Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System

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Creator: 

Shen, Jingyi

Date: 

2019

Abstract: 

In big data era, deep learning solution for predicting stock market price trend becomes popular. We collected two years of Chinese stock market data according to the financial domain, proposed a fine-tuned stock market price trend prediction system with developing a web application as the use-case, meanwhile, conducted a comprehensive evaluation on most frequently used machine learning models and concludes that our proposed solution outperforms leading models. The system achieves an overall trend predicting accuracy of 93%, also achieves significant high scores in other machine learning metrics in the meantime. Thus, this work provides a solid foundation for further price prediction by classifying the price trend accurately. With the detail-designed evaluation on prediction term-lengths, feature engineering and data preprocessing methods, this work also contributes to the stock analysis research community in both financial and technical domain.

Subject: 

Computer Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Information Technology: 
M.I.T.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Digital Media

Parent Collection: 

Theses and Dissertations

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