Research Methodology
Portfolio construction runs as three explicit stages — screen, select, allocate — rather than a single opaque scoring step. Screening keeps assets with sufficient price history over the evaluation window. Selection ranks the eligible universe by a method you choose. Allocation then sets weights using a portfolio optimizer, subject to the position bounds and risk-profile constraints attached to the run.
Keeping the stages separate is deliberate: it means an allocation can be traced back to the universe it came from and the ranking that produced it, and that changing one stage does not silently change the others. Constraints are applied as bounds on the optimizer rather than penalties in the objective. Where a set of bounds is arithmetically impossible — a 5% maximum weight across ten assets cannot sum to 100% — the binding limit is relaxed to the nearest feasible value rather than the run failing.
Allocation methods
- Hierarchical Risk Parity (HRP) — clusters assets by correlation structure and allocates across the resulting hierarchy. It avoids inverting the covariance matrix, which makes it less sensitive to estimation error than classical mean-variance on noisy or highly correlated universes.
- Mean-Variance Optimization (MVO) — the classical risk/return trade-off. Included because it is the reference every other method is judged against, with the caveat that it concentrates hard when return estimates are noisy.
- Conditional Value at Risk (CVaR) — optimizes against expected loss in the tail rather than variance, which matters more when the return distribution is skewed than when it is not.
Ranking methods available at the selection stage include market capitalization, Sharpe ratio, twelve-month momentum, and a composite quantitative ranking. No method is treated as universally correct; the right choice depends on the universe and the objective, which is why the choice is exposed rather than hidden.
Backtesting
Backtests are walk-forward. At every rebalance date the engine re-runs the same screen, select, and allocate steps using only data available up to that date, on a rolling lookback window, then holds the resulting weights until the next rebalance. Nothing in the decision at a given date can see beyond it.
This matters because a single in-sample fit over the whole history will almost always look better than anything achievable in practice. Rebalance frequency is configurable, and round-trip transaction costs can be applied in basis points per unit of turnover so that a high-turnover strategy is charged for its turnover rather than being compared against a frictionless benchmark.
Data Sources
Market data — historical prices, market capitalization, company profiles, and treasury yields used as the risk-free rate — is sourced from our licensed market-data infrastructure. The returns used for allocation and backtest simulation are computed on dividend-adjusted prices. The earlier screening and ranking pass runs on unadjusted closes, which is materially cheaper across a full universe; the adjusted panel is then used for every return series that drives allocation and simulation.
Data quality checks include missing-value handling, field normalization, and timestamp validation. Assets without adequate price coverage over the window under evaluation are dropped at the screening stage rather than being carried forward with interpolated history, since gap-filled prices understate realized risk.
Model Limitations
These are the limitations we consider material. They are properties of this class of model, not defects to be tuned away, and no output should be read without them in mind.
- Historical performance does not guarantee future outcomes.
- Model assumptions may degrade in structural market regime changes. Correlation structure estimated over a rolling window is a description of the past, and correlations tend to converge toward one in exactly the drawdowns diversification is meant to protect against.
- Execution quality in live markets can differ from simulated assumptions. Backtests fill at historical closing prices; live orders face spreads, partial fills, and timing differences a daily series cannot represent.
- Universes assembled from currently listed instruments carry survivorship bias. Assets that were delisted or acquired are absent from a present-day universe, and their absence flatters historical results.
- Testing many configurations against the same history raises the chance that a strong result reflects selection rather than signal. Walk-forward validation reduces lookahead bias; it does not eliminate the effect of repeatedly searching the same data.
Compliance and Disclaimers
Platform outputs are informational and educational. They are not individualized investment advice, and Acubic is not acting as your fiduciary or registered investment advisor through this interface alone.
Before making investment decisions, consult a licensed professional and evaluate suitability for your financial situation, jurisdiction, and risk tolerance.
Review and Updates
Methodology documentation is reviewed when models, data pipelines, or core assumptions materially change. Significant updates should be reflected in corresponding guide pages.