Authors:
Ying Bai
1
and
Dali Wang
2
Affiliations:
1
Johnson C. Smith University, 100 Beatties Ford Rd., Charlotte, U.S.A.
;
2
Christopher Newport University, One Avenue of the Arts, Newport News, U.S.A.
Keyword(s):
ANFIS Algorithm, Deep Learning Model, Estimate and Predict Current Stock Prices, AI Applications in Financial Implementations.
Abstract:
To correctly and accurately predict and estimate the stock prices to get the maximum profit is a challenging task, and it is critical important to all financial institutions under the current fluctuation situation. In this study, we try to use different AI methods and algorithms, such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Deep Learning (DL), to easily and correctly predict and estimate the current and future possible stock prices. Combining with some appropriate pre-data-processing techniques, the current stock prices could be accurately and quickly estimated via those models. In this research, both algorithms are designed and built to help decision makers working in the financial institutions to easily and conveniently predict the current stock prices. The minimum training and checking RMSE values for ANFIS model can be 0.0009828 and 0.001713. The minimum MSE value for DL model is 0.0000047 with a regression value of 0.9958.