Arima Models In Finance: Predicting Markets With Historical Series

This report explores the application of ARIMA models to financial time series, from the theoretical foundations developed by Box and Jenkins to their practical use in market analysis. The paper covers the key components of the ARIMA(p, d, q) framework - autoregression, integration, and moving average - and examines how these tools can be applied to financial data such as log returns and price indices, along with their main strengths and limitations in real-world contexts.