Stock Movement Prediction Based On Technical Indicators Applying Hybrid Machine Learning Models

The prediction of stock price movements is one of the most challenging tasks in financial market field. Stock price trends depended on various external factors like investor's sentiments, health and political crises which can make stock prices more volatile and chaotic. Lately, two crises affected the variation of stock prices, COVID-19 pandemic and Russia-Ukraine conflict. Investors need a robust system to predict future stock trends in order to make successful investments and to face huge losses in uncertainty situations. Recently, various machine learning (ML) models have been proposed to make accurate stock movement predictions. In this paper, a framework including five ML classifiers (Gaussian Naive Bayes (GNB), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and K-Nearest Neighbors (kNN))) is proposed to predict the closing price trends. Technical indicators are calculated and used with historical stock data as input. These classifiers are hybridized with Principal Component Analysis method (PCA) for feature selection and Grid Search (GS) Optimization Algorithm for hyper-parameters tuning. Experimental results are conducted on National Association of Securities Dealers Automated Quotations (NASDAQ) stock data covering the period from 2018 to 2023. The best result was found with the Random Forest classifier model which achieving the highest accuracy (61%).