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A methodology of structuring a garment production simulation model using a spreadsheet is described to minimize the average daily production cost through the investigation of various man‐machine combinations. The capability and usability of an easily available modern spreadsheet Excel 7.0 to simulate a simple garment production system is accessed with an attempt to demonstrate the simulation model building in a user friendly environment rather than learning and using costly simulation programming languages or simulation software packages. Simulation has evaluated the resource utilization and measured the system performance and developed strategies for taking operational decisions in a logical and better way to minimize the garment production cost. It may also assist and benefit the garment production managers to plan, design and operate their systems in an efficient manner in a competitive environment.

This study examines the impact of social media investor sentiment on the stock market performance through qualitative and quantitative proxies.

The authors use a sample of daily stock performance related to S&P 500 Index for the period from December 18, 2017, to December 18, 2018. The social media investor sentiment was assessed through qualitative and quantitative proxies. For qualitative proxies, the study relies on three social media resources”: Twitter, Trump Twitter account and StockTwits. The authors proposed 3 methods to reflect investor sentiment. For quantitative proxies, the number of daily messages published from Trump Twitter account and StockTwits is considered as a signal of investor sentiment. For regression model, the study adopts the autoregressive distributed lagged to determine the relationships between the nonstationary series.

Empirical findings provide evidence that quantitative measures of investor sentiment have significant effects on S&P’500 performances. The authors find that Trump's tweets should be interpreted with caution. The results also show that the number of Trump's tweets on t−1 day have a positive effect on performance on day t.

Social media sentiment contains information for predicting stock returns and transaction activity. Since, the arrival of new information in capital markets triggers investor sentiment on social media.

This study investigates the investors’ sentiment through social media and explores quantitative and qualitative measures. The amount of information on social media reflects more the investor sentiment than content analysis measures.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0818

The main purpose is to explore the impact of major stock markets on China's market where major markets are represented by former G8 nations (current G7 and Russia).

The article makes use of: stationarity tests (ADF and PP unit root); long-run correlation tests (Johansen integration involving trace and maximum eigenvalue); impact of G8 markets on China (VECM test); influence of G8 markets on volatility in China's market (variance decomposition analysis) and, effect from shocks in G8 markets on China (impulse response function).

Using a period of 2009–2019 that avoids detecting linkages caused by interdependencies created by two major international crises, the article offers four major findings. First, except for Germany and Russia, G8 markets have a significant causal influence on China with UK having the greatest. Second, G8 markets are not the major source of short-run fluctuation in China's market but over time exercise a noteworthy collective impact with UK having the greatest impact. Third, there are occasions for international portfolio diversification with China's market providing greater diversification than G8 nations. Fourth, all markets provide a short-run window of abnormal profit.

The indexes used to represent national markets are assumed to be adequate representations.

Short-term abnormal profits exist. Investing in China, compared to G8 countries, offers greater portfolio diversification possibilities.

Removal of trade and investment barriers cause greater market integration.

By using recent data, this study reveals that G8 stock markets influence China's market.

The political influence on the determinants of capital structure has been under-researched for a long time. Taking the turnover of secretary of municipal committee as a political factor in China, this paper studies the effect of local government officials' turnover on firm's capital structure.

Starting with all A-shares listed firms in the Shanghai and Shenzhen Stock Exchanges from 2001 to 2018, this paper implements the OLS estimation, staggered difference-in-difference approach to investigate the effects of political turnover on the choice of capital structure.

The results show that, driven by government officials' turnover, firms will significantly reduce their leverage. When comparing between formal finance (bank loans) and informal finance (payables), the reduction of capital structure is mainly driven by banks, not by suppliers. Furthermore, two possible channels have been investigated. First, the reduction effects are mainly driven by the SOEs when classifying the types of corporate ownership into SOEs and non-SOEs. Second, the reduction effects exist in areas with the more intense government intervention when considering the heterogeneity of the development of institutional environment in provinces.

This paper first contributes to the literature on the determinants of corporate choice on capital structure. Second, this paper enriches the studies on the economic consequences of local government officials' turnover.

The purpose of this paper is to study the following: short-run and long-run associations between the terror-affected country’s stock market index and other global countries’ equity indices and gold; the volatility of stock market indices when one of the countries is affected by a terrorist attack; and the linkages between terrorism and the returns in the selected stock markets.

To study the impact of the Taj attack on other global indices, the authors selected top five countries’ stock market indices, namely, FTSE, DJI, NIKKEI, SSEC and DAX. The short-run and long-run associations are also compared with gold. The authors used the autoregressive distributed lag model, LM test and bounds test for analyzing the short-run and long-run impact; ARCH family models to study the volatility impact; and the MAR model to study the impact on returns.

The authors found that all the global indices had a short-run association with the terror-affected country’s benchmark index, i.e. BSE. Gold moved as expected, with it having a short-run impact on the terror-affected country. All the global indices except DJI have volatility of share price movement either positively or negatively. As the benchmark of the terror-affected country fell, NIKKEI, HSI, IXIC, DAX and CAC also fell; that is, it had a positive influence on the terror-affected country’s index. Post the Mumbai attacks, DJI, NIKKEI, SSEC, DAX, BSE and CAC performed well in performance measure returns compared with the pre-attack period. Whereas, FTSE and GOLD performed well in performance measure returns in the pre-attack period compared with the post-attack period. GOLD proved that it is the best avenue to invest in, as it has only a short-term association with the terror-affected country’s index.

The authors studied the short-run and long-run associations with only five countries’ benchmark indices.

The authors found that all the global indices had long- and short-run associations with the terror-affected country’s benchmark index, i.e. BSE. Global indices like DJI, NIKKEI, SSEC, DAX and FTSE had a short-term association with the affected country’s index. Gold moved as expected, with it having a short-run impact on the terror-affected country. All the global indices except DJI have volatility of share price movement either positively or negatively. As the benchmark of the terror-affected country fell, NIKKEI, HSI, IXIC, DAX, TSX, BVSP and CAC also fell; i.e., it had a positive influence on the terror-affected country’s index. Post the Mumbai attacks, DJI, NIKKEI, SSEC, DAX, BSE and CAC performed well in performance measure returns compared with the pre-attack period. Whereas, FTSE and GOLD performed well in performance measure returns in the pre-attack period compared with the post-attack period. GOLD proved that it is the best avenue to invest in, as it has only a short-term association with the terror-affected country’s index. In all the relationships were mixed with respect to terror attacks, and GOLD took the lead run out of all the associations it had in the 16-year time span from 2000 to 2016.

The research has got an important implication to the investors. It shows that patience is the key, as all the indices had only short-term associations with the BSE. It implies that investors’ returns will be negative in the short run, but if they continue investing, in the long run, the impact of terrorism tapers out and the returns will increase.

There is a lot of research done on the impact of the US attacks on the stock markets of other countries, but on the impact of the Taj attack in India, there is hardly any research.

This study aims to assess the performance of domestic and foreign Islamic banks as well as the influence of foreign banks’ home country development influence on the performance of foreign banks.

This study uses data of 17 domestic Islamic banks and 11 foreign Islamic banks from Bahrain, Malaysia and Pakistan from 2010 to 2018. Pooled ordinary least square is used to investigate the relationship between home country development and performance of foreign Islamic banks.

Results from this paper suggest domestic Islamic banks in Malaysia have higher performance than their foreign counterparts while foreign Islamic banks from Pakistan have higher performance than their domestic counterparts. However, as a whole, the performance of domestic and foreign Islamic banks is not significantly different from one another. Development of foreign banks’ home country measured by gross national income per capita and gross domestic product per capita show significant positive influence on the performance of foreign Islamic banks.

Higher level of development of the home country of foreign banks appears to influence the performance of foreign banks in the host country.

Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework.

The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”.

Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin.

The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.

This paper aims to analyze systemic risk in and the effect of capital regulation on the European insurance sector. In particular, the evolution of an exposure measure (SRISK) and a contribution measure (Delta CoVaR) are analyzed from 1985 to 2016.

With the help of multivariate regressions, the main drivers of systemic risk are identified.

The paper finds an increasing degree of interconnectedness between banks and insurance that correlates with systemic risk exposure. Interconnectedness peaks during periods of crisis but has a long-term influence also during normal times. Moreover, the paper finds that the insurance sector was greatly affected by spillovers from the process of capital regulation in banking. While European insurance companies initially at the start of the Basel process of capital regulation were well capitalized according to the SRISK measure, they started to become capital deficient after the implementation of the model-based approach in banking with increasing speed thereafter.

These findings are highly relevant for the ongoing global process of capital regulation in the insurance sector and potential reforms of Solvency II. Systemic risk is a leading threat to the stability of the global financial system and keeping it under control is a main challenge for policymakers and supervisors.

This paper provides novel tools for supervisors to monitor risk exposures in the insurance sector while taking into account systemic feedback from the financial system and the banking sector in particular. These tools also allow an evidence-based policy evaluation of regulatory measures such as Solvency II.

The purpose of this study is to determine the impact of disaggregate official development aid (ODA) on economic growth, and ascertain whether bilateral and multilateral aid played complementary role with private sector, government sector and external sector in driving growth of sub-Saharan African economies.

The role of bilateral and multilateral aid in economic growth of sub-Saharan Africa (SSA) is investigated in this study. The vector error correction model (VECM) and generalized method of moments (GMM) techniques are employed in estimating the short-run and long-run impacts, over the period 1980–2020.

The estimation results reveal that the effect of bilateral aid is positive, and more significant than multilateral aid. Their effect on economic growth is, however, less significant than the effects of domestic private investment and government spending. Nonetheless, aid complemented private and government sectors in facilitating growth. External trade is the only exogenous variable in estimation that is insignificant. The results further reveal that economic growth is unable to significantly respond to its own lag. Generally, the estimation results conform to theoretical expectations.

One major implication of the findings is that SSA countries have benefited substantially from development aid. It is, therefore, important for these countries to develop stronger institutions that would attract more inflows of development aid.

The study was motivated by the fact that less attention has been given to the role of disaggregate ODA in economic growth of African countries. Previous research works have tended to focus more on aggregate ODA. Furthermore, adequate research has yet to be done on how ODA complements the private sector, government sector and external sector in facilitating growth of African countries. These issues are investigated in the study.

The authors make a fundamental initial effort to conduct a systematic review analysis on “cryptocurrency,” mainly to analyze the way it has been changing the “stereotype” financial transactions, and also identify the probable unexplored research avenues on this innovative investment regime. The study aims to draw the landscape of the current state, prospects, challenges, trends and possible agendas of cryptocurrency in the global market.

Using a quali-quantitative approach widely known as meta-literature review, the synthesis analysis on “cryptocurrency” is conducted. Methodologically, the authors review and analyze the most recent and relevant papers preferably published between 2016 and 2020 in leading business and finance journals of ISI Web of Science (ISI WOS) through bibliometric analysis particularly coupled with content analysis.

The findings of the meta-analysis summarize the relevant stylized facts of the cryptocurrency market: distinctive features of blockchain technology, decentralized payment method, low-cost facility, ensuring pseudo-anonymity, independence from central authority, double spending attack protection, organic and instantaneous nature, among others. In addition, the analysis identified several future research regimes: pricing model, prospect of investment regime, hedging properties, volatility dynamics, information asymmetry, underlying risk factors and bubble-like nature in global cryptocurrency market.

This academic novelty significantly contributes to enhance our knowledge on the current state-of-the-art of digital finance, outlines the research agenda and eventually provides important investment implications for financial managers, research analysts, investors, market practitioners, regulatory compliance professionals and policymakers. Therefore, the findings shed the lights on new investment opportunity in the global market.

Cryptocurrency, virtual currency or digital asset having cryptography for idiosyncratic security features, seems to be a persistent paradigm shift in the digitalized financial system. Despite the continuing growth, the academic research on cryptocurrency is still at nascent stage, particularly because researchers did not deeply draw attention at this financial innovation. In addition, the authors argue that none of the earlier studies yet conducted a meta-analysis on this latest investment regime. Therefore, this review study is the initial attempt to fill up the gap in the finance literature.