Cross Listing of a Finnish Company on an Exchange in the United States of America

Finnish companies cross listing in the United States is an exceptional phenomenon. This study examines the cross listing decision, cross listing choice and cross listing process with associated challenges and critical factors. The aim is to create an in-depth understanding of the cross listing process and the required financial information. Based on that, the aim is to establish the process phases with the challenges and the critical factors that ought to be considered be- fore establishing the process plus re-evaluated and further considered at points in time during the process. The empirical part of this study is conducted as a qualitative study. The research data was collected through the adoption of two approaches, which are the interview approach and the textual data approach. The interviews were conducted with Finnish practitioners in the field of accounting and finance. The textual data was from publicly available publications of this phenomenon by the two BIG5 accounting companies worldwide. The results of this study demonstrate the benefits of cross listing in the U.S. are the better growth opportunities, the reduction of cost of capital and the production of higher quality financial information. In the decision making process companies should assess whether the benefits exceed the increased costs, the pressure for performance, the uncertainty of market recognition and the requirements of management. The exchange listing is seen as the most favourable cross listing choice for Finnish companies. The establishment of the processes for producing reliable, transparent and timely financial information was seen as both highly critical and very challenging. The critical success factors relating to the cross listing phases are the assessment and planning as well as the right mix of experiences and expertise. The timing plays important role in the process. The results mainly corroborate the literature concerning cross listing decision and choice. This study contributes to the literature on the cross listing process offering a useful model for the phases of the cross listing process.

Seed quality parameters of three interspecific hybrids of elephant grass x pearl millet

An interspecific hybrid resulting from the crossing of elephant grass (Pennisetum purpureum Schumach) x pearl millet (Pennisetum glaucum (L.) R. Brown) has been developed. This hybrid, however, revealed low phenotypic uniformity and low production of pure seeds. Through recurrent selection, two improved populations were obtained (genotypes Corte and Pastoreio). The aim of this study was assessing seed quality of the three hybrids (genotypes Corte, Pastoreio and Paraiso) by tests of: seed purity; seed germination; accelerated aging test, at 42 ºC; 1,000 seeds weight; drying curves; and sorption and desorption isotherms. Recurrent selection altered the seed size and increased initial quality of population for genotype Pastoreio. Drying curves for the three hybrids have shown similar behavior and reached moisture contents of 2.1%, 1.9%, and 1.8%, respectively, after 63 days. The accelerated aging test showed that hybrid Pastoreio was the most vigorous.

How Is Credit Risk Estimated? Discussion and Evidence based on Cross-Industry Sample Utilizing the KMV Model

The purpose of this thesis is to focus on credit risk estimation. Different credit risk estimation methods and characteristics of credit risk are discussed. The study is twofold, including an interview of a credit risk specialist and a quantitative section. Quantitative section applies the KMV model to estimate credit risk of 12 sample companies from three different industries: automobile, banking and financial sector and technology. Timeframe of the estimation is one year. On the basis of the KMV model and the interview, implications for analysis of credit risk are discussed. The KMV model yields consistent results with the existing credit ratings. However, banking and financial sector requires calibration of the model due to high leverage of the industry. Credit risk is considerably driven by leverage, value and volatility of assets. Credit risk models produce useful information on credit worthiness of a business. Yet, quantitative models often require qualitative support in the decision-making situation.