I spent a semester reading Ilmanen's Expected Returns and wondering, do these strategies actually hold up when you implement them yourself with real data? Here's what I found.
The carry trade is one of the oldest tricks in FX: borrow in low-rate currencies (JPY, CHF) and invest in high-rate ones (NZD, AUD, NOK). Uncovered interest parity predicts these differentials should be offset by exchange rate moves, empirically they aren't, creating the “UIP puzzle.”
I added a momentum overlay, if a currency's 4-week return is negative, the weight is zeroed for that month. Vol-weighting ensures Norwegian krone doesn't dominate purely because of high carry. Post-2020 volatility has made this trade rockier, which shows in the flat equity curve.
NEUTRAL = momentum overlay active (4-week return negative)
Time-series momentum (TSMOM), go long assets trending up, short those trending down, is one of the most replicated factors in finance. Signal is 12-month return minus 1-month (the “12-1” skip-month momentum), applied across 12 asset classes.
Each position is inverse-volatility weighted so every asset contributes equally to portfolio risk. Trend following is famous for crisis alpha: it gets short early in a down-trend. The strategy ran across equities, bonds, commodities, and FX with monthly rebalancing.
What does “cheap” mean at the country level? CAPE, dividend yield, book-to-market all have blind spots. My proxy: the negative of the past year's price return. Countries that underperformed tend to be cheaper relative to fundamentals, a crude mean-reversion assumption.
Annual rebalance, long the 5 cheapest, short the 5 most expensive. Equal-weight within each leg. US tech dominance has made cross-country valuation spreads wider and stickier, which has hurt this factor over the past decade.
CAPM says higher beta equals higher expected return. Frazzini and Pedersen showed this is empirically backwards within equities: low-beta stocks deliver better Sharpe ratios than high-beta stocks. The explanation: leverage-constrained investors reach for beta to hit return targets, bidding up high-beta stocks.
The strategy levers up the low-beta quintile and de-levers the high-beta quintile to create a market-neutral portfolio. Rolling 52-week betas computed on a universe of 60 large-cap stocks. Floor beta at 0.2 to cap max leverage at 5x. Monthly rebalance. This is the strongest performer of the five.
“The single clearest finding: low-risk assets earn higher risk-adjusted returns than high-risk assets.”
, Frazzini & Pedersen (2014)
Blue = LONG (low-beta), Red = SHORT (high-beta), Grey = middle quintiles (neutral)
If carry, trend, and value each have mediocre Sharpe ratios individually, what happens when you combine them? Carry tends to struggle in risk-off environments precisely when trend does best. Value moves on long cycles mostly uncorrelated with both. Combining with equal weights smooths the equity curve.
Each signal is z-scored cross-sectionally across 20 assets, combined with 1/3 weights, then the portfolio goes long the top 7 and short the bottom 7 by composite score. The chart below shows the Sharpe improvement through diversification, Ilmanen's central thesis.
BAB showed consistent alpha, Sharpe of 1.06 across the backtest window. The low-beta premium is real. Signal Stack outperformed all individual components through diversification.
FX Carry suffered from post-2020 carry crash volatility. Country Value struggled as US tech dominance made cross-country spreads wider and stickier than any mean-reversion model expected.
Incorporate PE/DY data for richer value signals. Test crypto factor premia. Implement regime detection to tilt factor weights. Add ML-based signal combination.