arXiv subjects

Mathias Mesfin

Publications and source records attributed to Mathias Mesfin.

3 recordsLinked to original sources

arXiv2026

A Validated Volatility-Volume-Gap Classifier for Regime Identification in MNQ Intraday Data

This paper builds and tests a day-classification system for MNQ (Micro E-Mini Nasdaq 100) futures based on three simultaneously elevated pre-market conditions: absolute overnight gap, absolute first-30-minute return, and first-bar volume relative to a 20-day rolling baseline. The Volatility-Volume-Gap (VVG) classifier is evaluated on 947 trading days of five-minute data from 2021-2025, with all thresholds computed on an expanding window to prevent lookahead bias. The classifier activates on roughly 4.4% of sessions (40 days). Those days exhibit measurably distinct behavior: 77.6% reverse from their intraday peak before the close, mean peak-to-close giveback of 11.73 points, and a 25.6 basis point next-day return spread versus non-classifier days. Year-by-year analysis reveals substantial path heterogeneity -- 2024 classifier days closed at mean +40.74 points while 2025 crashed to -42.48 -- which is the core obstacle for any directional strategy. Eight directional configurations were tested. None passed. Best result: T = 1.46, mean net +7.80 points, 127 OOS trades, reversal entry with OLS regression filter. 2024 broke year stability. Binding constraints are the 40-day sample (roughly 10 per year) and regime-dependent intraday behavior that no fixed rule survives across all test years. The classifier is preserved as a research asset: it identifies a real behavioral phenomenon but cannot generate a deployable directional signal under current constraints.

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arXiv2026

Structural Limits of OHLCV-Based Intraday Momentum Signals in MNQ Futures: A Systematic Falsification Study

This paper tests whether common intraday momentum signals built from OHLCV data generate a tradable edge in Micro E-Mini Nasdaq 100 (MNQ) futures after realistic execution costs. Fourteen signal families were evaluated on 947 trading days of five-minute data from 2021-2025 under expanding-window walk-forward validation. Each signal had to clear five criteria: T-statistic >= 2.0 on out-of-sample net returns, >= 30 trades per out-of-sample fold, positive net returns after instrument-appropriate friction, consistent direction across test years (2023, 2024, 2025), and permutation p < 0.001 where applicable. None passed all five. The failures divide into three groups. Eleven families fail because gross return before friction is 0.07-1.50 points, below the 2.0-point friction floor. Three clear friction but fail elsewhere: the Opening Range Breakout long (T = 0.88, year-unstable), the VVG classifier reversal (T = 1.26, year-unstable), and gap continuation short (gross +16.53 pts, T = 1.46, fails year stability and per-fold sample minimum). Two positive controls confirm the methodology detects genuine edge: the RTH Confluence Signal (OOS T = 3.11, mean net +11.82 pts, N = 196) and London Session Signal B (OOS T = 4.30, mean net +4.09 pts, N = 247, p = 0.000025).

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arXiv2026

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

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.

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