Multiscale Bootstrap Correction for Random Forest Voting: A Statistical Inference Approach to Stock Index Trend Prediction

This paper proposes a novel multiscale random forest model for stock index trend prediction, incorporating statistical inference principles to improve classification confidence. Traditional random forest classifiers rely on majority voting, which can yield biased estimates of class probabilities, especially under small sample sizes. To address this, we introduce a multiscale bootstrap correction mechanism into the ensemble framework, enabling the estimation of third-order accurate approximately unbiased p-values. This modification replaces naive voting with statistically grounded decision thresholds, improving the robustness of the model. Additionally, stepwise regression is employed for feature selection to enhance generalization. Experimental results on CSI 300 index data demonstrate that the proposed method consistently outperforms standard classifiers, including standard random forest, support vector machine, and weighted k-nearest neighbors model, across multiple performance metrics. The contribution of this work lies in the integration of hypothesis testing techniques into ensemble learning and the pioneering application of multiscale bootstrap inference to financial time series forecasting.