Author: Huang, Weihai
Title: Deep learning for financial market prediction
Advisors: Chi, Zheru (EIE)
Degree: M.Sc.
Year: 2021
Subject: Stock price forecasting
Machine learning
Hong Kong Polytechnic University -- Dissertations
Department: Department of Electronic and Information Engineering
Pages: viii, 64 pages : color illustrations
Language: English
Abstract: In order to effectively predict the trend of stock prices, a method is proposed by using fundamental analysis and deep learning algorithm. Firstly, S&P 500 data from 1980 to 2019 is collected and analyzed via Yahoo Finance API and the logarithmic return rate, volatility and trading volume on different dates are obtained. Secondly, the S&P 500 from 1980 to 2019 is also converted into image data. The quantitative data is converted into f-line pictures, and the daily trading volume histogram is merged to obtain 224×224×3 images. Thirdly, several deep learning algorithms, such as Long Short-Term Memory (LSTM) and convolutional neural network (CNN), are compared to determine the trend of stock price after 21 days through parameter optimization. The classification results show that the CNN model with quantitative data as input has strong robustness and obtains the classification results (66.70% in accuracy and 0.5744 in AUC). Finally, by randomly removing some tags and improving the training model, the maximum accuracy of 78.37% and the best AUC of 0.73 can be obtained.
Rights: All rights reserved
Access: restricted access

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