Implementation of a Type-2 Fuzzy Logic Based Prediction System for the Nigerian Stock Exchange
- February 3, 2022
- Posted by: RSIS
- Categories: Computer Science and Engineering, IJRIAS
International Journal of Research and Innovation in Applied Science (IJRIAS) | Volume VII, Issue I, January 2022 | ISSN 2454–6194
Implementation of a Type-2 Fuzzy Logic Based Prediction System for the Nigerian Stock Exchange
Isobo Nelson Davies, Donald Ene, Ibiere Boma Cookey, Godwin Fred Lenu
Department of Computer Science, Rivers State University, Port Harcourt Nigeria
Abstract: Stock Market can be easily seen as one of the most attractive places for investors, but it is also very complex in terms of making trading decisions. Predicting the market is a risky venture because of the uncertainties and non-linear nature of the market. Deciding on the right time to trade is key to every successful trader as it can lead to either a huge gain of money or totally a loss in investment that will be recorded as a careless trade. The aim of this research is to develop a prediction system for stock market using Fuzzy Logic Type-2 which will handle these uncertainties and complexities of human behaviour in general when it comes to buy/hold/sell decision making in stock trading. The proposed system was developed using VB.NET programming language as frontend (interfaces) and Microsoft SQL Server as backend (database).A total of four different technical indicators were selected for this research. The selected indicators are the Relative Strength Index (RSI), William Average (WA), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (SO).These indicators serve as input variable to the Fuzzy System. The MACD and SO are deployed as primary indicators, while the RSI and WA are used as secondary indicators. Fibonacci retracement ratio (Tuning Factor) was adopted for the secondary indicators to determine their support and resistance level in terms of making trading decisions. The input variables to the Fuzzy System is fuzzified to “Low”, “Medium”, and “High” using the Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference rules were used for combining the trading rules for each input variable to the fuzzy system. The developed system was tested using sample data collected from ten different companies listed on the Nigerian Stock Exchange (NSE) for a total of fifty-two periods. The dataset collected are Opening, High, Low, and Closing prices of each security. These datasets were used for calculating the technical indicators and also for evaluating the performance of the system. The developed system outperformed other existing system and therefore the output can be used to draw inference in terms of making buy/hold/sell trading decisions.
Keywords: Fuzzy Logic, Stock Exchange, Relative Strength Index, Moving Average, Gaussian
