Nepal Stock Market Movement Prediction with Machine Learning

Abstract

Financial market predicting is a popular theme of lots of researches in recent years. However, the majority of previous studies are focus on markets in great countries like China and United States, while some small countries are drawn less attention. To cover this shortage in current literature, we determined to use and compare 17 types of machine learning models to foresee Nepal market in this paper. Based on stock prices, 10 technical indicators were computed as input features. In addition, we also added emotional factors extracted from financial news to improve the prediction performance, which was evaluated by accuracy and F1 score. We predicted whether the closing price would rise or descend after three horizons: 1-day movement, 15-day movement and 30-day movement. From our experiment results, we found that linear SVM and XGBoost perform best and are the best options for further consideration in the trading process.