Short-term stocks movement prediction with technical analysis and machine learning - Wongso, Jonathan Anthony (Nanyang Technological University, 2024)
In the financial market, predicting stock price movement has always been a challenge. Many investors and analysts use different analyzing techniques, trying to predict the volatile market. This project presents a technological approach to short-term stock price prediction using Long Short-Term Memory (LSTM) neural networks. The primary objective of this research is to develop and evaluate the accuracy of an LSTM-based machine learning model in predicting short-term stock movement. The model was trained and tested using a several datasets, comprising of stock indexes and individual stocks from the US, China and Hong Kong. The LSTM model was configured and implemented using the Keras API, with GridSearchCV hyperparameter tuning to optimize performance. The model’s accuracy was evaluated using Mean Squared Error (MSE) and R-Squared (R2) metrics. Additionally, technical analysis was conducted to provide further information into the stock movement. The results demonstrate great performance in predicting movement of stocks. However, it is emphasized that the model’s prediction is a gauge and should be used in conjunction with other analytical techniques, such as technical analysis, to make well-informed decisions. The findings of this study highlight the potential of machine learning like LSTM to predict future stock price movements and offer valuable indicators for investors. Future work may include refining the model, exploring additional data sources and different analytical techniques such as fundamental analysis to improve prediction performances.