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arXiv Machine Learning By Bingqi Lian, Linfeng Cheng, Mei Lu, Jerry Wu

Signal Simplification Is Not Predictive Simplification: Diagnosing Residual Neural Forecasting in Short-Horizon Volatility

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The paper investigates whether simplifying a forecasting target via a statistical first stage improves neural network performance in short‑horizon volatility prediction. Using a signal‑forecast‑system diagnostic framework on five liquid U.S. assets, a volatility‑aligned HAR‑style model outperforms AR, MA, and ARIMA, and its residuals exhibit markedly lower variance and weak autocorrelation. However, augmenting a residual‑LSTM with these simplified targets actually worsens mean squared error compared to a pure LSTM, revealing a forecaster‑preconditioner asymmetry where statistical simplification does not translate into neural preconditioning benefits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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