A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and
  XGBoost for Speculative Stock Price Forecasting
Riaz Ud Din Salman Ahmed Saddam Hussain Khan
Forecasting speculative stock prices is essential for effective investment
risk management that drives the need for the development of innovative
algorithms. However, the speculative nature, volatility, and complex sequential
dependencies within financial markets present inherent challenges which
necessitate advanced techniques. This paper proposes a novel framework, CAB-XDE
(customized attention BiLSTM-XGB decision ensemble), for predicting the daily
closing price of speculative stock Bitcoin-USD (BTC-USD). CAB-XDE framework
integrates a customized bi-directional long short-term memory (BiLSTM) with the
attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages
its learning capabilities to capture the complex sequential dependencies and
speculative market trends. Additionally, the new attention mechanism
dynamically assigns weights to influential features, thereby enhancing
interpretability, and optimizing effective cost measures and volatility
forecasting. Moreover, XGBoost handles nonlinear relationships and contributes
to the proposed CAB-XDE framework robustness. Additionally, the weight
determination theory-error reciprocal method further refines predictions. This
refinement is achieved by iteratively adjusting model weights. It is based on
discrepancies between theoretical expectations and actual errors in individual
customized attention BiLSTM and XGBoost models to enhance performance. Finally,
the predictions from both XGBoost and customized attention BiLSTM models are
concatenated to achieve diverse prediction space and are provided to the
ensemble classifier to enhance the generalization capabilities of CAB-XDE. The
proposed CAB-XDE framework is empirically validated on volatile Bitcoin market,
sourced from Yahoo Finance and outperforms state-of-the-art models with a MAPE
of 0.0037, MAE of 84.40, and RMSE of 106.14.