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Does More History Help? A Market-Regime HMM and the Trouble with Out-of-Sample Evaluation This article investigates whether longer historical training periods improve the out-of-sample performance of a Hidden Markov Model (HMM) used for identifying financial market regimes. Four four-state Gaussian HMMs are compared, differing only in the starting point of their training data (1970, 1980, 1990, and 2000), while being evaluated on the same test period. The model uses five market-based features capturing volatility, drawdowns, credit conditions, and sector rotation to identify latent risk regimes. The results show that selecting models solely by out-of-sample likelihood can lead to misleading conclusions, favoring shorter training histories that fail to capture crisis regimes. When evaluated using regime stability, economic interpretability, and strategy-based metrics, the model trained on the longest history provides better generalization and avoids overfitting. The study highlights that validation methodology and evaluation criteria are as important as model design and data length when assessing regime-based investment models. quantpedia.com/does-more-his… #HMM #market #timing #risk #regimes #model #quant #trading