The aim of this research is to analyze the potential relationship between corporate governance and dividend policy. To conduct this research, a sample of 19 corporations from the Borsa Istanbul (BIST) Corporate Governance Index (XKURY), which is composed of listed companies who accomplished a certain level of Corporate Governance Principles over the period of 2007-2014, were selected. OLS (Ordinary Least Squares) panel regression analysis has been performed. The potential relationship between ownership structure and dividend policy has also been analyzed by utilizing the independent variables of ownership concentration, managerial ownership and total foreign ownership. In addition to our independent variables, we also included return on equity (ROE) and firm size to our research in order to increase the explanatory power of our model. This study finds an insignificant relationship between corporate governance and dividend policy. On the other hand, we obtained significant positive relationship between total foreign ownership and dividend policy and significant negative relationships between ownership concentration and dividend policy and managerial ownership and dividend policy. Finally, we obtained significant negative association between return on equity (ROE) and dividend policy and significant positive association between firm size and dividend policy.
Researches on technological development and innovation indicators that are used as different criteria for measurement such as multivariate statistics methods have increased rapidly in the field of social sciences since 1990s. The concept of indicators is an interesting field of science, which are used to inform us about things that are difficult to measure. Indicators for technology development and innovation may be defined as statistics, which measure quantifiable aspects of technological development and innovation creation. In this research, indicators help us to describe technological development and innovation clearly and enable us to have a better understanding of the impact of policies and programs on technological development and innovation and on the society and the economy in general. The objective of the present paper is to examine whether technological development indicators, which are used as a proxy for economic growth, innovation and the development level of countries, are influenced by the used variables in this analysis. The study is conducted by using a very large data set. It covers a monthly time period of 1996 and 2011. The study includes a variety of variables such as research and development expenditure (RDE), high-technology exports (HTE), long-term unemployment (LTU), patent applications-residents (PA), patent applications-nonresidents (PAF), health expenditure (HE), GNI per capita (PPP), share of women employed in the non-agricultural sector (SWE), stocks traded (ST), internet users (IU), scientific and technical journal articles (STJ). The empirical results which were obtained by using MDS (Multidimensional Scaling) and HCA (Hierarchical Cluster Analysis) methods suggest that the variables of RDE, PA, HE, PPP, SWE, IU and STJ have significant impacts on technological development and innovation and should be reviewed all together.
Prediction of economic crisis, financial distress or bankruptcy has attracted great deal of attention in financial literature and in many other fields among the researchers over the past few decades. Although there are a variety of different methods that can be used to predict the future financial crisis, due to the complexity of the existing factors, prediction of financial crisis is a very difficult case. With the advent of Artificial Neural Networks (ANNs), researchers had the chance to solve various problems in finance. ANN approach is the application of artificial intelligence, which has been improved by the simulation of cognitive learning process of human brain. ANNs are commonly used in recent years, due to major advantages that they offer such as their ability to perform nonlinear statistical modeling that provides new alternative to other statistical methods and to learn directly from examples without needing or providing an analytical solution to the problem. In this study, a monthly dataset covering the period of 1990 and 2014 that belong to the Turkish economy will be used. The purpose of this study is to develop an early-warning system to predict financial crisis. To realize this aim, multi-layered feedforward neural networks (MLFNs) will be used. By using monthly data of 7 key macroeconomic and financial indicators of Turkish economy during 1990 and 2014, we find that predictive power of ANN is quite striking. Our out-of-sample forecasts indicate that the Turkish economy remains at high risk due to major negative developments and potential political instability between 2014 and 2016.
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