A Short-Term Fuzzy Inference System for Stock Market Prediction

Citation:

Tealab A, Hefny H, Badr A. A Short-Term Fuzzy Inference System for Stock Market Prediction. International Arab Journal of Information Technology (IAJIT). 2017.

Abstract:

This study describes a short-term stock fuzzy inference system for predicting and trading stock market indices. The proposed system trading strategy is based on technical analysis by using a mixture of technical indicators. The selected technical indicators for designing trading rules consist of commonly used indicators and new developed one which is based on daily candlestick information produces short and long entry signals. Fuzzy logic is applied for both technical indicators and trading rules definition. The purpose of this study to develop a fuzzy trading system for predicting market trends by using fuzzy rules. The system is aiming to maximize profit and minimize loss using a portfolio management technique. The proposed system is tested using the gold prices data. The results are compared to classical non-fuzzy system. The proposal performance produced less losses and better profits. The results demonstrate that the fuzzy logic is promising in stock market prediction with steady upward profit and low losses.