Market Crash Research Articles

Monitoring anomaly for time series of volatility index(VIX)

This study investigates a monitoring procedure that aims to capture a change of model parameters in time series regarding the volatility index (VIX). To solve this problem, we harness a cumulative sum (CUSUM) process using residuals designed to simultaneously detect changes in both the conditional mean and variance of the time series. To obtain the control limits, limit theorems are used for the CUSUM monitoring process. Thereafter, the scheme of statistical process control (SPC) is applied to the time series of the log-returns of VIX by fitting it with an ARMA-GARCH model to obtain further knowledge regarding the financial market’s participants. Our study indicates the proposed monitoring process’ potential as a tool to allude to imminent significant market crashes.

COVID-19 Pandemic and the Performance of Listed Securities: Evidence from the Nairobi Securities Exchange in Kenya

The Novel Covid-19 disease led to panic that resulted to volatility of stock prices across the securities exchange in the world. This saw foreign investors liquidating their positions for fear of a market crash in Nairobi Securities exchange (NSE). The specific objective of this research was to find out whether Covid-19 pandemic had an effect on the performance of listed securities at the NSE in Kenya. Daily data for 5 months, 154 days since the period 13th March 2020 to 14 August 2020 was utilized. The study applied linear regression analysis model to find out the effect of Covid-19 daily cases on the performance of listed securities at the NSE in Kenya. The study findings revealed that Covid-19 daily cases and weather changes had a negative and statistically significant effect on the performance of listed securities at the NSE. The results inferred that Covid-19 daily cases affected the performance of listed stocks at the NSE since the announcement of the first Coronavirus cases in Kenya. The findings from this study are useful for policy implications as it provides guidance to policy makers to institute applicable policy reactions in line with the course of current coronavirus pandemic or during future pandemics of similar nature.

Hedge and safe-haven properties of FAANA against gold, US Treasury, bitcoin, and US Dollar/CHF during the pandemic period

The sudden market crash around 20 February 2020 on the dawn of the COVID-19 pandemic has accelerated the digitalization of all human communication and revived the interest for risk mitigation during stress periods. Interestingly, FAANA (Facebook, Apple, Amazon, Netflix, and Alphabet) stocks exhibited positive returns with remarkable resilience throughout the pandemic period, suggesting a change in their investing risk. In this paper, we take a different step from the existing literature and examine the hedging, diversifying, and safe haven properties of FAANA stocks against four alternative assets, namely gold, U.S. Treasury bonds, Bitcoin, and U.S. Dollar/CHF. Our analysis covers an extended sample period comprising the heightened uncertainty during the recent pandemic period. It involves conditional correlations, optimal weights, hedge ratios, and hedging effectiveness for the pairs of FAANA stock and alternative asset during the full sample period and the COVID-19 pandemic period. The results show that the majority of FAANA stocks serve as weak/strong safe havens against gold, Treasury bonds, Bitcoin, and Dollar/CHF in the full sample period. Further, few FAANA stocks serve as strong safe havens against the U.S. Treasury and Dollar/CHF during the pandemic. Our findings suggest that FAANA, once thought as risky high growth tech stocks, have gained maturity and became a safe blanket during the latest turbulent period.

Do listed companies fulfill their public commitments? Evidence from the stake-raising commitments of Chinese companies

This study investigates the motivations of listed companies to make public commitments and examines if they do fulfill them. A suitable research scenario exists for this in China. During the stock market crash of 2015 in China, the China Securities Regulatory Commission (CSRC) encouraged major shareholders and executives of listed companies to increase their shareholdings to stabilize stock prices. Since then, many listed companies have officially released stake-raising commitments. This study examines the motivations behind these promises and whether they are fulfilled. I examine the stake-raising plans issued by China's A-share listed companies from July 2015 to June 2019, and find that the main motivations for these commitments are self-interest and external pressure (including political and peer pressure), rather than the signaling of undervaluation. Subsequently, the motivations of value signaling and peer effect significantly increase the possibility of commitments being fulfilled, whereas self-interest decreases this possibility. Further research shows that the strength of commitment plays a mediating role between motivation and fulfillment. Moreover, a stronger commitment boosts the stock price more in the short term. If the commitment is fulfilled on time, the short- and long-term market performance of the company's stock improves, and the stock price crash risk decreases.

Government intervention model based on behavioral heterogeneity for China’s stock market

Active government intervention is a striking characteristic of the Chinese stock market. This study develops a behavioral heterogeneous agent model (HAM) comprising fundamentalists, chartists, and stabilizers to investigate investors’ dynamic switching mechanisms under government intervention. The model introduces a new player, the stabilizer, into the HAM as a proxy for the government. We use the model to examine government programs during the 2015 China stock market crash and find that it can replicate the dynamics of investor sentiment and asset prices. In addition, our analysis of two simulations, specifically the data-generating processes and shock response analysis, further corroborates the key conclusion that our intervention model not only maintains market stability but also promotes the return of risk asset prices to their fundamental values. The study concludes that government interventions guided by the new HAM can alleviate the dilemma between reducing price volatility and improving price efficiency in future intervention programs.

The evolution of foreign exchange market: A network view

By analyzing the foreign exchange market data of various currencies, we investigate the topology of correlation networks among 45 major currencies using the minimal spanning tree (MST) and the planar maximally filtered graph (PMFG). Besides the geographical location, our paper uses interest rates as another labeling criterion to analyze the currency network. Some conclusions are given: the mean values of the correlation coefficients dropped dramatically in economic crisis, such as September 2008 and March 2020. Also, the overall correlation between currencies has gradually weakened in recent years; When interest rate is used as the labeling criteria, the currencies of countries with low interest rates are mostly surrounded by the EUR; The PMFG method contains more links that are filtered in the MST and displays different results on degree centrality analysis, providing complementary information for the MST result; the Commonwealth cluster is also found. Our findings can help to discover the economic cycles and market crashes by studying topological and statistical properties of the currency network.

Overconfidence bias in the Indian stock market in diverse market situations: an empirical study

Market situations affect investors’ sentiments. Sudden and sharp changes in the market may make investors adjust their beliefs and change their viewpoints. To quote, such sharp changes include the stock market crash of 2008 and the COVID-19 pandemic, which significantly impacted investors' emotions. In this direction, this study attempts to find investors’ overconfidence bias in different market situations, i.e., precrash period (2006–2008), crash period (2008–2010), and post-crash period (2010–2015, 2015–2020, 2020–2021). We have obtained secondary data for this study from the BSE 100 index (as a proxy of the Indian stock market). Econometric techniques used for data analysis include Vector Auto Regression (VAR), Granger causality VAR/Block Exogeneity Wald Test, and Impulse Response Function (IRF). The study finds that the investors were overconfident during pre-crash periods, i.e., before the global market crash of 2008 and the duration between 2015 and March 2020. In postcrash periods, i.e., during 2008–2010, 2010–2015, and 2020–2021, investors were not overconfident.

Hedging demand and near-zero swap spreads: Evidence from the Chinese interest rate swap market

Interest rate swap plays an essential role in the Chinese fixed-income market as a mainly hedging instrument. However, the market pricing anomaly of the near-zero swap spread violates the non-arbitrage assumption. Investigating the dynamics of pricing factors on interest rate swaps is meaningful to explain near-zero swap spreads. This paper employs a vector autoregressive model with time-varying parameters (TVP-VAR) model to examine the dynamic effects of pricing factors on swap spreads from a business cycle perspective. An analysis of the main three swaps with 1-year, 2-year, and 5-years maturities indicates that the TVP-VAR model accurately describes the time-varying features of the economic determinants. The TVP-VAR model has higher forecasting accuracy than the TVP model, particularly in the first-three months after the start of Covid-19 pandemic. The level, interest rate volatility (or economic policy uncertainty index), and stock market volatility as the proxies for hedging demands show opposite effects to the theoretical predictions. These effects are evident before 2010 (international financial crisis), during a short period in 2015 (stock market crashes), and after 2019 (Covid-19 pandemic). Thus, we provide different explanations of the effects of the hedging demand on near-zero swap spreads.

Fighting for Farmers

Fighting for Farmers

Dynamic connectedness of China’s green bonds and asset classes

This paper quantifies the co-movement and time-varying integration between China's green bonds and other asset classes across different time domains using the wavelet coherence and time-frequency connectedness model based on the time-varying parameter VAR (TVP-VAR). First, we predominantly detect a strong positive co-movement of green and conventional bonds, especially in the medium and long term. Second, strong bidirectional spillovers exist between green bonds and treasury, corporate, and financial bonds regardless of the time horizon. Lastly, cross-market spillovers between the green bonds and the stock, energy, low-carbon stock market were quite limited in the short-run but strengthened towards the long-term except during the 2015 China stock market crash and the COVID-19 recession when short-term integration rose sharply. The results document some practical enlightenment for investors and policymakers with various time horizons.

Dynamic connectedness between China's commodity markets and China's sectoral stock markets: A multidimensional analysis

AbstractConsidering the heterogeneity of China's different sectoral stock markets, this paper adopts the time‐domain spillover index model, extreme spillover index model, and frequency‐domain spillover model to analyse the spillover effects of China's commodity and sectoral stock markets under normal conditions, extreme conditions, and frequency‐domain conditions. The empirical results highlight three interesting and noteworthy aspects for investors and regulators: first, the spillover behaviours of China's sectoral stock markets reveal significant heterogeneity. The Energy, Materials, Industries, Optional, Consumption, Information, and PublicUtilities markets are net transmitters, while the Pharmaceutical, Finance, and Telecom ones are net receivers. Second, the spillover effects between China's commodity and sectoral stock markets are enhanced under extreme conditions and are approximately 18.49% higher than those under normal conditions, and there is asymmetry between left‐tail and right‐tail spillovers, which was observed during China's stock market crash. Finally, the spillover effect between China's commodity and sectoral stock markets is dominated by short‐term spillovers, and there is a positive correlation between short‐term and long‐term spillovers.

A system dynamics approach to venture capital investments illustrated by cases from singapore

Despite the amount of research on VC decision-making, very little is known about the dynamic decision processes venture capitalists (VCs) execute in the real world, paying attention to the systemic interactions within the industry. The importance of dynamic decision processes lies in recognizing that investment decisions in VC take place in complex, rapidly changing, and highly competitive markets where growth and returns are expected to be significantly higher. The objective of this paper is to create and simulate a quantitative model of VC investment dynamics drawing on cases from Singapore. The geographic focus is on Singapore and in terms of methodology, the paper makes use of System Dynamics. As key findings, our model captures how the boom-and-bust phenomenon may be generated by the economic agents’ intendedly rational decisions within a competitive VC market, that, however, leads to unintended poor performance for the industry as a whole. Future research could then compare our simulation results not only with each other or to the base run, but also to other kinds of scenarios; for example, an external shock like a market crash scenario.

Exploring the causal links between investor sentiment and financial instability: A dynamic macro-financial analysis

Investor sentiment may not only influence financial instability, it may also be shaped by it. Moreover, such causal links may differ over time, in crisis and non-crisis periods. This is a groundbreaking paper that tests these two hypotheses from a macro-financial perspective using the bootstrap rolling window sub-sample Granger causality approach to look at the case of the U.S. financial market between January 1990 and January 2021. We find that bullish sentiment can reduce financial instability as it promotes financial market entry and then liquidity during non-crisis periods, while higher financial instability is associated with less bullish sentiment leading to a shift to crisis periods. The results also reveal a positive (negative) effect of bearish (bullish) investor sentiment on financial instability during crisis periods, including the 2007–2008 financial crisis, the 2010 flash crash, the 2015–2016 Chinese stock market turbulence, and the February 2020 stock market crash. Finally, the study highlights the important role of the “betting against beta” strategy in the U.S. financial market, showing a negative effect of financial instability on bearish investor sentiment during several pre- and post-crisis periods.

Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash.

Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.

Study on the Pakistan stock market using a new stock crisis prediction method.

A Stock market collapse occurs when stock prices drop by more than 10% across all main indexes. Predicting a stock market crisis is difficult because of the increased volatility in the stock market. Stock price drops can be triggered by a variety of factors, including corporate results, geopolitical tensions, financial crises, and pandemic events. For scholars and investors, predicting a crisis is a difficult endeavor. We developed a model for the prediction of stock crisis using Hybridized Feature Selection (HFS) approach. Firstly, we went for the suggestion of the HFS method for the removal of stock’s unnecessary financial attributes. The Naïve Bayes approach, on the other hand, is used for the classification of strong fundamental stocks. In the third step, Stochastic Relative Strength Index (StochRSI) is employed to identify a stock price bubble. In the fourth step, we identified the stock market crisis point in stock prices through moving average statistics. The fifth is the prediction of stock crises by using deep learning algorithms such as Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Root Mean Square Error (RMSE), Mean Squared Error (MSE) and Mean Absolute Error (MAE) are implemented for assessing the performance of the models. The HFS-based GRU technique outperformed the HFS-based LSTM method to anticipate the stock crisis. To complete the task, the experiments used Pakistan datasets. The researchers can look at additional technical factors to forecast when a crisis would occur in the future. With a new optimizer, the GRU approach may be improved and fine-tuned even more.

Sustainable mutual fund performance and flow in the recent years through the COVID-19 pandemic

Sustainable investing has gained significant momentum over the past few years. In this paper, we study the performance and flows of sustainable equity mutual funds in recent years through the COVID-19 pandemic. We find that the high-sustainable funds perform better than the low-sustainable ones by between 1.32% and 6.96% annually. This outperformance significantly increases during the COVID-19 pandemic-induced market crash and the post-crash pandemic. Similarly, we find that high-sustainable funds attract significantly more investments (or suffer less outflow) than the low-sustainable funds by between 5.28% and 5.76% per annum. These flow differences increase considerably during the market crash, consistent with the ‘flight-to-quality’ effect. We also find that the high-sustainable funds attract significantly more investment during the post-crash pandemic than before the crash. This suggests that investors consider sustainable investing a necessity (not a luxury good), and their taste/attitude towards sustainable investing has changed – now they prioritize ‘investing with a conscience.’

Volatility spillovers from the Chinese stock market to the U.S. stock market: The role of the COVID-19 pandemic

The COVID-19 pandemic, which originated in Wuhan, China, precipitated the stock market crash of March 2020. According to published global data, the U.S. has been most affected by the tragedy throughout this outbreak. Understanding the degree of integration between the financial systems of the world's two largest economies, particularly during the COVID-19 pandemic, necessitates thorough research of the risk transmission from China's stock market to the U.S. stock market. This study examines the volatility transmission from the Chinese to the U.S. stock market from January 2001 to October 2020. We employ a variant form of the EGARCH (1,1) model with long-term control over the excessive volatility breakpoints identified by the ICSS algorithm. Since 2004, empirical evidence indicates that the volatility shocks of the Chinese stock market have frequently and negatively affected the volatility of the U.S. stock market. Most importantly, we explore that the COVID-19 pandemic vigorously and positively promoted the volatility infection from the Chinese equity market to the U.S. equity market in March 2020. This precious evidence endorses the asymmetric volatility transmission from the Chinese to the U.S. stock market when COVID-19 broke out. These experimental results provide profound insight into the risk contagion between the U.S. and China stock markets. They are also essential for securities investors to minimize portfolio risk. Furthermore, this paper suggests that globalization has carefully driven the integration of China's stock market with the international equity markets.

Financial Stability and Economic Activity in China: Based on Mixed-Frequency Spillover Method

To improve financial sustainability and promote economic stability, it is important to understand the intricate relationship between finance and macroeconomy. Thus, focusing on financial stress and macroeconomic sectors, this paper investigates macro-financial spillovers in China. First, we develop a high-frequency financial stress index based on eight daily financial indicators to measure the stability of China’s financial markets. Through event identification, we find that China’s Financial Stress Index can effectively reflect the stress situation of China’s financial market. Then, given that the traditional co-frequency method fails to deal with financial stress index and macroeconomic data with different frequencies, we employ the mixed-frequency spillover method to evaluate macro-financial spillovers to examine the connectedness between China’s financial market and the real side of the economy. We find that financial stress is the leading net risk output and primarily affects the loan sector; deterioration of economic conditions can lead to more apparent fluctuations in spillover effects, with spillovers from financial stress to others being the most susceptible; within the sample, the 2015 stock crash, U.S.–China trade friction, and COVID-19 have the most impact on macro-financial spillover effects. In addition, we track the results of different risk events on spillover effects across sectors.

Covid Crisis and Stock Market Crash: Evidence from Selected European Hospitality Firms

This article investigates the performance of 12 European firms active in the hospitality industry during the financial crisis triggered by COVID-19. The analysis adopts weekly data on the total number of confirmed COVID cases in EU countries for the period between the first week of March and the last week of December 2020 to estimate the impact of this crisis on the stock returns of firms with various financial characteristics. The estimations are based on the generalized estimating equations (GEE) method, which is a convenient approach for analyzing correlated variables. The findings confirm the negative impact of the crisis on stock returns. However, the larger firms with more cash flows can derive investors' positive market valuation through better self-funding. This study contributes to the literature by first identifying the drivers of the stock price returns of hospitality firms during an unprecedented economic depression and second by introducing the most practical approaches to help the hospitality industry to survive future health crises considering the design of this industry in the postpandemic world. The findings of this study provide vital themes for investment performance in the case of similar crises in the future.

News sentiment and crash risk

ABSTRACT This paper uses firm-specific released news data in the Chinese market to examine the relationship between news sentiment and stock price crash risk between 2007 and 2017. To do so, we develop a firm-specific news sentiment index by quantifying the textual contents in the news. Our results show that firms with better news sentiment are less likely to be involved in future stock crash risk. Also, we find that CEOs approaching retirement are an important mechanism for explaining the relationship between news sentiment and firm crash risk. Our main findings still hold after conducting several robustness tests.