The diploma thesis analyzes available methods of financing of a new housing construction with the focus on the possibilities of concurrent investment. The above analysis is demonstrated in a model case study. Practical part contents selection of the products to be analyzed from each evaluated field: suitable mortgage loan, finance market investment, the investment to the alternate heating equipment of the home and photovoltaic power plant. Than demonstrate the possibilities of decreasing the price of the loan with the assistance of concurrent investment into the financial markets or the alternative investment. The risk of investing has been taken into consideration as well. At the end are demonstrated comparative analysis and evaluation of all financing possibilities resulting in suggesting the most suitable method of home loan financing.

The theme of this thesis is financing of purchase of a flat in the contemporary conditions in the Czech Republic. The thesis is focuses mainly on an analysis of mortgage loans and building savings products. The thesis is also dedicated to comparison of the possible options of financing the own housing in the particular case in order to find the optimal solution and propose the final recommendations.

This diploma thesis engages in evalution of benefits in financing of real estate. It deals with comparasion of offered forms in financing of loans, especially mortgage loans and their combinations with another products like building savings and unit trusts for needs of model clients. The outcome of a thesis is a proposal and recommendation of the most advantageous variant for financing of given real estate.

This diploma thesis deals with the financing options of housing in the Czech Republic with the focus on building society saving accounts and mortgage loans. At first, it explains the basic parameters of these products. Then, the second part is aimed at the possibilities of credit products offered on the market. Finally, the third part shows the representative example of a family and it suggests a possible way to finance their own housing on the basis of assessment of major advantages and disadvantages of credit types.

The aim of this thesis is the first part to gather theoretical and methodological knowledge about sources of funding in their own home and family planning personal finances. In the second part are then used to build the knowledge model situations three types of funding for each property separately. Produced models are primarily based on a comparison of the total financial cost and burden on the family budget. Consequently, the aim of using these evaluation and based on other findings in the final stage to propose recommendations that option purchase their own property will be best for the family.

The financial crisis facing the United States savings and loan industry has been steadily escalating over the last decade. During this time, accounting treatments concerning various thrift institution transactions have also attracted a great deal of attention. The specialized accounting treatments used in the thrift industry, known as regulatory accounting practices (RAP) have been blamed as one of the culprits hindering the regulators' ability to detect serious financial problems within many institutions. Accordingly, RAP was phased out, and all federally insured savings and loan associations began preparing their financial statements in accordance with generally accepted accounting principles (GAAP) as of January 1, 1989. The purpose of this dissertation is to compare the relative predictive values of the two historical cost based accounting conventions (RAP and GAAP) available to the savings and loar? industry during the 1980's. For purposes of this dissertation, predictive value is defined as the usefulness in assessing future financial health and viability. The sample consisted of all the institutions reporting to the Federal Home Loan Bank of Dallas between 1984 and 1989. Year-end thrift financial report data, obtained from Sheshunoff Information Services, Inc. (Austin, Texas) was used to calculate several financial ratios. The Federal Home Loan Bank of Dallas provided a comprehensive listing of all institutions that failed between January 1, 1985 and March 31, 1989. The null hypothesis tested in this study was: no significant differences existed between the predictive values of RAP and GAAP financial statements. Using a dichotomous dependent variable (failed/not failed) and independent variables from prior research, several multinomial logistic models were developed to test the null hypothesis. All models developed failed to reject the null hypothesis.

Machine learning and neural networks have recently become very popular in a large category of domains, partly thanks to their ability to solve complex problems by finding patterns in data, but also due to an increase in computing power and data availability. Successful applications of machine learning include for example image classification, natural language processing, and product recommendation. Despite the potential upside of machine learning applied to financial data there exists relatively few articles published while the ones that do exist exhibit that there exists a potential for the tools that it provides. This thesis utilizes neural networks to price collateralized loan obligations which is a type of bond that is backed by a large pool of corporate loans, rather than being issued by a single company or government like a regular bond. The large pool of corporate loans and structure of a collateralized loan obligation makes it a good candidate for this type of research as it involves regressing a large number of variables into a final single real-valued price of the bond where the relations are not necessarily linear. The thesis establishes a relatively simple model and builds upon this using a state-of-the-art ensemble method while also exploring a volatility scaled loss function. The findings of this thesis are that artificial neural networks can price collateralized loan obligations using only their structural and loan pool data with an accuracy close to that of a human. Ensemble methods outperform non-ensemble methods and boost performance by up to 28% when only considering mean squared error while scaling the loss function with the inverse of market volatility does not boost performance. The best performing model can price a collateralized loan obligation tranche rated AAA with an average absolute error of 0.88 and an equity tranche with an average mean absolute error of 4.67. / Under de senaste åren har maskininlärning samt artificiella neurala nätverk blivit väldigt populära i många olika domäner. Detta är delvis tack vare deras förmåga att lösa komplexa problem genom att hitta mönster i data, men även tack vare en ökning i beräkningskraft samt att tillgängligheten av data har blivit bättre. Några exempel på områden där maskininlärning har applicerats med framgång är klassificering av bilder, språkteknologi samt produktrekommendationer. Trots att maskininlärning skulle kunna erbjuda en stor potentiell uppsida vid lyckad tillämpning på finansiella data finns relativt lite studier publicerade kring ämnet. De studier som däremot är publicerade visar på stora möjligheter inom området. Den här studien använder artificiella neurala nätverk för att prissätta ”collateralized loan obligations” (CLOs), som tyvärr inte har någon bra svensk översättning. En CLO utfärdar obligationer vars underliggande värde härstammar från en portfölj av företagslån, och är därmed ett finansiellt instrument. Strukturen av en CLO och dess underliggande lånportfölj ger upphov till en stor mängd data, vilket gör instrumentet till en bra kandidat för maskininlärning. Studien etablerar ett relativt enkelt neuralt nätverk som sedan används för ett jämföra med en ensemblemetod samt en modifierad loss funktion som tar höjd för volatilitet. Slutsatserna av den här studien är att neurala nätverk lyckas prissätta instrumenten näst intill lika bra som vad en människa skulle kunna göra med befintliga metoder som bygger på Monte Carlo simulering. Däremot är studiens metod inte lika beroende av antaganden som gör den befintliga metoden väldigt känslig. Vidare så bidrar ensemblemetoden som används till att minska det genomsnittliga felet i kvadrat med upp till 28%. Att ta höjd för volatilitet vid inlärning bidar inte till att minska felet.

No description available.

This study is a descriptive study which identifies the variables that inhibit participation by black merchants in a Retail Revitalization Loan Program (RRLP) that utilizes the public/private partnership concept. A sample of fifteen (15) black merchants were administered self-report questionnaires from a population of seventy (70) in Dallas, Texas through the stratified sampling method, utilizing SIC codes. From the response patterns of those surveyed, a Z-value was computed and tested at 1% level of significance, revealing that at least 85% of the black merchants identified conventional underwriting criteria as the primary cause for their lack of participation in the RRLP. Therefore, the null hypothesis was upheld.

本篇論文的研究動機。由於許多文獻提到債權人在公司治理上從過去的被動位置轉變為主動利用聯合貸款合約中的限制性條款(covenant) 來規範公司營業活動。小至限制公司資本支出、股利發放，大則影響董事會決定公司 CEO 的去留。限制性條款扮演越來越重要的角色。本文探討從 SEC EDGAR filings 中搜尋公司是否面臨限制性條款的違約(in violation of covenants)。本篇論文能降低未來研究在資料建立上需要人工處理的時間，藉由降低錯誤標記的筆數來達成。 / Covenant violations in syndicated loan agreements are a key factor which demonstrates a shift in control rights to debt holders before a company enters into events of default. This paper focuses on the methodology of identifying incidents of covenant violations using both programming codes and manual searches. We dedicate to minimize the time in hand-collecting while pursue high hitting rates of true covenant violations in SEC EDGAR (Electronic Data Gathering, Analysis, and Retrieval) database. Our findings provide a way to access the U.S. firms’ public financial statements reported to SEC and extends the list of companies provided by Nini, Smith and Sufi (2012).