| Proj No. | A1084-251 |
| Title | Machine learning and deep learning methods for stock trading |
| Summary | Stock market trends are difficult to predict even for experienced analysts due to the complexity of the market and unknown factors. There has been increasing interest in the use of machine learning to predict stock prices as it has shown promising results in predicting time series data. The aim of the project is to explore and compare the performance of different machine learning models such as regression, recurrent neural networks (RNN or LSTM) and reinforcement learning in performing stock price forecasting and yielding profitable trading results. The student can also experiment on major factors from technical, fundamental, and sentiment analyses. |
| Supervisor | A/P Wong Jia Yiing, Patricia (Loc:S1 > S1 B1B > S1 B1B 58, Ext: +65 67904219) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Internet of Things Laboratory (Loc: S1-B4c-14, ext: 5470/5475) |
| Single/Group: | Single |
| Area: | Intelligent Systems and Control Engineering |
| ISP/RI/SMP/SCP?: |