Thesis (M.Com.(Taxation))--University of Pretoria, 2008. / Summary in English and Afrikaans. Includes bibliographical references.

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Eftersom dagens kund är krävande och medveten om vad hon eller han vill ha är det viktigare än någonsin att verkligen förstå vad kunden verkligen efterfrågar. Ett företags returhantering kan därför ha avgörande betydelse då det gäller att samla in rätt information ifrån kunden med målet att minska framtida returer. Ett företags sätt att arbeta med returhantering är avgörande, detta är speciellt sant för företag inom e-handeln. Dessa företag kommer alltid att ha en viss retur nivå eftersom deras kunder använder sina hem som provrum eftersom det är svårt för kunderna att veta om plagget kommer att passa och motsvara deras förväntningar. Det är dock viktigt att jobba för att ha en så låg returandel som möjligt, för att också kunna ha nöjda kunder. Målet är att vara proaktiv och undvika returer. Detta är något som bör göras både långsiktigt och kortsiktigt i hela försörjningskedjan. Kundförståelse spelar också en stor roll i returhanteringen. Rätt information ska samlas in från kunderna, så att företaget kan bilda sig en uppfattning om kundernas behov och önskningar. Genom att skapa returkoder som ger en mer exakt bild av returanledningen kan denna information sedan användas i produktutvecklingen med målet att minimera framtida returer. Hänsyn behöver också tas till andra områden när man arbetar med att undvika returer. Kunden behöver till exempel få en klar bild av vad han eller hon köper, därför är det viktigt att produkten beskrivs på ett tydligt och lättförståelig vis. Det är också viktigt att måttlistor och måttinstruktioner är lätta att förstå och relatera till, så att kunden får möjligheten att välja rätt storlek. När man arbetar på ett bra sätt inom alla dessa områden kan man nå en högre avkastning genom ökad lojalitet från kunder och färre returer. / <p>As today’s customer is demanding and knows what he or she wants, it is more important than ever to really understand the customer’s true demand or need. A company’s returns management can therefore be crucial in order to gather the right information from the customer so that future returns can be minimized. A company’s work with returns management is very important, especially for companies within eCommerce. These types of companies will always have a certain level of returns, because the fact that the customers use their own homes as changing rooms. It is hard for the customer to know if the product will fit them and correspond to their expectations. It is although important to try to have as low return level as possible in order to have satisfied customers. The aim is to be proactive and avoid returns before they occur. This avoidance has to be considered both in short and long term ways within the whole supply chain.Consumer insight also plays an important role within returns management. It is here the right information should be collected from the customers in order to understand their wants and needs. By designing return codes that give a more precise picture of the return reason companies like Halens can use this information in the development process in order to minimize the risk for future returns.Also other areas within the business can be taken into consideration when working with returns avoidance. The customer needs to get a clear picture of what they actually are buying and therefore it is also important to describe the products in a clear and lucid way. It is also important to make measurement lists and instructions easy to understand and relate to, in order to give the customer the ability to choose the right size.When working efficiently within all these areas higher profit can be gained though increased customer loyalty and decreased returns.</p><p>Program: Magisterutbildning i Applied Textile Management</p>

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We propose an asset pricing model in a production economy where cash flows are determined by firms' optimal dividend and investment decisions. Extensive and intensive decision margins in dividend payout are modeled with cash holding and investment adjustment costs. The model implies that delays in dividend distribution of young and growing firms play instrumental roles in explaining various asset pricing anomalies. Quantitative results show that model-implied dividend policies and investments are consistent with data, and the cross sections of stock returns are well explained by the interactions between productivity shocks and the lumpy dividend policies. Additionally, the model produces countercyclical variations in the market risk premium. In addition, we empirically investigate the relevance of firm characteristics and aggregate productivity shocks in determining dividend payment propensity, thereby asset prices. It is found that excess returns for dividend payers over nonpayers are significantly linked to business cycles. Relative future returns are fairly predicted by the spread of lagged propensities to pay dividends. Furthermore, the empirical results document that each future return of payers and nonpayers increases in propensities to pay out cash to shareholders. These results are consistent to our rational expectations model of dividend policy, and contradictory to the catering theory of dividends.

The last decade has witnessed a great deal of research in modelling volatility of financial asset returns, expressed by time-varying variances and covariances. The importance of modelling volatility lies in the dependence of any financial investment decision on the expected risk and return as formalized in classical asset pricing theory. Precise evaluation of volatilities is a compulsory step in order to perform correct options pricing according to recent theories of the term structure of interest rates and for the construction of dynamic hedge portfolios. Models of time varying volatility represent an important ground for the development of new estimation and forecasting techniques for situations not reconcilable with the Gaussian or, more generally, a linear time series framework. This is particularly true for the statistical analysis of time series with long range dependence in a nonlinear framework. The aim of this thesis is to introduce parametric nonlinear time series models with long memory, with particular emphasis on volatility models, and to provide a methodology which yields asymptotically exact inference on the parameters of the models. The importance of these results stems from: (i) rigorous asymptotics was lacking from the stochastic volatility literature; (ii) the statistical literature does not cover the analysis of the asymptotic behaviour of quadratic forms in nonlinear non-Gaussian variates that characterizes our problem.

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