The aim of this diploma thesis is assessment of competitiveness in construction company with help of tools and methods of financial analysis. In the first part are defined theoretical terms related to examined problem, while attention is focused on terms construction company, competitiveness and financial analysis. In practical part are applied methods of financial analysis which are used to assess efficiency of the company. Achieved results are compared with the most important competitors on the market and also with branch. To comparing was used benchmarking diagnostic system of Ministry of Industry and Trade of the Czech Republic. As an output of this thesis are recommended tools for continuous assessment of competitiveness.

The Master’s thesis focuses on the evaluation of financial situation in selected company during the years 2011 – 2015. The first part of the thesis contains theoretical framework which is subsequently applied in evaluating the financial health of the company using suitable methods of the strategic and financial analysis. The next part consists of proposals to company’s improvement based on the overall assessment of the financial situation of the company.

Research question: Three models, Z``-score, O-score and Skogsvik HCA model, will be used in this study to examine Swedish companies who has gone bankrupt over the last decade within the manufacturing and retail branches. The study will examine how these models stand against each other when it comes to predict bankruptcy within these two branches one and two years in advance. Purpose: The purpose with this study is to examine these three models that are used for bankruptcy prediction and to get an understanding of why the accuracy differs between the models when it comes to predicting bankruptcy within the manufacturing and retail branches. Method: The study is based on a quantitative method with a deductive research approach to examine the accuracy of the three models when it comes to one and two years before bankruptcy. Conclusion: The study shows that Skogsvik’s model is the most accurate when it comes to predicting bankruptcy within the manufacturing and retail branches. / Problemställning: Tre modeller, Z``-scoremodellen, O-scoremodellen och skogsviks HCA modell, kommer att användas i vår studie för att undersöka svenska aktiebolag som gått i konkurs det senaste decenniet inom tillverkningsbranschen och detaljhandelsbranschen. Studien kommer undersöka hur dessa tre modeller står sig mot varandra när det kommer till att förutspå konkurser inom tillverknings- och detaljhandelsbranschen under en prediktionstid på både ett och två år i förväg. Syfte: Syftet med uppsatsen är att undersöka tre olika modeller som används för konkursprediktion och få en förståelse varför träffsäkerheten skiljer sig mellan de olika modellerna när det gäller att förutse konkurs inom tillverkningsbranschen och detaljhandelsbranschen. Metod: Studien bygger på en kvantitativ metod med en deduktiv ansats för att undersöka hur stor träffsäkerhet som redan befintliga modeller har vid förutsägelser av framtida konkurser på upp till två år. Slutsats: Studien visar att Skogsviks modell är den som är mest träffsäker när det gäller att förutse konkurser inom tillverknings- och detaljhandelsbranschen.

Bankruptcy prediction is an important research topic. The cost of incorrect decision making in companies and financial institutions can be great and could affect large parts of society. But while it is indeed a major research area, there are few studies which consider the effects of feature selection. This is an important step that could improve the performance of bankruptcy prediction models. This thesis therefore aims to find which feature selection methods perform best for bankruptcy prediction. Five feature selection methods will be compared and used to create datasets with fewer redundant features. To test these methods, a LSTM model is used to train on both an unaltered dataset and datasets created by the mentioned models. The predictive performance of these are then compared with the metrics AUC, Type I error, and Type II error. This study finds that the forward selection algorithm from the Stepwise regression method performed best with an increase in AUC score and decrease in both Type I and Type II error rates compared to the model trained on the unaltered dataset.

The field of bankruptcy prediction has experienced a notable increase of interest in recent years. Machine Learning (ML) models have been an essential component of developing more sophisticated models. Previous studies within bankruptcy prediction have not evaluated how well ML techniques adopt for data sets of companies with higher credit risks. This study introduces a binary decision rule for identifying companies with higher credit risks (abnormal companies). Two categories of abnormal companies are explored based on the activity of: (1) abnormal credit risk analysis (”AC”, herein) and (2) abnormal payment remarks (”AP”, herein) among small Swedish limited companies. Companies not fulfilling the abnormality criteria are considered normal (”NL”, herein). The abnormal companies showed a significantly higher risk for future payment defaults than NL companies. Previous studies have mainly used financial features for bankruptcy prediction. This study evaluates the contribution of different feature categories: (1) financial, (2) qualitative, (3) performed credit risk analysis, and (4) payment remarks. Implementing a Light Gradient Boosting Machine (LightGBM), the study shows that bankruptcies are easiest to anticipate among abnormal companies compared to NL and all companies (full data set). LightGBM predicted bankruptcies with an average Area Under the Precision Recall Curve (AUCPR) of 45.92% and 61.97% for the AC and AP data sets, respectively. This performance is 6.13 - 27.65 percentage units higher compared to the AUCPR achieved on the NL and full data set. The SHapley Additive exPlanations (SHAP)-values indicate that financial features are the most critical category. However, qualitative features highly contribute to anticipating bankruptcies on the NL companies and the full data set. The features of performed credit risk analysis and payment remarks are primarily useful for the AC and AP data sets. Finally, the field of bankruptcy prediction is introduced to: (1) evaluate if bankruptcies among companies with other forms of credit risk can be anticipated with even higher predictive performance and (2) test if other qualitative features bring even better predictive performance to bankruptcy prediction. / Konkursklassificering har upplevt en anmärkningsvärd ökning av intresse de senaste åren. I denna utveckling har maskininlärningsmodeller utgjort en nyckelkompentent i utvecklingen mot mer sofistikerade modeller. Tidigare studier har inte utvärderat hur väl maskininlärningsmodeller kan appliceras för att förutspå konkurser bland företag med högre kreditrisk. Denna studie introducerar en teknik för att definiera företag med högre kreditrisk, det vill säga avvikande företag. Två olika kategorier av avvikande företag introduceras baserat på företagets aktivitet av: (1) kreditrisksanalyser på företaget (”AK”, hädanefter), samt (2) betalningsanmärkningar (”AM”, hädanefter) för små svenska aktiebolag. Företag som inte uppfyller kraven för att vara ett avvikande företag klassas som normala (”NL”, hädanefter). Studien utvärderar sedan hur väl konkurser kan förutspås för avvikande företag i relation till NL och alla företag. Tidigare studier har primärt utvärdera finansiella variabler för konkursförutsägelse. Denna studie utvärderar ett bredare spektrum av variabler: (1) finansiella, (2) kvalitativa, (3) kreditrisks analyser, samt (4) betalningsanmärkningar för konkursförutsägelse. Genom att implementera LightGBM finner studien att konkurser förutspås med högst noggrannhet bland AM företag. Modellen presenterar bättre för samtliga avvikande företag i jämförelse med både NL företag och för hela datasetet. LightGBM uppnår ett genomsnittligt AUC-PR om 45.92% och 61.97% för AK och AM dataseten. Dessa resultat är 6.13-27.65 procentenheter högre i jämförelse med det AUC-PR som uppnås för NL och hela datasetet. Genom att analysera modellens variabler med SHAP-värden visar studien att finansiella variabler är mest betydelsefulla för modells prestation. Kvalitativa variabler har däremot en stor betydelse för hur väl konkurser kan förutspås för NL företag samt alla företag. Variabelkategorierna som indikerar företagets historik av genomförda kreditrisksanalyser samt betalningsanmärkningar är primärt betydelsefulla för konkursklassificering av AK samt AM företag. Detta introducerar området av konkursförutsägelse till att: (1) undersöka om konkurser bland företag med andra kreditrisker kan förutspås med högre noggrannhet och (2) test om andra kvalitativa variabler ger bättre prediktive prestandard för konkursförutsägelse.

Reformen för frivillig revision genomfördes med syfte att möjliggöra valfrihet för revisionsfrågan utefter småföretagens egna behov och resurser. Trots att reformen genomfördes för över ett decennium sedan är forskarna fortfarande oense om effekterna av den avskaffade revisionsplikten. Det senaste åren har debatten kring återinförandet av revisionsplikten varit aktuell där argumenten bakom återinförandet baseras på den ökade ekonomiska brottsligheten och minskade lönsamheten hos de bolag som valt bort revisionen. Även de olika fördelar som revisorns kompetens och legitimitet som skapar trygghet för företagens intressenter är argument för återinförandet av revisionsplikten. Denna studie bidrar med en ökad förståelse och vägledning för småföretag i valet om frivillig revision. Syftet med denna studie är därför att undersöka om reformen för frivillig revision har påverkat konkursrisken hos svenska aktiebolag, samt om det finns andra finansiella faktorer som kan påverka småföretagens långsiktiga överlevnad. Syftet besvaras med utgångspunkt i tre teorier: legitimitetsteori, informationsasymmetri och signalteori, som dessutom utgör en del av den teoretiska referensramen. Den kvantitativa studien utvecklar hypoteser genom teorierna och den tidigare forskningen. Dessa ligger till grund för dataanalysen som sker genom logistisk regression för att skapa en konkursprognostiseringsmodell. Datamaterialet som undersökts består av sekundärdata genererad från Retriever Business av småföretag i Västra Götaland under period 2019. Studiens resultat visar att det finns ett signifikant negativt samband mellan revisorn och konkursrisk. Studien visar därmed att revisorn har en effekt på konkursrisken, även om det är svårt att fastställa den exakta påverkan som revisorn har på konkursrisken. Resultatet ger trots det indikationer på att återinförande av revisionsplikten skulle gynna såväl småföretagare som myndigheter och bidrar på så sätt med stöd till debatten om återinförandet av revisionsplikten. / The reform for voluntary audit was implemented with the aim of enabling freedom of choice regarding audit matters based on the needs and resources of small businesses. Despite the reform being implemented over a decade ago, researchers are still divided on the effects of abolishing the mandatory audit requirement. In recent years, there has been a debate on reintroducing the mandatory audit requirement, with arguments based on increased economic crime and decreased profitability among companies that have opted out of audits. The various advantages of the auditor's expertise and legitimacy in providing security for stakeholders are also arguments for the reintroduction of the mandatory audit requirement. This study contributes to a better understanding and guidance for small businesses in the decision-making process regarding voluntary audits. Therefore, the purpose of this study is to examine whether the reform for voluntary audit has influenced the bankruptcy risk of Swedish limited liability companies and to identify other financial factors that may affect the long-term survival of small businesses. The purpose is addressed based on three theories: legitimacy theory, information asymmetry, and signaling theory, which also form part of the theoretical framework. The quantitative study formulates hypotheses based on these theories and previous research. These hypotheses form the basis for data analysis using logistic regression to create a bankruptcy prediction model. The data analyzed consist of secondary data generated from Retriever Business on small businesses in Västra Götaland during the period 2019. Therefore, this study is written in Swedish. The study's results show a significant negative correlation between the auditor and bankruptcy risk. Thus, the study demonstrates that the auditor has an effect on bankruptcy risk, although it is difficult to determine the exact impact of the auditor on bankruptcy risk. In spite of that, the results provide indications that reintroducing the mandatory audit requirement would benefit both small business owners and authorities, thus offering support to the debate on reintroducing the mandatory audit requirement.

This study explores the financial performance measurement of South Africa's Top Companies. It aims to find a conclusion on the research problem, that is 'Do South Africa's Top Companies use the available arsenal to measure their financial performance?' Commerce and industry are the cornerstones of the economy of a country. This study purports to contribute to the ways and means of minimising the risk of business failures due to the resultant effects on the economy. The sample comprises of sixty companies. The sampling frame is the first hundred companies of the Financial Mail 200 Top Performers for 2004. The arsenal that is available to measure financial performance is researched in the financial literature. Mainly, this covers ratio analysis and interpretation, and the bankruptcy prediction models. To arrive at a conclusion on the research problem, a research instrument is developed from the host of financial ratios in the literature, including the bankruptcy prediction models. The research instrument comprises of popular ratios that are also found to be 'logical', as well as the ratios that make up the Z-Score bankruptcy prediction model. The instrument is called the Ratio Map and Z-Score and is applied to test the financial strengths/weaknesses of the Top Companies. In addition to the Ratio Map and Z-Score, the measures applied by the Top Companies as 'highlights' are analysed. This is done to determine the extent at which the measures unearth the strengths/weaknesses of the Top Companies. The conclusion drawn is that the Top Companies do not utilise the available arsenal to measure their financial performance. The supporting evidence is that the most frequently applied 'highlights' measures by the Top Companies cover only one area of the many financial fields of a company, that is, share performance. On the other hand, the analyses per Ratio Map and Z-Score have not revealed major material weaknesses in the financial position of the Top Companies. It is proposed that: ïf  More information be given in the notes to the financial statements to facilitate meaningful analysis; and ïf  A follow-up research study be done to assess the trends of the Top Companies. / Business Management / D.Comm. (Business Management)

Från 1900-talets början har banker och låneinstitut använt nyckeltal som hjälpmedel vid bedömning och kvantifiering av kreditrisk. För dagens investerare är den ekonomiska miljön mer komplicerad än för bara 40 år sedan då teknologin och datoriseringen öppnade upp världens marknader mot varandra. Bedömning av kreditrisk idag kräver effektiv analys av kvantitativa data och modeller som med god träffsäkerhet kan förutse risker. Under 1900-talets andra hälft skedde en snabb utveckling av de verktyg som används för konkursprediktion, från enkla univariata modeller till komplexa data mining-modeller med tusentals observationer. Denna studie undersöker om det är möjligt att prediktera att svenska företag kommer att gå i konkurs och vilka variabler som innehåller relevant information för detta. Metoderna som används är diskriminantanalys, logistisk regression och överlevnadsanalys på 50 aktiva och 50 företag försatta i konkurs. Resultaten visar på en träffsäkerhet mellan 67,5 % och 75 % beroende på vald statistisk metod. Oavsett vald statistisk metod är det möjligt att klassificera företag som konkursmässiga två år innan konkursens inträffande med hjälp av finansiella nyckeltal av typerna lönsamhetsmått och solvensmått. Samhällskostnader reduceras av bättre konkursprediktion med hjälp av finansiella nyckeltal vilka bidrar till ökad förmåga för företag att tillämpa ekonomistyrning med relevanta nyckeltal i form av lager, balanserad vinst, nettoresultat och rörelseresultat. / From the early 1900s, banks and lending institutions have used financial ratios as an aid in the assessment and quantification of credit risk. For today's investors the economic environment is far more complicated than 40 years ago when the technology and computerization opened up the world's markets. Credit risk assessment today requires effective analysis of quantitative data and models that can predict risks with good accuracy. During the second half of the 20th century there was a rapid development of the tools used for bankruptcy prediction. We moved from simple univariate models to complex data mining models with thousands of observations. This study investigates if it’s possible to predict bankruptcy in Swedish limited companies and which variables contain information relevant for this cause. The methods used in the study are discriminant analysis, logistic regression and survival analysis on 50 active and 50 failed companies. The results indicate accuracy between 67.5 % and 75 % depending on the choice of statistical method. Regardless of the selected statistical method used, it’s possible to classify companies as bankrupt two years before the bankruptcy occurs using financial ratios which measures profitability and solvency. Societal costs are reduced by better bankruptcy prediction using financial ratios which contribute to increasing the ability of companies to apply financial management with relevant key ratios in the form of stock , retained earnings , net income and operating income.

This study explores the financial performance measurement of South Africa's Top Companies. It aims to find a conclusion on the research problem, that is 'Do South Africa's Top Companies use the available arsenal to measure their financial performance?' Commerce and industry are the cornerstones of the economy of a country. This study purports to contribute to the ways and means of minimising the risk of business failures due to the resultant effects on the economy. The sample comprises of sixty companies. The sampling frame is the first hundred companies of the Financial Mail 200 Top Performers for 2004. The arsenal that is available to measure financial performance is researched in the financial literature. Mainly, this covers ratio analysis and interpretation, and the bankruptcy prediction models. To arrive at a conclusion on the research problem, a research instrument is developed from the host of financial ratios in the literature, including the bankruptcy prediction models. The research instrument comprises of popular ratios that are also found to be 'logical', as well as the ratios that make up the Z-Score bankruptcy prediction model. The instrument is called the Ratio Map and Z-Score and is applied to test the financial strengths/weaknesses of the Top Companies. In addition to the Ratio Map and Z-Score, the measures applied by the Top Companies as 'highlights' are analysed. This is done to determine the extent at which the measures unearth the strengths/weaknesses of the Top Companies. The conclusion drawn is that the Top Companies do not utilise the available arsenal to measure their financial performance. The supporting evidence is that the most frequently applied 'highlights' measures by the Top Companies cover only one area of the many financial fields of a company, that is, share performance. On the other hand, the analyses per Ratio Map and Z-Score have not revealed major material weaknesses in the financial position of the Top Companies. It is proposed that: ïf  More information be given in the notes to the financial statements to facilitate meaningful analysis; and ïf  A follow-up research study be done to assess the trends of the Top Companies. / Business Management / D.Comm. (Business Management)

The diploma thesis is focused on evaluating the performance of the company KOHUT Třinec s.r.o. in period from 1. 4. 2007 to 31. 3. 2011. Evaluation is made by using selected indicators of financial analysis, namely pyramidal decompositions and systems of purposefully selected indicators. The theoretical part contains the definition of basic concepts for understanding the issues examined. Based on financial analysis in the practical section are made possible suggestions to improve the current situation of the company and suggestions to remove the identified problems.