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Using Long-horizon Returns to Predict/Time the Stock Market

| | Posted in: Big Ideas, Fundamental Valuation

Is use of a sampling interval much shorter than input variable measurement interval a useful statistical practice in financial markets research? In the April 2018 update of their paper entitled "Long Horizon Predictability: A Cautionary Tale", flagged by a subscriber, Jacob Boudoukh, Ronen Israel and Matthew Richardson examine statistical reliability gains from overlapping measurements of long-horizon variables (such as daily or monthly sampling of 5-year returns or 10-year moving average earnings). They employ the widely used cyclically adjusted price earnings ratio (CAPE, or P/E10) for some examples. Based on illustrations and mathematical derivations, they conclude that:

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