Adaptive Polynomial Regression Analysis For Predicting Indonesian Stock Exchange Movements With R-Square Optimization And Trading Channel Construction

Ade Putra Prima Suhendri (Universitas Pamulang)
Amin Hidayat (Universitas Pamulang)
Yuda Samudra (Universitas Pamulang)

Electronic Journal of Education, Social Economics and Technology
Vol 7, No 1 (2026)

Stock price movements in financial markets often reflect rapid macroeconomic shifts and liquidity-driven volatility. This is particularly evident in large-cap, highly liquid stocks within the Indonesian LQ45 index. Traditional deep learning models often struggle with this market's unique volatility, suffering from overfitting, lack of transparency, and look-ahead bias. To address these issues, this study proposes an adaptive rolling polynomial regression (APR) framework for non-linear trend filtering and automated trading signal generation. The method uses a sliding window with endpoint locking to prevent look-ahead bias. At each step, it finds the best lookback window by optimizing a local OLS polynomial regression and filtering out noisy market regimes using a strict R-squared threshold. The resulting trends are converted into trading signals (BUY, SELL, HOLD, CLOSE) through dynamic Z-score volatility channels. When evaluated on daily historical data from 2015 to 2024 for five LQ45 stocks ANTM, ASII, BBCA, PTBA, and TLKM the framework shows an excellent fit, with R-squared values between 0.9881 and 0.9966. Backtesting indicates that the strategies yield positive returns and profit factors above one for all assets. ASII performed best, with a total return of 2680.16%. Despite low win rates (10%), the long-term profitability was maintained as large trend-following gains offset small losses. This study highlights that simple, interpretable mathematical models can provide effective, computationally efficient systematic trading options in emerging equity markets.