Banks as the most evident financial institutions which provide a range of financial services in their primary role as intermediary from lenders and borrowers of money to sophisticated tools concerned with credit and liquidity provision, risk management and remittance of funds play a vital role in the economy of countries. Measuring the performance of banks, and identifying the factors which impact it, is an issue of major interest for regulators, policy makers, stakeholders, investors and the general public. Oil price movement as an external factor influencing the performance of banks, may affect macroeconomic events which, in turn, influence cash flows significantly in the finance and banking industry. Examining the performance of banks and how oil price movement impact their performance significantly those operating in oil exporting countries, is of interest of bank managers and policy makers. It will help top level managers of banks to be aware of relationship between oil price movement and the performance of their banks and will help them in formulating better policies and strategies in taking on opportunities and avoiding possible risks which this movement may cause. Moreover, it will help policy makers in oil exporting countries to understand how the banking industry of an oil exporting country can reap benefits from economic booms as a consequence of an increase in the price of oil. Therefore, this thesis attempts to investigate the impact of oil price movement on the performance of banks under different operational styles in oil exporting countries. The sample is consisting of 98 commercial, investment and Islamic banks in eight Middle Eastern oil exporting (MEOE) countries during the period 2000-2011. The research applies a two-stage Data Envelopment Analysis to examine the impact of oil price movement on performance of banks. In the first stage, four different efficiency scores of banks operating in the MEOE countries are derived and compared. The empirical results suggest that overall, MEOE banking industries mostly suffer from poor usage of and mal-location of resources by management to produce outputs, rather than a failure in operating at the most productive scale. A low level of overall technical efficiency in the MEOE banking industry means that management has poor skills in controlling operating expenses, marketing activities, absorbing deposits and the monitoring and effective screening of borrowers. In the second stage, to find out the impact of oil price movement on the performance of banks, technical efficiency scores obtained from the first stage are regressed over the oil price movement variable and environmental variables. The empirical results show that while oil revenue impacts the efficiency of the banks directly, positive oil price shocks impact efficiency of banks indirectly, and through inflation and economic growth. These findings suggest that when there is an increase in the price of oil, banks operating in oil exporting countries will derive benefit from the surplus income injected into the economy and their performance will be enhanced.

This master thesis is dedicated to Russian development and determinants of its growth. Just two decades ago Russia experienced a total change in the political and economic structure of the country. USSR fall, together will all its successes and failures. Russia went to the path of building a market economy, while the rest of the developed world was already living in such economy and using its benefits. With the heritage of the USSR, Russia had a huge potential to be successful in that road. The goal of this work is to answer the question, whether or not we were able to use it. In order to answer this question two-stage analysis was performed, combining theoretical and quantitative parts. Theoretical part contains the overall analysis of the areas of economic and social development. Quantitative part contains analysis of the main GDP drivers and endogenous powers of industrial production. Powered by TCPDF (www.tcpdf.org)

Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the Hierarchical Conceptual (HC) model; (2) the Artificial Neural Network-Quantitative (ANN-Q) model; (3) the Linguistic model; (4) the Rule-based Expert model; and, finally, (5) the Hybridisation of Linguistic and Quantitative (LQ) model. First, to understand the behaviour of the crude oil price market, the HC model functions as a platform to retrieve information that explains the behaviour of the market. This is retrieved from Google News articles using the keyword “Crude oil price”. Through a systematic approach, price data are classified into categories that explain the crude oil price’s level of impact on the market. The price data classification distinguishes crucial behaviour information contained in the articles. These distinguished data features ranked hierarchically according to the level of impact and used as reference to discover the numeric data implemented in model (2). Model (2) is developed to validate the features retrieved in model (1). It introduces the Back Propagation Neural Network (BPNN) technique as an alternative to conventional techniques used for forecasting the crude oil market. The BPNN technique is proven in model (2) to have produced more accurate and competitive results. Likewise, the features retrieved from model (1) are also validated and proven to cause market volatility. In model (3), a more systematic approach is introduced to extract the features from the news corpus. This approach applies a content utilisation technique to news articles and mines news sentiments by applying a fuzzy grammar fragment extraction. To extract the features from the news articles systematically, a domain-customised ‘dictionary’ containing grammar definitions is built beforehand. These retrieved features are used as the linguistic data to predict the market’s behaviour with crude oil price. A decision tree is also produced from this model which hierarchically delineates the events (i.e., the market’s rules) that made the market volatile, and later resulted in the production of model (4). Then, model (5) is built to complement the linguistic character performed in model (3) from the numeric prediction model made in model (2). To conclude, the hybridisation of these two models and the integration of models (1) to (5) in this research imitates the execution of crude oil market’s regulators in calculating their risk of actions before executing a price hedge in the market, wherein risk calculation is based on the ‘facts’ (quantitative data) and ‘rumours’ (linguistic data) collected. The hybridisation of quantitative and linguistic data in this study has shown promising accuracy outcomes, evidenced by the optimum value of directional accuracy and the minimum value of errors obtained.

Submitted by Yuri Barboza Cocchiarale (yuriufrj1410@hotmail.com) on 2017-07-18T13:48:06Z No. of bitstreams: 1 Dissertação FGV - Yuri Barboza 2017.pdf: 7367125 bytes, checksum: f7c2e1f50effbdca635ca98fe61d1862 (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2017-07-21T14:50:15Z (GMT) No. of bitstreams: 1 Dissertação FGV - Yuri Barboza 2017.pdf: 7367125 bytes, checksum: f7c2e1f50effbdca635ca98fe61d1862 (MD5) / Made available in DSpace on 2017-07-27T12:28:16Z (GMT). No. of bitstreams: 1 Dissertação FGV - Yuri Barboza 2017.pdf: 7367125 bytes, checksum: f7c2e1f50effbdca635ca98fe61d1862 (MD5) Previous issue date: 2017-05-31 / The aim of this study is to show that the creation of the Law no. 12,858 / 2013 has an irrelevant effect in the short term, regarding issues related to health and education problems. By adopting some assumptions capable of modeling the obtained database and estimating oil production using the SARIMA, Holt-Winters and Kalman Filter models, combined with the price forecast, the portrayed results can consistently reflect the impacts brought by this law. The problem becomes even bigger in a long-term horizon, in which the migration of revenue from royalties will overwhelmingly affect the entities who benefit from it. / Este trabalho tem como objetivo mostrar que a criação da Lei n° 12.858/2013 possui um efeito irrelevante no curto prazo, no que tange as questões ligadas aos problemas da saúde e educação. Adotando algumas premissas capazes de modelar o banco de dados obtido e estimando a produção do petróleo utilizando os modelos SARIMA, Holt-Winters e Filtro de Kalman, combinadas com a previsão dos preços, os resultados apresentados conseguem refletir de forma consistente os impactos trazidos por esta lei. O problema ainda se torna maior em um horizonte de longo prazo, onde a migração da receita provida dos royalties afetará de forma devastadora os entes que se beneficiam dela.