Risk transmission between equity market of China and its trading partners: new evidence from various financial crises

PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

Novel Method for Estimating Time-Varying COVID-19 Transmission Rate

The transmission rate is an important indicator for characterizing a virus and estimating the risk of its outbreak in a certain area, but it is hard to measure. COVID-19, for instance, has greatly affected the world for more than 3 years since early 2020, but scholars have not yet found an effective method to obtain its timely transmission rate due to the fact that the value of COVID-19 transmission rate is not constant but dynamic, always changing over time and places. Therefore, in order to estimate the timely dynamic transmission rate of COVID-19, we performed the following: first, we utilized a rolling time series to construct a time-varying transmission rate model and, based on the model, managed to obtain the dynamic value of COVID-19 transmission rate in mainland China;second, to verify the result, we used the obtained COVID-19 transmission rate as the explanatory variable to conduct empirical research on the impact of the COVID-19 pandemic on China's stock markets. Eventually, the result revealed that the COVID-19 transmission rate had a significant negative impact on China's stock markets, which, to some extent, confirms the validity of the used measurement method in this paper. Notably, the model constructed in this paper, combined with local conditions, can not only be used to estimate the COVID-19 transmission rate in mainland China but also in other affected countries or regions and would be applicable to calculate the transmission rate of other pathogens, not limited to COVID-19, which coincidently fills the gaps in the research. Furthermore, the research based on this model might play a part in regulating anti-pandemic governmental policies and could also help investors and stakeholders to make decisions in a pandemic setting.

Impact of Pandemic Outbreak and Targeted Required Reserve Ratio Cut Policy on Stock Return Rate: Evidence from China

This study analyzed the impact of COVID-19 outbreak and targeted required reserve ratio cut policy on stock returns of Chinese listed companies. This paper uses the data of 3,449 A-share listed companies from February 3, 2020 to December 31, 2020 for research, the empirical results showed that stock prices of private enterprises with stronger debt-paying ability and looser financing constraints, and state-owned enterprises with less supply chain credit risks performed better, in the central and western regions, enterprises with stronger solvency and looser financing constraints have better stock price performance during the early stages of pandemic. After the implementation of the targeted RRR cut policy, the stock prices of enterprises with poor solvency, private enterprises, and enterprises in central and western regions with strong financing constraints, state-owned enterprises, and enterprises in eastern region with high credit risks all showed significant reversals, and the stock prices reflected the effect of the targeted RRR cut policy in the short and medium term. Over time, the pandemic has been controlled, and the resumption of work and production has freed most enterprises from financial difficulties. However, due to sporadic outbreaks, large private enterprises and eastern enterprises with strong risk resistance and loose financing constraints enjoy better stock price performance. This study is helpful for enterprises to understand the value of financial flexibility and solvency and provides a reference for enterprises to make financial decisions: how to balance the benefits and costs of solvency. © Tian Wang, Fang Fang and Linhao Zheng, 2023.

Performance of IPOs During Covid-19 Pandemic and Factors Affecting Investors' Perception

Initial Public Offering (IPO) is a fund-raising tool through which a company gets listed for the first time under SEBI regulation and issues IPOs to raise funds from the public. The shift from a privately-owned to a publicly-owned firm via an IPO is the most significant event in a company's life (Pagano et al., 1998). In an IPO investment, there is limited historical data to analyze and predict the future performance of the company;hence it becomes a risky investment for the investors as they cannot predict how the shares will perform in the future. Most companies that go for an IPO are in the growth or expansion phase so it becomes more difficult to predict their market position and performance in the future, which leads to uncertainty in deriving their future value. Also, most IPOs are of companies going through a transitory growth period, and are therefore subject to additional uncertainty regarding their future value. This study analyzes the performance of the IPOs issued during the Covid-19 pandemic, when the markets across the world faced massive disruptions. The IPOs from various sectors like finance, technology, service, infrastructure, food, pharmaceutical and information technology were considered for the study. The study also analyzes the factors affecting investor perception towards investment in an IPO. The study considered the IPOs issued during the pandemic, and their performance on the listing day was measured by considering issue price, listing price and closing price. It was observed that 90% of the IPOs selected performed well during the listing day and 10% underperformed. It was also found that factors like company brand, company sector, fundamental analysis, company ratings, expert opinion and stock market conditions had a positive impact on the investors' decision to invest in an IPO. The study also revealed that factors like risk factor in primary market, returns on IPO on the listing day and Gray Market Premium have no significant impact on the investors' perception.

Does ESG Disclosure Transparency Mitigate the COVID-19 Pandemic Shock? An Empirical Analysis of Listed Firms in the UK

This paper examines whether the ESG reporting transparency of listed firms in the UK can play a role in mitigating the impact of the COVID-19 pandemic. We investigate 350 UK firms in the FTSE350 index from 2016 to 2021 with daily data on stock performance and annual data on financial performance. The empirical results show that firms with a high ESG disclosure score have a lower volatility of stock performance during the COVID-19 pandemic. For these firms, the negative relationship between stock performance, as well as financial performance, and their main driving factors, is lower during the COVID-19 pandemic. Among these factors, we identify the lockdown announcement, quantitative easing announcement, and the intensity of news media coverage of the company. These results tend to indicate that the quantity of ESG data reported by firms can contribute to mitigating the impact of the COVID-19 pandemic on stock performance volatility and financial performance. © 2023 Journal of Innovation Economics and Management. All rights reserved.

Japanese Economic Performance after the Pandemic: A Sectoral Analysis

The COVID-19 crisis battered the Japanese economy. The purpose of this paper is to investigate whether the pandemic has left scars. To this end, it employs out-of-sample forecasting models and detailed stock market data for 30 sectors and disaggregated current account data for the 3 years after the first case occurred. The findings indicate that stock prices in sectors such as tourism, education, and cosmetics remain far below forecasted values after three years. Office equipment and semiconductor stock prices initially fell more than predicted but have since recovered. Other sectors such as bicycle parts and home appliances gained at first but are now performing as expected. Sectors such as home delivery and electronic entertainment continue to outperform. The results also indicate that income flows from Japanese investments abroad are much larger than forecasted, keeping the Japanese current account in surplus even as imports of oil and commodities have created persistent trade deficits. Since the travails of hard-hit sectors such as tourism reflect their exposure to the COVID-19 pandemic rather than bad choices made by firms, policymakers should consider employing cost-effective ways to stimulate economic activity in these sectors. © 2023 by the author.

Dynamic Dependency between the Shariah and Traditional Stock Markets: Diversification Opportunities during the COVID-19 and Global Financial Crisis (GFC) Periods

The aim of the present research is to highlight whether there exist any diversification opportunities from investing in developed and developing countries' Shariah-compliant and non-Shariah-compliant stock markets during global financial crisis (GFC) and the COVID-19 pandemic periods. For this purpose, we employ daily data for both Shariah and non-Shariah indices from 29 October 2007 to 31 December 2021. The study uses multivariate GARCH-DCC and wavelet approaches to examine if there exist diversification opportunities in the selected markets. Evidence from this study shows that although the developing markets' stock returns experience high volatility of a similar degree, the conventional indices of Malaysia have the highest volatility among them. This shows that Shariah indices have less exposure to risk and higher possibilities of diversification compared to their conventional counterparts. Regarding developed markets, the Japanese conventional index and the U.S. Shariah indices are more volatile compared to other indices in the market. Moreover, the results of the wavelet power spectrum show significant and higher volatility during the COVID-19 pandemic rather than the GFC. Similarly, the Chinese conventional market experienced minimum variance during the GFC and COVID-19 pandemic period. On the other hand, the results of wavelet-coherence transform indicate that the Japanese Shariah-based market offered better portfolio opportunities for U.S. traders during the GFC and the COVID-19 pandemic periods. Hence, opportunities for investment in this selected market are basically close to zero. Therefore, investors should carefully choose which stocks they can include in their investment portfolio. © 2023 by the authors.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Zero-COVID policy and stock market sectoral performance in China

With the outbreak of COVID-19, the Chinese government implemented the "zero-COVID” policy as a measure to curb the spread of the virus. The different measures of the policy include widespread testing, contact tracing, and strict quarantine and isolation protocols. In view of recent changes in COVID-19 trends and other economic indicators, the Chinese government withdrew significant provisions of the zero-COVID policy in China. The present study investigates the sectoral performance of the Chinese stock market after the withdrawal of the zero-COVID policy. The study considers eighteen sectoral indices of the Shenzhen Stock Exchange of China as a sample and applies the event study methodology to study the impact of the policy withdrawal on the stock prices performance. The results of the study indicate that sectors such as hotel, consumer staples, the financial sector, real estate, media, and culture have reported significant positive movement after the withdrawal of the zero-COVID policy, while other sectors such as consumer discretionary, energy, healthcare, information technology, manufacturing, mining, technology, telecom, transportation, utilities, wholesale, and retail have shown insignificant reactions. These results also indicate that when the COVID-19 outbreak happened in China, different sectors of the economy reacted negatively except the retail and wholesale sectors, while with the withdrawal of the zero-COVID policy by the Chinese government, the reaction of investors is optimistic as different sectors are reporting either positive reactions in the stock price movement or no reaction. © Prashant Sharma, Surender Kumar, 2023.

A study of optimal portfolio selection using quadratic programming modelling: Evidence from Indian pharmaceutical industry during COVID-19 times

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically. Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio. Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks. Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model. Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks. © 2023 by Chetna and Dhiraj Sharma.

Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

This paper examines the presence of a contagion effect between Chinese and G20 stock markets as well as its intensity over a recent period from 1(st) January 2013 to 7 April 2022. The empirical study is conducted using the time-varying copula approach. The obtained results show strong evidence of a contagion effect between China and all countries except United States America, Argentina and Turkey during the COVID-19 period. In particular, the Chinese stock market exhibits the highest level of dependence with the Asian and European stock markets in addition to the greatest variability in dependence. These findings are interesting and have important implications for several financial applications.

Altın ve Kripto Paraların BÍST100 Endeksi için Hedge ve Güvenli Liman Özellikleri: Covid-19 Salgını Etkileri

Covid-19 salgınının ortaya çıkmasından sonra dünya çapında ekonomik çalkantılar ve şiddetli piyasa düşüşlerinin ortaya çıktığı görülmüştür. Bu dönemde hisse senedi piyasalarına yatırım yapmış ajanlar için hedge ve/veya güvenli liman araçları arayışları artmıştır. Kripto paralar ve altın özellikle gelişmekte olan ülke piyasalarındaki yatırımcılar için hedge ve/veya güvenli liman olma konusunda iyi potansiyellere sahiptir. Bu çalışmada, Borsa Ístanbul için Bitcoin, Etherium, Ripple, Litecoin ve altın piyasalarının hedge ve/veya güvenli liman piyasalar olup olmadığı, GARCH(1,1) hata terimleri varsayımı altında modellenmiş regresyon sistemi yardımı ile araştırılmıştır. Analizlerde örneklem olarak 4 Eylül 2017 – 30 Mart 2022 tarihleri arasındaki günlük frekanslı verilerden faydalanılmıştır. Ayrıca, Covid-19 salgın dönemi etkilerini ayrıştırmak için örneklem iki alt gruba ayrılmış ve tahminler Covid-19 öncesi dönem (31 Aralık 2019 öncesi) ve Covid-19 dönemi (31 Aralık 2019 ve sonrası) için ayrı ayrı analiz edilmiştir.A.B.D. doları cinsinden elde edilmiş kripto para (BTC, ETH, XRP, LTC), altın ve BÍST100 endeks getirileri kullanılarak bulunan tahmin sonuçlarına göre, tüm örneklem için Litecoin zayıf güvenli liman olarak ortaya çıkarken, Covid-19 öncesi dönemde Bitcoin ve Etherium zayıf hedge, Covid-19 salgın döneminde de Etherium zayıf güvenli liman olma özellikleri göstermektedir. Tüm örneklem ve salgın öncesi dönem verileri söz konusu olduğunda, BÍST100 endeks getiri dağılımının %10 çeyrek değerinden az olduğu durumlarda Bitcoin, Etherium ve Ripple güvenli liman piyasalar olarak gözlemlenirken, salgın döneminde altın, BÍST100 endeks getiri dağılımının %1 çeyrek değerinden az olduğu durumlarda güvenli bir liman olarak ortaya çıkmıştır. Fakat tüm analizlere dayanarak, altının genel görünümüyle BÍST100 endeksi için hedge veya güvenli limandan çok bir çeşitlendirici varlık olarak öne çıktığı söylenebilir.Alternate :After the Covid-19 outbreak, economic turmoil and severe market crashes have been observed around the world. During this crisis period, cyriptocurrencies and gold have become potentially good hedge and/or safe haven assets for especially the stock investors in emerging markets. This study investigates whether or not Bitcoin, Etherium, Ripple, Litecoin and gold markets have hedge and/or safe-haven properties for Borsa Ístanbul through a regression system modeled under the assumption of GARCH(1,1) error terms. Daily frequency data covering the period September 4, 2017 through March 30, 2022 is used in the sample analysis. In addition, to separate out the effects of the Covid-19 pandemic on the analysis, full sample is divided into two subgroups and the estimations are made separately for the pre-Covid-19 period (before 31 December 2019) and the Covid-19 period (31 December 2019 and later).According to the estimation results, Litecoin emerges as a weak safe haven for Borsa Ístanbul over the entire sample period, while Bitcoin and Etherium appear to be weak hedges in the pre-pandemic period. During the Covid-19 pandemic period, Etherium is shown to be a weak safe haven for the BÍST100 index. Full sample and pre-pandemic data analysis reveal that, Bitcoin, Etherium and Ripple act as safe-haven markets in some cases when the BÍST100 index returns hit lower than their 10% quantile value. After the outburst of the Covid-19 however, gold seems to act as a safe haven asset for Borsa Ístanbul when the BÍST100 index returns hit lower than their 1% quantile value. Based on the overall estimation results, gold stands out as a diversifier rather than a hedge and/or a safe haven asset for the BÍST100 index.

Volatility spillovers among major tourism stock indices during Covid-19 pandemic

This paper examines the dynamics of volatility spillovers among five major tourism stock indices during the Covid-19 period. Our paper enriches the current literature as it is the first paper to investigate the volatility spillovers among major global tourism stock indices by adopting Diebold and Yilmaz (2012. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. ), and Barunik and Krehlik (2018. Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16(2), 271–296.) time and frequency domain methods. Results suggest that total spillovers of the tourism stock indices rose significantly during the pandemic. Turkey and Italy are net volatility spillover transmitters, and others are net volatility spillover receivers. Findings of this study also indicates that the effect of volatility transmission among tourism stock markets is temporary (short-lasting). The results suggest that short-term investors and portfolio managers should avoid investing in the tourism indices in the short term.

The Spillover Effects of US Unconventional Monetary Policy on Inflation and Non-Inflation Targeting Emerging Markets

This study employs the panel vector autoregressive (PVAR) model to examine the spillover effect of US unconventional monetary policy on inflation and non-inflation targeting emerging markets post credit crunch and during COVID-19 from 2000Q1 to 2020Q4. Unlike other analyses, this paper adds to the existing body of knowledge by employing a dummy variable to represent the United States' quantitative easing. Other included control variables are equity prices, the federal reserve rate, the exchange rate, central bank assets and the short-term interest rate. This paper estimated two-panel VARs, Model one and Model two, for inflation and non-inflation targeting emerging markets, respectively. Model one consists of eight inflation-targeting markets, and Model two consists of four non-inflation-targeting countries. Other included control variables are equity prices, the federal reserve rate, the nominal effective exchange rate, and the central bank policy rate. According to the empirical results, the US unconventional monetary policy induces a surge in the exchange rate and a decrease in the central bank policy rate for both inflation and non-inflation targeting emerging markets. However, there was no significant impact on the equity prices. The empirical results are statistically significant, robust, and consistent with previous studies except for the response of equity prices. Unconventional monetary policy is effective in steering macroeconomic variables in developed economies. The monetary policymakers in emerging markets must also use the currency reserve to stabilise the macroeconomic variables in response to US unconventional monetary policy shocks.

Manufacturing Innovation

(ProQuest: ... denotes non-USASCII text omitted.) The annual Global Innovation Index released in September 2021 ranked China twelfth, surpassing developed economies such as Japan, Israel, and Canada and raising fears in the United States amidst sluggish growth in North America and strong growth in the Asia Pacific region.1 Interestingly, the United States government responded by boycotting the Beijing Olympic Games, citing human rights abuses as the main reason.2 A tech war between China and the United States brewed beneath the diplomatic rancor over the attendance at the Olympic Games. Part I documents how the United States has assisted China's tech and intellectual property domination through President Nixon's historic visit to China, giving China Most Favorite Nation (MFN) status and ascending China to the World Trade Organization (WTO). [...]under Deng Xiaoping's leadership during the reform period, China rapidly developed its special economic zones (SEZs), laying the foundation for subsequent tech innovation and production. [...]broadcasting, telecommunications, office machines, computers, integrated circuits, and cell phones are among China's notable exports to the world.9 China dominates in commodities and raw materials, exporting refined petroleum, cotton, plywood, and tea.10 For agricultural products, China occupies the perch as the world's largest producer. Shenzhen rose as the largest among the four.18 Shenzhen, a small fishing locale in the southern part of China's southern province, Guangdong, served as the pioneer of Deng Xiaoping's embrace of economic reforms.19 A market-oriented economy took root in Shenzhen, allowing foreign companies and entities from Hong Kong and Macau to operate and allowing Chinese talents the freedom to leave their hometowns and move into the SEZs.20 Cheap labor proved to be another significant factor facilitating China's rise as a global manufacturer.21 In the 1980s, multinational corporations from Taiwan, Japan, and South Korea, as well as domestic Chinese companies, opened their factories in the SEZs and other cities in China to take advantage of the cheap and plentiful labor force.22 Indeed, when Deng Xiaoping began his pilot SEZs, China's young workers who wished to lift themselves out of poverty descended into the economic zones in search of better opportunities.23 Shenzhen grew from a population of 59,000 in 1980 to a population of 12,357,000 in 2020.24 The new migrants became the workers, participants, and stakeholders in the global manufacturing frontier.25 Because of the abundance of cheap labor, manufacturers in China have no difficulty keeping production prices low and pleasing consumers and businesses worldwide.26 China's currency manipulation is another factor propelling China to its domination in global manufacturing.27 The United States Congress attempted numerous times to introduce legislation to combat China's currency manipulation.28 China artificially devalued its currency through government control of the exchange rate and refused to let the Chinese Renminbi (RMB) float.29 Despite strong criticisms from the United States, China refuses to allow its currency to freely float.30 China's currency manipulations, according to critics, caused the widening of trade deficits between the United States and China.31 China's currency manipulation allows products to be manufactured at lower prices, hampering competitors and thereafter replacing them.32 In order to cope with China's currency practices, United States manufacturers facing their own existential crises must decide to either outsource jobs overseas or face large risks, including financial ruin.33 The United States lost millions of manufacturing jobs due to massive job outsourcing as the trade deficits between the United States and China continued to persist.34 Geopolitically, in shaping post-Cold-War powers, the United States decided to assist China in its transformation from a poverty-stricken country to a global manufacturer.

Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

Relationship among economic crisis, stock market crashes and accounting scandals

Purpose: This work investigates the relationships between stock exchange crashes and accounting scandals.Design/methodology: We analyze the main accounting scandals and stock exchange crashes that occurred between 1980 and 2020. Findings: First of all, it was verified that a stock market crash occurred in the years in which most of the accounting scandals took place (or within the next three years). This evidence is consistent with much of the previous literature. Second, an average of 5.4 years has been estimated as the period of time that elapses between the time a company starts engaging in accounting deception and the moment when it is discovered and the scandal breaks out. Third, it has been found that accounting deception is more likely to occur in years with stock market crashes and in the years immediately following. The literature review revealed no evidence supporting the two latter hypotheses.Research limitations/implications: This exploratory work has several limitations. First of all, only scandals that have been reported on websites in Spanish and English have been analyzed. Therefore, the sample may be biased, giving more weight to companies from Anglo-Saxon and Spanish-speaking countries. Second, the sample was made up of a small number of companies (53), which are those that have met the search criteria used.Practical implications: The findings of this work are relevant today, since a major stock exchange crash has occurred as a result of the coronavirus. Therefore, if the pattern of the most recent decades is repeated, it would be expected that more accounting scandals will come to light in the coming years.Social implications: The conclusions obtained are of great relevance for the different users of the financial information from companies, and also for auditors, consultants and supervisory bodies, since due to the stock exchange crash triggered by COVID-19, they will need to exercise extreme caution in the coming years in relation to financial information.Originality/value: The work provides evidence on the relationship between stock market crashes and accounting scandals, which is a highly relevant topic. The literature review revealed no study using the same methodology or a similar sample of companies.

Combination forecast based on financial stress categories for global equity market volatility: the evidence during the COVID-19 and the global financial crisis periods

The 2008 global financial crisis and the COVID-19 pandemic both decrease economic growth and lead to high uncertainty in global stock markets, and financial stress information is closely linked to financial crises. To improve the predictability of the realized volatility of the global equity indices during crises, we examine the predictive role of the Global Financial Stress Index (GFSI) and its categories. We find that the combination predictions based on GFSI's five incorporated categories and three region-based categories outperform the predictions based on the raw GFSI for most indices. Specifically, the DMSPE combination model with a low discount factor has accurate forecasts for 5- and 22-day-ahead realized volatility, and it also performs better than the equal-weighted and the trimmed mean combination methods. In this study, we present a comprehensive analysis of the predictive role of financial stress information in stock market volatility during crises, and the empirical evidence provides a positive case against the ‘forecast combination puzzle'. Our findings are very instructive for policymakers and investors to make their own short-term and long-term plans in crisis. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

ANALYSIS OF MORTALITY AMONG PATIENTS SUFFERING FROM HEART FAILURE, USING THE EXAMPLE OF A MEGALOPOLIS

The medical documentation (n = 146912) introduced into the system "Regional fragment of the unified state information system in the field of healthcare” of Saint Petersburg for 2019–2021 was analyzed. To evaluate the mortality of patients due to heart failure, all deceased patients from 2019 to 2021 in Saint Petersburg (n = 192133) were taken as a basis, and based on a thorough study of medical documentation, patients who died from cardiovascular diseases and because of heart failure were singled out separately. The total mortality from all causes in Saint Petersburg in 2019 was 53025 people;in 2020, 66468 people;and in 2021, 72640 people. The analysis of mortality due to cardiovascular diseases from 2019 to 2021 showed an upward trend of 20.1% over the 3-year period of data analysis. When analyzing the prevalence of heart failure among deceased patients, an increase of 129.4% was noted over this period. The obtained results of the prevalence, mortality, and mortality of patients due to heart failure on the example of a megalopolis are the most relevant at the current time;they indicate a steady increase in the number of patients suffering from heart failure with an increase in the burden on the city's healthcare system. Simultaneously, there is insufficient continuity in the provision of medical care to patients suffering from heart failure, which is because of not only a shortage of medical personnel at all stages of medical care but also insufficient compliance of patients who either do not want to be treated or cannot continue treatment. Moreover, a significant disconnect was found in the continuity of medical care at the stages of pre-hospital and hospital treatment, as well as further outpatient follow-up of patients suffering from heart failure in the metropolis. All this leads to a significant increase in the mortality and mortality of patients suffering from heart failure, despite all the existing modern effective drug therapies. It appears critical to create a unified register platform for recording patients with heart failure, which will allow for a more accurate understanding of epidemiological aspects, the solution of which will improve the quality of medical care, identify the need for the crucial medicines, and reduce mortality, and mortality rates due to heart failure. All rights reserved © Eco-Vector, 2022.

The stock market and NO2 emissions effects of COVID‐19 around the world

In this paper, we study the impact of the coronavirus disease 2019 pandemic in estimated panel vector autoregression models for 92 countries. The large cross‐section of countries allows us to shed light on the heterogeneity of the responses of stock markets and nitrogen dioxide emissions as high‐frequency measures of economic activity. We quantify the effect of the number of infections and four dimensions of policy measures: (1) containment and closure, (2) movement restrictions, (3) economic support, and (4) adjustments of health systems. Our main findings show that a surprise increase in the number of infections triggers a drop in our two measures of economic activity. Propping up economic support measures, in contrast, raises stock returns and emissions and, thus, contributes to the economic recovery. We also document vast differences in the responses across subsets of countries and between the first and the second wave of infections.