| Author: | Lau, Wai Tak |
| Title: | Forecasting Hong Kong Hang Seng Index stock price movement using social media data analysis |
| Advisors: | Chan, Chun Chung Keith (COMP) |
| Degree: | M.Sc. |
| Year: | 2018 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Stock price indexes -- China -- Hong Kong -- Forecasting Stock index futures -- China -- Hong Kong Social media Data mining |
| Department: | Department of Computing |
| Pages: | viii, 104 pages : illustrations (some color) |
| Language: | English |
| Abstract: | Intelligence and high-tech algorithms are the evolution of predicting stock price movement to inherit the historical researches - Social Media Data Analysis (SMeDA). It is perfect to collect Hong Kong social media data in the public text. Hang Seng Index (HSI) of stock price prediction movement based on the stock analysts expressed on financial and economic social media website has been an intriguing field of research. Their emotional text expressed their perspective and point of view in the future of stock price movement. The thesis of this dissertation is to examine and study creditability and referencing of stock analysts' prediction movement. In this paper, applied text and data analysis, including supervised machine learning algorithms extracted from social media articles, as well as analyze the correlation features between stock price prediction and stock commentary, which compared with historical stock data prediction. In an elaborate way, the level of creditability from the commentary of stock analysts can be roughly assorted the stock market development trend in HSI, it can definitely encourage the public for references (Appendix V and Appendix VI provide the programming coding of all algorithms and feature of raw data for algorithm). |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022144642803411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.71 MB | Adobe PDF | View/Open |
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