A Mobile Application For Stock Price Prediction - Choy, Yi Tou (2021)
Investing in stocks’ markets is risky and it needs a lot of research and time to make the right decision for earning money in stock markets. Novice investors are limited to specific investment knowledge and lack investment tools to gain wealth in the stocks’ markets. A mobile application for stock price prediction using time series algorithms is developed to tackle the problem mentioned. There are a few prediction algorithms being evaluated which were Long Short-Term Memory (LSTM), Holt Winter, AutoRegression Integrated Moving Average (ARIMA), Seasonal Auto-Regression Integrated Moving Average (SARIMA) and Prophet. These prediction algorithms were evaluated using 6 sectors of Bursa Malaysia stocks which are in total 216 stocks. The evaluation methods were Root Mean Square Error and Mean Absolute Error. The results show ARIMA has the least error among all five prediction algorithms. Therefore, ARIMA is the best prediction model to implement in the mobile application for stock price prediction. The mobile application for stock price prediction was developed with prototyping methodology. There were three iteration in this project to develop and enhance the functionality of the mobile application. Although the mobile application for stock price prediction had been developed, it contains some limitations like the model is inaccurate for some time and users cannot set the prediction period. All these limitations can be improved in the future. In conclusion, this project’s objectives were achieved by developing the mobile application for stock price prediction using the best time series algorithm evaluated, which is ARIMA.