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Pattareeya Piravechsakul. Stock trading approaches using LSTM and GANs. Master's Degree(Information and Communication Technology for Embedded System). Kasetsart University. Office of the University Library. : Kasetsart University, 2021.
Stock trading approaches using LSTM and GANs
Name: Pattareeya Piravechsakul
Classification :.LCCS: QA76.87
LCSH:
Machine learning
LCSH:
Moving average
LCSH:
Stock price forecasting
Abstract:
Growth in Artificial Intelligence (AI)-related financial research has been growing exponentially. Examples of using AI in the financial market, portfolio management, stock market prediction, trading strategy, and capture real-time patterns. A stock market prediction is one of the most challenging tasks. There are a lot of affected factors to make the financial market price go up or down such as economy, politics, dynamic environment. Although, a lot of free technical indicator tools available for traders and investors are provided by brokers, some technical indicator tools might not be able to capture market behaviors and understand the complexity of stock data. To address the challenging task, we proposed the deep learning models, namely, Generative Adversarial Networks (GANs), and Long Short-Term Memory (LSTM) that applied as stock price predictor to predict future prices based on learning historical price data from Stock Exchange of Thailand SET50 index. The condition GANs (CGANs) and the Wasserstein GANs (WGANs) are a type of GANs were combined and used in this work. The SET50 index data between August 2011 to July 2019 was used for the training models. After that, the models were tested through our trading operations to find a suitable trading strategy in back testing and forward testing. The trading operations were separated into 2 cases, long-only case, and long-short case. We also compared the profitable model performance based on the Conditional Wasserstein GANs (CWGANs) and the LSTM with buy & hold strategy and four technical indicators strategy, namely, Moving Average (MOV), Moving Average Convergence Divergence (MACD), Bollinger Bands (BB), and Relative Strength Index (RSI). Moreover, we studied ability of the GANs model based on con the different architectural designs of the GANs based on LSTM and CNN model. We found that the CNN-CNN architecture of the CWGANs algorithm that received the best performance in our data. The results show that the CWGANs and the LSTM trading strategy are promising. Mostly, both models in long-only case and long-short case can achieve a higher return than buy & hold, MOV, MACD, BB, and RSI strategy in back testing and forward testing.
Kasetsart University. Office of the University Library
Address:
Bangkok
Email:
tdckulib@ku.ac.th
Name: Teerasit Kasetkasem
Role:
Thesis Advisor
Name: Sanparith Marukatat
Role:
Thesis CO-Advisor
Name: Itsuo Kumazawa
Role:
Thesis CO-Advisor
Created:
2021
Modified:
2025-07-17
Issued:
2025-07-17
วิทยานิพนธ์/Thesis
application/pdf
URL: https://www.lib.ku.ac.th/KUthesis/2564/pattareeya-pir-all.pdf
CallNumber:
QA76.87 .P38
eng
DegreeName: Master of Engineering
Level: Master's Degree
Grantor: Kasetsart University
©copyrights Kasetsart University
RightsAccess:
ใช้เวลา
0.030937 วินาที
Pattareeya Piravechsakul
| Title | Contributor | Type |
|---|---|---|
| Stock trading approaches using LSTM and GANs
มหาวิทยาลัยเกษตรศาสตร์ Pattareeya Piravechsakul | Teerasit Kasetkasem | วิทยานิพนธ์/Thesis |
Teerasit Kasetkasem
Sanparith Marukatat
| Title | Creator | Type and Date Create |
|---|---|---|
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| Title | Creator | Type and Date Create |
|---|---|---|
| Land cover mapping classification based on deep-learning
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| Land cover mapping using a random forest classification
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