LAUSR

Stock price forecasting has always been a classic and challenging task, attracting widespread attention from stakeholders such as market regulators, financial practitioners, and individual investors. Developing new models to improve the accuracy of stock price forecasting is also a persistent goal pursued by researchers. In recent years, quantum computing has developed rapidly. The emergence of quantum machine learning (QML) and quantum neural network (QNN) models has made it possible to develop new stock price forecasting models that leverage the advantages of quantum algorithms. To improve predictive accuracy, this study proposes a novel decomposition‐ensemble approach based on multivariate empirical mode decomposition and QNN. In the prediction, multi‐source big data from the stock market, search engines, and social media are employed to represent investor anticipation, attention, and sentiments, respectively. Using the daily average stock price in the Shenzhen Stock Exchange, an empirical analysis is conducted to illustrate the proposed approach. The results suggest that the proposed approach outperforms benchmark models, indicating that it is a promising method for forecasting stock price series with high volatility and nonlinearity.