Cross-sectional momentum: the formation/holding grid, and the crash
The best-supported idea technical trading points at, run as its original experiment: rank a synthetic cross-section on its trailing J-month return, hold K months, and read the whole J × K grid at once — including the rare, violent drawdowns that come with it.
Self-contained simulation on randomly generated synthetic data — not market data, not a live signal, and not a record of trading. It runs entirely in your browser; nothing is sent anywhere. Educational information only — not investment advice. Hypothetical backtested results; past performance does not guarantee future results. Trading involves risk of loss. Full-page version, with the findings and citations
What the research actually found
The support rating below is the same one this concept carries in our research corpus, and the quoted findings are the corpus text — not a summary written to flatter the simulation. Every citation was verified against its source.
Momentum & trend-following (the anomaly itself)
also called: time-series momentum, cross-sectional momentum, 'the trend is your friend'
Where it comes from. Not a chart pattern but a return regularity: assets that performed well keep performing well for months. Documented in stocks by Jegadeesh & Titman (1993) and generalized across 58 futures/FX/bond markets as time-series momentum by Moskowitz, Ooi & Pedersen (2012).
What the research supports. This is the best-supported idea 'technical' trading points at. Buying 3–12-month winners and selling losers earned significant excess returns in US stocks (Jegadeesh & Titman 1993); 12-month time-series momentum is positive in nearly every liquid futures market (Moskowitz, Ooi & Pedersen 2012); and a reconstruction back to 1880 finds positive trend-following returns in every decade since (Hurst, Ooi & Pedersen 2017).
What it does not support. It is not a free lunch and not a precise entry signal. Momentum suffers rare, violent crashes — losing over 70% in months during 1932 and 2009 rebounds (Daniel & Moskowitz 2016) — and the academic effect is a diversified portfolio phenomenon at monthly horizons, not a promise that any single chart's trend will continue.
Sources: Jegadeesh & Titman (1993) · Moskowitz, Ooi & Pedersen (2012) · Hurst, Ooi & Pedersen (2017) · Daniel & Moskowitz (2016) · full concept-by-concept evidence
Also embedded on
4 sources — all verified
- Jegadeesh, Narasimhan, & Titman, Sheridan (1993). “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48(1), 65–91.
- Moskowitz, Tobias J., Ooi, Yao Hua, & Pedersen, Lasse Heje (2012). “Time Series Momentum.” Journal of Financial Economics, 104(2), 228–250.
- Hurst, Brian, Ooi, Yao Hua, & Pedersen, Lasse Heje (2017). “A Century of Evidence on Trend-Following Investing.” Journal of Portfolio Management, 44(1), 15–29.
- Daniel, Kent, & Moskowitz, Tobias J. (2016). “Momentum Crashes.” Journal of Financial Economics, 122(2), 221–247.
This simulation demonstrates a mechanism on synthetic data; it does not reproduce any paper's dataset or reported magnitudes. Educational information only — not investment advice. Educational information only — not investment advice. Hypothetical backtested results; past performance does not guarantee future results. Trading involves risk of loss. See the methodology and the full disclaimer.
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