| Author: | Po, Lun Ho |
| Title: | Stock trading strategy using artificial neural network |
| Advisors: | Chan, Henry (COMP) |
| Degree: | M.Sc. |
| Year: | 2021 |
| Subject: | Stocks -- China -- Hong Kong Stock price forecasting -- China -- Hong Kong Finance -- Data processing Machine learning Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | ix, 57 pages : color illustrations |
| Language: | English |
| Abstract: | An Artificial Neural Network (ANN) has been constructed with various features, such as the percentage difference between close price and several technical indicators like Simple Moving Average (SMA), Bollinger Band, together with other parameters with the aim of predicting the stock price's upcoming trend and formulating the corresponding trading strategy to optimize the profit. This study aims to predict the stock price trend in the Hong Kong stock market, a market in which a wide range of companies are listed with free capital movement from worldÂwide. Using the 50 Hang Seng Index stocks in the Hong Kong stock market as the data, we have built several features by calculating the distance between close price and different technical indicators, such as SMA and Bollinger Band. Our model consists of two parts. The first part is the ANN model with the features built as input and data split into training data and validation data. After the model has been trained, the second part is to select a trading strategy which is based on the buying, selling and holding probabilities as determined from the ANN model. Based on these probabilities, the action as well as the number of shares to buy or sell can be determined. We have conducted experiments to evaluate the performance of the trading strategy. According to the experimental results, our model outperforms the daily buy-and-hold strategy and the stock price's overall trend. In particular, the performance of the proposed trading strategy was better when there was a significant downtrend of the stock market. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5829.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.25 MB | Adobe PDF | View/Open |
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