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Science and Studies of Accounting and Finance : Problems and Perspectives
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2029-1175 - ISSN (Online) 2351-5597
Published by Aleksandras Stulginskis University Homepage  [3 journals]
  • A SYSTEMATIC STUDY OF BIOLOGICAL ASSETS AND AGRICULTURAL PRODUCTS COSTING

    • Authors: Dalia Juočiūnienė, Danutė Zinkevičienė, Neringa Stončiuvienė
      Pages: 1 - 14
      Abstract: In the context of modern agribusiness, it is important to form suitable management accounting policy for agricultural business entities and properly collect information on costs in order to use it for cost calculation of biological assets and agricultural products, prepare financial and tax reports, and analyse and make innovative management decisions. Therefore, the aim of this study is to propose a model for the formation of costing methodology for biological assets and agricultural products and determine the impact of formed cost calculation scenarios to the financial performance of the entity. Based on the results of the scientific literature analysis and the questionnaire survey of the persons managing the accounting of agricultural business entities, a matrix of scenarios for calculating the cost of agricultural production was created. After applying it to the case study and combining different indirect cost allocation alternatives with different methods of costs allocating to costing objects, the influence of the formed alternatives on the indicators of the main agricultural production cost and financial result of the agricultural entities was determined. A comparison of the alternatives for calculating the cost of agricultural production revealed significant fluctuations in indirect manufacturing costs and manufacturing production cost, which had a significant impact to changes in financial results. It was found, that the choice of the allocation base for indirect manufacturing costs has a more significant effect. Therefore, in order to prepare financial statements that give a true and fair view of the company’s operations, it is important to select differentiated bases for allocating indirect manufacturing costs as accurately as possible that would reflect the fairest possible relationship between accounting objects of manufacturing costs and certain indirect manufacturing cost items, as well as to choose the method of allocating costs to cost calculation objects that best reflects the production technology. Keywords: biological assets, agricultural products, cost accounting, cost calculation, costing methodology. JEL codes: D24, M41, Q14.
      PubDate: 2021-03-26
      DOI: 10.15544/ssaf.2021.01
      Issue No: Vol. 15, No. 1 (2021)
       
  • P2P LENDING PLATFORM EVALUATION OF OPERATING PECULIARITIES

    • Authors: Rosita Šiaulytė, Aušrinė Lakštutienė
      Pages: 15 - 22
      Abstract: Rapidly evolving financial technologies (FinTech) are changing established, time-tested financial services delivery and competition strategies. The growing diversity of financial services is evolving entrepreneurial ecosystems, making it a challenge for all financial market participants. One of the largest Fintech markets in Lithuania is the lending market. This market acts as an alternative to traditional financial institutions’ credit and distinguish by its relatively new and developing. Therefore, the research aims to evaluate the activities and peculiarities of operating lending platforms in Lithuania. The analysis uses P2P lending market development indicators, return and risk identified and analyzed in the research. The study revealed that the portfolio of consumer loans provided through operators of P2P lending platforms is growing steadily every year. AB NEO Finance maintains the same tendencies and secures a leading position in the Lithuanian P2P lending market. AB NEO Finance stands out as the only one of all P2P lending platforms, because it is listed on Lithuania’s stock exchange. It covers more than half of the P2P lending market regarding the amount and number of disbursed consumer loans. Were found that the remaining loan repayment is proliferating in the P2P lending market. However, AB NEO Finance is experiencing a lower number of overdue payment days than the P2P lending market, which indicates a lower risk. Keywords: P2P lending platform, AB NEO Finance, P2P lending market. JEL codes: G2.
      PubDate: 2021-03-31
      DOI: 10.15544/ssaf.2021.02
      Issue No: Vol. 15, No. 1 (2021)
       
  • EFFECT OF OWNERSHIP STRUCTURE ON CAPITAL STRUCTURE OF LITHUANIAN LISTED
           COMPANIES

    • Authors: Rimantė Vaičekauskaitė, Rasa Norvaišienė
      Pages: 23 - 33
      Abstract: The aim of this study is to investigate the impact of ownership structure on the capital structure of Lithuanian listed companies. A ten-year period from 2009 to 2018 has been selected for this study. The study includes non-financial companies listed on the Nasdaq Vilnius Stock Exchange. The ownership structure was assessed according to the concentration of ownership and according to the identity of the owner. In the first part of the study, a comparative analysis of the capital structure of companies with different ownership structures was performed. In the second stage of the study, a regression analysis was performed to determine the impact of the ownership structure on capital structure of listed companies. The research showed significant differences in the capital structure of Lithuanian listed companies in different groups of ownership concentrations and different trends in capital structure in individual groups of companies: medium-sized companies try to use less financial debt and also to reduce the share of borrowed capital in the capital structure, and small and large ownership concentration companies tend to borrow more. Summarizing the results of the comparative analysis, it is assumed that the dependence of the level of indebtedness of Lithuanian listed companies on the ownership concentration is in the form of U (parabola), i. e., when the ownership concentration in companies is low (less than 25%) or high (more than 50%), companies tend to use more borrowed funds, and in the case of medium ownership concentration (when the first largest shareholder holds between 25% and 50% of shares) , efforts are being made to reduce the level of indebtedness as well as the level of financial debt. The results of the regression analysis refuted this assumption and led to the conclusion that there is a statistically significant negative linear relationship between ownership concentration and companies indebtedness, i. e. the increasing concentration of equity in Lithuanian listed companies has a statistically significant negative impact on both the total share of borrowed capital in the capital structure and the share of financial debt in the capital structure. Nevertheless, the results of the regression analysis didn’t show a statistically significant influence of the ownership identity on the level of indebtedness of Lithuanian listed companies, as well as on the level of financial indebtedness. Keywords: ownership structure, capital structure, ownership concentration, ownership identity, leverage ratio. JEL codes: G32.
      PubDate: 2021-04-02
      DOI: 10.15544/ssaf.2021.03
      Issue No: Vol. 15, No. 1 (2021)
       
  • EARNINGS MANAGEMENT IN NASDAQ VILNIUS STOCK EXCHANGE COMPANIES

    • Authors: Silva Katutytė
      Pages: 34 - 43
      Abstract: Financial information is one of the most important sources of information used for decision making of internal and external interested parties. Therefore, the quality of financial information determines the quality of decisions based on this information. Managers experience the pressure of various interested parties. On one hand, interested parties expect receiving qualitatively prepared financial reports disclosing precise and true information. On the other hand, managers feel pressure to reach the set targets. Striving to combine expectations of all interested parties may encourage managers using various earning management patterns. The aim of this research is to detect whether Nasdaq Vilnius stock exchange companies are using earnings management and which pattern of earnings management – accrual based or real earnings management – they use. Discretionary accruals estimation model is used for detecting usage of accrual based earnings management. The abnormal cash flow, abnormal production costs and abnormal discretionary expenses valuation model is used for detecting real earnings management. The research in this paper is implemented by using comparative analysis, generalization, content analysis, monography and statistical methods. Prior implemented empirical researches’ analysis shows that managers apply earnings management in order to show better results. Accrual based earnings management is implemented through accounting policy. Real earnings management is implemented through operations that differ from typical structure and timing of the company. The results of this research show that Nasdaq Vilnius stock exchange companies use earnings management by applying both patterns of earnings management - accrual based or real earnings management. The companies did not give priority to any of the pattern of earnings management. The results of this research imply the expediency of evaluation of both patterns of earnings management for obtaining more precise earnings management valuation results. Not applying of one of the earnings management patterns by itself does not mean that the other pattern of earnings management is not applied. Keywords: earnings management, accrual based earnings management, real earnings management, stock exchange. JEL codes: M21, M41, G30.
      PubDate: 2021-04-06
      DOI: 10.15544/ssaf.2021.04
      Issue No: Vol. 15, No. 1 (2021)
       
  • ANALYSIS OF THE ACCURACY OF BANKRUPTCY PREDICTION MODELS: THE CASE OF
           LITHUANIAN COMPANIES

    • Authors: Jurgita Stankevičienė, Gabija Prazdeckaitė
      Pages: 44 - 53
      Abstract: The aim of this research is to analyze the accuracy of selected bankruptcy prediction models on the example of Lithuanian companies. The research involves financial statements of 23 companies that have gone bankrupt over the period of 2013-2019. We used three different groups of models. The first two are considered as classic models which were developed using discriminant analysis (Altman, modified Altman, Springate, Taffler and Tishaw, and Grover models) and logistic regression (Ohlson, Zmijewski, and Grigaravičius models). The third group is based on artificial intelligence (we used a decision tree model, which is the most innovative and the least explored model of all used). The analysis evidenced that the logistic regression models, such as Zmijewski and Ohlson, demonstrated the best results in the group of classic prediction models, i.e., high probability of bankruptcy even earlier than one year prior to actual bankruptcy in the case of most companies. However, the decision tree must be considered as the most accurate model as it predicted bankruptcy of all analyzed companies one year before actual bankruptcy; this could be interpreted as 100% accuracy. Too late bankruptcy process causes many negative consequences for company’s employees, partners, and the state. Though problems with financial resources such as growing accounts payable and the shortfall of working capital which contribute to insolvency can be seen in the financial statements, in addition to the analysis of financial indicators, it is particularly important to use the above-mentioned bankruptcy prediction models, which help to detect financial problems in time and make the right decisions concerning future activities. Keywords: bankruptcy, bankruptcy prediction models, insolvency, Lithuanian companies. JEL Codes: G32, G33.
      PubDate: 2021-04-09
      DOI: 10.15544/ssaf.2021.05
      Issue No: Vol. 15, No. 1 (2021)
       
  • FINANCIAL DISTRESS DETECTION AT THE CONSTRUCTION SECTOR COMPANIES

    • Authors: Jūratė Savickienė, Jovita Baliūnė
      Pages: 54 - 64
      Abstract: Financial distress detection at business companies is becoming the focus of an increasingly large number of researchers and practitioners with each year. The analysis of scientific research works has suggested the research problem, namely, the lack of a sufficiently reliable financial distress detection model, which could be used for early detection of financial distress for the companies to promptly undertake preventive procedures. The logical scheme for the empirical study was developed based on previous research findings. It enabled the authors to design the financial distress detection model. The model variables were selected, the analysis of closenss of relationships between the independent variable and dependent variables was performed; correlation between the dependent variables was verified, and the problem of multicollinearity was solved. Following the selection of significant indicators (return on total assets, equity-to-debt ratio, and modified indebtedness), the logistic regression function was developed for calculation of the financial distress probability at the company. The model was verified on the basis of data of other companies with the bankruptcy detected within three years before the bankruptcy was declared. To design the financial distress detection model, other companies with or without financial difficulties were selected. The research period was 2014-2018. The designed logistic regression model enables reliable calculation of the financial distress probability at the construction sector companies. The empirical verification of the applicability of the designed model to the constrution sector companies showed that the correct classification rate Ar of the model was 0,97, model sensitivity Se – 1, model specificity Sp – 0,94. The results of model assessment have reasonably suggested that the designed model enables reliable detection of financial distress at the construction sector companies. By using the designed model, the construction sector companies would be able to perform early detection of financial distress at the company and identify the necessity of implementation of the preventive procedures. Keywords: financial distress, financial distress detection model, construction sector companies. JEL codes: G32, G33, L74.
      PubDate: 2021-04-27
      DOI: 10.15544/ssaf.2021.06
      Issue No: Vol. 15, No. 1 (2021)
       
 
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