BIO

Applied mathematics post-baccalaureate student with prior education in economics at Florida State University. I am interested in machine learning, neural networks, deep learning, financial math, and statistics. My current academic journey involves synthesizing prior knowledge of economics with current interests in advanced computational fields.

Stock Price Prediction: Deep Neural Network LSTM

Abstract

This study explores the predictive capabilities of Long Short-Term Memory (LSTM) models in forecasting the stock price movements of the S&P 500. The model utilizes a comprehensive set of features, including historical stock prices, macroeconomic indicators, and technical indicators. By conducting a comparative analysis, this research assesses the performance of the models and the significance of different features. The findings reveal that the LSTM model's effectiveness varies with the inclusion of specific features and the chosen timeframes. Additionally, the study examines the impact of market volatility events, such as the 2008 financial crisis and the COVID-19 pandemic, on predictive accuracy. Highlighting the potential of technical indicators in market condition predictability, the project offers insights for future advancements in financial market forecasting models.

Keywords: Machine Learning, Neural Networks, LSTM