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).

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 The current extremely volatile business environment requires companies to manage a wide range of risks. Poor management of the company’s main risks can lead to significant value losses for key stakeholders. Companies strive to preserve and protect their value by developing risk management models based on organisational culture, processes and structure. The main objective of this paper is to assess the maturity of risk management, explore its determinants and examine its impact on firm value. In order to quantify the maturity of the risk management model, we have created an index based on 31 reference components whose weighting values have been determined by a group of experts using the Delphi technique. In addition, this paper aims to identify the determinants of the risk management model maturity in companies in Bosnia and Herzegovina (B&H). Based on the estimated ordinary least squares (OLS) model, the results confirm that companies from the financial sector have more mature risk management models compared to the real sector. Moreover, the size of the firm and the type of auditor were identified as additional determinants of risk management maturity. The OLS model confirms the positive and statistically significant impact of risk management model maturity on Tobin’s Q value.

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.