Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.
Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.
School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh.
Shipra
S
Banik
School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh.
A F M
AF
Khodadad Khan
School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh.
Mohammad
M
Anwer
D001185:Artificial Intelligence; D001459:Bangladesh; D003657:Decision Making; D005544:Forecasting; D006801:Humans; D007449:Investments / Q000191:economics*; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D013997:Time Factors