Enhancing Stock Price Forecasting With a Hybrid SES-DA-BiLSTM-BO Model: Superior Accuracy in High-Frequency Financial Data Analysis

Abstract

Stock price forecasting is a critical component of financial market analysis and plays a key role in formulating investment strategies, affecting a wide range of stakeholders. To address the inherent complexities and dynamic fluctuations in stock prices, innovative modeling techniques are required. Deep Learning (DL) approaches, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, have shown notable promise in enhancing prediction accuracy for stock prices. This study introduces a novel hybrid Single Exponential Smoothing with Dual Attention based Bi-directional LSTM optimize with Bayesian Optimization (SES-DA-BiLSTM-BO) designed to predict future stock prices accurately using Life Insurance Corporation of India (LIC) stock price dataset with 1-minute, 3-minute, and 10-minute intervals. The model aims to forecast stock prices, identify market trends, and reveal influential factors more effectively. The performance of the proposed SES-DA-BiLSTM-BO model was evaluated using key metrics: Directional Accuracy (DA=0.4816), Mean Absolute Percentage Error (MAPE=0.0006), Win/Loss Ratio (WLR=0.7517), and Theil’s U-Statistic (TUS=0.0006). These results demonstrate its superior performance compared to traditional models such as Moving Average (MA), Auto-Regressive (AR), Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA), Single Exponential Smoothing (SES) and LSTM models. By outperforming baseline model on various error metrics, the proposed SES-DA-BiLSTM-BO hybrid model exhibits improved predictive capabilities and lower loss functions, making its highly reliable for forecasting stock prices under diverse market conditions. The integration of statistical methods with deep learning in this hybrid approach offers a scalable and efficient solution for optimizing investment strategies, enhancing market efficiency, and contributing to economic stability by reducing the impact of market volatility.

Keywords

• Single exponential smoothing (SES)
• dual attention mechanism (DA)
• Bi-directional LSTM (BiLSTM)
• Bayesian optimization (BO)
• stock price prediction