Soft Computing Analysis for Stock Price Dynamism Using Gaussian Membership Function: A Case Study of Nigerian Stock Exchange

Stock Prices are dynamic and vulnerable to quick changes because of the fundamental nature of the financial field and in part because of the mix of known parameters (Previous Days Closing Price, High Price, etc.) and unknown factors (like Election Results, Rumors, etc.). Its prediction is generally regarded to be a very arduous task as the process is time-varying, thus, creating uncertainty as its characteristic nature. Uncertainty results from the limited capability to resolve details and encompass the notions of partial, vague, noisy and incomplete information about the real world. Soft Computing techniques operate in an environment that is subject to uncertainty and imprecision with the aid of Fuzzy logic that aims at formalization of approximate reasoning. Gaussian fuzzy membership function was employed to capture the stock dynamism by the function’s smoothness at its edges. The system was simulated using MATLAB. 256 production rules of the technical indicators and 16 of the external factors were generated. The simulated system gave a good insight into stock market for investors to know at what point in time is best to buy and sell stocks as well as make a good profit or loss.