AQI Process

A Cap Weighted portfolio gives more weight to larger companies based on their market capitalization, meaning performance is heavily influenced by the biggest stocks. In contrast, an Equal Weighted portfolio assigns the same weight to every security, regardless of size.

At AQI, we prefer Equal Weighting because it aligns with our conviction that every position should contribute equally to performance. This helps reduce concentration risk, maintain portfolio balance, and ensure that returns are driven by the strength of our selection process, not just the size of a few companies.

We identify the prevailing economic regime, such as expansion, slowdown, or contraction, by systematically analyzing macroeconomic data like GDP trends, interest rates, inflation, and employment. This regime informs how different asset classes and factors tend to behave.

Retitling refers to how we dynamically label and adjust portfolio exposures based on these regime signals. By doing so, we ensure our strategies stay aligned with macro conditions, enhancing adaptability and increasing the probability of capturing favorable return profiles.

A single-factor strategy focuses on one specific driver of returns, such as value, momentum, or quality, while a multi-factor approach blends several of these to balance exposures and reduce risk.

AQI employs a multi-factor framework designed to identify persistent return drivers across different regimes. By combining factors, we seek to capture the strengths of each while minimizing cyclicality and reducing the risk of overreliance on any single signal.

AQI’s investment process is built around identifying the factors most likely to outperform, such as quality, momentum, or valuation, regardless of sector. This means our portfolios may look very different from traditional market-cap-weighted indices, which are often heavily tilted toward the largest sectors or companies.

Because our holdings are factor-driven rather than sector-constrained, this can lead to tracking errors, or a noticeable deviation from the benchmark in the short term. However, this intentional divergence is designed to enhance the portfolio’s risk-adjusted return profile over time, as we aim to capture persistent sources of excess return that are not bound by index composition.

We mitigate bias through a quantitative-first approach, where security selection begins with data-driven models designed to be objective and repeatable. When fundamental insights are applied, they follow a structured, collaborative framework that emphasizes consistency over discretion.

By combining systematic modeling with rigorous review, we reduce behavioral biases and maintain fidelity to our investment thesis—ensuring decisions are based on facts, not opinions.