Tail-event driven NETwork dependence in emerging markets

This paper employs the Tail Event NETwork (TENET) to identify financial markets with greater potential risk, and simultaneously investigate the interdependence between them. We find strong time-varying connectedness across 23 emerging markets during the main crisis episodes, including the most recent COVID-19 pandemic, using data from January 1995 to May 2021. The network analysis revealed that emerging European markets are top risk transmitters, whereas emerging Asian markets are top risk receivers. China showed disconnection from the network, reflecting its diversification potential for investors. Our findings offer several policy and regulatory implications. • We investigated the tail-event network dependence of 23 emerging markets;• Tail-Event NETwork (TENET) technique has been employed;• We show that European emerging markets are top risk transmitters, while Asian economies are top risk receivers;• Chinese market is decoupled from the rest of markets analysed. [ FROM AUTHOR] Copyright of Emerging Markets Review is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

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.

EMPLOYING ARTIFICIAL NEURAL NETWORKS AND MULTIPLE DISCRIMINANT ANALYSIS TO EVALUATE THE IMPACT OF THE COVID-19 PANDEMIC ON THE FINANCIAL STATUS OF JORDANIAN COMPANIES

Aim/Purpose This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman's (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations Decision makers and top management are encouraged to focus on the identified for Practitioners highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendations This research can be considered a stepping stone to investigating the impact of for Researchers COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results. © 2023 Informing Science Institute. All rights reserved.

COVID-19 vaccination and response of Latin American financial markets

The COVID-19 pandemic has had major economic consequences in the markets. This paper analyzes the relationship between the progress of vaccination programs and Latin American financial markets. A Wavelet coherence analysis approach is used to evaluate the co-movement of markets and the progress of inoculation strategies based on daily data from Argentina, Brazil, Chile and Mexico. The results show that the progress of vaccination programs in Latin American countries has positive and significant effects on the returns of their financial markets. © 2023 Universia. All rights reserved.

Unveiling commodities-financial markets intersections from a bibliometric perspective

The prominence of commodity markets within the domains of empirical finance and energy economics is well established, largely due to oil's importance and its relationship with other commodities and financial markets. In this study, we present a bibliometric examination of 437 journal articles addressing the phenomenon of commodity connectedness, spanning the period from 1994 to 2022. The research methods include a blend of qualitative and quantitative approaches, incorporating bibliometrics and content analysis. Based on the findings of the analysis, four primary research streams have been identified within the literature concerning commodity connectedness, namely (1) commodity interconnectivity, (2) the relationship between traditional commodities, renewable energy, and cryptocurrencies, (3) the relationship between oil and stock markets, and (4) studies utilizing copula methods to examine the interconnectivity between oil and financial markets. We proposed 15 future research questions for further investigation in the domain of commodity connectedness, including topics such as the impact of the post-COVID era and global uncertainties on commodity markets, how commodities can address the issue of climate change, the exponential growth of cryptocurrencies as a new financial asset, and the impact of the ongoing Russia-Ukraine conflict on commodity and financial markets.

Predicting Stock Price Movement for the Inefficient Market: Case of Hanoi Stock Exchange (HNX)

If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model. © 2023 IEEE.

Comparison of the Performances for CAPM and Fama-French models During COVID-19

Since the outbreak of COVID-19, it has caused a startling stun to both society and economy in numerous nations, where different industries suffered unequally. This paper reviews the various performance of the Capital Asset Pricing Model (CAPM), and the Fama-French three-factor model and the five-factor model in different regions and industries. To metric the performance, various statistics models and scaling are applied including Pearson correlation, linear regression, R2 scores, t-test, etc. Specifically, this paper demonstrates the different performances of the CAPM model on the US and Egyptian stock markets, whereas using generalized method of moments in a panel data analysis to evaluate the performance in the U.S. market and the paired sample t-test and Wilcoxon signed-rank to evaluate the performance in the Egyptian market. The Fama-French three-factor model and five-factor model are both based on the U.S. market and analyze the model's performance (measured by significant level) in the U.S. market in general and in individual sectors, respectively. Whereas, in terms of three-factors model, the OLS estimation and relapse expected excess return are used onto the variables and multiple linear regression method was used to study the significance of factors in three sub-industries. Regarding to five-factors model, a multivariate regression with covariates and OLS estimation are the method for evaluation. These results shed light for deeply understanding the model and recognizing the impact on the security market of the COVID-19. © 2023 SPIE.

China: The Bankable State

The volume on China: The Bankable State rejects neoliberal consensus and focuses on crucial contributions of the Chinese state in shaping Chinese economy. This book makes crucial theoretical contributions to the study of local political economy of China. This book engages with Chinese state responses to challenges China faces in the processes of reform, transition and development of both commercial and non-commercial banks. This book explores Chinese economic growth and development policy processes and its uniqueness in the wider world economy. The book examines Chinese financial policy praxis and offers an insightful account of its successes for the wider resurgence of alternative political economy of local development. Additionally, this book also showcases state led entrepreneurship in China. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Time-Frequency Volatility Spillovers among Major International Financial Markets: Perspective from Global Extreme Events

In the context of the gradual intensification of the Russia-Ukraine conflict and the continuous spread of the COVID-19 pandemic, this paper concentrates on the impact of global extreme events such as the COVID-19 pandemic and the Russia-Ukraine conflict on the risk spillovers among major international financial markets. First, to measure the impact of the extreme events the on the volatility spillovers among major international financial markets in the time-frequency domain, we combine the TVP-VAR-based connectedness method and BK frequency connectedness approach and focus on the total, directional, and net volatility spillovers. Second, the network visualization method is applied to outline the structural change in the risk contagion, paths, and roles among international financial markets during different periods of global extreme events. The empirical results indicate that the risk spillovers (total, directional, and net spillovers) among international financial markets and the roles played by each market in the process of risk contagion have changed significantly in different periods of global extreme events. Furthermore, volatility spillovers among international financial markets are driven mainly by the high-frequency component (short-term spillovers) during the full sample time. However, the effects of the extreme events also persist in the medium and long terms. Our findings may help understand the dynamics among international financial markets under extreme shocks and provide significant implications for portfolio managers, investors, and government agencies in times of extreme events. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Analytical Study on Volatility and Impact of Selected Sectoral Indices on National Stock Exchange during Covid-19

The main objective of this paper is to analyze the COVID-19 effects in selected companies in NSE. Ten companies based on market capitalization for the sectoral i ndices analyses are studied. The company's growth in sectoral Indices was analyzed for the pre and post COVID-19 period by collecting one and half a year data from July 2019 to October 2020 using two hypotheses. The unit test, Co-integration test, serial correlation, heteroscedastic tests, and GARCH tests were performed to perform this hypothesis. Generally, regression tests are used for analysing the company's growth. Based on the results, the cointegration results reveal moderate Volatility during the pandemic period from February 2020 to October 2020, whereas during the pre-period, it shows high Volatility. This result is identical to both the volatile and regression tests. © Indian Institute of Finance.

A Novel Machine Learning-assisted Pairs Trading Approach for Trading Risk Reduction

The cryptocurrency market has been growing rapidly in recent years. The volume of transactions and the number of participants in the cryptocurrency market makes it huge enough that we cannot ignore it. At the same time, the global stock market has also reached a new height in the past two years. However, due to the COVID epidemic and other political and economic-related factors in the last two years, the uncertainty in the capital market remains high, and short-term large fluctuations occur frequently;thus, many investors have suffered substantial losses. Pairs trading, an advanced statistical arbitrage method, is believed to hedge the risk and profit off the market regardless of market condition. Amongst the vast literature on pairs trading, there have been investors trading a pair of cryptocurrencies or a pair of stocks using machine learning or empirical methods. This research probes the boundary of utilizing machine learning methods to do pairs trading with one stock asset and another cryptocurrency. Briefly, we built an assets pool with both stocks and cryptocurrencies to find the best trading pair. In addition, we applied mainstream machine learning models to the trading strategy. We finally evaluated the accuracy of the proposed method in prediction and compared their returns based on the actual U.S. Stock and Cryptocurrency Market data. The test results show that our method outperforms other state-of-the-art methods. © 2022 IEEE.

The impact of simultaneous shocks to financial markets and mortality on pension buy-out prices

In this paper, we determine the fair value of a pension buyout contract under the assumption that changes in mortality can have an impact on financial markets. Our proposed model allows for shocks to occur simultaneously in mortality rates and financial markets, so that strong changes in mortality rates can affect interest rates and asset prices. This approach challenges the common but very strong assumption that mortality and market risk drivers are independent. A simulation-based pricing framework is applied to determine the buyout premium for a hypothetical fully funded pension scheme. The results of an extensive sensitivity analysis show how buyout prices are affected by changes in mortality and financial markets. Surprisingly, we find that the impact of shocks is similar whether or not these shocks occur simultaneously or not, although there are some differences in annuity prices and buyout premiums. We clearly see that the intensity and severity of shocks, and asset price volatility play a dominant role for buyout prices.

Learning financial survival from disasters

This study examines how firms learn financial survival from experience, and how stock markets price this learning. We study American firms during the Covid turmoil which had prior exposure to the 2008 Global Financial Crisis. Our results show firms exposed to the 2008 Crisis had 95% higher monthly stock returns during Covid compared to their unexposed peers. This highlights the role major crises play in shaping organisational resilience. The organisational learning we illustrate includes a strong element of CEO learning but is not exclusive to senior management. Our empirical findings are stronger for firms in ‘shutdown sectors' and persist after controlling for state interventions, as well as other control factors and estimation windows.

The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model

This study employs a non-linear framework to investigate the impacts of central bank digital currency (CBDC) news on the financial and cryptocurrency markets. The time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005) is estimated based on weekly data from the first week of January 2015 to the last week of December 2021. The vector of endogenous variables in the VAR estimation contains the Central Bank Digital Currency uncertainty index (CBDCU), cryptocurrency policy uncertainty index, S&P 500 index, VIX, and Bitcoin price. The TVP-VAR model's time-varying responses demonstrated that the reactions of the cryptocurrency market to central bank digital currency announcements vary remarkably over time. The impacts of the CBDC shocks on the financial market have been increasingly visible during the COVID-19 pandemic. According to the time-varying forecast error decompositions, CBDCU and VIX shocks have accounted for most of the variance in cryptocurrency uncertainty and Bitcoin return shocks, notably during the COVID-19 period.

Assessing the impacts of Covid-19 on stock exchange, gold prices, and financial markets: Fresh evidences from econometric analysis

This research analyses the relative efficacy of gold price, financial market, and stock exchange hedging against sectoral and industry-level global stock market returns. Incorporating Gold into equity-based asset allocation techniques and assessing the stock market and financial sector during the COVID-19 epidemic is one way to diversify your portfolio and reduce risk. After orthogonalizing raw returns concerning a robust collection of relevant universal variables, we conduct our analysis inside a bivariate GARCH(p, q) framework. To further assess ideal portfolio proportions and the efficacy of hedging methods, we expand the volatility spillovers study by calculating the optimal weights for a minimal risk portfolio and determining the hedge ratio. In high-volatility environments, our results show which financial market and stock exchange sectors and industries investors should prioritize to minimize the risk and maximize reward. Use of country-specific macroeconomic variables indices to supplement the worldwide index, (3) separate analysis for the COVID-19 first wave due to the existing argument that the pandemic raises unexpected market events and our early data showing co-movement among the three unpredictability metrics during the pandemic. These findings have important implications for portfolio entrepreneurs and business investors looking to buy international equities.

The Covid-19 Pandemic and Digitalization in Financial Markets

The COVID-19 pandemic, which began in China at the end of 2019, quickly spread worldwide. Since the emergence of the coronavirus (Covid-19), all countries have faced unprecedented challenges in many areas such as health, the economy, supply chains, and finance. In addition to all these problems caused by the pandemic, it has accelerated digitalization in many areas and has led to new global trends. One of the areas affected by the acceleration of digital transformation is the financial markets. The development of technologies such as Big Data, Cloud Computing, the Internet of Things (IoT), Artificial Intelligence (AI), Super Computing, Blockchain, and Virtual Reality (VR) has a crucial role in increasing digitalization. This article examines how Covid-19, which affects the whole world, accelerates digitalization in financial markets. According to the research results, digitalization, in other words, the dehumanization of work and processes, has become a top priority for companies during the Covid-19 pandemic. The rapid increase in digital payment systems and the increasing investments of businesses in technological infrastructure and cybersecurity are just a few of the changes that Covid-19 has accelerated in the financial markets.

The Financial Impact of COVID-19 from the Perspective of Media Coverage: Evidence from China

This paper merges three textual models to construct a series of indicators, which can yield more refined proxies for financial media coverage, to measure the impacts of COVID-19 on Chinese financial markets. Results show that the basic indicator Granger causes the volatilities of bond and stock markets and contributes more to the stock market after the outbreak of COVID-19. Next, four specific market-related indicators have significant effects on the corresponding financial market after the outbreak. Finally, the policy-related indicator has a significant effect on four financial markets after the outbreak, and it causes greater volatility in the stock market. This paper can help the government to stabilize the financial market by managing financial media attention. © 2023 Taylor & Francis Group, LLC.

Asymmetric volatility spillover among global oil, gold, and Chinese sectors in the presence of major emergencies

The volatility of international crude oil and gold markets has affected stock markets through several economic channels, and the impact tends to be more evident with the appearance of emergencies. However, the volatility linkages between commodities and Chinese sector stocks in the presence of emergencies are understudied. To examine the asymmetric relationship and time-varying connectedness between commodities and Chinese sector stocks, this paper first employs GJR-GARCH to capture the realized volatility of international oil, gold, and Chinese sector stocks. Secondly, we decompose the realized volatility of international oil and gold into bad and good volatility and then employ the TVP-VAR-DY approach to obtain the connectedness index. The final result shows asymmetric volatility spillover among oil, gold, and Chinese sector stocks. During the COVID-19 outbreak, the gold good volatility transmission is intenser than bad volatility. Thirdly, the analysis is also carried out under different subperiods. They include three international events: the global financial crisis and the European debt crisis, the oil crisis, and COVID-19. The result reveals heterogeneity exists in the impact of international oil and gold on the Chinese sector stocks under different emergencies. These findings are of great significance for policymakers to improve the sector management under the impact of different emergencies and for investors to design diversified portfolios according to the commodity-sector risk spillover effects. © 2023 Elsevier Ltd

On the efficiency of the gold returns: An econometric exploration for India, USA and Brazil

As a precious metal and investment commodity, gold has been signified to be important for risk management, diversification, and hedging. The gold market has undergone considerable structural changes in the facet of the pandemic and other geopolitical developments, attracting the interest of investors. Thus, it is crucial to look into how these structural changes affect the efficiency of the market. Accordingly, the study examines and compares the evolution of the gold market efficiency in three major economies from January 1, 2018, to August 31, 2022: India, USA, and Brazil. For this, we first decompose the time series using Loess Smoother's Seasonal and Trend Decomposition and then employ a multifractal detrended fluctuation analysis approach. The estimates are strengthened by the alternative approach of the rolling window method of wild bootstrap automatic variance ratio. The findings indicate a considerable decline in the efficiency of the gold returns across three economies, with the highest decline in India, followed by USA and Brazil. Notably, during covid and post covid periods, India and USA show persistence in small fluctuations, while Brazil displays persistent behavior in large fluctuations. Thereby, the market panic makes the gold market unstable, and its use as a safe haven is "erratic”. © 2023 Elsevier Ltd

Connectedness between emerging stock markets, gold, cryptocurrencies, DeFi and NFT: Some new evidence from wavelet analysis

This paper examines the dynamic connectedness between Gulf countries and BRICS stocks markets with a sample of cryptocurrencies, as well as two newly developed digital assets, namely NFT and DeFi, and Gold. The period under examination spans from January 2019 until September 2022. Our analysis is based on wavelet coherence, which is a suitable methodology considering the nonlinear dynamics present in data. Our empirical results clearly identify nontrivial time-varying connectedness between different assets and the stock markets. Asymmetric patterns in the interconnections of newly developed digital assets, cryptocurrencies, Gold and emerging market indices are well-documented, especially during the advent of the health and political events. Our empirical findings have relevant implications for portfolio managers, investors and researchers about portfolio allocation, investment strategies and potential diversification benefits of NFT and DeFi digital assets. © 2023 The Author(s)