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The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

This paper attempts to investigate the impact of the Russia–Ukraine war on the returns and volatility of the United States (US) natural gas futures market.

The study uses secondary data of 996 trading day provided by the US Department of Energy and investing.com websites and applies the event study methodology in addition to the generalized autoregressive conditional heteroscedastic (GARCH) family models.

The findings from the exponential EGARCH (1,1) estimate are the best indication of a significant positive effects of the Ukraine–Russia war on the returns and volatility of the US natural gas futures prices. The cumulative abnormal returns (CARs) of the event study show that the natural gas futures prices reacted negatively but not significantly to the Russian–Ukraine war at the event date window [−1,1] and the [−15, −4] event window. CARs for the longer pre and post-event window display significant positive values and coincides with the standard finance theory for the case of the US natural gas futures over the Russia–Ukraine conflict.

This is the first study to examine the impact of the Russia–Ukraine war on natural gas futures prices in the United States. Thus, it provides indications on the behavior of investors in this market and proposes new empirical evidence that help in investment analyses and decisions.

The purpose of this paper is to investigate the question whether democratic transition elections influence currency returns. Also, the paper examines the behavior of the currency market around these elections in Tunisia.

Empirical data are collected from the International Monetary Fund, the Central Bank of Tunisia and the Tunisian stock market websites. The paper employs event study analysis using a market model and investigates abnormal currency returns around the four election events that occurred during the period of democratic transition in Tunisia (2011–2015). A robustness test is also conducted to control for monetary policy effects.

The results indicate that democratic transition does impact currency returns. The authors did not find any significant effect on the events dates (t0). However, event windows around the elections days reacted significantly to the events. The authors notice a significant decrease in cumulative abnormal returns (CARs) at event periods leading up to the elections. Post-event windows perceived negative CARs in the first and second election, and positive CARs in the last two elections. The authors also find that the change in the victors of the elections does not cause major differences to CARs. Further, the authors do not find significant results when controlling for inflation and interest rate.

There is no evidence yet on how democratic transition elections can affect currency returns. Given that currency is a leading indicator of the performance of the financial sector, this paper should provide policymakers with new evidence on the response of currency returns to democratic transition.

The purpose of this study is to investigate the relationship between suicide rates and institutional quality.

This study used autoregressive distributed lag model and error correction model to examine short-run and long-run relationships during 1996–2017 in Lebanon.

The study shows that in the long-run, suicide rates are significantly associated to the control of corruption, voice and accountability and rule of law, but not to regulatory quality, political stability and government efficiency. In the short-run, nearly all variables of institutional quality have a negative and significant relationship to suicide rates. Results are discussed in detail.

This research is the first to inspect the relation between the quality of institutions and suicide rates. Empirical results of this study add new evidence to the literature and provide support to policymakers.

The purpose of the study is to ascertain the mediating role of meaning between servant leadership and work engagement. The study also explores the relationship between servant leadership and work engagement.

The data were collected from 704 service sector employees working in Pakistan through survey design. The data analysis was done through structural equation modeling using PLS-Smart and hierarchical regression using SPSS.

The results revealed that servant leadership was related to meaning and work engagement. Moreover, meaning was found to be related to work engagement. Finally, meaning was found to mediate the relationship between servant leadership and work engagement.

The research has ascertained the previously unexplored mediating role of meaning between servant leadership and work engagement.

To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.