Deep learning with its contributions to Artificial Intelligence has drawn researchers and investors to utilize it to predict stock price movement. In this project, we employ traditional machine learning models including linear regression, logistic regression and SVM as well as deep learning models including MLP and 1D CNN to predict the value and trend of Dow Jones Industrial Average. .
Supervisor: Kwok-Ping CHAN
Students: LIU Jiayao(3035142596)
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Date | Events |
---|---|
Oct 1st | Deliverables of Phase 1: Inception
- Complete the project plan - Build a project website |
Sept 1st to Oct 15th | Study the theory & related knowledge
- Deep learning - Python programming - Financial products |
Oct 16th to Jan 10th | Collect & preprocess the data
- Determine a predicted objective - Collect data from WSJ and Yahoo Finance - Clean and label the data |
Jan 12th | First Presentation |
Jan 21th | Deliverables of Phase 2: Elaboration
- Conduct preliminary implementation - Complete detailed interim report |
Jan 10th to Feb 25th | Build the models based on the data
- Fit the model - Tune the corresponding hyperparameters |
March to April | Finalize & Improve the results:
- Optimize the model - Prepare for final presentation - Start to write final report |
Apr 15th | Deliverables of Phase 3: Construction
- Conduct finalized tested implementation - Complete the final report |
Apr 17th | Final presentation |
May 2nd | Project exhibition |
In this project, given the past stock data of Dow Jones Industrial Average (DJIA), we apply regression models to predict the value of DJIA and classification models to predict the trend of DJIA on the 31st day. The stock data of DJIA from Oct 5, 1987 to Sept 29, 2017 are collected from Yahoo Finance and split into training and test sets. The Mean Squared Error (MSE) of regression models as well as the accuracy of classification models on the test set are selected as the evaluation metrics. The model training results are as follows:
Model | Test MSE |
---|---|
Linear Regression | 18352.1540 |
MLP (regression) | 18226.6978 |
Model | Test Accuracy |
---|---|
Logistic Regression | 0.5355 |
Support Vector Machine | 0.5319 |
MLP (classification) | 0.5487 |
1D CNN (univariate) | 0.5548 |
1D CNN (multivariate) | 0.5548 |
Name | Phone | |
Kwok-Ping CHAN (supervisor) | kpchan@cs.hku.hk | |
LIU Jiayao | jadeliu@hku.hk | (+852) 67649723 |