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The diploma thesis handles economic analysis of the company MD logistika, a.s. The work is divided into theoretical and practical part. The theoretical part describes the indicators of profitability, liquidity, activity, debt, costs and market indicators. DuPont analysis breaking down ROE was used and was followed by a more detailed decomposition of ROA. Within the value indicatiors was used economic value added, which was calculated by two methods. The credibility, bankruptcy and credibility - bankruptcy models were applied to evaluate the company. The operational leverage, financial leverage and more detailed analysis of stocks analysis were performed. In the practical part each procedure, which was elaborated in the theoretical part of company, was applied to the MD logistika, a.s. In the conclusion the company was compared with the closest competitors and the industry averages.

This Master's thesis presents the external financial analysis of the chosen hotel. The main aim of this thesis is both to determine the current financial situation, and evaluate the dependency of the development from fluctuation in the macroeconomic environment. The first chapter based on professional literature depicts that financial analysis is important business management tool and explains the basic methods of analysis. Practical part specifies the economic situation of the hotel, especially from its relevant market share view. Subsequently the financial analysis (balance sheet, income statement, and cash flow statement) and financial gap analysis follows. The core of the thesis is ratio analysis of liquidity indicators, long-term financial balance, profitability and activity. Kralicek quick test and bankruptcy model IN05 provides the summary view on company's financial health. At the conclusion, company's major strengths and weaknesses are highlighted altogether with the future development outline and presentation of proposals solving the identified deficiencies.

The topic of this thesis is the financial analysis of companies Madeta, a. s. and Mlékárna Hlinsko, s. r. o. from 2007 to 2010. In the theoretical part are described the basic methods and techniques of financial analysis. In the practical part are applied each methods of financial analysis to the companies and after that the results are compared not only between each other but also in relation to the industry. At the end of the thesis the obtained results are evaluated.

Leasing är idag en av de största finansieringskällorna och i synnerhet för branscher med hög kapitalintensitet. Redovisningen av leasing har länge reglerats av standarden IAS 17 som exkluderar operationella leasingavtal från balansräkningen. Enligt den nya leasingstandarden IFRS 16 måste alla leasingavtal redovisas på balansräkningen från och med den 1 januari 2019. Då leasingavtal måste kapitaliseras förväntas IFRS 16 ge markanta effekter på redovisningen, i synnerhet för kapitalintensiva branscher som flygbranschen.  Syftet med studien har varit att undersöka hur kapitalstruktur och nyckeltal har påverkats sedan implementeringen av den nya leasingstandarden IFRS 16. För att uppnå studiens syfte har en kvantitativ metod använts där finansiella rapporter från 19 europeiska flygbolag har samlats in. Utifrån de finansiella rapporterna har nyckeltalen skuldsättningsgrad, EBIT, EBITDA och ROA beräknats som sedan har skapat grunden till en statistisk analys. För att svara på syftet har fyra hypoteser formulerats som har testats genom Wilcoxons teckenrangtest och t-test. Studien bidrar med en tvådelad bild av hur IFRS 16 anses ha påverkat kapitalstruktur och nyckeltal. Resultat som ligger i linje med studiens hypoteser är skuldsättningsgrad samt ROA som ökar respektive minskar mellan räkenskapsåren 2018 och 2020. Det går dock inte att påvisa samma resultat i testen mellan räkenskapsåren 2018 och 2019. Resultaten för EBIT och EBITDA talar emot studiens hypoteser genom att påvisa signifikanta minskningar mellan räkenskapsåren 2018 och 2020. Liknande resultat kan indikeras för EBIT och EBITDA mellan räkenskapsåren 2018 och 2019. / Leasing is today one of the largest sources of financing and especially for industries with high capital intensity. The reporting of leases has long been regulated by the IAS 17 standard, which excludes operating leases from the balance sheet. According to the new leasing standard IFRS 16, all leasing agreements must be reported on the balance sheet from 1 January 2019. When leasing agreements must be capitalized, IFRS 16 is expected to have significant effects on the accounts, especially for capital-intensive industries such as the aviation industry. The purpose of the study has been to examine how capital structure and key ratios have been affected since the implementation of the new leasing standard IFRS 16. To achieve the purpose of the study, a quantitative method has been used where financial reports from 19 European airlines have been collected. Based on the financial reports, the key ratios debt-to-equity ratio, EBIT, EBITDA and ROA have been calculated, which has then formed the basis for a statistical analysis. To answer the purpose, four hypotheses have been formulated that have been tested through Wilcoxon signed rank test and t-test. The study provides a two-part picture of how IFRS 16 is considered to have affected capital structure and key ratios. Results that are in line with the study's hypotheses are the debt-to-equity ratio, which increases and ROA, which decreases between the financial years 2018 and 2020. However, it is not possible to demonstrate the same results in the tests between the 2018 and 2019 financial years. The results for EBIT and EBITDA contradict the study’s hypotheses by showing significant reductions between the financial years 2018 and 2020. Similar results can be indicated for EBIT and EBITDA between the financial years 2018 and 2019.

Capital structure is one of the most frequent topics in the finance literature. This literature has its origins in studies of the manufacturing industry. Much of the results of this work have been applied indiscriminately to other industries without thorough validation. Only limited studies have considered financial structure in hospitality industry. The service industry is different than manufacturing industry, and even the hospitality industry is not homogeneous. The restaurant industry and lodging industry are quite different from each other. Of interest to this present study is to seek to understand how the patterns of capital structure are shaped within the context of the multi-unit casual themed restaurant industry. Restaurant industry is well known for a high bankruptcy rate. Many multi-unit restaurants exist in the casual themed restaurants strategic group in the Unites States, and many small independent restaurants are also present. The firm's strategic choice and its relationship with financial structure became a topic for my research. Publicly traded casual themed restaurants have been selected in this study. Hypothetically a common capital structure exists among firms within this strategic group. In this study, an investigation can consider the relationship among financial ratios as well as the uniqueness of the financial structure of the casual themed restaurants. / Master of Science

The objective of this study investigated the successful prediction of business failure of JSE listed companies using financial ratio analysis. During the research, financial statement data of failed and non-failed JSE listed companies during 2007-2012 financial periods were analysed, compared and interpreted. The interpretation of the trends and comparisons is of a quantitative nature, together with a qualitative genre which examines the tables, figures and equations in order to get the entire picture of the company’s performance for a five year period. The combination of literature on various failure predictor models and experience of these models resulted in the development of a modified model. The conclusion from the study indicated that financial ratio analysis successfully predicts failure and non-failure of the 16 companies that were investigated. These companies were grouped into eight delisted (failed) and eight listed (non-failed) JSE companies, which were paired in accordance to industry, fiscal period and closest asset size. The adoption of the traditional ratio analysis methods and EMS model yielded some interesting findings. The traditional ratio analysis methods (trend and comparative ratio analysis) were used with the Emerging Market Score (EMS) Model. The outcomes indicated the traditional methods are viable company failure prediction tools and the EMS model points out companies at a score of 2.60 and above as being financially stable. Between 2.60 and 1.10 the results are not very dependable because it is known that the company is in distress, yet uncertain whether the company has financially failed and below 1.10 the company has failed. It was concluded that a combination of the various prediction models enhances the accuracy of failure prediction. Therefore further research is required to assist stakeholders of South African companies to predict business failure by developing an adjusted model in a South African context. / MCom (Accountancy)--North-West University, Vaal Triangle Campus, 2015

The objective of this study investigated the successful prediction of business failure of JSE listed companies using financial ratio analysis. During the research, financial statement data of failed and non-failed JSE listed companies during 2007-2012 financial periods were analysed, compared and interpreted. The interpretation of the trends and comparisons is of a quantitative nature, together with a qualitative genre which examines the tables, figures and equations in order to get the entire picture of the company’s performance for a five year period. The combination of literature on various failure predictor models and experience of these models resulted in the development of a modified model. The conclusion from the study indicated that financial ratio analysis successfully predicts failure and non-failure of the 16 companies that were investigated. These companies were grouped into eight delisted (failed) and eight listed (non-failed) JSE companies, which were paired in accordance to industry, fiscal period and closest asset size. The adoption of the traditional ratio analysis methods and EMS model yielded some interesting findings. The traditional ratio analysis methods (trend and comparative ratio analysis) were used with the Emerging Market Score (EMS) Model. The outcomes indicated the traditional methods are viable company failure prediction tools and the EMS model points out companies at a score of 2.60 and above as being financially stable. Between 2.60 and 1.10 the results are not very dependable because it is known that the company is in distress, yet uncertain whether the company has financially failed and below 1.10 the company has failed. It was concluded that a combination of the various prediction models enhances the accuracy of failure prediction. Therefore further research is required to assist stakeholders of South African companies to predict business failure by developing an adjusted model in a South African context. / MCom (Accountancy)--North-West University, Vaal Triangle Campus, 2015

From different points of view, including Resource-Based Theory, Resource-Advantage Theory, and Structure Conduct Performance, the research was firstly discussed if the iron and steel enterprises would have different competitive advantages with different strategy segmentations. In addition, how investors make investment decision and how management level distribute resource into which financial indicator in order to strengthen competitive advantage were discussed as well. The research was collected financial data (from year 2004 to 2008) of 40 domestic iron and steel companies which are listed in public stock market (including OTC) as sample pool. The sample pool was segmented into four groups, such as products, value chain of upstream to downstream, and top-to-down consolidated strategic groups. Competitive advantage was classified by average of equity return ratios of shareholders, and financial ratios were applied to analyze the source of competitive advantage in iron and steel industry. Firstly, started to analyze financial ratios of each group by description statistic method and implemented factor analysis to figure out common factors. Furthermore, from investors point of view, to clarify how those factors to be reflected on operating performance by applying multiple regression analysis. Moreover, from business operating level¡¦s eyes, to distinguish enterprises with competitive advantage from each strategic group. The result was shown that each strategic group has different sources of competitive advantage. Therefore, recommendations of the research are listed: I. Investors should invest in enterprises with better current ratio, quick ratio, and guarantee of multiplied interest. II. Aggressive investors should invest in enterprises that have stainless steel products and consolidated business from upstream, mid-stream, to downstream. III. Conservative investors should invest in enterprises that have plate steel products and consolidated business from upstream, mid-stream, to downstream. IV. In order to create resources of competitive advantage, management level of each strategic group should increase financial signals as first priority: 1. Plate steel and downstream enterprises: value-added of unit salary and percentage of employee bonus out of profit. 2. Bar iron, stainless enterprises with consolidation of upstream to mid-stream and top-to ¡Vdown conformity: current ratio, quick ratio and guarantee of multiplied interest. 3. Alloy steel enterprises: ratio of debt out of net assets, percentage of loan, stock and account receivable out of net assets ratio, and net assets out of assets ratio.

Den första januari 2019 trädde en ny redovisningsstandard IFRS 16 i kraft gällande leasing som ersatte den tidigare standarden IAS 17. Implementeringen innebar att leasingavtal skall endast redovisas som finansiell leasing och inte som operationell med visst undantag som analogt med tidigare forskning resulterar till förändringen i företagets finansiella rapporter. Standarden förväntas därmed bidra till en mer transparent bild av finansiell ställning. Syftet med studien är att undersöka vilken påverkan IFRS 16 har på de finansiella nyckeltalen som baseras på de faktiskt siffror för nordiska börsnoterade bolag under två tidsperioder: 2018 och 2019, samt 2018 och 2020. Urvalet omfattar 340 nordiska börsnoterade företag som är listade på Nasdaq inom fyra branscher: finans, detaljhandel, logistik och telekom. Nyckeltalen som undersöktes var soliditet, skuldsättningsgraden, EBIT, EBITDA, ROE och ROA. Valet av nyckeltal grundades på de tidigare forskning, där Morales-Díaz och Zamora-Ramírez (2018) är den huvudsakliga källan. För att undersöka sambandet mellan införandet av IFRS 16 och förändringen i nyckeltal genomfördes ett beroende t-test med jämförelse mellan fyra branscher och fem länder inom Norden. Studien finner statistik samband mellan införandet av IFRS 16 och förändringen i samtliga nyckeltal för tre av fyra branscher under tidsperioden 2018 och 2019. En vidare påverkan på tre av sex nyckeltal kunde beaktas för två av fyra branscher under tidsperioden 2018 och 2020. Undersökningen resulterade till ett statiskt stöd mellan IFRS 16 och förändringen i fyra av sex nyckeltal för fyra av fem länder under första tidsperioden. Under andra tidsperioden har tre av fem länder bekräftat statistiskt stöd för tre av sex nyckeltal. / On the first of January 2019, a new accounting standards IFRS 16 was introduced to replace the former standard IAS 17 which prescribes the accounting policies that are applied to leasing. The new standard is expected to conduce a more transparent image of the company's financial status. The purpose of this study is to identify what effect the new standard IFRS 16 has on the financial ratio which is based on the actual numbers of the Nordic publicly listed companies over the course of three years. The selection of the study includes 340 Nordic publicly listed companies in Nasdaq in four different industries: finance, retail business, logistics and telecom. The ratios that were examined during this study are solidity, debt-to-equity ratio, EBIT, EBITDA, ROE and ROA. To study the correlation between the insertion of IFRS 16 and the alteration in the ratio, t-test was carried out with comparisons between four industries and five Nordic countries. The study finds static correlation between the insertion of IFRS 16 and the alteration in the ratio in three out of four industries over the course of 2018 to 2019. An additional effect on three out of six ratios was found in two out of four industries during the period from 2018 and 2020. As a result of this study a static correlation between the IFRS 16 and the alteration of ratio in four out of six countries in four out of five countries over the first period of time. Over the second period of time three out of five countries has confirmed a static correlation for three out of six ratios.