A Stock Price Trend Prediction Method Based on Market Sentiment Factors and Multi-layer Stacking Ensemble Learning with Dual-CNN-LSTM Models and Nested Heterogeneous Learners

Investor sentiment, as a factor influencing stock price volatility, has received increasing research attention in recent years. This study proposes a more comprehensive representation of sentiment by incorporating social attributes when constructing investor factors. Notably, a novel market sentiment factor, γ, is introduced in this paper, which combines investor sentiment, stock data, and policy influences to enhance prediction accuracy beyond individual models. A multi-level nested ensemble model based on stacking is constructed in this study, which integrates the sentiment-stock Dual-CNN-LSTM model with learners to improve the accuracy of stock price volatility prediction. The experimental results demonstrate that: (1) The proposed market sentiment factor γ shows improved predictive accuracy compared to using investor sentiment factors alone, with an average increase of 5.55%; (2) The Dual-CNN-LSTM model outperforms the CNN-LSTM model using stock data alone in terms of volatility prediction accuracy, with an improvement of 9.81%. (3) The proposed multi-level nested ensemble algorithm, which adopts stacking nested Learner, achieves an accuracy of 88.24% in stock trend prediction. Overall, this research constructs a better sentiment indicator factor γ and provides a new approach for predicting stock price volatility through the integrated nested model, highlighting the effectiveness of hybrid architectures in addressing financial forecasting challenges.