Many kinds of research has suggested that innovation is positively linked to business performance and that it acts as an intermediary between organizational variables and financial performance measured by earnings achieved. Researchers worldwide have paid great attention to identifying and exploiting the main drivers of innovation management, which has led to many research articles that have adopted different approaches and identified several factors that are related to innovation. Nevertheless, there is some ambiguity about the critical behavioral factors for innovation. Therefore, this study aims to identify behavioral incentives, or key factors, that impact business innovation and financial stability, mainly in the field of strategic management, and to reveal the latest trend in corporate innovation policy by using bibliographic mapping. The purpose is to precisely define specific incentives that can influence the overall productivity and profitability of a business, and this list of innovation factors can be of benefit to a strategic manager in introducing or supporting innovative activities. The analysis is preceded by an in-depth study of publications from the Web of Science and Scopus databases and based on the VOS Viewer method (which is a mapping and clustering program for network data), the available keywords are analyzed, and then a list of incentives in strategic innovation is compiled.

Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the enterprise’s competitiveness. Managers purposely manipulate business earnings to achieve the required status of the enterprise. The consequence of these activities is to provide a positive perspective for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. This article aims to identify parallels and differences in earnings management of enterprises in the Visegrad Four and the Baltics in terms of competitiveness for the nineyear period 2010-2018. The research uses a final sample of 4,543 observations from the EBITs of Slovak, Czech, Hungarian and Polish enterprises as well as 1,633 observations from the EBITs of Latvian, Lithuanian and Estonian enterprises. Time-series methods with all necessary assumptions have been run for the analyzed financial dataset. The results of the econometric modeling of unit roots show significant parallels in these groups of countries. The enterprises from the Visegrad group and the Baltics group use the apparatus of earnings management to be competitive. The obtained results confirm the systematic but legal manipulation from the side of management. A quantitative analysis of homogeneity tests using 1,000,000 Monte Carlo simulations indicates significant time differences of manipulation in these emerging countries. The year 2014 signaled a radical “accelerando” in earnings management for the V4, and the year 2016 is highlighted for the Baltics.

Research background: The trade sector is considered to be the power of economy, in developing countries in particular. With regard to the Czech Republic, this field of the national economy constitutes the second most significant employer and, at the same time, the second most significant contributor to GNP. Apart from traditional methods of business analyzing and identifying leaders, artificial neural networks are widely used. These networks have become more popular in the field of economy, although their potential has yet to be fully exploited. Purpose of the article: The aim of this article is to analyze the trade sector in the Czech Republic using Kohonen networks and to identify the leaders in this field. Methods: The data set consists of complete financial statements of 11,604 enterprises that engaged in trade activities in the Czech Republic in 2016. The data set is subjected to cluster analysis using Kohonen networks. Individual clusters are subjected to the analysis of absolute indicators and return on equity which, apart from other, shows a special attraction of individual clusters to potential investors. Average and absolute quantities of individual clusters are also analyzed, which means that the most successful clusters of enterprises in the trade sector are indicated. Findings & Value added: The results show that a relatively small group of enter-prises enormously influences the development of the trade sector, including the whole economy. The results of analyzing 319 enterprises showed that it is possible to predict the future development of the trade sector. Nevertheless, it is also evident that the trade sector did not go well in 2016, which means that investments of owners are minimal.

The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks.

The paper deals with the possibilities of company goodwill valuation and verification. The value of company goodwill is still an actual issue for the scientific community. Goodwill as an economic phenomenon has attracted the attention of economic experts since the nineteenth century. Nowadays, there are many approaches to goodwill valuation. However, its identification and quantification are still a challenge. The paper aim is to identify significant sources of company goodwill creation and their verification on the sample of 2 European countries with the similar business environment, political stability and regulatory platform - Slovak and Czech companies. The sample for the identification of significant sources of company goodwill creation consists of the financial statements of Slovak companies in 2015. The sample for data verification consists of the financial statements of Slovak and Czech companies in 2016. The paper identifies the determinants of goodwill creation by multiple regression analysis. The paper also verifies the total explanatory power of these determinants by matrixes of changes. Volatility and deviation of the results are captured by descriptive statistical methods. The paper’s results point to a necessity to identify the key determinants of goodwill creation. They bring the construction of an econometric model for company goodwill valuation. It could be used to compute the value of company goodwill of the individual companies in the Slovak economic conditions.

From the time of Altman and the first bankruptcy prediction models, the prediction of default of companies is in the centre of interest of many economists and scientists all over the world. For companies, early detection of the possible threat of imminent financial difficulties or even bankruptcy is a very important part of financial analysis. Over the last few years, many predictive models have been created in the world. However, it has been shown that these models are not very well transferable to the conditions of the economy of another country and their prediction or rating power in another country is lower. Therefore, it is best to create a specific predictive model in the country that takes into account the situation of companies on the basis of real data on their financial situation. This paper is focused on creating a model of failure prediction of small companies in Slovakia using a well-known and widely used method of multivariate discriminant analysis. Discriminant analysis is one of the oldest multivariate statistical methods and sometimes it is difficult to fulfil certain assumptions for data. However, its results are easily interpretable and can be used to classify a company to the group of companies with risk of financial difficulties or, on the contrary, between well-prosperous companies. Prediction model is created based on real data on Slovak enterprises and has a strong classification ability in the specific conditions of the Slovak Republic.