AI RESEARCH
Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ
arXiv CS.LG
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ArXi:2605.17724v1 Announce Type: cross 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.