A Comparative Analysis on Leveraging K-Nearest Neighbours for Accurate Stock Price Forecasting

This research presents a comprehensive comparison of hybrid models combining K-Nearest Neighbours (KNN) with Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks for stock price prediction. Using historical data from major technology stocks (AAPL, GOOGL, MSFT) spanning 2020-2023, we implemented and evaluated KNN-SVM and KNN-LSTM architectures. Our experiments demonstrate varied performance across different stocks, with the KNN-SVM hybrid achieving optimal results for GOOGL (RMSE: 2.11, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> : 0.968) while showing moderate performance for AAPL (RMSE: 13.56) and MSFT (RMSE: 25.39). This variation in performance provides important insights into the relationship between stock characteristics and model efficacy. Through extensive experimentation and architectural optimization, we demonstrate that while simpler hybrid models can achieve superior performance in specific cases, their effectiveness varies significantly across different market conditions and stock characteristics. The study reveals important insights into the trade-offs between model complexity, computational efficiency, and prediction accuracy in financial time series forecasting.