Background: In recent years, reputation has become an important risk concern for companies around the world. Deloitte Global Survey highlights the reputation risk as the top strategic business risk in 2014. This is also proven by a research conducted by AON Global Risk Management Survey in 2015 and Allianz Risk Barometer Survey in 2016 which finds a loss of reputation as one of the biggest risks for business executives. Furthermore, the importance of reputation is confirmed by the fact that reputation accounts for more than 25 percent of a company's market value and the total market capitalization of the S&P500 companies. Objectives: To investigates the relationship between corporate reputation and financial performance. Methods/Approach: The survey of the paper was conducted in 2015 in Croatia. The questionnaire for assessing corporate reputation contained three reputational dimensions: products and services, corporate integrity, and organizational performance while the financial dimensions contained indicators of EVA, ROCE, ROA, ROE and the financial stability coefficient. Hierarchical regression methods were applied in the analysis. Results: This research leads to the conclusion that some dimensions of corporate reputation can be important predictors of financial performance. Conclusions: Results of the research could be a valid motivation for business executives to consider reputation risk as a critical issue of corporate business strategy.

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Background:The stock exchange, as a regulated financial market, in modern economies reflects their economic development level. The stock market indicates the mood of investors in the development of a country and is an important ingredient for growth. Objectives: This paper aims to introduce an additional statistical tool used to support the decision-making process in stock trading, and it investigate the usage of statistical process control (SPC) methods into the stock trading process. Methods/Approach: The individual (I), exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts were used for gaining trade signals. The open and the average prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the analysis. The statistical control charts capabilities for stock trading in the short-run were analysed. Results: The statistical control chart analysis pointed out too many signals to buy or sell stocks. Most of them are considered as false alarms. So, the statistical control charts showed to be not so much useful in stock trading or in a portfolio analysis. Conclusions: The presence of non-normality and autocorellation has great impact on statistical control charts performances. It is assumed that if these two problems are solved, the use of statistical control charts in a portfolio analysis could be greatly improved.

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The unemployment can be considered as one of the main economic problems. The aim of this article is to examine the differences in male and female unemployment rates in selected European countries and to predict their future trends by using different statistical forecasting models. Furthermore, the impact of adding a new data point on the selection of the most appropriate statistical forecasting model and on the overall forecasting errors values is also evaluated. Male and female unemployment rates are observed for twelve European countries in the period from 1991 to 2014. Four statistical forecasting models have been selected and applied and the most appropriate model is considered to be the one with the lowest overall forecasting errors values. The analysis has shown that in the period from 1991 to 2014 the decreasing trend of unemployment rates in the short-run is forecasted for more Eastern Balkan than the EU-28 countries. An additional data point for male and female unemployment rates in 2014 led to somewhat smaller forecasting errors in more than half of the observed countries. However, the additional data point does not necessarily improve forecasting performances of the used statistical forecasting models.