A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange. (February 2016) - G., Rubell Marion Lincy; C., Jessy John
Highlights: We develop a multiple fuzzy inference systems framework for daily stock trading. We apply the framework to NASDAQ stock exchange data. Gives an increase in profit return with decrease in the number of days of observations. Including earnings per share to the framework increases profit return. Developed framework performs better than the most popular technical indicators. Abstract: The aim of this study is to develop an expert system for predicting daily trading decisions in a typical financial market environment. The developed system thus employs a Multiple FISs framework consisting of three dedicated FISs for stock trading decisions, Buy, Hold and Sell respectively. As input to the Multiple FISs framework, the system takes the fundamental information of the respective companies and the historical prices of the stocks which are processed to give the technical information. The framework suggests the investor to Buy, Sell or Hold on a daily basis for a portfolio of stock taken into consideration. Experimenting the framework on selected stocks of NASDAQ stock exchange shows that including the fundamental data of the stocks as input along with the technical data significantly improves the profit return than that of the system taking only technical information as input data. Characterised as a stock market indicator, the framework performs better than some of the most popularly used technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO) and Chaikin Oscillator (CO). The developed framework also gives better profit return compared to an existing model with similar objective.