A Financial Time Series Forecasting Model Using Quasi-Recurrent Neural Networks and the Crown Porcupine Optimizer for Stock Market Risk Prediction

Stock is a financial product described by flexible trading, high risk, and high return, which is favored by many investors. Investors may obtain sufficient returns by precisely approximating stock price developments. However, a stock price is affected by several factors, such as market conditions, macroeconomic situation, major economic and social events, and managerial decisions of companies. As a result, Stock Price Prediction (SPP) has consistently attracted attention and is an important field of investigation. Currently, Machine Learning (ML) is extensively utilized in SPP, but considerably more appropriate techniques are suggested for the prediction of stock prices, as numerous investigations have proved that Deep Learning (DL) has better efficacy than other methods. The paper proposes a Financial Time Series Forecasting Using Quasi Recurrent Neural Network and Crown Porcupine Optimizer (FTSFQRNNCPO) method, offering an intelligent framework for financial stock market price prediction to accurately assess market volatility and forecast associated investment risks. The FTSF-QRNNCPO method begins with data preprocessing, comprising missing value handling, data cleaning, and normalization to prepare the input data effectively. The Secretary Bird Optimization Algorithm (SBOA) is employed for optimal Feature Selection (FS). Then, the Quasi-Recurrent Neural Network (QRNN) approach is employed for prediction. The Crown Porcupine Optimizer (CPO) approach is employed in the parameter tuning process. The FTSF-QRNNCPO technique was experimentally evaluated on a Tesla stock price dataset, demonstrating superior accuracy, achieving a significantly lower MAPE of 0.415% and outperforming previous models. © (c) by the authors