Research & Methodology

Ticker Inputs: Ticker inputs are entered as individual rows. Each row represents one ticker in the portfolio, benchmark, or custom factor set. Leveraged exposure can be specified with custom-ticker notation, such as "SPY?L=2".

Weight Inputs: When optimization is enabled, the selected model determines portfolio weights, subject to exposure limits.

Cash-flow Inputs: Positive cash flows are recurring contributions, negative flows are withdrawals, and the frequency dropdown schedules them.

Inflation adjustment: Inflation-adjusted views convert portfolio values into real purchasing power terms.

Factor sources: Fama-French Factor data is sourced from the Kenneth French Data Library; Q-factor data is sourced from the Hou-Xue-Zhang Global-Q library, and updated monthly.

Custom factor tickers: User-selected factors allow for any ticker to be added to the selected factor model or to be used to construct a custom factor model. This lets any ticker serve as a factor.

Uploaded return series: Uploaded daily return series can be saved as custom tickers and used anywhere a ticker input is accepted.

Ticker mapping: Some funds have limited historical data. The following table is a mapping catalog to provide viable proxies or simulated data/tickers for some of the most widely recognized funds, indexes, and asset classes. Long-history simulated tickers splice histories with live ETF/fund returns when available.

Backtest history limitation: A run begins at the latest first-available date among the portfolio, benchmark, and required proxy series. When a single ticker constrains the start date, Simfolio identifies that ticker beside the backtest inception date.

Live Performance: Provides a view of the portfolio's performance over user specified windows.

Actions: Previous and next scheduled optimization, rebalancing, or contribution/withdraw event for the modeled portfolio.

Live Portfolio Allocation: The current amount allocated to each holding. Allocations drift daily based on market fluctuations and are updated to reflect the current state of any modeled portfolio.

Transaction costs: Effective spreads are derived from each asset's daily OHLC using the Abdi-Ranaldo (2017) microstructure estimator and used to estimate slippage & transaction costs (.1% - .5% ∼ average) every one-way trade.

Walk-forward optimization: Simfolio exposes 16 academically grounded optimization models through a walk-forward process. Historical data is split into rolling training windows and forward evaluation windows with a purged execution gap between them. At each rebalance point, the selected model is fit only on the available training window, the resulting weights are applied to the next forward window, and the process repeats as the windows roll through time.

Lookback window: Each optimization model's training window rolls forward with a fixed trailing history of at least 12 months. Rolling emphasizes recent regimes while avoiding very short samples and all-history expanding fits that can slow live walk-forward runs.

Estimator stabilization: Covariance-sensitive models use Ledoit-Wolf covariance shrinkage (2004); mean-sensitive models shrink the expected-return vector toward a Bayes-Stein grand mean (Jorion, 1986). Both reduce optimizer over-reactions to sample noise in walk-forward fitting.

Constraints: Per-ticker minimums, maximums, and group constraints limit the optimization model during fitting and constrain the respective model from over/under allocating to tickers or groups when optimizing.

Factor model specification: Factor regressions use OLS with Newey-West HAC standard errors. Best Fit compares the available academic factor specifications and displays the one with the strongest adjusted R²; Best Fit + Custom adds user-selected custom factor tickers to that comparison; Custom uses only the selected custom factors.

How we selected our methodology: We tested over 200 unique forecasting models with out-of-sample validation across 80 multi-asset portfolios and more than 40 years of historical data. Our goal is to offer the most distributionally accurate, research-driven, plug-and-play portfolio forecasting solution for investors.

Not investment advice: Simfolio is not a registered investment adviser, broker-dealer, or fiduciary. Nothing produced by the platform is a recommendation to buy, sell, or hold any security. Decisions and their consequences belong to the user.

Past performance: Every backtest is a replay of one realized history and cannot be assumed to always represent the future.

Costs: Slippage is estimated via the Abdi-Ranaldo (2017) microstructure estimator on daily OHLC. This is an estimate of effective half-spreads averaged across the observed sample and is not any broker's actual fill data, does not vary with order size, and does not include market impact for large orders. Taxes and margin interest are not reflected in any of the backtested or forecasted portfolio performance.

Walk-forward optimization can be suboptimal for the future: A model that fits and performs well across in-sample periods (historically) can fail in any future period.

Optimizer sensitivity: Optimization models are highly sensitive to small changes in expected-return, covariance, and constraint inputs. Ledoit-Wolf shrinkage and mean shrinkage can reduce — but not eliminate this sensitivity. Two reasonable users running the same data with slightly different lookback windows, optimization frequencies, or constraints can experience materially different results.

Forecast stationarity assumption: The forecasting methodology is dependent on, and inferred only from the available portfolio history and proxy data. If the future market environment is vastly different, and outside anything represented in historical data, the forecast distribution will miss it.

Does Simfolio give investment advice?: No. Simfolio is a research tool for testing portfolio ideas, not an advisor or asset manager. Any investment decision remains yours, and you should consult a qualified professional for individualized advice.

What price data sources does Simfolio use?: Sourced from an institutional-grade data provider. We use adjusted close prices for all price data.

How accurate are long-horizon forecasts?: Long-horizon forecasts should be read as distributional ranges, not specific predictions. Simfolio reports percentile bands, loss probabilities, VaR, CVaR 1%, and CVaR 5% so users can see how wide the uncertainty is.

Can I import my existing portfolio from a broker?: Not yet. Enter tickers and weights directly, then hit the save button so that portfolio data will load upon login.

Do you offer refunds?: Yes. Paid plans include a 30-day money-back guarantee

What happens if I cancel?: Cancellation stops renewal. Access remains active until the end of the current billing period.

What happens if I downgrade with too many saved portfolios?: Existing saved data is not deleted automatically. Creating additional saved portfolios is blocked until the account is back under the active plan's limit or the plan is upgraded again.