HUBUNGAN ANTARA EFIKASI DIRI DAN PERAN GURU DENGAN BELAJAR BERDASAR REGULASI DIRI PADA AKSELERAN

This research is a quantitative approach with the aim at knowing: (1). The relationship between self-efficacy and the role of the teacher by learning based on self-regulation. (2). The contribution given by self-efficacy and the role of teachers in self-regulation based learning. (3). Levels of self-efficacy and the role of teachers in learning based on self-regulation. The subject of this study was acceleration in one of the Public High Schools in Surakarta as many as 115 people. This study is a principled study on population studies. Based on the results and analysis using multiple regression it is known that the correlation coefficient R = 0.375 (p = 0.000: p <0.05), which means that there is a significant relationship between self-efficacy and the role of teachers with self-regulation. The total contribution given by the two independent variables in this study amounted to 14.1%, and for both categorizations the independent variables namely self-efficacy and the role of the teacher were at a high level, and the dependent variable of learning based on self-regulation was at a very high level. Key word: Self-Regulated Learning, Self-Efficacy, The Role of Teachers

Impact of Technical Analysis on Karachi Stock Exchange of Pakistan

This thesis examines the impact of Technical Analysis on Karachi Stock Exchange by investigating the tools used for Technical Analysis for the sample period of 1997 to 2014. The KSE-100 index was examined to represent the market over the sample period for the following three aspects. First the results indicate that KSE-100 index do not follow random walk model by applying the Wright„s rank and sign variance ratio test. Secondly the study compared a variety of extremely popular technical trading rules based on simple moving averages, exponential moving averages, relative strength index and stochastic RSI to find the predictive ability of these indicators. The thesis also employed generalized regression neural network (GRNN) for stock prediction. The results show that these trading rules have predictive power over future price behavior. It is also evidenced that the inclusion of oscillators like RSI and RSIStochastic increase the performance in generating above average return. The combination of GRNN with simple moving averages also produced significant return. Based on these trading rules, the study proposed two trading strategy in order to know that whether investor beat buy-and- hold strategy. The results indicate that strategy based onthese rules have the ability to outperform the buy-andhold strategy. The results are significant even after considering the transactional cost. Technical Analysis is very effective for the investors in creating excess return for the sample period.