Purpose The purpose of this study is to measure financial inclusion (FI) and to examine the role of digital financial literacy (DFL) and its components, and various socio-demographics in relation to FI. In addition, the mediating effect of digital financial attitudes (DFA) on the relationship between digital financial knowledge (DFK) and digital financial behaviour (DFB), as well mediating effect of DFA and DFB on the relationship between DFK and FI, is being explored.Design/methodology/approach Using a cross-sectional research design, we utilize a dataset from the survey of adults’ financial literacy in Bosnia and Herzegovina, collected from the representative sample of 1,096 adults in 2022. The main methodology relies on logistic and ordinal logistic regression analyses and PROCESS for mediation analyses.Findings The findings suggest that the effect of DFK on DFB is partially mediated by DFA. In addition, the effect of DFK on FI is fully mediated through three pathways: DFA, DFB, and DFA and DFB in serial mediation. Age, education, employment status and residence are significantly related to FI. Internet access is significant only for FI scores but not for adults’ banking status. Although women are almost twice as unbanked as men, we find no gender-based differences in financial product holdings, FI or adults’ banking status.Practical implications There is a need to enhance DFK and DFA to enable adults to use financial products. Financial institutions could use our results in designing and promoting their services.Social implications Policy implications are seen in the need for developing national strategies for financial education, with an emphasis on strengthening DFL, especially DFK and DFA, which will enhance the formal FI of adults. Also, governments should work on expanding Internet access.Originality/value The results make a contribution to the theory of planned behaviour. They contribute to the limited empirical evidence of the mediating role of DFA in relationship to DFB, as well as the mediating role of DFA and DFB in relationship to FI.

Abstract This study investigates the benefits of international diversification in the stock markets of the 28 European countries (the EU and the UK) over two five-year periods: a stable period from 2014 to 2019 and a turbulent period from 2019 to 2024. The analysis draws on the Markowitz mean-variance, Sharpe reward-to-variability, and naive diversification models, based on which different investment strategies were developed and implemented. We find that actively managed portfolios perform significantly better than naively diversified portfolios. The analyzed markets exhibit positive short-term associations, with an average correlation coefficient of 0.29 in the first period and 0.46 in the second period. However, these markets do not show long-term cointegration. Recent crises have reduced diversification benefits, yet significant opportunities for diversification remain. Diversification benefits are almost halved in the second period: average single-market standard deviation can be reduced by 60.5% with investments in 20-indices portfolios in the stable period, and only by 33.7% with the same portfolio size in the turbulent period.

Financial literacy is a critical life skill that is essential for achieving financial security and individual well-being, economic growth and overall sustainable development. Based on the analysis of research on financial literacy, we aim to provide a balance sheet of current research and a starting point for future research with the focus on identifying significant predictors of financial literacy, as well as variables that are affected by financial literacy. The main methods of our research are a systematic literature review, and bibliometric and bibliographical analysis. We establish a chronological path of the financial literacy topic in the scientific research. Based on the analysis of the most cited articles, we develop a comprehensive conceptual framework for mapping financial literacy. We identified a large number of predictors of financial literacy starting with education, gender, age, knowledge, etc. Financial literacy also affects variables such as retirement planning, financial inclusion, return on wealth, risk diversification, etc. We discuss in detail the main trends and topics in financial literacy research by involving financial literacy of the youth, financial literacy from the gender perspective, financial inclusion, retirement planning, digital finance and digital financial literacy. Our research can help policymakers in their pursuit of improving the levels of individual financial literacy by enabling individuals to make better financial decisions, avoid financial stress and achieve their financial goals. It can also help governments in their efforts in achieving sustainable development goals (SDGs).

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

Abstract Background: Due to strong empirical evidence from different markets, existence of value premium became a financial theory standpoint. Although previous studies found that value stocks beat growth stocks in bearish and bullish markets, during the GFC, value stocks underperformed growth stocks. Objectives: This paper aims to examine the performance of value and growth stock portfolios after the GFC. Subjects of our analysis are constituent companies of the DJIA index, out of which portfolios of large-cap value and growth stocks have been constructed and evaluated. Methods/Approach: We measure the performance of stock portfolios, which are created based on the naïve diversification rule and random weighting approach. Statistical testing includes Levene’s homogeneity test, the Mann-Whitney U test, T-test, and the One-Sample T-test. Results: Growth stock portfolios outperform value stock portfolios after the GFC. The dominance of growth stock portfolios compared to value stock portfolios is significant, and the value premium disappears. Conclusions: Financial theory and investment management implications show that growth stocks have overtaken the dominance over value stocks since 2009. Causes might be in (1) expansionary monetary policy characterized by very low long-term interest rates and (2) high performance of the tech industry to which most growth stocks belong.

Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.

We analyzed the efficiency of the insurance industry in Bosnia and Herzegovina (BiH) in the period from 2015 to 2019 in order to identify good and bad practices, sources of inefficiency and to propose guidelines for the necessary efficiency improvements based on the results. Efficiency measurement was performed using the nonparametric Data Envelopment Analysis (DEA) technique as the most commonly used tool for efficiency analysis in finance. We used one output and two input variables according to the input-oriented approach assuming a variable return to scale (VRS). Empirical research was conducted on all insurance companies from BiH, which are grouped according to the size of assets, type of insurance, and headquarters in order to determine whether there are differences in the efficiency of insurance companies in terms of their size, type of insurance, or depending on whether it operates in the Federation of Bosnia and Herzegovina (FBiH) or Republic of Srpska (RS). The results of the analysis indicate significant inefficiencies in the insurance sector in BiH, but also differences among the observed groups. The insurance sector is more efficient in FBiH compared to RS, and insurance companies in the composite insurance market are significantly more efficient than companies in the non-life insurance market. Finally, the research has showed a relatively high level of positive correlation between the size of an insurance company and its efficiency. According to all efficiency indicators, there is significant potential for efficiency improvement. Based on the analysis, the main causes of inefficiency were identified and guidelines for improving efficiency were proposed.

Abstract This paper presents linear and goal programming optimization models for determining and analyzing the food basket in Bosnia and Herzegovina (BiH) in terms of adequate nutritional needs according to World Health Organization (WHO) standards and World Bank (WB) recommendations. A linear programming (LP) model and goal linear programming model (GLP) are adequate since price and nutrient contents are linearly related to food weight. The LP model provides information about the minimal value and the structure of the food basket for an average person in BiH based on nutrient needs. GLP models are designed to give us information on minimal deviations from nutrient needs if the budget is fixed. Based on these results, poverty analysis can be performed. The data used for the models consisted of 158 food items from the general consumption of the population of BiH according to COICOP classifications, with average prices in 2015 for these products.

Abstract Diversification potential enables investors to manage their risk and decrease risk exposure. Good diversification policy is a safety net that prevents a portfolio from losing its value. A well-diversified portfolio consists of different categories of property with low correlations, while highly correlated markets have the feature of low possibilities for diversification. The biggest riddle in the world of investments is to find the optimal portfolio within a set of available assets with limited capital. There are numerous studies and mathematical models that deal with portfolio investment strategies. These strategies take advantage of diversification by spreading risk over several financial assets. Modern portfolio theory seeks to find the optimal model with the best results. This paper tries to identify relationships between returns of companies traded in South-East European equity markets. A Markowitz mean-variance (MV) portfolio optimization method is used to identify possibilities for diversification among these markets and world leading capital markets. This research also offers insight into to the level of integration of South-East European equity markets. Principal component analysis (PCA) is used to determine components that describe the strong patterns and co-movements of the dataset. Finally, we combined MV efficient frontier and equity, which represent PCA components, to draw conclusions. Our findings show that PC analysis substantially simplifies asset selection process in portfolio management. The results of the paper have practical applications for portfolio investors.

Entrepreneurs play an important role nowadays as well as in the history of economic thought - entrepreneur used to be the one bearing the risk of buying at certain prices and selling at uncertain prices, protagonist of economic activity and innovating so the new combinations of inputs are used in order to create new output. Nevertheless, relationship between entrepreneurial activity and national growth is not so straightforward. This study aims to contribute to the stream of research that tries to uncover the ultimate results of entrepreneurship. This paper seeks to explore influence of TEA in countries that belongs to different categories regarding ‘uncertainty avoidance’ cultural dimension. Our results could not confirm that entrepreneurial activity increases with increase of per capita income. We argue that entrepreneurial activity could contribute to GDP growth regardless of current level of development. We find that the entrepreneurial activity can increase or decrease GDP growth rates depending on level of preferences for uncertainty avoidance. We argue that TEA in countries with lower and higher preferences for uncertainty avoidance will negatively influence the GDP growth. Possible explanation is that less innovative ventures are created in countries with lower preference for uncertainty avoidance thus no considerable influence on GDP could be expected while more ventures fail as the entrepreneurs starts riskier business in the countries with higher preferences for uncertainty avoidance. Countries in the middle of these two extreme values can expect positive effect of TEA on GDP growth since preference for uncertainty is neither too high nor too low. Nevertheless, these results should be taken with caution since not all results were statistically significant. The main limitation is lack of data on entrepreneurial activity for all countries so instead of using TEA in the period preceding the GDP growth, average TEA for 2010-2011 was used.