Statistical Methods in Finance 2025

Forecasting High Frequency End-of-Day Volume with the CoFES Seasonal Nonlinear Autoregressive Neural Network With Exogenous Variables (CoFES S-NARX)

By:Michale Jackson
Rice University, USA

We formulate a hybrid NARX-based sea-sonal predictive model, Seasonal Nonlinear Autoregressive Neural Network with Exogenous Variables (S-NARX ), for end-of-day volume, where end-of-day volume is directly driven by the end of the day auctions. The S-NARX model will seek to take advantage of the information found in the data up until the auction time and high-frequency intraday trading volume’s diurnal seasonal pattern to predict end-of-day volume. Volume is well known to be a leading indicator of price changes and the two metrics are simultaneously positively correlated. Algorithmic traders rely on accurate volume predic-tions to deploy algorithmic trading algorithms, especially when utilizing a Volume Weighted Average Price (VWAP) algorithm, that allows the execu-tion of large orders with minimal slippage. Fundamental and quantitative investors are also interested in trading volume because it is a measure of trading intensity and an indicator of market liquidity. The S-NARX aug-ments the NARX with the feature set from a seasonal ARMA(P,Q)[s] and offers quantitative traders a flexible machine learning model for forecasting time series with both longer dependencies and seasonality.