About Portfolio
Research has consistently shown that smaller companies, often referred to as small-cap stocks, have historically outperformed larger companies over the long term. This phenomenon, known as the “size effect,” is a cornerstone of many investment strategies. xSize, our meticulously crafted small-cap focused portfolio, is designed to capture this potential for outperformance by systematically investing in companies with lower market capitalizations.
Why Invest in Size?
Growth Potential:
Smaller companies are often in earlier stages of their growth cycle, with greater potential for expansion and market share gains compared to their larger, more established counterparts.
Inefficiency and Opportunity:
The small-cap market is often less efficiently priced than the large-cap market, creating opportunities for skilled investors to identify undervalued companies with significant upside potential.
Diversification Benefits:
Small-cap stocks can exhibit lower correlation to large-cap stocks, providing valuable diversification benefits to a broader portfolio.
The xSize Advantage
Our approach is grounded in academic research and empirical evidence supporting the size effect. We utilize a robust quantitative model that focuses on market capitalization to identify and weight companies within the portfolio. Our key methodology involves:
Market Capitalization Focus: We systematically target companies with lower market capitalizations, reflecting our commitment to capturing the small-cap premium.
Inverse Logarithmic Weighting: We employ an inverse weighting scheme based on the natural logarithm of each company’s market capitalization. This approach gives greater weight to smaller companies while still maintaining a diversified portfolio.
Quantitative Selection Methodology
The xSize portfolio employs a rigorous, quantitative model to capture the potential for outperformance associated with smaller companies. Our methodology is designed to be objective, transparent, and consistent with the well-established size effect. Here’s a breakdown of the key steps:
- Universe Definition: We define our investment universe, typically consisting of stocks within a specific market or region (e.g., all publicly traded companies in a particular country or index).
- Market Capitalization Calculation: We calculate the market capitalization for each company within our investment universe. Market capitalization is calculated by multiplying the company’s current share price by its total number of outstanding shares.
- Natural Logarithm Transformation: We apply a natural logarithm (ln) transformation to each company’s market capitalization. This transformation has the effect of reducing the influence of extremely large companies and giving proportionally greater weight to smaller companies.
- Inverse Weighting: We calculate portfolio weights based on the inverse of the natural logarithm of market capitalization. Specifically, the weight of each company is proportional to:
Weight = 1 / ln(Market Capitalization)
This inverse weighting scheme ensures that smaller companies receive a higher weight in the portfolio, consistent with the size effect.
- Data Preparation and Winsorization: As with our other factor portfolios, we implement a data quality check. To mitigate the impact of extreme outliers, we apply a winsorization process to the natural log of the market capitalization. We rank the transformed market cap values within each country, and values falling below the 5th percentile or above the 95th percentile are capped at the 5th and 95th percentile values, respectively.
- Portfolio Construction: The portfolio is constructed by allocating capital to each company based on its calculated weight, subject to liquidity and other portfolio construction constraints.
This systematic, data-driven approach allows us to effectively target the small-cap segment of the market and capture the potential for outperformance associated with the size effect. Our methodology is continuously monitored and refined to ensure its ongoing effectiveness.
Strategy Available For:
