Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-art method for analyzing the structure and risk within stock markets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stock market in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson's correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts.

The risk of distortion of financial statements has been growing. Following the 2008 crisis, recipients of financial information are increasingly focusing on the likelihood of financial statements being distorted through fraudulent presentation of financial information. Therefore, scientific research pays more attention to models capable of detecting financial statement manipulation.The paper aims to present the principles of functioning and the possibility of using the Beneish M-score model in Polish realities. It analyzes the history of more than 30 companies listed on the Warsaw Stock Exchange to select those whose history indicates that they can be classified as manipulators, and to select the same number of companies from the control group that are considered as non-manipulators.The research method involves the analysis of empirical data on companies listed on the Warsaw Stock Exchange. The analysis showed the 8-factor Beneish model identified manipulators with 100% accuracy and succeeded in identifying non-manipulators. The effectiveness of the 5-factor model was much lower. To serve the purpose of the study, the effectiveness of the Beneish model was tested on a small sample of Polish listed companies as an introduction to a planned larger scale research. The results obtained are consistent with the results of numerous studies by authors from various countries and confirm the effectiveness of the Beneish model in detecting financial statement manipulation. AcknowledgmentThe publication is sponsored by funds from the Cracow University of Economics for the maintenance and development of research potential.

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The bankruptcy of an individual is a standard situation in a free market economy. The aim of this study is to present the reasons for the bankruptcy of entrepreneurs in Poland, in the light of literature research and the authors' own research. These studies were carried out in two ways – the first stage concerned the analysis of court files of entrepreneurs who filed for bankruptcy. The second element of the research included conducting surveys among entrepreneurs and trustees. In total, 331 surveys were collected among entrepreneurs and 87 from syndicates. The results of the surveys presented in the article focus on the analysis of the reasons for the bankruptcy of entrepreneurs in Poland in the opinions of trustees and management. Conducted research in the field of comparative analyses (syndics, management) is the essence of the study, where the determinants of the bankruptcy of entrepreneurs from various points of view have been diagnosed.

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