# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
[PERSON]  
1 Department of Electrical Engineering, National Kaohsing University of Science and Technology, Kaohsing City 80778, Taiwan; [EMAIL_ADDRESS] (C.-L.J. ); [EMAIL_ADDRESS] (Y.-K.T. ); [EMAIL_ADDRESS] (Z.-E.S. ); [EMAIL_ADDRESS] (C.-C.H.) [PERSON]  
1 Department of Electrical Engineering, National Kaohsing University of Science and Technology, Kaohsing City 80778, Taiwan; [EMAIL_ADDRESS] (C.-C.L.J. ); [EMAIL_ADDRESS] (Y.-K.T. ); [EMAIL_ADDRESS] (Z.-E.S. ); [EMAIL_ADDRESS] (C.-C. Since the establishment of the \"Dow Theory\" by [PERSON] [2], modern technical analysis has flourished, providing investors with valuable indicators and insights. The advent of artificial intelligence (AI) has led to the emergence of new technologies, such as heuristic algorithms, machine learning, and deep learning. Among these, machine and deep learning are the most commonly used. Machine learning [3; 4] involves processing and learning considerable amounts of data, analyzing them, and solving problems through self-learning and improvement through continuous analysis and learning. Deep learning, a subset of machine learning, employs a neural network comprising numerous neurons, each of which represents a function. They are trained on vast amounts of data to generate prediction models. Owing to its powerful learning capabilities, deep learning has been successfully employed across various financial domains, such as stock-market forecasting [5; 6; 7; 8; 9], cryptocurrency-trend forecasting [10], trading-strategy optimization [11; 12], and portfolio optimization [13]. Among these, stock-market forecasting is one of the most popular. By analyzing the time-series data of stock markets, deep learning can help uncover the rules and reasons for the changes over time, which can be used to predict future trends, capture data characteristics, and assist professionals in making informed decisions. Researchers who employ technical indicators in the stock market believe that history will repeat itself, and past experience can be used to forecast future trends. However, they can be interpreted differently by different investors, and overly subjective analyses or insufficient understanding of their meaning can lead to inaccurate predictions. The substantial advancements in computing powers and the emergence of AI technologies allow for the development of more objective approaches. AI algorithms can extract meaningful knowledge from data and identify rules, thereby mitigating the subjectivity inherent in human interpretations of technical indicators. Owing to the robustness and computing prowess of deep-learning methods, numerous researchers have explored their application in predicting stock-market trends. However, only a few focused on price forecasting. Accurate prediction of future stock prices, especially next-day prices, can certainly help investors make substantial profits. Therefore, a stock-price prediction method that combines deep-learning models with technical analysis indicators can offer valuable decision support for investors. Therefore, we developed a hybrid stock-price prediction system, called CSLSTM, based on the long short-term memory (LSTM) network [14] and crow search algorithm (CSA) [15]. The LSTM network, which is widely used for time-series analysis owing to its memory capabilities, was used to make predictions from various data. CSLSTM employs a stacking ensemble of LSTM networks and a CSA to optimize the weights of predictions from multiple LSTM models. This weighted combination of individual LSTM predictions helps enhance the overall prediction accuracy. CSA was chosen for optimization due to its robust performance in finding optimal solutions in high-dimensional search spaces, enabling the fine-tuning of the weights of the LSTM models. The prediction performance of the proposed model was evaluated on five different types of stocks and compared with those of other neural networks commonly employed for time-series analyses.