Stock Market Prediction Using Generative Adversarial Networks(GANs):Hybrid Intelligent Model

作     者：Fares Abdulhafidh Dael Omer CagrıYavuz Ugur Yavuz 

作者机构：Department of Management Information SystemsAtaturk UniversityErzurum25030Turkey Department of Management Information SystemsKaradeniz Technical UniversityTrabzon61080Turkey 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第10期

页      面：19-35页

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

主　　题：Stock markets statistics machine learning sentiment analysis investment decisions 

摘      要：The key indication of a nation’s economic development and strength is the stock *** and economic expansion affect the volatility of the stock *** the multitude of factors,predicting stock prices is intrinsically *** the movement of stock price indexes is a difficult component of predicting financial time *** predicting the price movement of stocks can result in financial advantages for *** to the complexity of stock market data,it is extremely challenging to create accurate forecasting *** machine learning and other algorithms to anticipate stock prices is an interesting *** purpose of this article is to forecast stock market values to assist investors to make better informed and precise investing ***,Machine Learning(ML),Natural language processing(NLP),and sentiment analysis will be used to accomplish the study’s *** both qualitative and quantitative information,the study developed a hybrid *** hybrid model has been handled with *** on the model’s predictions,a buy-or-sell trading strategy is *** conclusions of this study will assist investors in selecting the ideal choice while selling,holding,or buying shares.