PREDICTION OF STOCK VALUE MOVEMENT USING TECHNICAL ANALYSIS AND SENTIMENT ANALYSIS OF HEADLINES NEWS - KURNIAWAN MUTTAQIN NIM: 23514072, HANNIF
Stock price movements have random fluctuations that are influenced by speculative factors that are difficult to predict accurately. The behavior and opinions of investors have an influence on stock price movements. The time series model can be used to solve the problem. This study uses technical analysis features from historical stock prices, and sentiment analysis features from headlines news that can be used to create forecasting models. Multiple Kernel Learning (MKL) is used because the data source comes from different sources and has different data distribution. The MKL method can improve the effectiveness of choosing the kernel, it can minimizing the error value because uses less precise kernel. The feature extraction results against the data source generate eleven features. These eleven features can be used in making predictive models using the MKL method. The sentiment analysis feature can improve the best MAE accuracy by 24.72% in the MKL method, and 39.84% in the SVR method.