Journal of Economics, Management and Trade <p><strong>Journal of Economics, Management and Trade (2456-9216)</strong> publishes manuscripts with valuable insight to research, ideas and strategies of economics, management and trade. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer reviewed, open access INTERNATIONAL journal. This journal aims to publish high quality papers (<a href="/index.php/JEMT/general-guideline-for-authors">Click here for Types of paper</a>) in all below mentioned areas.</p> en-US (Journal of Economics, Management and Trade) (Journal of Economics, Management and Trade) Fri, 13 Sep 2019 11:22:31 +0000 OJS 60 Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines <p>Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.</p> Jasleen Kaur, Khushdeep Dharni ##submission.copyrightStatement## Fri, 13 Sep 2019 00:00:00 +0000 An Analysis of the Effectiveness of Inflation Targeting Monetary Policy Framework in South Africa <p>This study is premised on investigating the effectiveness of inflation targeting in South Africa. The methods of analysis include the Vector Autoregressive model (VAR), the unit root test and cointegration test. The analysis was conducted with the use of EViews version 9. The findings from the study revealed that the response of inflation is not consistent with the Taylor rule hence increases in the repo rate meant to reduce inflation actually increase the inflationary pressures in the economy. This is due to the composition of the Consumer Price Index. Housing constitutes the largest weight on the CPI hence this has an impact on how the Repo rate affects inflation. The autoregression model of inflation showed that the sum of the coefficients is less than one (0.965) showing that inflation targeting has effectively reduced the persistence of inflation in South Africa. Thus monetary framework in South Africa seems to be effective and should thus be advanced for wider economic benefit.</p> Leonard Makuvaza, Jacob M. Nyambe, J. P. S. Sheefeni ##submission.copyrightStatement## Fri, 13 Sep 2019 00:00:00 +0000