Weighted Category Average

The universe is built from investable, open-ended Indian direct mutual fund schemes. Eligible schemes must have sufficient history for evaluation, consistent NAV reporting, and regular portfolio disclosure. The basket is designed for investors with a moderately higher risk tolerance and targets a balanced asset mix, typically incorporating meaningful equity exposure alongside stabilizing allocations to high-quality debt and cash-equivalents (aligned to a moderately aggressive risk band). To keep the portfolio practical and scalable, we prefer liquid, well-established funds and avoid schemes where mandate complexity, low transparency, or persistent style drift can make outcomes harder to predict. The investable set is categorized across core equity (large-cap/flexi/multi-cap), satellite equity (mid/small-cap), and debt-oriented stabilizers (short duration/high-quality where appropriate). Each fund is mapped to risk drivers such as volatility, drawdown behavior, and correlation so diversification is intentional—not incidental.

Research combines quantitative fund analytics with portfolio-construction checks. We evaluate rolling returns across market regimes, downside capture, maximum drawdown, and consistency versus category peers. Risk is modeled through volatility, correlation structure, and stress-period behavior, alongside practical attributes like expense ratio and concentration. Our approach as data-driven and AI-assisted, using models to forecast risk and reward and to analyze baskets alongside expert selection. These signals are translated into forward-looking expectations and then tested for robustness, so the basket does not depend on a single style (growth/value), market-cap bucket, or factor regime. Scenario checks (risk-on vs risk-off) and overlap analysis help ensure the final portfolio is diversified rather than a collection of similar funds.

Constituents are screened in three layers. (1) Eligibility: operationally available schemes with adequate history, regular disclosure, and investability through the platform. (2) Quality &amp; risk: we remove funds with persistent peer underperformance, unusually deep drawdowns for their category, unstable risk profiles, or excessive concentration/turnover that can amplify downside. (3) Portfolio fit: each remaining fund is scored on expected contribution to return, impact on portfolio risk, and diversification benefit (how much it reduces reliance on any single driver). Overlap analysis penalizes funds that largely replicate existing exposures, helping the basket combine core holdings (broad equity) with complementary satellites (select mid/small or diversifying styles) and stabilizers (debt/cash sleeves) only when they improve the total risk/return profile. The final list is kept compact for clean implementation and easier monitoring.

Weights are determined using a constrained optimization framework designed for aggressive growth with diversification controls. Inputs include expected return, expected risk, and cross-fund correlations from the research layer. Constraints limit concentration in any single scheme and ensure exposure is spread across complementary equity categories, while guardrails reduce unintended bets from overlap and correlated style tilts. The basket is rebalanced on a semi-annual cadence (every 6 months), aligning the portfolio back to target weights while aiming to avoid unnecessary churn. After optimization, a validation step checks stress behavior (drawdowns and downside capture) and confirms that no single theme dominates the outcome. This keeps the portfolio aligned to its stated objective: higher-return potential for investors who can tolerate higher volatility and interim drawdowns.

This smallcase will be rebalanced on an as-needed basis

Wright Moderately Aggressive Asset Allocation

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Moderately aggressive mutual fund basket balancing growth potential with measured risk.

Wright Moderately Aggressive Asset Allocation

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