Question-3: SP500

Title

Forecasting and Optimization of S&P 500 Component Stocks

Abstract

Machine learning has been applied across diverse fields to develop predictive models. Although forecasting stock prices or market directions remains challenging due to volatility and noise, numerous studies have demonstrated the effectiveness of machine learning algorithms in financial prediction tasks.

In Alzaman, historical data from stocks traded on the Toronto Stock Exchange (TSE) was used to train machine learning models aimed at predicting future price movements and directions. An important next step is to examine whether the methods employed in that study can be successfully adapted to other markets.

This project focuses on the S&P 500, a benchmark index comprising 503 common stocks issued by 500 of the largest publicly traded companies in the United States. The research involves building predictive machine learning models for S&P 500 component stocks and evaluating their performance relative to models developed using TSE data. The study aims to provide insights into the generalizability of machine learning techniques across different stock exchanges and market conditions.