The objective of this study is to establish whether companies that utilise their resources more efficiently present specific characteristics in their financial profile, and whether on the basis of these characteristics a classification model can be constructed that includes, alongside resource utilisation measures, predictors related to other financial dimensions calculated from published information. The- research proceeds by examining the factors influencing companies' performance, and the reliabilty of published accounts. Discriminant analysis is chosen as the most appropriate technique of analysis. Its applications in the field of financial analysis are discussed -and an examination of the discriminant analysis technique is undertaken. For reasons of comparability and access to a large quantity of information, the analytical part of the study is based on data extracted from a computer readable tape provided by Extel Statistical Services Ltd. It starts by describing the financial variables to be used later on in the study, and proposing a classification framework that would be of assistance in identifying the financial dimensions of importance in relation to the problem under investigation. A discriminant model that correctly classifies 85 per cent of the companies is then constructed. It includes, besides measures of resources utilisation, measures of financial levarage, working capital management, cash position and stability of past performance. The-part of the analysis on the identification of potential well performing companies indicates that, although specific characteristics can be noticed up to five year before, it is only possible to construct a classification model with sufficient accuracy one year before a high level of performance is actually reached. Finally, an index of financial performance based on normal approximations of the z-score distributions from the model used to identify well performing companies is suggested and an assessment of the structural change experienced by companies rising from a less well to a well performaing status is presented. / Algerian Ministere de l'Hydraulique

The objective of this study is to establish whether companies that utilise their resources more efficiently present specific characteristics in their financial profile, and whether on the basis of these characteristics a classification model can be constructed that includes, alongside resource utilisation measures, predictors related to other financial dimensions calculated from published information. The- research proceeds by examining the factors influencing companies' performance, and the reliabilty of published accounts. Discriminant analysis is chosen as the most appropriate technique of analysis. Its applications in the field of financial analysis are discussed -and an examination of the discriminant analysis technique is undertaken. For reasons of comparability and access to a large quantity of information, the analytical part of the study is based on data extracted from a computer readable tape provided by Extel Statistical Services Ltd. It starts by describing the financial variables to be used later on in the study, and proposing a classification framework that would be of assistance in identifying the financial dimensions of importance in relation to the problem under investigation. A discriminant model that correctly classifies 85 per cent of the companies is then constructed. It includes, besides measures of resources utilisation, measures of financial levarage, working capital management, cash position and stability of past performance. The-part of the analysis on the identification of potential well performing companies indicates that, although specific characteristics can be noticed up to five year before, it is only possible to construct a classification model with sufficient accuracy one year before a high level of performance is actually reached. Finally, an index of financial performance based on normal approximations of the z-score distributions from the model used to identify well performing companies is suggested and an assessment of the structural change experienced by companies rising from a less well to a well performaing status is presented.

Session ID: SA-G4-2 / Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, September 17-21, 2008, Nagoya University, Nagoya, Japan

Travel and tourism marketers face a highly competitive environment brought on by the changing demographics of the U.S. population, the most significant change being the growth in size of the mature segment of the population. In terms of market size, there are currently 73 million people age 50 and older, comprising nearly one-fourth of the U.S. population (U.S. Census Bureau 2000). That number is expected to rise to 96 million by 2010, representing one-third of the population (Rasmusson 2000). A swelling population is not the only enticement that this age group offers. It is important to note that many mature consumers have deep pockets and a strong desire to spend. In fact, they control more than three-quarters of the wealth and one-half of the discretionary income in the nation. It is also estimated that they lay claim to three-fourths of the country's financial assets and boast more than $1 trillion in annual buying power. When all is said and done, this age group accounts for 40 percent of the total consumer demand in the United States (Swartz, 1999). However, even though recognizing the significance of the mature market in terms of their market size and economic potential, little research has been conducted to identify and understand the mature travelers who use the Internet.The main purpose of this study is to profile mature travelers on the basis of Internet use. More specifically, the intention is to examine the demographic and socio-economic characteristics of mature travelers who use the Internet compared to those who do not use the Internet. In addition, the purpose of the present study is to examine whether or not differences exist between Internet users and Internet non-users among mature travelers with respect to travel behavior. Attention is paid to investigate types of trip selected, the preferred activities participated in during the travel, length of stay, travel-related expenditures, type of lodging, type of transportation, number in the travel party, and type of travel party in explaining the differences between Internet users and Internet non-users of the mature market.Data were collected by utilizing a mailed questionnaire. 433 responses (23.44 percent of the total target population) were coded and used for data analysis. Data were analyzed by employing three types of data analysis: chi-square tests of independence; t-tests; and multiple discriminant analysis.The findings in the present study suggest that there are numerous differences in demographics, socio-economic characteristics, and travel characteristics between Internet users and Internet non-users among mature travelers. As a whole, for example, the results revealed that mature travelers who use the Internet were more likely to be younger, have higher annual household incomes, and have higher levels of education than mature travelers who do not use the Internet. Also, the results indicated that mature travelers who are still working are more likely to use the Internet than those who are not working. By understanding and utilizing information gathered from Internet users' and Internet non-users' demographics, socio-economic characteristics, and travel characteristics, tourism planners and marketers can develop appropriate and effective marketing strategies that appeal to mature travelers. / Master of Science

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The purpose of this study is to investigate the usefulness of statistical decomposition measures in the prediction of industrial bond rating changes. Further, the predictive ability of decomposition measures is compared with multiple discriminant analysis on the same sample. The problem of this study is twofold. It stems in general from the statistical problems associated with current techniques employed in the study of bond ratings and in particular from the lack of attention to the study of bond rating changes. Two main hypotheses are tested in this study. The first is that bond rating changes can be predicted through the use of financial statement data. The second is that decomposition analysis can achieve the same performance as multiple discriminant analysis in duplicating and predicting industrial bond rating changes. To explain and predict industrial bond rating changes, statistical decomposition measures were computed for each company in the sample. Based on these decomposition measures, the two types of analyses performed were (a) a univariate analysis where each decomposition measure was compared with an industry average decomposition measure, and (b) a multivariate analysis where decomposition measures were used as independent variables in a probability linear model. In addition to statistical decomposition analysis, multiple discriminant analysis was used in duplicating and predicting bond rating changes. Finally, a comparison was made between the predictive abilities of decomposition analysis and discriminant analysis.

<p>Corporate failures pose a problem for banks, investors, customers, employees andinsurers. With a multivariate discrimination method, the study aims to find the specificfinancial ratios that most accurate reveals a company's financial health, which is ofinterest to all of the above parties. The data consist of 1042 Swedish small enterprisesand 30 different financial ratios between the years 2005‐2007. The result shows thatbankrupt firms three years before bankruptcy have a disadvantaged capital structurewith poor solvency and high debt. The year before bankruptcy, profitability and liquiditydeclined for bankruptcy group, and rates vary considerably in comparison with thesurviving companies. Our model can correctly classify 91,2 % of the total sample one yearbefore bankruptcy.</p> / <p>Företagskonkurser är dyrt för samhället och utgör ett problem för bland annat banker,investerare, kunder, anställda, försäkringsbolag och leverantörer. Studien syftar till attfinna de specifika nyckeltal vilka avslöjar ett företags finansiella hälsa, vilket är av intresseför alla ovanstående parter. Med en kvantitativ multivariat analysmetod har 30 olikanyckeltal från sammanlagt 1042 svenska mindre aktiebolag mellan åren 2005 och 2007studerats.Resultatet visar att konkursföretagen tre år innan konkurs har en missgynnandekapitalstruktur med svag soliditet och hög skuldsättning. Året innan konkurs harlönsamheten och likviditeten minskat för konkursgruppen och talen uppvisar storaskillnader i jämförelse med de överlevande företagen. Modellen klassificerar 91,2 % avföretagen korrekt ett år innan konkurs.</p>

Corporate failures pose a problem for banks, investors, customers, employees andinsurers. With a multivariate discrimination method, the study aims to find the specificfinancial ratios that most accurate reveals a company's financial health, which is ofinterest to all of the above parties. The data consist of 1042 Swedish small enterprisesand 30 different financial ratios between the years 2005‐2007. The result shows thatbankrupt firms three years before bankruptcy have a disadvantaged capital structurewith poor solvency and high debt. The year before bankruptcy, profitability and liquiditydeclined for bankruptcy group, and rates vary considerably in comparison with thesurviving companies. Our model can correctly classify 91,2 % of the total sample one yearbefore bankruptcy. / Företagskonkurser är dyrt för samhället och utgör ett problem för bland annat banker,investerare, kunder, anställda, försäkringsbolag och leverantörer. Studien syftar till attfinna de specifika nyckeltal vilka avslöjar ett företags finansiella hälsa, vilket är av intresseför alla ovanstående parter. Med en kvantitativ multivariat analysmetod har 30 olikanyckeltal från sammanlagt 1042 svenska mindre aktiebolag mellan åren 2005 och 2007studerats.Resultatet visar att konkursföretagen tre år innan konkurs har en missgynnandekapitalstruktur med svag soliditet och hög skuldsättning. Året innan konkurs harlönsamheten och likviditeten minskat för konkursgruppen och talen uppvisar storaskillnader i jämförelse med de överlevande företagen. Modellen klassificerar 91,2 % avföretagen korrekt ett år innan konkurs.

This thesis provides a comprehensive ranking of 11 Czech statistical and 4 foreign credit scoring models. The ranking is based on the predictive performance of individual models, as measured by the area under curve, evaluated on a randomly sampled set of 250 training and validation samples. After establishing a baseline comparison, 3 avenues of estimation setup optimization are explored, namely missing value treatment, estimation method and the use of additional non-financial variables. After being optimized, the models are once again ranked based on their predictive performance. Statistical inference is drawn using ANOVA and the Friedman test, along with the corresponding Tukey and Nemeyi pos-hoc tests. In their baseline form, the Czech credit scoring models are found to be outperformed by the foreign benchmark model. Treating the missing values by OLS imputation and estimating the models by probit, significantly is found to significantly improve their predictive performance. In their optimized form, the difference in predictive performance between Czech and foreign credit scoring model is found to be only marginal. JEL Classification G28, G32, G33, G38 Keywords credit scoring, multiple discriminant analysis, logit analysis, probit analysis Author's e-mail 71247263@fsv.cuni.cz Supervisor's e-mail...

This thesis uses data of more than 180 banks from CEE region to identify the main determinants of long term credit ratings assigned to these banks in period between 2010 - 2012. This is done by employing two frequently used classification methods - Multiple Discriminant Analysis and Ordered Logit Model. The main contribution lies in including explanatory variables from various areas which have impact on financial health of examined banks. Apart from standard spheres of banks' performance such as capital adequacy, asset quality or profitability we investigate relevance of macroeconomic and qualitative factors as well. Although our results suggest that all mentioned areas are relevant for credit risk and hence rating assignment process the bank specific variables, both quantitative and qualitative, still play the key role.