Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ

TL;DR

This study evaluates whether five-minute OHLCV data can reliably predict intraday futures movements using LSTM and gradient boosting, finding no statistically significant predictive advantage over a four-year period.

Contribution

It provides an empirical assessment of a Kronos-inspired architecture on real-world data, establishing a lower bound on data scale needed for effective sequential financial ML.

Findings

No configuration achieved statistically significant out-of-sample accuracy above 51.8%.

Permutation tests yielded high p-values, indicating no significant predictive edge.

Feature importance was unstable, suggesting noise fitting rather than capturing stable signals.

Abstract

This paper compares gradient boosting and long short-term memory (LSTM) architectures for intraday directional prediction in Micro E-Mini Nasdaq 100 futures (MNQ). Motivated by recent foundation-model research on financial candlestick data, including the Kronos architecture, we test whether five-minute OHLCV bar sequences contain exploitable sequential predictive structure at the scale of a single instrument dataset. Using 944 trading days from 2021-2025, four model configurations are evaluated under strict expanding-window walk-forward validation across three out-of-sample periods. The target variable is whether the session close exceeds the 10:30 AM open by more than ten points. No configuration produces statistically significant out-of-sample accuracy above the 51.8% base rate. Combined OOS accuracies range from 50.00% to 50.89% across gradient boosting variants, while the LSTM achieves 50.59%. Permutation tests yield p-values of 0.135 for the best gradient boosting model and 0.515 for the LSTM, indicating no statistically significant predictive edge. Feature importance instability across walk-forward folds suggests noise fitting rather than stable structural signal capture. The results indicate that four years of single-instrument five-minute OHLCV data are insufficient for reliable sequential ML-based intraday forecasting. The primary contribution is a documented evaluation of a Kronos-inspired architecture on a constrained real-world dataset, providing an empirical lower bound on data scale requirements for sequential financial ML.