A model for determining stock purchase decisions based on gated recurrent units and decision tree C4.5

Abstract

The stock market is highly volatile but offers high profit potential. This makes it difficult for novice investors to make investment decisions. Current studies mostly focus on stock price prediction or trading signal classification, while interpretable hybrid frameworks to support stock purchase decisions are still limited. Many machine learning models offer limited interpretability. Numerical predictions are often generated by forecasting models, which are difficult to translate into investment decisions. Therefore, this study aims to design a hybrid decision support system with an interpretable model by combining a Gated Recurrent Unit (GRU) and a C4.5 Decision Tree classifier in stock purchase decision making. The proposed framework consists of two phases. The first step is to predict the closing price of stocks using the historical daily data with the GRU model. The predicted price is then combined with technical indicators such as Simple Moving Average (SMA), Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to produce trading signals using C4.5 Decision Tree. The dataset used in this study is BBNI.JK stock data from 2015 to 2025 with a walk-forward validation scheme and evaluation using RMSE, MAE, MAPE, Accuracy, Precision, Recall and F1-Score. The experimental results show that the MAPE of the GRU model is 6.16% and the proposed DT-GRU strategy produces the highest trading return of 83.07% with a Sharpe ratio of 2.75. These results indicate that the combination of interpretable forecasting and classification can provide effective and practical trading decision-making support.