Purpose: The paper aims to determine the impact of bank-specific, industry-specific and macro-specific determinants on the profitability indicators – return on assets (ROA) and ratio net-interest margin (RNIM).Design/methodology/approach: This research sample includes selected Croatian banks, and the empirical analysis covers the period 2007-2014. Based on the reliable and robust econometric tests, dynamic estimation technique (DOLS) was run to estimate the profitability models, by using of ROA and RNIM as dependent variables, which also include lagged dependent variables to capture the speed of mean reversion in terms of profitability, respectively.Findings: The results proved the crucial positive impact of assets size (economies of scale), loan portfolio and GDP growth on the banks’ profitability. Further, the negative impacts on profitability have risks and administrative costs. This paper shows the positive impact of capital adequacy ratio (CAR) and leverage on ROA and RNIM, as well as the correlation between market concentration and banks’ profitability.Practical implications: Basically, Croatian banks should improve operative efficiency and risk management practice to increase their profitability. In addition, banks should carefully balance between capital base and risk exposure on the one hand and take advantage of using relative cheaper deposits and borrowed funds instead of using more expensive equity. This conclusion is reasonable, keeping in mind that the Croatian financial market does not punish banks for an extra risk exposure caused by market imperfections. Finally, the regulatory authority in Croatia should impose some additional antitrust measures to increase competition in the banking market.Originality/value: Although a bunch of existing studies explain the determinants of bank profitability from different perspectives, this paper conducts a specific empirical analysis about the determinants of bank profitability in Croatia. In addition, this paper provides a good synthesis of the relevant empirical and theoretical studies from this domain.

This article investigates the impact of institutional variables on economic activity (GDP) in 19 Central and Eastern Europe (CEE) countries, covering the period 1999–2016. We utilise Principal Component Analysis (PCA) to construct a hybrid measure of economic freedom, and the Random Effect model to estimate the causalities. The analysis shows that many CEE countries have improved their institutional setting, especially the control of corruption. The regression results indicate that the control of corruption and democracy have a positive and significant impact on the GDP. Also, political stability promotes growth, while the quality of regulation is statistically insignificant. Finally, the rule of law and economic freedom accelerate economic growth. Overall, the legal framework, political stability and economic freedom shape the economic reality in the CEE region. We conclude that institutions matter for growth in the CEE countries.

The paper deals with the efficiency dynamics of the Croatian banking industry, covering the period from 2006 to 2015. We have implemented the intermediation approach, using interest and non-interest expenses and revenues as the input and output variables, respectively. The variable return to scale (BCC) Data Envelopment Analysis (DEA) output-oriented model has been implemented, and we have estimated the crisis-driven efficiency trends, as well as the impact of the EU membership. We further estimated the efficiency effects of the relative market power/size, ownership structure, and origin of capital. The global crisis had detrimental effects since the overall efficiency score dropped by about 3%. On the contrary, Croatian banks have largely benefited from the EU membership, and the efficiency score after the EU association increased by about 45%. The market leaders are more efficient than the competitive fringe, which is in line with the efficiency structure hypothesis. In addition, the biggest banks are the most efficient ones, meaning that the scale efficiency hypothesis has also been upheld. Contrary to the agency theory hypothesis, state-owned banks are permanently more efficient than private banks. Finally, the results support the home-field advantage hypothesis exclusively for the pre-crisis period (2006–2009).

The study relies on an extensive dataset (≈1.86 million news headlines) to investigate the heterogeneity and predictive power of explicit sentiment signals (TextBlob, VADER, and FinBERT) and implied sentiment (VIX) for stock market trends. We find that news content predominantly consists of objective or neutral information, with only a small portion carrying subjective or emotive weight. There is a structural market bias toward upswings (bullish market states). Market behavior appears anticipatory rather than reactive: forward-looking implied sentiment captures a substantial share (≈45–50%) of the variation in stock returns. By contrast, sentiment scores, even when disaggregated into firm- and non-firm-specific subscores, lack robust predictive power. However, weekend and holiday sentiment contains modest yet valuable market signals. Algorithm-wise, Gradient Boosting Machine (GBM) stands out in both classification (bullish vs. bearish) and regression tasks. Neither FinBERT news sentiment, historical returns, nor implied volatility offer a consistently exploitable edge over market efficiency. Thus, our findings lend empirical support to both the weak-form and semi-strong forms of the Efficient Market Hypothesis. In the realm of exploitable trading strategies, markets remain an enigma against systematic alpha.