A strategy that harvests the positive correlation between an asset's own past returns and its future returns, applied across many assets and sized by volatility. We dig into its two sources of edge, concrete numeric rules, and the conditions under which it breaks.
Time-Series Momentum (TSMOM) harvests one specific property: an asset's own past return is positively correlated with its future return.
The rule is almost anticlimactically simple to state. If the trailing 12-month return is positive, go long; if it is negative, go short. Apply that to a large set of futures across equity indices, government bonds, currencies, and commodities, and size each position by its volatility so they all contribute comparable risk.
A crucial distinction: this is not the same as cross-sectional momentum (relative momentum), which buys what is strong relative to other assets. Time-series momentum looks only at the asset's own history. Whether USD/JPY has been rising is judged by USD/JPY's own past return, not by comparing it to the euro or the pound.
There are two things being harvested. One is the lagged trend itself. The other is a risk premium earned for holding trends through their reversals. And the defining feature of this strategy is its "crisis alpha": it tends to make money precisely when equities are falling apart.
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