Optimized Artificial Neural Network Model for the Prediction of Domestic Companies Index Direction under the Botswana Stock Market

The business sector has always encountered some challenges in predicting exact daily prices for stock market index and therefore more research methodologies have been proposed this far to address this problem. In every nation, there are several factors such as the state of politics, economic situations and trade expectations that have great impact on the stock market index. In this paper, we compare two types of input variables useful in the prediction of stock mar- ket path for daily markets index. Our main contribution presented through this study is the ability to predict the path ow for the next day's price in Botswana stock market index through the use of optimized Artificial Neural Network (ANN) model. To enhance efficiency in the prediction accuracy on future stock market trends, we employ Genetic Algorithms (GA) to optimize the ANN model. We further reveal and substantiate the predictability of stock price ow by employing the hybrid GA-ANN model and compare its perfor- mance to pre-existing methods. Practical results indicate that proper selection of input variables enhances efficiency in the optimized ANN model performance.