The value investing philosophy, which can be traced at least to the teaching of Graham and Dodd in the 1930?s, entails identifying and investing in potentially under valued stocks with a potential for extraordinary returns. The focus of this thesis is to identify patterns and characteristics in financial accounting data preceding creation of shareholder value. The authors of this thesis utilize a Multivariate discriminant analysis in order to identify indicators of value creation and subsequent extraordinary returns in value stocks. A discriminant function is derived which successfully identifies which value stocks will eventually become growth stocks.

Very often the interesting variables are explained by several underlying variables and in statistical analyses it is common to study the relationship between variables and groups of variables. Because of this multivariate analysis is commonly used in both science and industry. There are two problem with both univariate and multivariate analyses. One is when the variables are correlated. The other is when the number variables of exceeds the number of observations which makes the matrices algebra used in the analysis impossible to execute.

Very often the interesting variables are explained by several underlying variables and in statistical analyses it is common to study the relationship between variables and groups of variables. Because of this multivariate analysis is commonly used in both science and industry. There are two problem with both univariate and multivariate analyses. One is when the variables are correlated. The other is when the number variables of exceeds the number of observations which makes the matrices algebra used in the analysis impossible to execute.

Very often the interesting variables are explained by several underlying variables and in statistical analyses it is common to study the relationship between variables and groups of variables. Because of this multivariate analysis is commonly used in both science and industry. There are two problem with both univariate and multivariate analyses. One is when the variables are correlated. The other is when the number variables of exceeds the number of observations which makes the matrices algebra used in the analysis impossible to execute.

Managing credit risk might be the single most important business area for any commercial bank. The assessment of "good" and "bad" corporate clients is a important task for a creditor. A bad debtor is a corporate client with hardships in meeting the continous claims (interest payments) that a creditor requires. One way of evaluating or separating a "bad" client from a "good" client is to assess the propensity for the client to file for bankruptcy. This thesis examines 226 firms in the Swedsh market in the quest of predicting corporate bankruptcy.

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.

Managing credit risk might be the single most important business area for any commercial bank. The assessment of "good" and "bad" corporate clients is a important task for a creditor. A bad debtor is a corporate client with hardships in meeting the continous claims (interest payments) that a creditor requires. One way of evaluating or separating a "bad" client from a "good" client is to assess the propensity for the client to file for bankruptcy. This thesis examines 226 firms in the Swedsh market in the quest of predicting corporate bankruptcy.

Large companies in today?s business environment are to a great extent depending on their credit score by rating institutes like Moody?s and Standard & Poor?s. There is however indications that these big institutes are not always rating companies accurately. This is why this thesis aims at developing a new credit rating model, CRITA-Score, with the objective of determining a company?s financial health more accurately.

Sweden?s District councils and Country councils are in itself to a large extent responsible for the state financed welfare. Because of this, Sweden has a contribution- and adjustment system. The reason for this system is to create equal chance, for all District councils and Country councils, to be able to give the citizens service independently of their income and non-influential structural differences. The adjustment is built upon the so-called standard cost method, which contains four different parts.

The snow leopard (Panthera uncia) is an elusive felid, native to the mountains in central Asia. Basic knowledge about the snow leopards? ecology has long been lacking but is advancing with the help of the GPS-technology. GPS cluster analysis can provide insight in the diet and prey selection of elusive predators, such as the snow leopard. Acceleration data from GPS collars can be used to study animal behavior but the two have never been combined to gain more detailed information of the feeding behavior of large carnivores.

The thick-shelled river mussel (Unio crassus) is a red listed species classified as Endangered (EN) and is also considered within the Habitats Directive. The distribution in Sweden is fragmented and it is mostly occurring in the south eastern parts. We investigated the presence of Unio crassus in a section of the stream Storån, Östergötland County, from Falerum to the inflow into Lake Åkervristen. The environmental parameters investigated were water depth, bottom substrate, shading, water velocity and the slope over the water surface. In this thesis I have focused mainly on water depth and shading, comparing sites with and without mussels.

A multivariate cross-sectional model is used in this thesis to proxy for expected earnings and to estimate return on equity for 214 companies over the period 2009-2013, using Swedish data. The model, which has never been tested on Swedish data before, is first confirmed to function as a forecasting model for expected return. Furthermore, the model is evaluated through a comparison with two univariate models based on the assumption that return on equity follows a mean reversion process. Forecast accuracy is calculated as the difference of estimated returns and actual returns. The results show that the univariate models' forecasts are superior to the multivariate model's..

This thesis examines a sample of Pitted Ware pottery from the Gotlandic site of Ajvide in regard to the spatial and temporal relationship of the shards. Ajvide Stone Age site have been extensively excavated over the past 25 years and the most recent analysis of pottery was carried out by Inger Österholm in 1987 and new analyses of the material are therefore greatly needed. Pottery from three sample areas was analysed in regard to their distribution, decoration, fragmentation, part of pot and quality of the goods. To analyse the material in a contextual way a multivariate statistical analysis called correspondence analysis was used. Two areas denominated ?dark areas?, which Österholm defined previously, as possible ritual sites, and one reference area with mixed material were chosen for the analysis.

Hallsta paper mill uses large amounts of water during the pulp and paper making processes. The wastewater is treated in two separate activated sludge processes referred to as BIO 1 and BIO 2. The main aim is to reduce the organic substance content, measured as COD (Chemical Oxygen Demand) before the wastewater is released into the environment. Since the effluent produced is very rich in organic substances, the addition of nitrogen (N) and phosphorus (P) are necessary to give satisfactory treatment results. Problems with sludge bulking sometimes occur which lead to increased levels of suspended matter and nutrients in the effluent.

To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Va?ljarbarometern. A risk associated with surveys is satisficing ? sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to ? through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population.