@Article{csse.2023.037903,
AUTHOR = {Fares Abdulhafidh Dael, Ömer Çağrı Yavuz, Uğur Yavuz},
TITLE = {Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {1},
PAGES = {19--35},
URL = {http://www.techscience.com/csse/v47n1/53044},
ISSN = {},
ABSTRACT = {The key indication of a nation’s economic development and strength
is the stock market. Inflation and economic expansion affect the volatility of
the stock market. Given the multitude of factors, predicting stock prices is
intrinsically challenging. Predicting the movement of stock price indexes is a
difficult component of predicting financial time series. Accurately predicting
the price movement of stocks can result in financial advantages for investors.
Due to the complexity of stock market data, it is extremely challenging
to create accurate forecasting models. Using machine learning and other
algorithms to anticipate stock prices is an interesting area. The purpose of
this article is to forecast stock market values to assist investors to make better
informed and precise investing decisions. Statistics, Machine Learning (ML),
Natural language processing (NLP), and sentiment analysis will be used to
accomplish the study’s objectives. Using both qualitative and quantitative
information, the study developed a hybrid model. The hybrid model has
been handled with GANs. Based on the model’s predictions, a buy-or-sell
trading strategy is offered. The conclusions of this study will assist investors
in selecting the ideal choice while selling, holding, or buying shares.},
DOI = {10.32604/csse.2023.037903}
}