M.Comm. (Financial Management) / There is a wide perception that bank net interest margins (NIMs) in Sub-Saharan Africa in general and South Africa in particular, are higher compared to other regions. The study investigates four commercial banks in South Africa with the aim of identifying the relevant factors affecting the behaviour of NIMs in commercial banking in South Africa, and draws comparisons with other markets. The study employs the Classical Linear Regression Model (CLRM) using the Ordinary Least Squares (OLS) data estimating technique to analyse net interest margins over the period 2000 to 2010. The study takes note of Ho and Saunders’s seminal work produced in 1981, and subsequent extensions and modification by other authors and researchers. Net interest margins are modeled in a single-step together with explanatory variables driven from the theoretical model. Using data obtained from the Bankscope data base, the variables examined in the study are; competitive structure of the market, average operating costs, management’s propensity for risk aversion, credit risk exposure, the quantum of the bank’s operations, short-term money market interest rate volatility, the opportunity cost of holding reserves and quality of management running the institution. The findings of the study suggest that market power, average operating costs, degree of risk aversion, credit risk exposure, and size of operations are major factors explaining the behaviour of NIMs in South Africa. These variables are major in terms of the number of banks that exhibit statistical significance. Market power, interest rate volatility and opportunity cost of holding reserves are also relevant factors, although they affect fewer banks than the major factors. Comparison of South African net interest margins determinants with those from other regions reveals some fundamental differences. These differences indicate that banks from different countries and regions are faced with different operating environments and risk profiles that drive net interest margins.

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R2) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape.

In this diploma thesis I constructed generally usable program for processing economical data through mathematical methods of linear regression in Maple system. Program is used for trend dependency analysis of examined quantities. Via multi-stage algorithmization and implementation of information criterion I created interactive form with user-friendly interface with possibility of straight data import from office suite applications. Functionality of this program is verified on example with specific data collection.

The main subject of the diploma thesis is a market price modeling by real estates. As a tool for modeling, is used a multiple linear regression. As starting points, are used an econometrical theory and knowledge about real estate valuation. The main goal is to find optimal model for best capture in the time and place.

MCom / Department of Accountany / The relationship between the Equity Market Risk Premium (MRP) and macroeconomic variables has been a subject of extensive discussion in the finance literature. The MRP is a central component of the main asset pricing models which are used to estimate the cost of equity which is mainly used in investment appraisal, performance measurement and valuation of equity assets. Past studies have identified inflation rate, interest rate, foreign exchange rate and political risk as the key macroeconomic variables that determine the size of the MRP. The test of the impact of these variables on the MRP have however been based mainly on data from developed countries and a few emerging countries. To the researcher’s knowledge, there are no studies that have investigated the impact of these macroeconomic variables on the MRP in South Africa. It is necessary to test the impact of these variables in the context of South Africa as these variables vary across countries. Using time series secondary data that was obtained from the SARB database, JSE database and World Bank database for the period 2002 to 2017, this study investigated the impact of these variables on the MRP in South Africa. A total of 192 observations per series of the inflation rate, interest rate, foreign exchange rate, political risk, JSE-ALSI and 91-days Treasury bill was used in the study. The data used were tested for possible misspecification errors that could arise from using a time series secondary data and the regression model was fitted using the Ordinary Least Square (OLS) estimator. The misspecification tests and models were both implemented on STATA 15 software. The results shows that inflation rate, interest rate and foreign exchange rate have a negative impact on the MRP whilst political risk has a positive impact on the MRP. Furthermore, the result shows that the inflation rate is the only variable amongst other variable tested that has a significant influence on the MRP for the study period. The study, therefore, concludes that inflation rate has the highest impact on the MRP in the context of South Africa. The study recommends that inflation rate should be monitored and kept within its target of 3-6% amongst other variables tested in order to increase investors’ confidence in the security market and also foster economic growth. The main limitations to the study were the limited data sources and insufficient funds. / NRF

This thesis employs the globalization and integration-demarcation cleavage theory formulated by Huttar [2014] and Kriesi [2012], conception of populism formulated by Mudde [2017] and draws on Gramscian conception of civil society and hegemony. Aim of this thesis is to build a model of causal influence of globalization on cleavage and on populism, as was suggested by Hutter [2014]. After building this model, the aim is to explore how this theoretical relationship hold's over the 30 years since 90's, when the connection between globalization and new sociocultural cleavage had been theoretically suggested. For this model I am using KOF Globalization Index, European Values Survey datasets and Authoritarian Populism Index constructed and published by Timbro in years 1990, 1999, 2008 and 2017. This model is built on a dataset containing 38 countries on European continent or being a candidate country for EU. I am elaborating Hutter's theoretical suggestion and framing it in Gramscian conception of civil society. This allows me to suggest that populists are using organic crisis in a society to attract people who feel disjointed from current hegemonical elite and to create counterhegemony. The theory is, that globalization increases the tension between winners and losers of globalization sides of cleavage...

Congestion on roadways and high level of uncertainty of traffic conditions are major considerations for trip planning. The purpose of this research is to investigate the characteristics and patterns of spatial and temporal correlations and also to detect other variables that affect correlation in a freeway setting. 5-minute speed aggregates from the Performance Measurement System (PeMS) database are obtained for two directions of an urban freeway – I-10 between Santa Monica and Los Angeles, California. Observations are for all non-holiday weekdays between January 1st and June 30th, 2010. Other variables include traffic flow, ramp locations, number of lanes and the level of congestion at each detector station. A weighted least squares multilinear regression model is fitted to the data; the dependent variable is Fisher Z transform of correlation coefficient. Estimated coefficients of the general regression model indicate that increasing spatial and temporal distances reduces correlations. The positive parameters of spatial and temporal distance interaction term show that the reduction rate diminishes with spatial or temporal distance. Higher congestion tends to retain higher expected value of correlation; corrections to the model due to variations in road geometry tend to be minor. The general model provides a framework for building a family of more responsive and better-fitting models for a 6.5 mile segment of the freeway during three times of day: morning, midday, and afternoon. Each model is cross-validated on two locations: the opposite direction of the freeway, and a different location on the direction used for estimation. Cross-validation results show that models are able to retain 75% or more of their original predictive capability on independent samples. Incorporation of predictor variables that describe road geometry and traffic conditions into the model works beneficially in capturing a significant portion of variance of the response. The developed regression models are thus transferrable and are apt to predict correlation on other freeway locations.

Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas.

The main objective of this study was to test if the applicability of known capital structure theories holds water in abnormal economic environments, in particular, in Zimbabwe. Using secondary data collected for listed manufacturing firms from 2009-2018, results from a fixed effects regression model concluded that profitability, company size, non-debt tax shields, firm liquidity, inflation and GDP were significant in explaining capital structure decisions in Zimbabwe. In the context of South Africa, company size, asset tangibility, firm liquidity and inflation were found to be significant. The pecking order and trade-off theories were the only two theories that were found to be applicable in the Zimbabwean context, and the application of both theories indicated the use of internally generated funds as opposed to external finance sources, such as debt and equity. These results attribute to the abnormality and instability of the Zimbabwean economy, especially with regards to limited access to capital. / Business Management / M. Com. (Business Management)

В работе были определены детерминанты, формирующие прибыль банков разных форм собственности, степени их воздействия на прибыль, получаемую банками, а также определены специфические особенности деятельности государственных банков. / The paper identifies the determinants that form the profit of banks of different ownership forms, the degree of their impact on the profit received by banks, as well as the definition of specific features in the activities of state banks.