Comparative Analysis of Optimization Techniques in Ensemble Models for Stock Price Prediction

Stock price prediction is a pivotal task in fiscal requests, abetting investors make educated judgments. This research project proposes an ensemble model that combines sentiment analysis with the sliding window system for enhanced forecast accuracy. The paper involves extracting textual features similar as polarity and subjectivity through sentiment analysis and deriving historical data features using the sliding window method. These features are also integrated into a single dataset for model input. Three Recurrent Neural Network (RNN) models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN), are trained using this dataset, and their performance criteria, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-Squared (R²), are estimated. Also, ensemble models are constructed using Particle Swarm Optimization (PSO) and the Differential Evolution (DE) system for integrating the labors of different models. The finding advances the understanding on stock price forecasting techniques and offer insights into opting optimal models for real-world applications.

I. Introduction

Stock price prediction is an abecedarian task in financial requests, playing a vital part in guiding investment opinions and managing pitfalls. Over the times, experimenters have explored various ways and methodologies to enhance the efficacy of forecasting stock price methods. Among these, sentiment analysis and sliding window system have surfaced as promising approaches for landing request dynamics and literal trends, independently. In this research project, we present an ensemble model which integrates sliding window system and sentiment analysis to advance the predictive capabilities of stock price prediction models. Sentiment analysis is a natural language processing approach that evaluates textual data to discern the sentiment expressed within it. By extracting features similar as polarity and subjectivity from textual sources such as newspapers, social media postings, and reports on finances, sentiment analysis offers insights into market sentiment and capitalists' sentiment, that can affect stock price changes. In parallel, the sliding window system is employed to extract features from historical stock data. This system involves partitioning the historical data into fixed- length intervals, or windows, and calculating summary statistics or derived features within each window. One similar point, termed “5daymean”, which is the mean stock price during a five-day span. The sliding window system enables the capture of temporal patterns and trends in stock prices, furnishing precious input for predictive modeling. Combining the sentiment- extracted features and sliding window- extracted features into a single dataset, three RNN models are trained and estimated are Long Short-Term Memory (LSTM), SimpleRNN and Gated Infrequent Unit (GRU). These algorithms use a pooled dataset to identify intricate patterns and correlations among text sentiment and previous stock data [8]. Each model's correctness is assessed using performance measures such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-Squared (R2) in predicting stock prices. Furthermore, ensemble modeling ways are employed to work the strengths of individual models and improve predictive accuracy. Specifically, Particle Swarm Optimization (PSO) and the Differential Evolution (DE) system combine independent model outputs to create ensemble models Comparing individual and ensemble models reveals how successful ensemble approaches are for anticipating stock prices. Overall, this research effort tries to advance stock price prediction through incorporating sentiment analysis with the sliding window approach. We assess individual and ensemble models to determine the best technique for predicting stock prices in real-world markets.