Prediksi Gap-Up Saham Berbasis Logistic Regression Menggunakan Indikator Teknikal - Yuan Anisa; Muhammad Hafiz; Hadijah Hadijah; Abdul Gani (Ali Institute or Research and Publication, 2025)
This research aims to construct and test a predictive model for the gap-up phenomenon in stocks on the Indonesia Stock Exchange (IDX), with a case study on PT Bank Mandiri Tbk. (BMRI) stock. The research uses a quantitative approach by applying a Binary Logistic Regression model to analyze 1,213 daily historical data points from January 1, 2019, to January 1, 2024.Five technical analysis-based independent variables—vol_spike, rsi, prev_return, macd, and stochastic—were used to predict the probability of a stock gap-up occurrence.The analysis results show that the model as a whole is statistically significant (LLR p-value < 0.05), with an LLR p-value of (8.544e-11) and a Pseudo R-squared of 0.03389. Of the five variables, stochastic, macd, and prev_return were identified as significant predictors. Specifically, a high Stochastic Oscillator value has a strong positive influence on the probability of a gap-up. On the other hand, Moving Average Convergence Divergence (MACD) and Previous Return show a significant negative influence.These findings provide empirical evidence that a combination of technical indicators can be used to model and predict stock price movements at market opening. The implications of this research offer valuable insight for investors who rely on technical analysis as a basis for decision-making.