A3T-GCN for FTSE100 Components Price Forecasting

TL;DR

This paper introduces a novel hybrid A3T-GCN model for stock price forecasting of FTSE100 companies, demonstrating improved accuracy and efficiency by leveraging sector-based graphs and various financial features.

Contribution

The paper presents a new A3T-GCN architecture tailored for stock prediction, integrating sector and correlation graphs with multiple financial indicators.

Findings

A3T-GCN with annualized log-returns enhances prediction accuracy.

Shorter sequence lengths reduce computational load without sacrificing performance.

Longer historical data offers limited benefits for short-term forecasts.

Abstract

We examine the predictive power of a novel hybrid A3T-GCN architecture for forecasting closing stock prices of FTSE100 constituents. The dataset comprises 79 companies and 375,329 daily observations from 2007 to 2024, with node features including technical indicators (RSI, MACD), normalized and log returns, and annualized log returns over multiple windows (ALR1W, ALR2W, ALR1M, ALR2M). Graphs are constructed based on sector classifications and correlations of returns or financial ratios. Our results show that the A3T-GCN model using annualized log-returns and shorter sequence lengths improves prediction accuracy while reducing computational requirements. Additionally, longer historical sequences yield only modest improvements, highlighting their importance for longer-term forecasts.