scispace - formally typeset
Search or ask a question
Author

Luka M. Filipović

Bio: Luka M. Filipović is an academic researcher. The author has contributed to research in topics: Financial analysis & Computer science. The author has an hindex of 1, co-authored 3 publications receiving 1 citations.

Papers
More filters
Journal ArticleDOI
01 Jan 2016
TL;DR: In this paper, a credit solvency evaluation was performed in the case of a joint stock company called "Aleva" from Novi Kneževac, with the aim of comparing the results of the credit analysis with the company's reliability.
Abstract: There is a large number of developed methods and indicators of financial analysis i.e. analysis of financial reports. Therefore, it was useful to develop a basic template indicator, that would include all the indicators together and that would have the same meaning for all of its users. However, the requirements of users of financial information determine the level and form of desired information. Today, the favorable characteristics of a company are primarily seen as its solvency. Solvency represents the degree of certainty in making predictions related to its survival and development, as well as a review of its quantitative and qualitative skills, primarily yielding abilities, but also material and financial capacities. Solvency, in the narrow sense of a word, represents a company's credit ability, and the ability to maintain current liquidity, while in the broader sense it represents a company's overall market position. In practice, there are two models of credit solvency evaluation: the traditional model of credit solvency evaluation, and the Altman's 'Z'-model (score). Since these two models differ, in this paper we performed credit solvency evaluation in the case of a joint stock company called 'Aleva' from Novi Kneževac, with the aim of comparing the results of credit solvency evaluation with the company's reliability.

2 citations

Journal ArticleDOI
01 Jan 2018
TL;DR: In this article, the authors apply the technical analysis of investments in securities on the example of Ripple company Ripple XRP is the currency used in the payment network for all transactions, reducing the time needed, as well as the money for cross-border payments.
Abstract: The subject of the research in this paper is empirical testing of the possibility of applying the technical analysis of investments in securities on the example of Ripple company Ripple XRP is the currency used in the payment network for all transactions, reducing the time needed, as well as the money for cross-border payments The aim of this research is to find an optimal method that will improve the trading efficiency through tested specific examples with possibilities of improving the portfolio management made of Ripple XRP digital currency, with a special focus on the optimal choice of methods of technical analysis The analysis period was one year of observations, from April 2017 to March 2018, with a special focus on the last six months, from October 2017 to March 2018 The results of the research will be useful to the academic community for further research in the field of technical analysis of digital currencies, as well as institutional and individual investors in the function of creating certain instruments that are focused on effective trading with this or similar digital currency
Proceedings ArticleDOI
22 Nov 2022
TL;DR: In this paper , several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications, based on gained results, couple of techniques for improving Artificial Neural Networks are proposed to get better accuracy results than statistical predictive methods.
Abstract: Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction's accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.
Journal ArticleDOI
TL;DR: In this article , the authors evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications, and propose a couple of techniques for improving suggested ML methods and Artificial Neural Networks are proposed to get better accuracy results.
Abstract: Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results
Proceedings ArticleDOI
16 Feb 2022
TL;DR: An example of upgrade to the user account management system at the University of Montenegro (UoM) Information System involves the integration of SMS service that provides automatic sending of credentials to users via SMS messages.
Abstract: The paper presents an example of upgrade to the user account management system at the University of Montenegro (UoM) Information System. This upgrade involves the integration of SMS service that provides automatic sending of credentials to users via SMS messages. Usage statistics of the developed service from its launch until today is presented and recommendations for the protection of the service from unauthorized use are given. Also, description of possibilities for integration of SMS services into other UoM Information System's services is given.

Cited by
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors examined the financial situation, the place and the role of large companies in the overall economy of the Republic of Serbia, where, having in mind the fact that it is a transition economy, large enterprises, both private and public, significantly contribute to the growth of the economic activity and employment.
Abstract: In contemporary business conditions, advanced market economies are characterized by the increasing contribution of the SMEs sector to the economic development. Having that in mind, a large number of papers have been aimed at analyzing business doing in this sector in the last couple of years. In this way, the focus has been shifted from large business entities and their importance to the economic growth to the SMEs sector. In this regard, the aim of this paper is to examine the financial situation, the place and the role of large companies in the overall economy of the Republic of Serbia, where, having in mind the fact that it is a transition economy, large enterprises, both private and public, significantly contribute to the growth of the economic activity and employment. Before all, the degree of the liquidity, efficiency, indebtedness and profitability of these companies in the period 2014-2017 was determined by using the ratio analysis. After that, on the basis of the selected ratios, a comparative financial performance analysis of large business entities in relation to those small, medium-sized and microwas carried out by applying the Entropy and the PROMETHEE II methods. The obtained results pointed out that large business entities had a favorable business economy in relation to the other groups of business entities by size, and their performances had improved to a certain extent in the observed period.

3 citations

Journal ArticleDOI
TL;DR: In this article , the authors point out the importance of solvency assessment and explain how information is collected so that it can be used to avoid business risks and how to assess the current financial situation as well as to assess future business and development.
Abstract: Modern business is characterized by turbulence and unpredictability. The position of a company is influenced by internal and external factors. Management has significant opportunities to influence internal factors while it cannot influence external ones. The prerequisite for quality management is timely insight into the strengths and weaknesses of the company. In order for a corporate company to be successful, it is necessary to analyze all elements that guarantee general material stability, good reputation and prospects, as well as a good competitive position in the market, good development and production programs that guarantee a long life cycle and its right strategy. Solvency refers to business analysis that aims to determine and assess the quality of business. It shows how successful a certain company is, so it serves to assess the current financial situation as well as to assess future business and development. The aim of the research is to point out the importance of solvency assessment and to explain how information is collected so that it can be used to avoid business risks.