An Automated Dual-Module Pipeline for Stock Prediction: Integrating N-Perception Period Power Strategy and NLP-Driven Sentiment Analysis for Enhanced Forecasting Accuracy and Investor Insight

The financial sector has witnessed considerable interest in the fields of stock prediction and reliable stock information analysis. Traditional deterministic algorithms and AI models have been extensively explored, leveraging large historical datasets. Volatility and market sentiment play crucial roles in the development of accurate stock prediction models. We hypothesize that traditional approaches, such as n-moving averages, may not capture the dynamics of stock swings, while online information influences investor sentiment, making them essential factors for prediction. To address these challenges, we propose an automated pipeline consisting of two modules: an N-Perception period power strategy for identifying potential stocks and a sentiment analysis module using NLP techniques to capture market sentiment. By incorporating these methodologies, we aim to enhance stock prediction accuracy and provide valuable insights for investors.