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1 – 10 of 270David Korsah, Godfred Amewu and Kofi Osei Achampong
This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress…
Abstract
Purpose
This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.
Design/methodology/approach
This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.
Findings
The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.
Originality/value
This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.
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Оleksandra Kohut, Nataliia Tokareva and Olha Poliakovska
The purpose of this study is to determine the psychological features of providing initial psychotherapeutic assistance to victims, in particular to military servants.
Abstract
Purpose
The purpose of this study is to determine the psychological features of providing initial psychotherapeutic assistance to victims, in particular to military servants.
Design/methodology/approach
Theoretical analysis of scientific works; observation of servicemen in hospital conditions; conversations with servicemen and doctors; and interviews with foreign colleagues.
Findings
As a result of theoretical and empirical research, it was found that initial psychological assistance is more effective if it is provided in a timely and comprehensive manner in cooperation with medical assistance, if the recommended exercises are performed systematically by the victim and if a certain algorithm for providing initial psychotherapeutic assistance is used.
Originality/value
The authors present their own algorithm for providing initial psychological assistance to military servants: psychophysiological stabilization; adjustment of emotional balance; restoration of cognitive processes and acquisition of constructive coping strategies; and formation of life meanings that provide an opportunity to survive the crisis period of life. In this paper, the authors also note the importance of providing psychological first aid to victims of extreme situations in a timely manner, which helps reduce the intensity of symptoms of acute stress disorder and reduces the likelihood of post-traumatic stress disorder.
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Eley Suzana Kasim, Noor Rohin Awalludin, Nurazilah Zainal, Allezawati Ismail and Nurul Huda Ahmad Shukri
This study aims to investigate the effects of financial literacy, financial behaviour and financial stress on awareness of investment scams among retirees.
Abstract
Purpose
This study aims to investigate the effects of financial literacy, financial behaviour and financial stress on awareness of investment scams among retirees.
Design/methodology/approach
Using a questionnaire survey, data was distributed to 200 retirees. A total of 53 responses were obtained. The data was subsequently analysed using PLS-SEM version 3 software.
Findings
Findings indicated that while financial literacy has a significant influence on awareness, there is no conclusive evidence to support the relationship between financial behaviour and financial stress on awareness. These results highlighted the critical need to strengthen financial literacy among retirees as a prevention mechanism for them to avoid from being scammed.
Research limitations/implications
The finding from this study is relevant to regulators and law enforcement agencies to aid potential and actual retirees by educating them on the danger of investment scams.
Originality/value
As there are relatively few studies conducted on investment scams specifically among retirees, this study extends the investment scam literature by examining the underlying factors that affect their awareness towards the fraudulent activities.
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Felipe Sales Nogueira, João Luiz Junho Pereira and Sebastião Simões Cunha Jr
This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg…
Abstract
Purpose
This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.
Design/methodology/approach
This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.
Findings
The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.
Originality/value
This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.
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Sanshao Peng, Catherine Prentice, Syed Shams and Tapan Sarker
Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.
Abstract
Purpose
Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.
Design/methodology/approach
A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.
Findings
The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.
Originality/value
This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.
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Robert Owusu Boakye, Lord Mensah, Sanghoon Kang and Kofi Osei
The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.
Abstract
Purpose
The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.
Design/methodology/approach
The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.
Findings
The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.
Practical implications
The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.
Originality/value
Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.
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Taicir Mezghani, Mouna Boujelbène and Souha Boutouria
This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020…
Abstract
Purpose
This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020. The authors also compare the hedging performance of in-sample and out-of-sample analyses.
Design/methodology/approach
For the modeling purpose, the authors combine the GARCH-BEKK model with the machine learning approach to predict the transmission of shocks between the financial markets and the oil market. The authors also examine the hedging performance in order to obtain well-diversified portfolios under both Financial Stress cases, using a One-Dimensional Convolutional Neural Network (1D-CNN) model.
Findings
According to the results, the in-sample analysis shows that investors can use oil to hedge stock markets under positive Financial Stress. In addition, the authors prove that oil hedging is ineffective in reducing market risks for bond markets. The out-of-sample results demonstrate the ability of hedging effectiveness to minimize portfolio risk during the recent pandemic in both Financial Stress cases. Interestingly, hedgers will have a more efficient hedging performance in the stock and oil market in the case of positive (negative) Financial Stress. The findings seem to be confirmed by the Diebold-Mariano test, suggesting that including the negative (positive) Financial Stress in the hedging strategy displays better out-of-sample performance than the in-sample model.
Originality/value
This study improves the understanding of the whole sample and positive (negative) Financial Stress estimates and forecasts of hedge effectiveness for both the out-of-sample and in-sample estimates. A portfolio strategy based on transmission shock prediction provides diversification benefits.
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Gaurav Kumar, Molla Ramizur Rahman, Abhinav Rajverma and Arun Kumar Misra
This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.
Abstract
Purpose
This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.
Design/methodology/approach
The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.
Findings
The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.
Practical implications
The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.
Originality/value
This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.
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Muhammad Asim, Muhammad Yar Khan and Khuram Shafi
The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the…
Abstract
Purpose
The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.
Design/methodology/approach
For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.
Findings
The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.
Originality/value
In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.
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Xiaodong Yu, Guangqiang Shi, Hui Jiang, Ruichun Dai, Wentao Jia, Xinyi Yang and Weicheng Gao
This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as…
Abstract
Purpose
This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.
Design/methodology/approach
The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).
Findings
Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.
Originality/value
This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.
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