momentum
We ran the 52-week high proximity strategy on Indian stocks (BSE + NSE) from 2000 to 2025. The result: 26.73% annualized, 23.4% down capture, Sharpe of 0.735. A $1 investment grew to $444.67. The S&P 500 returned $7.30 over the same period. Contents 1.
working capital
We tested working capital efficiency as a stock selection signal across the full US equity universe from 2000 to 2025. The result: 10.10% CAGR vs the S&P 500's 7.64%, with a down capture ratio of just 22%. For every dollar the market lost in
momentum investing
The academic literature says momentum works everywhere. The data from 18 exchanges says it's more complicated. Pure 12-month price momentum (top 30 by 12M-1M return, semi-annual, no quality filters) was backtested across 18 exchanges covering every major equity market from 2000 to 2025. 8 exchanges outperform the S&
momentum investing
Pure 12-month price momentum is one of the most replicated factors in academic finance. Jegadeesh and Titman documented it in 1993 and the finding has held in 40+ countries for three decades. On US stocks, it doesn't work. The strategy returned 4.06% annually from 2000 to 2025,
momentum investing
The UK equity market doesn't generate as much momentum research attention as the US or India. That's a gap. Pure 12-month price momentum on LSE stocks from 2000 to 2025 delivered 10.82% annually, 3.08% above the SPY benchmark, with a down capture of 58.
momentum investing
Pure price momentum is supposed to work in emerging markets. India confirms the theory. Buying the top 30 Indian stocks by 12-month trailing return, semi-annually, delivered 22.74% annually from 2001 to 2025. That's +15% per year over the S&P 500 benchmark, one of the largest
momentum investing
German equities don't make many headlines in factor investing literature. Most momentum research focuses on the US, Japan, or global markets. The XETRA data tells a different story. Pure 12-month momentum on German stocks from 2000 to 2025 delivered 12.58% annually with a Sharpe ratio of 0.
pairs-trading
We ran a multi-pair portfolio backtest across 11 exchanges, testing portfolio sizes of 5, 10, 15, and 20 pairs with equal-weight and inverse-volatility allocation. The strategy generated positive returns on 5 of 11 exchanges at the 20-pair inverse-vol configuration, but only Korea and Sweden beat a low risk-free rate. India
pairs-trading
We ran a multi-pair portfolio construction backtest on US stocks from 2005 to 2024, testing portfolio sizes from 5 to 20 pairs with two allocation methods. The result contradicts the standard diversification narrative: adding more pairs made returns worse, not better. Transaction costs erode marginal convergence gains, and the best
pairs-trading
We ran the same multi-pair pairs trading backtest on Indian stocks (BSE + NSE) from 2005 to 2024. With 5 pairs, the strategy returned +11.80% annually, beating the S&P 500's 9.81%. With 20 pairs, it dropped to +6.45%. Each pair added beyond the top
pairs trading
We tested active z-score pairs trading on 7 major exchanges from 2005 to 2024. Convergence rates ranged from 77% to 87%. Every exchange lost money. Canada lost the most (-2.84% CAGR). Taiwan came closest to flat (-0.09% CAGR). The underlying cause is the same everywhere: transaction costs absorb
pairs trading
We ran an active z-score pairs trading backtest on US large caps from 2005 to 2024. 134 trades per year. 86.6% of spreads converged back to the mean. The strategy returned -1.22% CAGR over 20 years, vs 9.81% for the S&P 500. Contents 1. Method