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Momentum Strategies Across Markets: Methods, Evidence, and Explanations

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Summary

This review surveys momentum investing across equities, funds, commodities, bonds, currencies, and cryptocurrencies. It explains cross-sectional momentum, which buys past winners and sells losers, and time-series momentum, which takes positions based on each asset’s own past return. It also discusses residual, industry, style, and factor momentum, along with volatility scaling as a way to manage strategy risk. The cited studies report momentum effects across markets and describe improvements from adjusting for factor exposures or changing position size with volatility.

The review presents behavioral explanations, including delayed reactions, overconfidence, and anchoring, alongside risk-based accounts in which persistent return differences compensate for exposure to risk. Evidence on industry and factor momentum suggests that systematic exposures may explain part of stock-level results. The article emphasizes that momentum can suffer severe losses during sharp market rebounds and that its underlying causes remain disputed. It is a literature overview, not a tested trading specification; reported historical findings do not establish future performance, and isolating company-specific returns from changing factor exposures remains difficult.

Key ideas

  • Cross-sectional momentum ranks assets by past returns and buys winners while shorting losers.
  • Time-series momentum takes long or short positions according to an asset’s own recent return direction.
  • Residual momentum and volatility scaling are proposed ways to reduce factor exposure or manage risk.
  • Behavioral models attribute momentum to delayed information processing, while risk-based models treat returns as compensation for risk.
  • Industry and factor momentum may account for part of the observed stock momentum effect.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.