Adaptive Performance-Weighted Ensemble for Robust Next-Day Stock Direction Prediction

Prediction of stock markets is challenging due to the presence of factors like non- linearity, dependencies, volatilities, and others in financial time series. Various machine learning/deep learning models capture different features of stock price time series dynamics. In this paper, we propose the framework called Adaptive Performance-Weighted Ensemble (APWE) that utilizes the information about the future day stock price direction via LSTM, GRU, and XGBoost models. APWE uses historical daily OHLCV data plus some technical indicators, like daily return, moving averages, RSI, MACD, volatility, and volume change. Output of each model is the probability of an upward price movement. Rather than keeping static ensemble weights, APWE determines the weight of each model depending on its recent performance and produces an ensemble output from the individual model outputs. In our experiment, APWE performance was tested for a particular time interval using TCS. Historical data of NS stock were obtained from Yahoo Finance in the period from January 1, 2018, to September 1, 2026. Cleaned data consisted of 2,117 observations, including 1,693 observations for training and 424 observations for testing. Based on the results, it can be stated that GRU is the most accurate individual model with 49.76% accuracy, and APWE has 44.05% accuracy equivalent to equal weight ensembles. It can be concluded from the results that the use of adaptive weighting does not improve the accuracy as compared to equal weighting. It is necessary to conduct additional research to reveal the cases when adaptive weighting proves to be beneficial.