Earlier this month I wrote about an econometric tool—Hidden Markov model (HMM)—for identifying the start of bear markets, as early as possible and with a relatively high degree of confidence. The record looks encouraging for the past 50 years with the US stock market (S&P 500), although some readers wondered if this upbeat in-sample analysis would hold up in an out-of-sample context. That’s always a relevant question when forecasting (or nowcasting or even backcasting). Many models look wonderful with historical numbers only to stumble when applied in real time going forward. Is that fate for the HMM process I outlined a few weeks ago? Not necessarily, as a bit of testing suggests.
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