Inverse-Volatility Allocation
Global core
Spreads capital across the universe, giving more weight to lower-volatility names. Recomputed monthly.
This strategy's universe (121) →
Worst stretch in the backtest: -27.2% — a simulated $1,000 would have dipped to ~$728 before recovering.
Backtest window: 2020-12-31 → 2026-09-30 · as of 2026-09-30 (updated quarterly)
This window does not include the COVID crash (Feb–Mar 2020) and the 2008 financial crisis.
Full methodology & limitations
Track records are simulated backtests, not live results. Methodology and current limitations: (1) Universe membership is real point-in-time index constituents - Nasdaq-100 (US) and WIG20 (PL), with dated add/drop history - so backtests see each index as it actually was, with no survivorship from later additions. Prices are adjusted close (total-return). When the backtest runs on the licensed, delisted-capable feed (EODHD), index members that later delisted or renamed are still priced and INCLUDED, closing the survivorship gap on the price side; when it runs on the keyless fallback feed, some delisted names are unpriced and dropped, which can flatter drawdowns. Smaller residuals remain: index changes after the last sourced revision are not yet reflected (Nasdaq-100 membership as pulled on 2026-06-30, WIG20 as of its September 2025 revision), and a few long-delisted tickers may still be unresolved. A name with no real quote for 14 days is treated as untradable - never picked and never held on a frozen price - and a renamed listing is held under its new code with its full history. (2) Costs are charged per market on each rebalance's turnover - 5 bps for US names, 10 bps for EU, 20 bps for Warsaw (GPW) - the conservative end of typical half-spread-plus-fees for liquid names; market impact and FX conversion are not modelled (negligible at retail size, understated for large money). (3) Prices are total-return (dividends reinvested) where the data feed supports it. (4) Sharpe and Sortino use a flat 3%/yr cash (risk-free) rate, approximately the average US T-bill yield over the backtest window; actual cash rates varied 0-5% within it and Polish rates were mostly higher, so ratios are approximate. (5) Cross-market calendar gaps are bridged by carrying the last known price forward, which can modestly understate day-to-day volatility. (6) Rebalances are simulated at the same closing price the signal is computed from (close-to-close execution); a live follower trades at the next session's prices, so simulated results may differ from what next-bar execution would have produced. Past simulated performance does not predict future results.
Hover any metric above — I'll explain what it means.
How you'd carry it out in your broker
Every model portfolio update publishes as a plain list of tickers and weights — the exact same file for every subscriber. Here's the general shape of loading that into four popular brokers; the actual tickers and weights only appear once you unlock this strategy.
This strategy has averaged 1.2 changes in the model portfolio per month, over its last publications.
XTB, Trading 212, IBKR, Revolut
XTB
- Each published ticker is searchable in the XTB app/web platform.
- XTB has no basket/pie import — each ticker's published weight is its share of the model portfolio; whether and how much to trade is your own decision.
Trading 212
- Portfolio → Pies → Create Pie.
- Each published ticker goes in with its published weight % — the Pie then tracks that allocation for you.
Interactive Brokers (IBKR)
- Once unlocked, the model portfolio downloads as a CSV.
- Classic TWS: File → Import Watchlist reads that file — each ticker's published weight is its share of the model portfolio.
Revolut
- Each published ticker is searchable in the Invest tab.
- Revolut has no basket import — each ticker's published weight is its share of the model portfolio; whether and how much to trade is your own decision.
General information about loading a published, identical-for-everyone list into a broker — not a personal recommendation. Whether and how much to trade is your own decision, in your own account; we never trade on your behalf.
Track record
Chart data (year by year)
| Year | Simulated return |
|---|---|
| 2020 (from Dec) | +0.0% |
| 2021 | +22.0% |
| 2022 | -18.1% |
| 2023 | +30.0% |
| 2024 | +6.4% |
| 2025 | +17.1% |
| 2026 | +13.5% (to 2026-09-30) |
Dashed line = Equal-weight universe (same window and costs): CAGR 11.9%, max drawdown -32.3%.
Simulated backtest — methodology & limitations above, next to the stats.
This month's model portfolio
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How it works — in plain words
Every month, each name in the universe gets a weight proportional to 1 ÷ its recent volatility: the jumpier a stock, the smaller its slice; the calmer, the bigger. Money is spread so no single wild mover dominates the portfolio's risk. No return forecasting at all.
Three stocks: a utility that wobbles about ±1% a day, a bank ±2%, and a hot tech name ±4%. Split the money equally ($333 each) and the tech name runs the show — it swings four times as hard, so your portfolio's fate basically IS the tech name. Inverse-vol instead hands weight ∝ 1/volatility: 1/1 for the utility, 1/2 for the bank, 1/4 for the tech name. Normalised, that's roughly 57% / 29% / 14%. Now each name contributes a SIMILAR share of the total risk — no single wild mover hijacks the book.
Put $500 in a sleepy utility and $500 in a wild tech name and your portfolio's fate is mostly the tech name — it moves 3x as much. Risk-based weighting flips the question from 'how much money in each?' to 'how much RISK from each?'. That reframe is the seed of risk parity, used by some of the world's largest funds.
Bridgewater's famous All Weather fund is built on the same core intuition — balance the RISK contributions, not the dollars.
Live since 2026-07-13 through 2026-11-06: 3.3% (3 mo) — the result of the published portfolios, entering at the next session's close. This is not a backtest.
2 positions: marked at their last available close (no print after the rebalance).
1 position: top-up not executed — the extra weight stayed in cash.
Update history
| As of | What changed |
|---|---|
| 2026-11-01 | Model update: rotated the basket (2 added, 2 removed) |
| 2026-10-01 | Model update: adjusted the weightings |
| 2026-09-03 | Model update: adjusted the weightings |
| 2026-08-03 | Model update: removed 1 name(s) |
| 2026-07-21 | Model update: adjusted the weightings |
Grounded in published research
The peer-reviewed findings this strategy is built on:
- Maillard, S., Roncalli, T. & Teiletche, J. (2010). The Properties of Equally Weighted Risk Contribution Portfolios. Journal of Portfolio Management, 36(4).
- Baker, M., Bradley, B. & Wurgler, J. (2011). Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly. Financial Analysts Journal, 67(1).
A documented, peer-reviewed effect — cited so you can read the source yourself, not take our word for it. Implementation and results are ours (see methodology); the underlying finding is the literature's.
Understand this strategy
Plain-language explainers behind the mechanics above.
This strategy's rules and every model-portfolio update are written and run by HedgeYourOwn's founder.