Influence of Legal Form and Non-Anonymous Ownership Structure on Corporate Financial Performance

The paper represents a continuation of our previous results, which were closely linked to the topic of automated search for factors of corporate competitiveness and financial performance. As opposed to the results presented at ICMLG 2014 and ECMLG 2014, the current research deals with other characteristics of organizations not investigated before and related to Corporate Governance, like their legal form, the international diversity of top management (domestic only or also foreign) and particularly the non-anonymity of the ownership structure.The data were gathered in the period 2011 to 2013 from 222 companies with various legal forms. The main purpose of our research was to investigate which of these characteristics have an impact on corporate financial performance that has been assessed by the Return on Assets (ROA) index. The paper attempts to answer the research question of whether the ownership structure, particularly the fact whether the organization or enterprise owners are or are not anonymous, has a major influence on corporate financial performance. The methodology used in analysing and processing the data had to respect the fact that the characteristics (variables) of the companies which were individually investigated are not mutually independent, thus multidimensional methods have to be used. Therefore, we used here our non-linear kernel regression model which had already been successfully verified, having been developed in the field of statistical pattern recognition. Prediction error of the proposed model is then used as a feature selection criterion in the process of identifying factors that affect Corporate Governance and the financial performance the most. The results presented in the paper demonstrate that the type of ownership structure (anonymous or non-anonymous) has a dominant influence on the financial performance among the investigated characteristics. The target audience includes researchers in the fields of management and business science.Příspěvek představuje pokračování našich předchozích výsledků, které úzce souvisí s tématem automatizovaného hledání faktorů firemní konkurenceschopnosti a finanční výkonnosti. Na rozdíl od výsledků prezentovaných na ICMLG 2014 a ECMLG 2014, se současný výzkum se zabýval jinými charakteristikami organizacemi, které také souvisí s Corporate Governance - právní forma, mezinárodní diversita vrcholového managementu (tuzemský versus zahraniční) a zejména anonymita vlastnické struktury. Z údajů vlastníků jsou shromážděny z období 2011 až 2013 a týkají se 220 podniků s různou právní formou. Hlavním účelem našeho výzkumu bylo zjistit, které z těchto charakteristik mít dopad na podnikovou finanční výkonnost, která byla hodnocena ukazatelem ROA. Článek se pokouší odpovědět na výzkumnou otázku, zda anonymizovaná struktura vlastníků, má vliv na firemní finanční výkonnost. Metodika použitá při analýze a zpracování dat musela respektovat skutečnost, že jednotlivé zkoumané podniky vlastnosti (proměnné) nejsou na sobě nezávislé z hlediska použité vícerozměrné metody. Z uvedeného důvodu jsme použili námi již úspěšně ověřený non-lineární regresní model v oblasti statistického rozpoznávání obrazců. Chyba predikce navrhovaného modelu se pak používá jako kritérium výběru funkce v procesu identifikace faktorů, které mají nejvíce vliv na řízení podniků a finanční výkonnost. Dosažené výsledky ukazují, že typ vlastnické struktury (anonymní nebo neanonymní) má dominantní vliv na finanční výkonnost u zkoumaných charakteristik. Cílovou skupinou jsou výzkumní pracovníci v oblasti managementu a podnikání.The paper represents a continuation of our previous results, which were closely linked to the topic of automated search for factors of corporate competitiveness and financial performance. As opposed to the results presented at ICMLG 2014 and ECMLG 2014, the current research deals with other characteristics of organizations not investigated before and related to Corporate Governance, like their legal form, the international diversity of top management (domestic only or also foreign) and particularly the non-anonymity of the ownership structure.The data were gathered in the period 2011 to 2013 from 222 companies with various legal forms. The main purpose of our research was to investigate which of these characteristics have an impact on corporate financial performance that has been assessed by the Return on Assets (ROA) index. The paper attempts to answer the research question of whether the ownership structure, particularly the fact whether the organization or enterprise owners are or are not anonymous, has a major influence on corporate financial performance. The methodology used in analysing and processing the data had to respect the fact that the characteristics (variables) of the companies which were individually investigated are not mutually independent, thus multidimensional methods have to be used. Therefore, we used here our non-linear kernel regression model which had already been successfully verified, having been developed in the field of statistical pattern recognition. Prediction error of the proposed model is then used as a feature selection criterion in the process of identifying factors that affect Corporate Governance and the financial performance the most. The results presented in the paper demonstrate that the type of ownership structure (anonymous or non-anonymous) has a dominant influence on the financial performance among the investigated characteristics. The target audience includes researchers in the fields of management and business science

Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy

The article focuses on the analysis of motivation principles in human resource management and their further verification by factor analysis. The objective is to identify the main motivation principles and their impacts on employee turnover as well as formulate suggested practices to eliminate the negative impact of employee disaffection and turnover. The identification of motivation principles is based on a content analysis of professional and scientific publications aimed at motivation. The results and conclusions of this study were consequently verified by a quantitative survey, the data of which were statistically processed. As a suitable statistical analysis to assess the data from the survey, a factor analysis was chosen. The data for the factor analysis were collected and analysed based on two quantitative surveys focused on the causes of employee turnover. The results of both analyses proved and verified identical principles of employee management that affect job satisfaction and the decisions of employees to stay or leave their current job positions

Feature subset selection in large dimensionality domains

Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of Simulated Annealing with the very high rate of convergence of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of Generalized Regression Neural Networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms

Avast-CTU Public CAPE Dataset

There is a limited amount of publicly available data to support research in malware analysis technology. Particularly, there are virtually no publicly available datasets generated from rich sandboxes such as Cuckoo/CAPE. The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution. The machine can be infected by malware hence there is a good chance of capturing the malicious behavior in the execution logs, thus allowing researchers to study such behavior in detail. Although the subsequent analysis of log information is extensively covered in industrial cybersecurity backends, to our knowledge there has been only limited effort invested in academia to advance such log analysis capabilities using cutting edge techniques. We make this sample dataset available to support designing new machine learning methods for malware detection, especially for automatic detection of generic malicious behavior. The dataset has been collected in cooperation between Avast Software and Czech Technical University - AI Center (AIC)

Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems

The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data

Feature Selection - A Very Compact Survey Over the Diversity of Existing Approaches

Feature Selection has been a subject of extensive research that nowadays extends far beyond the boundaries of statistical pattern recognition. We provide a concise yet wide view of the topic including representative references in an attempt to point out that important results can be easily overlooked or duplicated in a variety of – even indirectly related – research fields

Nový algoritmus hledání cesty pro sešívání obrazů a pokročilé dlaždicování textur

We propose a fast and adjustable sub-optimal path search algorithm for finding minimum error boundaries between overlapping images. The algorithm may serve as an alternative to traditional slow path search algorithms like the dynamical programming. We use the algorithm in combination with novel adaptive blending to stitch image regions. The technique is then exploited in a framework for sampling-based texture synthesis where the learning phase is clearly separated and the synthesis phase is very simple