Prediction of Stock Market performance by using machine learning techniques

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. The model was built to predict performance of KSE-100 index. The prediction model predicts market as positive or negative with the help of different attributes. These factors include price fluctuation of fuel, commodity, foreign exchange, interest rate, general public sentiment, related NEWS and Auto-Regressive Integrated Moving Average (ARIMA) and Simple Moving Average (SMA) predicted values with help of historical data of the market. The techniques used for prediction include four different versions of Artificial Neural Network (ANN) including Single Layer Perceptron (SLP), Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Deep Belief Network (DBN). Other techniques include Support Vector Machine (SVM), Decision Tree and Naïve Bayes. All these techniques were compared to find the best predicting model. The results showed that MLP performed best and predicted the market with accuracy of 77%. Each factor was studied independently to find out its association with market performance. The change in Petrol prices showed the strongest association with market performance. The results suggested that behavior of market can be predicted using machine learning techniques.