Rethinking Financial Markets’ Forecasting: The Superiority of Neural Networks Over KNN and Random Forest

Abstract

This study compares the effectiveness of Random Forest, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) machine learning algorithms when applied to high-dimensional time-series panel data of daily stock exchange returns over different countries and exchange rates from January 1, 2001, to December 24, 2025. In general, ANN outscored the others significantly in accuracy and arrived at RMSE 0.002330863. The multicombzoned characteristic gives this cool performance, learning deeply in layers and generalizing complex and linear relationships quite effectively. The utility of this model in the financial market is that of crossing the heterogeneous market like those in China, the US, or the Brazil. The Random Forest model also showed competitive predictive performance for such heavy volume data, producing an RMSE of 0.003256111. This ensemble method proved helpful in capturing the data profile by acquiring non-linear interactions better compared to the alternatives and keeping a strong bias toward overfitting. In contrast, the KNN model, which is a slightly less accurate forecaster, achieved the highest RMSE of 0.003687131. The structure of KNN is simple, yet this model falls short in predictive accuracy due to its heavy dependence on parameter selection-the number of instances for nearest-neighbors (k) and the distance metric to be used. By presenting the comparative analysis, one may conclude that ANN outshines Random Forest and KNN. ANN leads with excellent performance, while other frameworks display perceptibly poor performance.