The core of the Axiom Quantitative Equity Strategy is a dynamic multi-factor econometric model. This sophisticated framework moves beyond simplistic measures of market risk (beta) to provide a comprehensive understanding of the key drivers, or “factors,” that influence portfolio returns and risk. By systematically identifying these factors and their influence on individual securities, we can construct a portfolio precisely tilted towards sources of expected outperformance.

Conceptually, our model is based on the principles of multiple linear regression, which can be expressed as:

y = β0 + β1×1 + β2×2 + … + βkxk + ε

In this framework, y represents the returns of an individual security. The x variables represent the quantifiable factors that we hypothesize influence those returns, and the β coefficients measure the security’s sensitivity to each factor. The final term, ε, represents the idiosyncratic return unique to that security, representing a potential source of alpha that our process seeks to identify.

Our research process investigates a comprehensive universe of factors grounded in established financial and economic theory. These factors are broadly categorized as follows:

• Value Factors: Metrics that identify securities trading at a discount to their intrinsic value (e.g., Sales/price, Cash flow/price).
• Size Factors: Measures related to a company’s scale and market footprint (e.g., Market capitalization).
• Momentum Factors: Indicators of persistent price trends and positive fundamental change (e.g., Price momentum, Analyst estimate revisions).
• Risk Factors: Quantifications of a security’s systematic and idiosyncratic risk (e.g., CAPM beta, Residual risk).

The objective of this modeling process is twofold: first, to build a dynamic model that identifies the market-wide payoff for each of these factors over time; and second, to measure each security’s unique sensitivity to them. We employ rigorous statistical validation, including t-tests and F-tests, to ensure that only statistically significant factors with clear economic justification are included in our final models. This process results in a powerful engine for forecasting returns and managing portfolio risk with precision.