The objective of the research was to identify the relationship between farm assets and the sources of financing used in agricultural operations, through the application of canonical correlation analysis (CCA). The study was conducted on a group of farmers in Central Pomerania, Poland. The analyses were based on data obtained from 150 farms during the pilot study that was implemented in 2023. The study employed the CATI (Computer-Assisted Telephone Interview) survey technique, which was based on an interview questionnaire. Calculations and graphical representation were performed using the R statistical program ver. 4.4.2, with the following packages: CCA (ver. 1.2.2), CCP (ver. 1.2), GGally (ver. 2.2.1) and corrplot (ver. 0.95). The study found that among the asset-related variables, the first two canonical dimensions are most strongly influenced by ASSET_BUILD and ASSET_STOCK. Among the financing-related variables, the first canonical dimension is most strongly influenced by DP_INVEST and DP_PROD, the second – by LEASE, COMMER_LOAN and EQUITY. The work is part of research on the use of intelligent information systems, particularly econometric methods and statistical computer packages, in studies on financial and investment decisions of farms and agricultural households.

The article analyzes the possibility of using bankruptcy risk forecasting models to assess the situation of food industry companies in Poland. The aim of the article is to assess the risk of bankruptcy of companies in the food industry using the example of WIG-Spożywczy index in 2020-2023. It presents the methodological assumptions of three discriminant models (D. Hadasik model, Poznań model, T. Maślanka model) and their application in the research carried out. The research is distinguished by its consideration of the recent economic turbulence during the COVID-19 pandemic and the armed attack by Russia on Ukraine. These events have had significant repercussions not only on social life but also on the global economy. The conducted study also stands out due to its use of three discriminant models, and the differing results for each model highlight the validity of this approach. Six companies out of fifteen exhibited stable financial conditions across all models, with no indication of bankruptcy risk. These entities operate in diverse fields, showcasing variability in their operations. The research was conducted using the Statistica 13.3 package. The use of discriminant analysis and IT tools enhances financial risk evaluation and decision-making in companies. Intelligent information systems are highly significant for the advancement of research in assessing the economic condition of enterprises, as they allow for the analysis of data across multiple periods, the identification of cause-and-effect relationships, and the evaluation of the impact of individual variables on the phenomenon under investigation.

The article evaluates the applicability of the Dynamic Financial Analysis (DFA) method for assessing the financial performance of insurers by incorporating into the analysis various areas of risk affecting the financial position, with a particular focus on solvency. DFA is, along with Asset Liability Management (ALM) and Data Envelopment Analysis (DEA), one of the most widely used methods. Intelligent information systems are of great importance for the advancement of research in the assessment of the financial situation and solvency of insurance companies, since they allow the analysis of data over several periods, the identification of cause-and-effect relationships, and the evaluation of the impact of individual variables on the phenomenon under study. DFA makes it possible to carry out an integrated and holistic quantitative analysis of the relevant risk factors and determine the interrelationships between the determinants. The aim of the research is to present the methodological assumptions and to demonstrate, on the basis of a critical literature review, the usefulness of the Dynamic Financial Analysis (DFA) for the assessment of the financial condition and solvency of insurers. The article presents the methodology for proceeding with DFA, and critically evaluates the cases of application of the method that have been described in the literature. Its advantages and disadvantages were indicated. It was confirmed that the DFA method can be an important tool for making financial decisions of insurers. The disadvantages of the method are the ambiguous approach to the treatment methodology, as well as the need to generate a large number of scenarios for the insurance sector. Systematizing the approaches and regulating the application of the DFA method would spread its use by insurers.

The objective of the paper was to evaluate the behavior of farmers in the utilization of financial services and products through the application of Latent Class Analysis (LCA). The study was conducted on a group of farmers in Central Pomerania, Poland. The data set comprised 150 farms, obtained from the pilot study conducted in 2023 using the CATI (Computer-Assisted Telephone Interview) survey technique based on an interview questionnaire. The research findings demonstrated the applicability of Latent Class Analysis in the assessment of farmers’ behavior in the market of financial services and products. The use of this method for unique survey results is innovative and contributes to the development of research on farmers’ financial decisions. The research identified three distinct groups of farmers. Group 1 is comprised of farmers who exhibit a high degree of caution in their utilization of financial services and products. Group 2 comprises farmers who are active in the market of financial services and products. In contrast, Group 3 represents those farmers who, in terms of their behavior on the market of financial services and products, take relatively high risks. The calculations were performed using the R statistical program with the poLCA package. Intelligent information systems are of high importance for the advancement of research in the evaluation of the behavior of farmers in the use of financial services and products, since they allow the identification of cause-and-effect relationships and the evaluation of the impact of individual variables on the phenomenon under study.

The following research focuses on the use of discriminatory models in predicting bankruptcy of energy companies in Poland. Over the past several years, this sector has faced unforeseen events, such as the COVID-19 pandemic or the conflict in Ukraine. Past studies have shown that foreign discriminatory models should not be used in the context of the Polish market. As a consequence, a T. Maślanka's model adapted for Polish companies was used. The aim of the article was to verify the financial condition (in the context of bankruptcy risk assessment) of companies in the energy sector using multi-criteria discriminant analysis. Statistica 13.3 package was used for calculations. The results of the research proved that discriminant models can be used to assess financial health and bankruptcy risk. Applying them in the empirical research presented in the article proved that the energy companies had varying financial health in 2019-2022, so in the period immediately before, and during the COVID-19 pandemic and the war in Ukraine. Most companies are not at risk of bankruptcy and have coped relatively well with the unstable situation in the global economy, including the commodity markets. Five companies (PGE, ENEA, PEP, ONDE and KOGENERA) obtained a T.Maślanka's model score above the cut-off point throughout the study period, indicating their good financial health. CEZ obtained results indicating a high risk of bankruptcy. The conclusions of the study are the first, known to the authors, researches about energy companies in Poland in the period of recent financial market fluctuations.

The article presents the use of mixed logistic regression models in the study of the effectiveness of investment funds. The aim of the research is to identify and evaluate the factors influencing the effectiveness of commodity funds in Poland using mixed logistic regression models. R 3.6.0 statistical package was used for calculations. The logarithmic rate of return was adopted as a measure of the effectiveness of the funds, and it also served as the dependent variable. Eleven internal and external factors influencing the effectiveness of funds were identified as explanatory variables. The time horizon of the study covered a 5-year period from 2015 to 2019. The subject of the study were commodity funds operating in Poland. The created mixed logistic regression model made it possible to conclude that three factors have a statistically significant impact on the chance of recognizing a commodity fund as efficient: the value of cash flows (CF), the value of the CRB Commodity Index (CRB) and the gold futures price (GC).

The aim of the research was to identify the characteristics of agricultural households at risk of financial self-exclusion using the Chi-squared Automatic Interaction Detector (CHAID) analysis. The source of data was the results of a survey conducted among 348 agricultural households of Central Pomerania (Poland). The obtained results prove that nearly 40% of the analyzed households did not use financial services and products of their own choice. Using the CHAID analysis, it was found that the strongest predictor differentiating the studied population in terms of financial self-exclusion was the level of average monthly household income (INC). Significant predictors were also those relating to the size and composition of the household (SHW and SHCH), the propensity to save (SAV) and features related to the production potential of a farm (AREA) and the effectiveness of using this potential, i.e. the value of produced output (PROD). The Statistica 13.3 software was used to build the algorithm and create a graphic form of the decision tree.

The article presents the methodology of applying factor analysis. The purpose of the research is to examine the applicability of factor analysis in the assessment of factors determining the return on equity of farms in the European Union, aimed at reducing the correlated variables without losing the information contained therein regarding the return on equity. The research used data published in the European FADN (Farm Accountancy Data Network). Factor analysis was performed with the R statistical program, ver. 4.2.2. On the basis of the conducted research, 4 factors were identified, into which 15 variables were classified. The factors were given the following names: Farm size and production potential (factor 1), Income and ability to self-finance operations (factor 2), Effects of long-term investment and financial decisions (factor 3) and Investment activity (factor 4). The first factor includes: Labor, Area, Subsidies, S_loans, Inputs, Output, Current_assets and Size. To the second: Income, Cash_flow 2 and Cash_flow 2. The third factor includes Fixed_assets and LM_loans, while the fourth one includes Net_inv and Gross_inv. The use of factor analysis allowed the reduction of variables and the identification of factors determining the return on equity of farms from European Union countries.

The paper presents the methodological assumptions for the linear mixed model and shows its usefulness in research on the factors determining the efficiency of investment funds. The aim of the study was to identify and evaluate the factors determining the efficiency of commodity funds in Poland using a linear mixed model. The Sharpe ratio was adopted as the fund's efficiency criterion. The subjective scope of the research covered all funds in Poland which at the end of December 2019 declared their investment portfolio exposure to commodity markets. The time scope of the study was 5 years, and annual data were used for the analysis. Based on the use of a linear mixed model, it was shown that there is a statistically significant negative relationship between the effectiveness of the studied funds and their operating time. This means that commodity funds that have been operating on the market for a shorter period of time achieve better investment results.

This paper surveys and critically evaluates the literature on the use of advanced methods to assess the financial effectiveness of insurance entities. The paper presents methodological assumptions for Data Envelopment Analysis (DEA) using the CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models. The usefulness of these methods for assessing the financial efficiency of insurance companies was also shown on the example of entities conducting insurance activities in Nigeria, Malaysia, Singapore, Indonesia, Serbia, Poland and Slovakia.

This paper presents the methodological assumptions of a logistic regression model and presents an example of the use of this type of models to assess the factors determining investment decisions of agricultural enterprises. The aim of the study was to identify and evaluate the factors influencing the decisions of agricultural enterprises in Central Pomerania with respect to tangible investments using the logistic regression model. The results of the research proved that the probability of making tangible investments by agricultural enterprises in Central Pomerania is influenced by financial factors: the structure of the use of capital expenditure (x1, x4); use of traditional sources of financing (x5, x6, x8); use of alternative sources of financing (x13); variable combining the use of traditional and alternative sources of financing (x14), as well as non-financial factors: characteristics of the enterprise (x17) and behavioral aspects (x18). It was also established that financial factors have a greater impact on the development of tangible investments in agricultural enterprises in Central Pomerania than non-financial factors.

The article deals with the problem of using the classification and regression trees (CRT) method to identify socio-economic features of agricultural households at risk of financial exclusion. Financial exclusion applies to people/households who do not use or use financial services and products to a small extent, both of their own choice and due to barriers in accessing financial products and services. The research used empirical data on agricultural households in Central Pomerania (Poland). Based on the use of the decision tree method, it was shown that nearly half of the surveyed farms do not use financial products and services or use them to a small extent, therefore they are financially excluded. Higher acreage indicates a lower risk of financial exclusion. The age of the household head influences the risk of financial exclusion. The fact of saving confirms the desire to multiply capital, hence in the group of farms where this occurs, the risk of financial exclusion is lower than in the remaining ones.

Insurance companies in their current operations focus mainly on business risks related to insurance products and the best matching of the offer to the needs of potential customers. The selection of appropriate methods to determine the socio-economic characteristics of people who use voluntary life insurance can increase the effectiveness of their sales, while affecting the increase in the efficiency of the insurance industry. Using the multidimensional correspondence method, this document comprehensively identifies the factors conditioning voluntary life insurance. The survey is based on a set of data from a questionnaire. Surveys were conducted among Polish households from the region of Central Pomerania. The results show that access to financial services and products, savings and education determine the existence of life insurance in households from the region of Central Pomerania. This study contributes to literature and practice by showing that multidimensional correspondence analysis can be an effective method for identifying conditions that will increase supply and demand for life insurance.

The paper presents methodological assumptions regarding the logistic regression model and an example of using this research method to evaluate financial decisions taken by households. The aim of the study was to identify and evaluate socio-economic factors determining the debt of households in Central Pomerania using a logistic regression model. The source of data was the results of a survey conducted among 1,000 households of Central Pomerania (Poland). The obtained results prove that the following factors related to the socio-economic characteristics of households: economic education of the head of the household, developmental phase of the household, socio-economic type of the household had a statistically significant positive impact on the likelihood of Central Pomeranian households using external sources of financing: household income and household income. These factors increase the likelihood of households using external sources of financing. In turn, a statistically significant negative impact on the analyzed phenomenon had the household income diversification and the age of the household head.