PREDICTION OF STOCK VALUE MOVEMENT USING TECHNICAL ANALYSIS AND SENTIMENT ANALYSIS OF HEADLINES NEWS
Stock price movements have random fluctuations that are influenced by <br /> <br /> speculative factors that are difficult to predict accurately. The behavior and <br /> <br /> opinions of investors have an influence on stock price movements. The time series <br /> <...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/22355 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Stock price movements have random fluctuations that are influenced by <br />
<br />
speculative factors that are difficult to predict accurately. The behavior and <br />
<br />
opinions of investors have an influence on stock price movements. The time series <br />
<br />
model can be used to solve the problem. <br />
<br />
This study uses technical analysis features from historical stock prices, and <br />
<br />
sentiment analysis features from headlines news that can be used to create <br />
<br />
forecasting models. Multiple Kernel Learning (MKL) is used because the data <br />
<br />
source comes from different sources and has different data distribution. The MKL <br />
<br />
method can improve the effectiveness of choosing the kernel, it can minimizing the <br />
<br />
error value because uses less precise kernel. <br />
<br />
The feature extraction results against the data source generate eleven features. <br />
<br />
These eleven features can be used in making predictive models using the MKL <br />
<br />
method. The sentiment analysis feature can improve the best MAE accuracy by <br />
<br />
24.72% in the MKL method, and 39.84% in the SVR method. |
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