19 financial market crash?

As financial institutions increasingly offer robo‐advisors (RAs) as a service, the empirical evidence on their performance, particularly during market downturns, remains limited. We study the impact of using RAs on investment performance during the 2020 financial crisis caused by the COVID‐19 global pandemic. We obtain daily portfolio and transaction data of investors on an online investment platform. Besides making investment decisions by themselves, investors can also leverage an RA system offered by the platform. We match RA users with other investors who did not use the RA with similar investor and portfolio characteristics before the market crash, then compare their portfolio returns after the crash. We find that RA users experienced significantly fewer losses during the market downturn, and this performance advantage came from the RA‐managed assets (rather than assets managed by RA users themselves). We further show that the RA system adjusted its portfolios to hold less risky funds, whereas human investors stayed with their status quo and did not reduce the risk of their portfolios, which partly accounted for the performance discrepancy. Importantly, the RA’s superior performance was not simply a continuation of its performance in a normal market; in fact, we find RA users to have similar portfolio returns as non‐RA investors during normal markets, and the benefits of RA's risk‐reduction trading strategy only manifested during the financial crisis. Our work offers an empirical assessment of RAs’ performance under different market conditions and advances the understanding of algorithmic decision making in financial markets.

Climate Risk, Stock Crash Risk, and Greenhouse Gas Emission Trading Scheme: Evidence From Korea

This study examines the relationship between climate risk and stock crash risk and the moderating effect of greenhouse gas emission trading scheme (ETS). Numerous studies have tested stock price crash risk measured by the negative skewness of return distributions in the areas of internal governance mechanism. While interest in environmental performance for sustainable growth of firms has increased, there are no studies on the effect of climate risk on stock crash risk. Thus, we find that climate risk (a firm’s greenhouse gas emissions and energy consumption) increases stock crash risk, and the positive effect of climate risk on stock crash risk is weakened after introduction of the emission trading scheme. This study theoretically extends the literature on the empirical determinants of stock crash risk, and practically provides investors with proven information about whether climate risk is worth considering during investment decisions. Furthermore, at the level of government policy, we can suggest that a monitoring system is needed to mitigate potential information asymmetry problems in firm with high climate risk.

Impact of COVID-19 on the Behaviour of Islamic and Conventional Investors: Evidence from the Indonesia Stock Market Crash 2020

Purpose — The aim of this paper is to investigate the influence of the COVID-19 outbreak on Indonesia’s conventional and Islamic stock markets through the lens of behavioural finance in the digital age. Design/Methodology/Approach — The analysis in this paper is focused on the short-run and long-run impact of variables associated with COVID-19—such as the number of COVID-19 cases and mortality, the Google Search Volume (GSV) for the search query associated with COVID-19, and the panic index related to COVID-19—on the returns of the LQ45 Conventional Index and Jakarta Islamic Index (JII), using the Autoregressive Distributed Lag (ARDL) model. Findings — In the short run, increasing mortality and GSV significantly decreases the returns on LQ45 and JII. By contrast, the returns of LQ45 and JII are unaffected by an increase in the number of cases or the panic index. In the long run, only the panic index affects the LQ45 returns. Originality/Value — This article makes three contributions to the literature. First, it compares the COVID-19 outbreak’s impact on conventional and Islamic stock markets. Second, it discusses the short-run dynamics and long-run impact of the COVID-19 outbreak on stock returns. Third, it provides an explanation of the empirical relationship between the COVID-19 outbreak and the stock market using a behavioural finance viewpoint. Practical Implications — Digital behavioural science-based policies are needed to prevent or lessen financial market crashes during future crises. ErratumArticle classification: Erratum from: ISRA International Journal of Islamic Finance Vol. 15 No. 3 (2023)Publisher: ISRA Research Management Centre, INCEIF University Shiddiqi, F.A. & Susamto, A.A. (2023), ‘Impact of COVID-19 on the Behaviour of Islamic and Conventional Investors: Evidence from the Indonesia Stock Market Crash 2020’, ISRA International Journal of Islamic Finance, Vol. 15 No. 3, pp. 142–159. https://doi.org/10.55188/ijif.v15i3.615It has been brought to our attention that an error in the affiliation of one of the authors has been inadvertently occurred during the publication process. The affiliation of author Faris Azzam Shiddiqi on page 142 should be changed from Graduate School Yogyakarta, Universitas Gadjah Mada, Yogyakarta, Indonesia to instead read: The Graduate School, Universitas Gadjah Mada, Yogyakarta, Indonesia. ISRA Research Management Centre, INCEIF University sincerely apologises to the authors for any inconvenience caused.This change has been effected in the online version of the paper. Citation:(2023), ‘Erratum’, ISRA International Journal of Islamic Finance, Vol. 15 No. 3, https://doi.org/10.55188/ijif.v15i3.615

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

Not all market participants are alike when facing crisis: Evidence from the 2015 Chinese stock market turbulence

We propose a novel framework to analyze the potentially heterogeneous roles played by different market participants in the fire-sale process during a market crash and illustrate the methodology with the 2015–16 Chinese stock market turbulence. Unlike conventional analysis focusing on one particular channel of fire sales, we establish a market-level measure of fire sales based on the decomposition of diffusion processes to quantitatively compare the contribution of various channels in driving stock prices to plummet. Empirical results identify mutual funds as the main fire-sale propagator, as well as the heterogeneities in response to the price crash among different market participants.

Measuring cryptocurrency moment convergence using distance analysis

AbstractThis study measures the convergence and divergence of major cryptocurrencies by applying two distance measures used in machine learning. Particularly, the time-varying Euclidean distance measure was constructed by combining the first four moments (i.e. mean, variance, skewness and kurtosis) of the return distributions of cryptocurrencies following the $$\ell ^{2}$$ ℓ 2 -normalisation. It was found that major cryptocurrencies converged to the centroid during the 2018 market crash, but diverged before and after the crash. Their divergence could be due to the uncertainty arising from market news and regulatory events. In addition, Bitcoin cosine similarity measure was developed to provide further insights into the relationship between Bitcoin and other cryptocurrencies. This cosine similarity shows how each cryptocurrency moves relative to Bitcoin, which is not captured by the Euclidean distance. More importantly, it was demonstrated that the divergence of major cryptocurrencies from their centroids can improve Markowitz’s efficient frontier and provide more diversification benefits to investors and portfolio fund managers. Finally, a profitable trading strategy was provided based on the Euclidean distance.

The great crypto crash in September 2018: why did the cryptocurrency market collapse?

The cryptocurrency crash on the 5th of September, 2018, resulted in price decreases in 95 of the 100 leading digital currencies. We obtained millisecond data of some of the more prominent cryptocurrencies–bitcoin, ethereum, ripple, bitcoin cash and eos–and some of the smaller cryptocurrencies–neo, nem, omg, tezos and lisk–that were most affected in the crash and investigated what caused the digital market to collapse. We find that the behaviour of the more prominent cryptocurrencies and bitcoin, in particular, was the dominant factor behind the crash. We also find that smaller cryptocurrencies followed the behaviour of the larger ones in the crash. Furthermore, our empirical findings show that the trading behaviour of cryptocurrency traders (CTs) did not trigger the digital market crash. We propose the introduction of a single-cryptocurrency circuit breaker most prominent largest cryptocurrency–bitcoin–that will halt trading during market disruptions.

A better start: board diversity matters in assessing stock price crash risk

PurposeThe determinants that contribute to reducing stock price crash risk have garnered attention from scholars and practitioners. However, our understanding of the relationship between board diversity and stock crash risk, as well as the contextual factors that influence this relationship, remains limited. To address this gap, this study aims to investigate how different attributes of board diversity affect stock price crash risk, particularly under conditions of higher performance hazard and ownership concentration.Design/methodology/approachUsing a two-stage least squares fixed-effects estimator, the authors analyze a panel data set of 1,792 firm-year observations across 282 firms listed on the KOSPI200 from 2010 to 2019.FindingsRelation-oriented diversity reduces future stock price crash risk, particularly when firms experience performance shortfalls and have concentrated ownership structures, but task-oriented diversity has no significant effects. The results imply that only relation-oriented diversity strengthens governance mechanisms by curtailing managerial bad news withholding behaviors, and the role of relation-oriented diversity in reducing stock crash risk becomes more crucial when firms have higher performance hazard and concentrated ownership.Originality/valueThis study makes crucial contributions as follows: the authors contribute to the stock crash risk literature by shifting the focus from how to when board diversity matters in assessing stock crash risk; the authors extend the board diversity research and enhance scholarly understanding of the effects of board diversity on corporate governance by highlighting that not all aspects of board diversity improve firm governance mechanisms; and the authors widen the lens from a single attribute to multiple attributes of diversity to reveal the effects of diversity on boards in assessing future crash risk.

Herding behavior and systemic risk in global stock markets

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.

Volatility feedback effect and risk-return tradeoff

Using the two alternative measures of the ex-ante unexpected volatility shock, we show that the volatility feedback effect plays an important role in the intertemporal risk-return tradeoff. The empirical results indicate that the volatility feedback effect reinforces the positive risk-return relation conditional on bad market news but attenuates the relation under good market news. The results provide strong evidence that an extremely heightened risk-return tradeoff caused by a high level of volatility feedback effect might lead to a market crash, even with no macroeconomic uncertainties in the markets. Also, the results show that the asymmetric volatility feedback effect is attributable to the negative correlation between the concurrent volatility and a price change. The state-dependent volatility feedback effect is observed for different market conditions such as high and low market sentiments and business cycles.

Assessing Housing Market Crashes over the Past 150 years

Assessing Housing Market Crashes over the Past 150 years

Short selling, informational efficiency, and extreme stock price adjustment

Using Chinese equity data from April 2009 to August 2020, we contribute to the literature by exploring the impact of short selling on stock prices in three aspects. First, we find that as short sellers become more active, stock price adjustments accelerate, and prices respond more swiftly to public information. Second, we investigate the trading behavior of short sellers during periods of extreme stock price fluctuations and reversals. Our empirical findings suggest that short sellers behave like contrarian traders during these periods and consequently, short sales reduce the stock price crash risk. Last, we find that in emerging markets dominated by individual investors, sentiment seems a key factor for short sellers to moderate market crashes. When the market experiences unexpected sudden plunges, short sellers become “support buyers” and reverse the previously bullish investor-dominated market sentiment, thus correcting mispricing and mitigating irrationality.

Market crash factors and developing an early warning system: Evidence from Asia

Market crashes pose significant risks to the stability and performance of financial markets, making the development of an early warning system crucial. This study utilizes exchange rate volatility and investor sentiment to predict market crashes. While several studies have examined factors affecting market crashes in developing countries. This study aims to develop an early warning system for investors to minimize investment risk using Exchange Rate Volatility and Investor Sentiment. The study focused on seven countries: Indonesia, Malaysia, Singapore, the Philippines, Thailand, Vietnam, and Mongolia. The stock exchanges examined included Jakarta Stock Exchange Composite, FTSE Malaysia KLCI, FTSE Singapore, SET Index, PSEi, HNX/HNXI, and MNE Top 20/MNETOP20. The analysis involved assessing early warning systems to provide valuable supplementary information for decision making and evaluating market vulnerabilities. The logistic regression equation was utilized to model market crashes, incorporating variables such as exchange rate volatility and investor sentiment while considering their interactions as moderating factors. The results indicate that exchange rate volatility and investor sentiment have a significant negative effect on market crashes, with probabilities of 0.0082 and 0.000 Furthermore, investor sentiment acts as a mediator for exchange rate volatility, amplifying its impact on market crashes. This suggests that higher exchange rate volatility and negative investor sentiment increase the likelihood of market crashes. Exchange rate volatility and investor sentiment can serve as early warning indicators, emphasizing the importance of monitoring these factors for market participants and policymakers.

A K-Means Classification and Entropy Pooling Portfolio Strategy for Small and Large Capitalization Cryptocurrencies.

In this study, we propose three portfolio strategies: allocation based on the normality assumption, the skewed-Student t distribution, and the entropy pooling (EP) method for 14 small- and large-capitalization (cap) cryptocurrencies. We categorize our portfolios into three groups: portfolio 1, consisting of three large-cap cryptocurrencies and four small-cap cryptocurrencies from various K-means classification clusters; and portfolios 2 and 3, consisting of seven small-cap and seven large-cap cryptocurrencies, respectively. Then, we investigate the performance of the proposed strategies on these portfolios by performing a backtest during a crypto market crash. Our backtesting covers April 2022 to October 2022, when many cryptocurrencies experienced significant losses. Our results indicate that the wealth progression under the normality assumption exceeds that of the other two strategies, though they all exhibit losses in terms of final wealth. In addition, we found that portfolio 3 is the best-performing portfolio in terms of wealth progression and performance measures, followed by portfolios 1 and 2, respectively. Hence, our results suggest that investors will benefit from investing in a portfolio consisting of large-cap cryptocurrencies. In other words, it may be safer to invest in large-cap cryptocurrencies than in small-cap cryptocurrencies. Moreover, our results indicate that adding large- and small-cap cryptocurrencies to a portfolio could improve the diversification benefit and risk-adjusted returns. Therefore, while cryptocurrencies may offer potentially high returns and diversification benefits in a portfolio, investors should be aware of the risks and carefully consider their investment objectives and risk tolerance before investing in them.

COVID-19 and commodity effects monitoring using financial & machine learning models

This article focuses on examining the effects of the COVID-19 pandemic and gold prices on the stock market. It primarily analyzes the relationship between COVID-19 cases and stock market prices, along with the impact on various commodity elements such as gold, oil, Chinese RMB, and US Dollar prices. These commodity elements are considered essential indicators of a country's financial health, and the study investigates how the increase in COVID-19 cases affects these financial elements. The research incorporates financial models, machine learning algorithms, and a financial Gaussian mixture model for data analysis and comparison. The findings shed light on the correlation between the virus, trading outcomes, and the importance of Karachi Stock Exchange-100 index data in preventing market crashes. The study also explores the implications of emergencies on the finance sector and provides insights for future financial predictions and the impact of social disasters on the economy.

Does stock market rescue affect investment efficiency in the real sector?

During China's stock market crash in 2015, the government purchased the stocks of over 1000 firms. We investigate how this government rescue affects investment efficiency and show that rescued firms experience a significant decrease in investment-q sensitivity. This rescue has an adverse impact on price efficiency and impedes managers from learning information for investment decisions. The learning channel is the main mechanism through which the rescue has a real effect. Our findings indicate that programs intended to stabilize the stock market could adversely affect the real efficiency, providing new insight into the consequences of government purchases.

Impact of Brand Capital on the Stock Price Crash Risk, an Empirical Study

The factors influencing the financial market are rapidly becoming more complex. The impact of non-financial factors on the performance of a company’s common stock can increase in ways that were not previously expected. This study investigated how brand capital affects the risk of stock prices in Iraqi private banks listed on the Iraq Stock Exchange failing by identifying the likelihood of a crash caused by a negative deviation in the distribution of returns on ordinary shares. As a result, the current study’s concept is to review an analytical knowledge framework of the nature of that relationship, its changes, and its impact on the pricing of ordinary shares of the banks of the researched sector for the years 2009 to 2017, as well as by the 21 banks listed during that time and by the 588 observations using the expanded market model to determine quarterly changes in stock prices. In addition to testing the negative coefficient of skewness and the down-to-up volatility models to test the contribution of brand capital in reducing the risk of stock collapse, The test results showed that brand capital is closely related to the significant and adverse risks of a stock crash. Additionally, the first’s impact is inverse, as its content highlights the role that the research sample banks’ brand capital played in lowering the dangers of stock price crashes.

Behavioural Biases in Equity, Futures, and Options Trading: Implications for Market Efficiency

This research paper presents a comprehensive analysis of behavioural biases among traders operating in the equity, futures, and options segments of financial markets. The study aims to understand the psychological factors influencing trading decisions, risk perception, and trading strategies. By exploring the impact of behavioural biases on market efficiency and stability, this research contributes to the growing field of behavioural finance. Using a mixed-method approach, the study collected data through surveys, interviews, and behavioural experiments from a diverse group of traders, including retail investors and institutional participants. The analysis focused on identifying common behavioural biases such as overconfidence, loss aversion, herding behaviour, anchoring, and the disposition effect. The findings reveal that behavioural biases are prevalent among traders in all three segments. Overconfidence leads to excessive trading and underestimation of risks, while loss aversion influences risk aversion and reluctance to exit losing positions. Herding behaviour results in the formation of market bubbles and crashes, and anchoring affects traders' reliance on past information to make decisions. The disposition effect influences traders to hold onto losing positions longer than profitable ones. The study also highlights the impact of behavioural biases on trading strategies and risk management practices. Traders tend to exhibit risk-seeking behaviour during periods of market euphoria and become risk-averse during times of uncertainty and volatility. Moreover, behavioural biases can lead to suboptimal portfolio allocation and performance outcomes. The implications of these findings for market efficiency and stability are significant. Behavioural biases can contribute to market inefficiencies, distorted price patterns, and increased market volatility. As a result, market participants and regulators must address these biases to foster more informed decision-making and improve market outcomes. To mitigate the impact of behavioural biases, the study recommends trader education programs focusing on behavioural finance principles and risk management techniques. Additionally, market regulators should implement measures to promote transparency and discourage excessive speculative trading. In conclusion, this research underscores the importance of understanding behavioural biases in equity, futures, and options trading. By shedding light on the psychological drivers of trading behaviour, this study provides valuable insights for traders, financial institutions, and policymakers to enhance market efficiency and stability in the face of behavioural challenges.