Search results

The purpose of this paper is to investigate the impact of human capital (HC), intellectual property rights (IPRs) and research and development (R&D) expenditures on total factor productivity (TFP), which leads to economic growth.

The panel data technique is used on a sample of 16 countries categorized into two groups, namely Brazil, Russia, India and China (BRIC) and Central and Eastern European (CEE) countries and, in order to make a comparison for the time period of 2007–2015, the researchers used a fixed effect model as an estimation method for regression.

The results indicate that HC, IPRs and R&D expenditures appear to be statistically significant and are strong factors in determining changes in TFP and exhibit positive results in all sample sets. Moreover, IPRs alone do not accelerate growth in an economy, especially taking the case of emerging nations.

Considering the importance of CEE and BRIC countries, and inadequate research on these regions with respect to current study’s variables and techniques, the present research provides valuable insights about the importance of HC, IPR and R&D activities and their impact on TFP, which leads to economic growth. IPRs create a fertile environment for R&D activities, knowledge creation and economic development. Distinct nations can attain better economic status via HC, R&D activities, innovation, trade and FDI, although the relative significance of these channels is likely to differ across countries depending on their developmental levels.

The goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).

Using Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.

According to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.

The findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.

The current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

The purpose of this paper is to assess the impact of the Ukraine–Russia military conflict on the returns and investment flows of equity funds across multiple countries.

Using a comprehensive sample of 1,281 equity funds in 40 countries. The countries were segregated into conflict states, members of NATO, and those which abstained from voting on the UN resolution on March 2, 2022. The authors employ a GARCH-based event study and estimate CARs for t−5, t−3, t, t + 3, and t + 5 event windows. Further, the authors use panel estimation to assess the link between the CARs and the investment exposure of the sample funds.

The findings highlight an adverse reaction of mutual funds in Russia, Ukraine, and the NATO States. On the contrary, the mutual funds in the countries that abstained during the voting on the UN resolution on March 2nd posted positive abnormal returns. Similarly, the investment exposure towards the conflicted countries and NATO states is unfavorable except for the abstained countries.

This is the primary study to evaluate the impact of the recent geopolitical tensions on mutual funds domiciled across various geographical locations.

This study examined the impact of; COVID-19 investor sentiment, COVID-19 cases, geopolitical risk (GPR), economic policy uncertainty (EPU), oil returns and Islamic banking on bank stock returns. In addition, it examined whether Islamic bank stock returns differed from conventional banks when interacting with selected variables.

This study consisted of 137 conventional and Islamic stock market listed banks in 16 Middle East and North Africa (MENA) countries from February 2020 to July 2021. Monthly data were used for bank stock returns, number of COVID-19 cases, COVID-19 investor sentiment, oil price and EPU, while GPR data were obtained annually. This paper used unconditional quantile regression (UQR) in its analysis.

COVID-19 investor sentiment and EPU negatively influenced bank stock returns. However, oil returns were only positive and significant in first quantile. Conversely, GPR negatively impacted bank returns up to the median quantile, while the impact was positive in upper quantiles. Islamic banks outperformed conventional banks in all quantiles. Additionally, GPR negatively influenced Islamic bank returns up to 75th quantile, while oil returns negatively impacted Islamic bank returns up to 95th quantile. Ultimately, COVID-19 investor sentiment and EPU positively influenced Islamic bank returns up to 95th quantile.

Market conditions must be considered when implementing investment decisions and policies, as the effects of market shocks are mostly asymmetrical. For example, it is important for international investors to take into consideration asymmetric factors, such as market uncertainty in oil market. Especially in bearish Islamic markets, bad news concerning uncertainty can be perceived as riskier than good news.

A change in health sentiment, such as COVID-19 cases and COVID-19 investor sentiment, can be used to determine future direction of conventional and Islamic stock markets. Asymmetric effects associated with market news can make portfolio management more effective. COVID-19 investor sentiment states can be used to predict Islamic market index dynamics in MENA region.

This paper offered insight into heterogeneity of market conditions and dependencies of Islamic banks' stock market returns on COVID-19 investor sentiment and uncertainty, among others that should be considered when implementing investment decisions and policies.