An effective approach for predicting daily stock trading decisions using fuzzy inference systems

To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted t -tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the t -tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO.