Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization Algorithm

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder, economic, and emotional behaviour. In the stock market, incessant unsettlement is the main reason why financiers give away at the wrong time and frequently fail to get a profit. While financing in the stock market, the stakeholders should not disremember the gamble of payment rule and reveal their assets to greater dangers. Discovering economic time series data and exhibiting the relationship between the stock trend and past data is the main method to resolve the issue. Machine learning (ML), a conventional technique, has also been considered for its ability to predict financial markets. This manuscript proposes a new Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization (PSPMCDL-TTMO) method. The PSPMCDL-TTMO methodology employs an optimal deep learning model to forecast stock price movements, determining whether prices will rise or fall. At the primary stage, the PSPMCDL-TTMO model utilizes data pre-processing using Z-score normalization to ensure that the input features are standardized for consistent performance. For feature selection (FS), the dingo optimizer algorithm (DOA) is employed to optimize the most relevant and impactful features from historical stock data. In addition, the multi-head attention bi-directional gated recurrent unit (MHA-BiGRU) model is used for stock price movement prediction. Finally, the hyperparameter range of the MHA-BiGRU model is implemented by the design of the equilibrium optimizer (EO) model. The experimentation outcome analysis of the PSPMCDL-TTMO approach takes place, and the results are inspected using various features. The investigational validation of the PSPMCDL-TTMO technique attained a superior CORR value of 0.9999 over existing models.