The Need of Changes in Traditional Accounting Systems Necessitated by Modern Intellectual Capital Conception

While economists, business people and policy analysts continue to debate the question of what is "new" about the so-called "New Economy", globalization, urgency of innovation and intensive use of Information technology, one important feature of modern Corporation in the early twenty-first century seems clear: intangible factors are playing an increasingly dominant role in business wealth creation. The drivers of tomorrow's wealth are brands, networks, knowledge, innovation, relationships, competencies, corporate culture and leadership, and these are the new critical assets - the weightless keys to business future wealth. But despite the growing awareness of the importance of intangible assets, they remain almost universally ignored in traditional accounting and reporting procedures. The authors in this article analyze the main problems concerning difficulties to reflect intangibles in traditional accounting statements and project the tendencies of reporting intangible-related information in future accountability.Podczas gdy ekonomiści, przedsiębiorcy i politolodzy dyskutują nad tym, co jest "nowego" w tzw. Nowej Ekonomii oraz nad zagadnieniami związanymi z globalizacją i potrzebą innowacji oraz szerokiego korzystania z technologii informatycznej, jedna kwestia nie ulega wątpliwości, jeśli chodzi o zmiany w przedsiębiorstwie XXI w.: czynniki niematerialne odgrywają coraz większą rolę w tworzeniu wartości. Czynniki kluczowe w kreowaniu przyszłego bogactwa to znaki firmowe, systemy połączeń i kooperacji, wiedza, innowacje, relacje, wartości, kultura organizacyjna i przywództwo. Ale chociaż świadomość znaczenia aktywów niematerialnych i prawnych jest coraz większa, tradycyjna rachunkowość i sprawozdawczość niemal całkowicie je ignoruje. Autorzy tego artykułu analizują najważniejsze problemy, wynikające z trudności odzwierciedlenia aktywów niematerialnych w tradycyjnych sprawozdaniach finansowych oraz ukazują kierunki przyszłego rozwoju rachunkowości w tym zakresie.Nepaisant augančio nematerialaus turto svarbos suvokimo, daugelyje šalių jis iślieka ignoruojamas tradicineje atskaitomybeje ir ataskaitų procedūrose. Praktiškai visos nematerialios investicijos finansinese ataskaitose yra fiksuojamos kaip iślaidos, bet ne kapitahzuojamos (traktuojamos kaip turtas), ir nuamortizuojamos prognozuojamame naudos laikotarpyje. Materialaus-nematerialaus turto asimetnja stingant informacijos apie investicijas į nematerialų turtą daro socialinę ir ekonominę żalą. Sio straipsnio autoriai analizuoja pagrindines nematerialaus turto atspindejimo tradicinese finansinese ataskaitose sunkumų problemas, pasiūlydami jų sprendimo būdus ir prognozuodami informacijos apie nematerialų turtą fiksavimo ateitį atskaitomybėse.Zadanie pt. Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej naukę

Influence of tax administration on financing decisions of Lithuanian companies

The main purpose of the article is to analyse, specific conditions of Lithuanian economy, which influence tax management by capital structure decisions. Additionally, the article focuses on transfer pricing used by large financial groups and its influence. Conclusion from analysis of theoretical substantiation can bc drawn, that (dis-)advantages debt tax shield depend on tax system, availability of other tax shields and operating risk, and thus can vary in different countries, industries and even companies. Analysis has shown certain collusion between interest of large blockholders and small shareholders, arising from different taxation, control power and required rate of return. The main conclusion of the conditions' analysis is that use of debt tax shield is inefficient and rarely used because of the following reasons: availability of other less costing and more efficient means of tax management; high level of uncertainty, lack of information and limited planning; specifics of operations of large financial structures

Evaluation of enterprise’s performance changes within the classification process

This paper is dealing with the application of statistical methods for the classification of enterprises according to their financial results. Cluster analysis was used for the classification of 100 Lithuanian enterprises. Enterprises were classified into classes of profitable, loss-making and mixed companies Dynamical changes of characteristics (class size and centroid value) of these classes were analyzed within the 5 year period. Selected variables of each class were generated using Monte Carlo simulation. Mahalanobis distances were calculated for these samples. The differences between Mahalanobis distances of the companies assigned to each class validated the reliability of the classification results. The investigation of enterprises’ migration between different classes was performed and the changes of classification variables’ values discussed. The investigation has argued the importance of determining necessary performance changes for the enterprise’s switchover from mixed or loss-making companies’ class to the class of profitable working enterprises

The stock market as a leading indicator in a small open economy : an application of granger causality

Akcijų rinka tradiciškai buvo laikoma patikimu įrankiu stebėti ekonomikos procesams. Yra manoma, kad didelis akcijų kainų kritimas reiškia būsimą recesiją, o didelis akcijų kainų augimas žada ekonomikos augimą ateityje. Tačiau skeptikai pateikia keletą prieštaringų faktų, kurie suteikia pagrindą abejoti akcijų rinkos prognostinėmis galimybėmis. Atsižvelgiant į akcijų rinką supančius prieštaravimus, yra svarbu toliau tirti šį klausimą. Teorinės priežastys, kodėl akcijų rinka gali iš anksto informuoti apie ekonominės veiklos tendencijas, apima tradicinį akcijų kainų vertinimo modelį ir taip vadinamą gerovės efektą. Pagal tradicinį akcijų kainų vertinimo modelį, jos atspindi būsimos ekonomikos lūkesčius ir todėl gali prognozuoti ekonomiką. Gerovės efektas reiškia, kad akcijų kainos iš anksto informuoja apie ekonominę veiklą ir iš esmės ir tuos ekonomikos pokyčius ir inicijuoja. Straipsnio tikslas yra įvertinti, ar akcijų kainos gali būti išankstiniu indikatoriumi mažoje atviroje ekonomikoje. Straipsnyje taikoma laikinių eilučių analizė ir "Granger priežastingumo“ sąvoka, siekiant apskaičiuoti ryšį tarp dviejų kintamųjų ir pamatyti, ar jie atitinka teoriją. Tiriant ryšį tarp akcijų kainų augimo tempo ir ekonomikos augimo tempo buvo taikomi C.J.Granger formalūs priežastingumo testai ir analizuojami Lietuvos ekonomikos specifiniai 2000 1 ketv. - 2007 4 ketv. duomenys. Akcijų kainos nedarė įtakos ekonomikos veiklai, tačiau infliacija ir pinigų pasiūla atskleidžia priežastinį ryšį su akcijų kainomis. Tyrimas atskleidė, kad statistiškai reikšmingi vėlavimai tarp svyravimų akcijų rinkoje ir pokyčių realioje ekonomikoje yra santykinai trumpi.Traditionally the stock market was considered a reliable tool for monitoring of economic processes. There is an opinion that a significant drop in stock prices means a future recession and a significant increase of stock prices indicates a growth of economy in the future. However skeptics present several contradictory facts, which give reasons to doubt the prognostic possibilities of the stock market. Taking into consideration the contradictions, surrounding the stock market, it is important to discuss the issue further. The theoretical reasons why the stock market can inform on the trends of economic activities in advance cover the traditional model of evaluation of stock prices and the so-called well-being effect. According to the traditional model they reflect the future economic expectations and thus, can forecast the economy. The wellbeing effect means that the stock prices provide advanced information on the economic activities and, in essence, initiate such changes in economy. The article aims at evaluating whether the stock prices can be an advanced indicator in a small open economy. The article employs an analysis of temporary lines and the concept of "Granger Causality“, in order to calculate the relation between the two variables and see whether they correspond to theory. When examining the relation between the rates of increase of stock prices and economy, C. J. Granger’s causality tests were used and the specific data of Lithuanian economy as of the 1st quarter of 2000 – 4th quarter of 2004 were analyzed. The stock prices had no influence on the economic activities however the inflation and the money supply reveals the causative relation with the stock prices. The study revealed that the statistically significant delays between the fluctuations on the stock market and the changes in the real economy are relatively short

Įmonių klasifikavimas įvertinant finansinius rodiklius

Classification of objects is often used in economic researches. It is indispensable when we analyse plentiful information. Classification problem also rises when we want to value and compare firms because every company is characterized by a lot of various ratios. So it is impossible in practice to take them all into account and make a versatile analysis of the firm's performance without corresponding statistical and mathematical methods. Thus this article presents an investigation of possible firms' estimation algorithms by focusing attention to the classes of firms according to the similarities of their financial ratios. The objects of this experimental research are joint stock companies in Lithuania and their division into four related groups. The establishment of the firm's class or its integrated ratio lets not just make objective and versatile valuation of the firm's results but also compare it with other firms. Such calculations that have been made for several years give us the information about the changes of financial conditions

Kredito rizikos vertinimas dirbtinių neuronų tinklų modeliais

The main risk banks face when lending money is credit risk. The purpose of the article is to establish the indicators defining the accuracy of classification of risk estimation models and to assess the accuracy of classification of artificial neural networks models. The analysis of scientific publications has shown that the most accurate methods to assess client credit risk are logistic regression and artificial neural networks. Models of credit risk estimation (artificial neural networks) were created in the work and applied to analyse the data of companies operating in Lithuania. Having calculated the efficiency indicators of these models, it has been established that the highest accuracy in the classification of companies is achieved when analysing the data of the last three years. Companies were rated by applying the developed models of credit risk estimation. Companies were rated according to the possibility of non-performance of their financial liabilities during the forthcoming year. Having rated companies, probabilities of non-performance of obligations of each group of companies were calculated. Those companies rated at total insolvency in all the models went bankrupt. This indicator reached 88.89–93.75% in the group of high probability of non-performance of liabilities, and 20.83–37.5% in the group of less reliable companies. None of the bankrupt companies from the group of reliable companies was rated as a totally reliable company by applying model 1 and model 3, and the probability of non-performance of liabilities in the group of totally reliable companies reached 3.33%

Application of classification methods in economic analysis

Statistinis grupavimas (klasifikavimas) yra vienas iš svarbiausių ir nepriklausomų socialinių - ekonominių reiškinių tyrimo metodų, plačiai taikomų daugelyje įvairių mokslo sričių, atliekant įvairius statistinius tyrimus. Jis nepakeičiamas, kai analizuojama gausi informacija. Šiame straipsnyje ir parodoma klasifikavimo pritaikymo galimybė atliekant ekonominę analizę. Toks tyrimas leidžia įvairiapusiškai įvertinti ir palyginti sudėtingus objektus, kurie apibūdinami daugybe įvairaus pobūdžio charakteristikų. Siame straipsnyje parodoma, kaip klasifikavimo metodas gali būti pritaikytas įvertinant bei palyginant Europos Sąjungos valstybes bei Lietuvos įmones. 23 ES valstybės skirstomos į keturias vienarūšes klases pagal 16 charakteristikų. Gauti rezultatai palyginami su klasėmis, gautomis pagal vieną iš dažniausiai naudojamų rodiklių - BVP, tenkantį vienam gyventojui. Lietuvos įmonės taip pat suskirstomos į 4 klases, atsižvelgiant į 192 finansinius rodiklius. Tokia analizė leidžia įmones įvairiapusiškai palyginti tarpusavyje, o atliekant tyrimą kasmet- išsiaiškinti jų finansinės padėties kilimą.Statistical grouping and classification is one of most important and independent research methods of social-economical occurrences and is widely used in statistical research of all or almost all spheres of science. It is indispensable when large data bases arc used in the analysis. It is also more accurate than the analysis by only one ratio. The mentioned method and its application in practice are described in this article. 23 states in the European Union are chosen as the object of this research and they are divided into four similar groups according to 16 various ratios. These results arc compared with the classes made according to the one ratio. The article presents an investigation of possible firms' estimation algorithms by focusing attention to the classes of firms according to the similarities of their financial ratios. The objects of this experimental research arc joint stock companies in Lithuania and their division into four related groups. The establishment of the firm's class enables not just make objective and versatile valuation of the firm's results but also compare it with other firms. Such calculations of several years provide the information about the changes of financial conditions