Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction (2204.02623v2)

Abstract: Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional time series model ARIMA can not describe the nonlinearity, and can not achieve satisfactory results in the stock prediction. As neural networks are with strong nonlinear generalization ability, this paper proposes an attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price. The model constructed in this paper integrates the time series model, the Convolutional Neural Networks with Attention mechanism, the Long Short-Term Memory network, and XGBoost regressor in a non-linear relationship, and improves the prediction accuracy. The model can fully mine the historical information of the stock market in multiple periods. The stock data is first preprocessed through ARIMA. Then, the deep learning architecture formed in pretraining-finetuning framework is adopted. The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Finally, the XGBoost model is adopted for fine-tuning. The results show that the hybrid model is more effective and the prediction accuracy is relatively high, which can help investors or institutions to make decisions and achieve the purpose of expanding return and avoiding risk. Source code is available at https://github.com/zshicode/Attention-CLX-stock-prediction.

Attention-based CNN-LSTM and XGBoost Hybrid Model for Stock Prediction: An Expert Overview

The paper introduces a sophisticated hybrid model, comprising an Attention-based Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture integrated with an XGBoost regressor, for stock price prediction. This hybrid model is designed to exploit the predictive capabilities of both neural networks and classical time-series analysis, aiming to improve prediction accuracy by addressing the complex nonlinear patterns present in stock market data.

Methodological Insights

The innovative approach of this paper involves a multi-stage processing pipeline. Initially, the stock data undergoes preprocessing with the AutoRegressive Integrated Moving Average (ARIMA) technique to stabilize and transform the data, which caters to the removal of non-stationarity inherent in stock prices. The traditional ARIMA model, often limited by its linearity assumptions, serves here primarily to provide a transformed input for more sophisticated modeling.

The primary model architecture leverages the strengths of CNN and LSTM. The Attention-based CNN functions as an encoder, adept at capturing local and global dependencies within the data with a multi-head attention mechanism that enhances its ability to discern salient patterns. This is followed by the LSTM decoder, which is essential in modeling the long-term dependencies typical of time-sequential data.

The advanced preprocessing through ARIMA, paired with deep feature extraction by the CNN, and the long-term dependency modeling of LSTM culminates in a robust hybrid design. This setup is not only theoretically sound but demonstrates significant efficacy in empirical evaluations.

Empirical Evaluation and Results

The paper extensively tests the proposed model on the stock price data of the Bank of China (601988.SH) from January 1, 2007, to March 31, 2022, using data sourced from publicly available Tushare datasets. It employs several error metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and $R^2$ to measure prediction accuracy.

The experimental results reflect that the hybrid Attention-based CNN-LSTM and XGBoost model, referred to as AttCLX, outperforms established models such as ARIMA, ARIMA-NN, and several Kalman Filter augmented models (LSTM-KF, Transformer-KF, and TL-KF). Notably, the AttCLX model achieves lower prediction errors and higher $R^2$ values, indicating its superior ability to model and predict the nonlinear and volatile nature of stock market time series data.

Theoretical and Practical Implications

This paper contributes to the field by empirically validating the integration of deep learning techniques, such as attention mechanisms and LSTM, with gradient boosting algorithms like XGBoost for financial time series prediction. The theoretical basis lies in the enhanced modeling capacity provided by non-linear, data-driven approaches that are adept in handling the asynchronous, high-dimensional nature of financial data.

Practically, this model offers tangible improvements over traditional models and could significantly benefit institutional investors by enhancing decision-making processes related to risk management and portfolio optimization. Its application extends beyond stock prediction to other domains requiring time-series forecasting under uncertainty.

Future Directions

The innovative integration put forth in this paper sets a precedent for future research, inviting exploration into further enhancements through additional feature engineering, alternative hybrid configurations, or the inclusion of external macroeconomic indicators. Furthermore, adaptive algorithms that respond to changing market conditions in real-time could optimize the utility of such hybrid models in dynamic trading environments.

Overall, this research marks a substantial contribution to the intersection of artificial intelligence and financial analysis, providing a robust framework for next-generation predictive modeling in financial markets.