How MEDGE Capital computes its numbers.

Conventions

The numerical conventions are fixed and apply to every report. Documenting them once here keeps the per-metric pages clean.

• Trading days per year252
• Volatility annualisationσ_d × √252
• Return annualisationμ_d × 252 (arithmetic) or (1 + μ_d)^252 − 1 (geometric)
• Risk-free rateSet per portfolio — defaults to 0 unless an explicit cash leg is included
• CVaR confidence levels95% and 99%, reported as a pair
• Monte Carlo paths5,000 by default (500–25,000); Bootstrap, GBM or t-copula
• Transaction costs0 by default; user-set, 0–500 bps on traded value at each rebalance
• Drawdown conventionPeak-to-trough on total return series, not calendar-based
• RebalancingYearly by default — none, monthly, quarterly, yearly or threshold (drift band)

Frequently asked

How does MEDGE compute Conditional Value at Risk (CVaR)?

CVaR is computed historically as the arithmetic mean of returns below the α-quantile, where α defaults to 95% and 99%. The sample is the full backtest window. For multi-asset portfolios the joint return distribution is preserved — we do not assume Gaussian marginals or constant correlations. CVaR is then converted to euro terms by multiplying by the current portfolio value.

What return model does the Monte Carlo simulator use?

Three methods, calibrated on the backtest window. Bootstrap (the default) resamples historical daily return rows with replacement, keeping the empirical joint distribution. Geometric Brownian Motion uses the mean and covariance of daily log-returns and assumes log-normal returns. The t-copula variant adds Student-t dependence to model fat tails. Paths are user-selectable from 500 to 25,000 (5,000 by default), in rebalanced or buy-and-hold mode, reported as P5–P95 fans over the chosen horizon.

Which annualisation convention is used?

All annualised figures use 252 trading days. Mean returns are multiplied by 252; standard deviations and Sharpe denominators are multiplied by √252. Drawdowns are computed peak-to-trough on the total return series, not calendar-based. Backtests charge transaction costs only when the user sets them (0 to 500 basis points on the traded value at each rebalance; 0 by default).

How is portfolio optimization implemented?

All 12 objectives are solved with the same search: random sampling of the weight space followed by local refinement, under per-asset weight bounds. The same universe and bounds apply to every objective, so results are directly comparable. It is a numerical search, not an exact solver: on large universes results can differ slightly between runs, and optimizations are in-sample.

Where does the regulatory data come from?

SEC Form 4 and Form 13F from EDGAR. CFTC Traders in Financial Futures from the weekly CFTC reports. Central-bank balance sheets (Fed, ECB, BoJ, BoE) and U.S. macro series via FRED. FCA UK short positions from the published SPR file. U.S. Treasury TIC and Daily Treasury Statement from the Fiscal Data API. BIS credit-to-GDP gaps from the BIS statistics API. Prices are daily end-of-day closes from MarketStack. MEDGE builds the total return series itself from the unadjusted close: splits are applied and cash dividends are reinvested on the ex-date. Spin-offs are not reconstructed; unexplained one-day moves above 40% and isolated bad prints are flagged in MCP responses.

Are backtests in-sample or out-of-sample?

Backtests are in-sample by default — the user-selected window is also the calibration window. The reports flag this explicitly. Out-of-sample and walk-forward testing are not offered today; to check robustness, rerun the same allocation on a different window, or compare with the Regime Summary and Period Analysis, which show how results change across market conditions.

Does MEDGE Capital ever provide investment advice?

No. MEDGE Capital is an analytics platform. The outputs are statistical descriptions of historical and simulated behaviour. Nothing on the site, in exported reports or in the AI-narrated PDF constitutes a personalised investment recommendation. Past performance does not predict future results. See the full Risk & Investment Disclaimer at /disclaimer.

References

The methodology builds on the standard quantitative finance literature. Each metric page in the glossary cross-references the relevant primary source.

1. [1]Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1). — Mean-variance optimization
2. [2]Sharpe, W.F. (1966). Mutual Fund Performance. The Journal of Business, 39(1). — Sharpe Ratio
3. [3]Sortino, F. & Price, L.N. (1994). Performance Measurement in a Downside Risk Framework. Journal of Investing, 3(3). — Sortino Ratio
4. [4]Artzner, P. et al. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3). — Coherent risk measures, sub-additivity
5. [5]Rockafellar, R.T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3). — Linear programming for CVaR optimization
6. [6]Fama, E.F. & French, K.R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1). — Fama-French 3-factor model
7. [7]Carhart, M.M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance, 52(1). — Carhart 4-factor extension
8. [8]Keating, C. & Shadwick, W.F. (2002). A Universal Performance Measure. The Finance Development Centre. — Omega Ratio
9. [9]Rachev, S.T. et al. (2004). Different Approaches to Risk Estimation in Portfolio Theory. Journal of Portfolio Management, 31(1). — Rachev Ratio
10. [10]Grinold, R.C. (1989). The Fundamental Law of Active Management. Journal of Portfolio Management, 15(3). — Information Ratio decomposition