Predicting stock prices with LSTM: A hybrid machine learning model for financial forecasting

Abstract

This article discusses the challenges of accurately predicting the direction of the stock market and proposes a new approach using machine learning and manual forecasting. The article explores the use of technical analysis and machine learning to predict current stock market indices’ values by training on historical data. The authors demonstrate how these methods can be used to influence investor judgments at different levels of consideration, including unrestricted, near, medium, high, and volumic. The article also explores the use of social media platforms like Twitter and the correlation between stock prices and local weather patterns to improve forecasting accuracy. The authors present their research in three phases, demonstrating the potential of machine learning and technical analysis to provide accurate and reliable predictions for investors seeking to protect themselves from market volatility.