The Trends Prediction of GEM Stock Prices Based on Time Series Analysis and Weibo Sentiment Analysis
(2024) DABN01 20241Department of Economics
Department of Statistics
- Abstract
- The study of stock market has always been a popular topic among researchers. In most common view, stock market are naturally noisy, non-parametric, non-linear, and deterministic chaotic systems that cannot be effectively predicted. However, many researchers are still trying to make more precise predictions in various ways. In China, these studies are mainly focus on main market, with fewer on GEM enterprises. To narrow the gap and provide a more effective way to make predictions on them, we will combine sentiment analysis of social media, time series analysis and deep learning methods and explore the answer based on the comparison between 3 different kinds of models: ARMA model, LSTM model and VAR model.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9173269
- author
- Su, Yiran LU
- supervisor
- organization
- course
- DABN01 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- sentiment analysis, time series analysis, ARMA model, LSTM model, VAR model
- language
- English
- id
- 9173269
- date added to LUP
- 2024-09-24 08:36:22
- date last changed
- 2024-09-24 08:36:22
@misc{9173269, abstract = {{The study of stock market has always been a popular topic among researchers. In most common view, stock market are naturally noisy, non-parametric, non-linear, and deterministic chaotic systems that cannot be effectively predicted. However, many researchers are still trying to make more precise predictions in various ways. In China, these studies are mainly focus on main market, with fewer on GEM enterprises. To narrow the gap and provide a more effective way to make predictions on them, we will combine sentiment analysis of social media, time series analysis and deep learning methods and explore the answer based on the comparison between 3 different kinds of models: ARMA model, LSTM model and VAR model.}}, author = {{Su, Yiran}}, language = {{eng}}, note = {{Student Paper}}, title = {{The Trends Prediction of GEM Stock Prices Based on Time Series Analysis and Weibo Sentiment Analysis}}, year = {{2024}}, }