~ Un-Common Sense In An Irrational World: We Challenge the Conventional WisdomTM ~
OUR FIRM WAS AN EARLY PIONEER IN MULTIFACTOR INVESTING WITH OUR CASSANDRA STOCK SELECTION MODELTM
Introduction
We are bringing our award-winning Cassandra Stock Selection ModelTM back to the Intrinsic Value Wealth Report Newsletter and for use in selecting stocks for our Northwest Quadrant Alternative Investments Venture Fund, LLC. We have a number of stocks that our model has identified for further analysis. We are putting these stocks through our extensive equity research and analysis framework and will be releasing our first stock picks soon.
Background
Our firm was an early pioneer in the development and use of quantitative, multifactor investment models with our Cassandra Stock Selection ModelTM, which we developed and deployed in 1994. It is a quantitative equity model which stems from the quantitative revolution that swept institutional asset management during the late 1980s and 1990s. Our model is designed to identify undervalued, high-quality companies by scoring stocks across five core criteria:
- Valuation: Measures if a stock is cheap relative to its fundamentals.
- Quality: Assesses the strength, stability, and profitability of the company.
- Growth: Looks for positive trends in earnings and cash flow.
- Momentum: Evaluates recent price performance and market sentiment.
- Capital Discipline: Analyzes management’s efficiency in deploying capital.
These distinct dimensions allow us to systematically screen global equity markets for potential Alpha generation, although we focus mainly on the US markets.
Our original 5-factor scoring system, which has evolved to include more factors than the original five, has allowed us to grade every stock in our investment universe on a standardized scale, creating a highly disciplined, risk-managed pipeline for stock selection.
The specific pillars used by our firm in the Cassandra Stock Selection ModelTM (Valuation, Quality, Growth, Momentum, and Capital Discipline) are virtually identical to the modern, standardized quantitative factor models used by institutional giants today. Firms like BlackRock, MSCI, and Vanguard deploy these exact metrics under the umbrella of “Smart Beta” or “Factor Tilting” strategies.
The Cassandra Stock Selection Model’sTM genesis can be traced through three core operational and historical drivers:
1. The Multi-Factor Revolution
Before multifactor models, institutional investing relied heavily on the Capital Asset Pricing Model (CAPM), which assumed a stock’s risk and return were driven almost entirely by one factor: its relationship to the broader market (Beta).
When academic research in the late 20th century proved that standard Beta could not fully explain why certain stocks outperformed, quantitative shops began building multi-variable frameworks. Institutions began building their models to capture a holistic snapshot of market anomalies that a single metric would miss.
2. Blending “Quants” with Fundamental Research
Unlike pure academic models that relied strictly on raw price and size data, many institutional models, including our Cassandra Stock Selection ModelTM, were engineered as Alpha-generating tools for active portfolio managers. We and others wanted a system that reflected how a human analyst evaluates a company, but scaled systematically across thousands of global stocks.
We achieved this by dividing our five factors (now more than five factors) into two distinct operational categories:
- Market Sentiment Factors: Value and Momentum were used to time entry points and avoid “value traps” (cheap companies whose stock prices keep falling or stay the same).
- Corporate Health Factors: Quality, Growth, and Capital Discipline acted as fundamental sanity checks to ensure the business was highly profitable, efficiently run, and growing sustainably.
3. The Institutional Mandate
Many institutions deployed this type of model primarily to manage large-cap institutional equity portfolios, pension funds, and endowments. Institutional clients required a repeatable, disciplined process that removed human emotion from the equation.
In addition to the factors discussed above, our Cassadra Stock Selection ModelTM has a small-cap and medium-cap emphasis, based on academic research by Fama and French (Fama & French, 1992, 2015) and others. What this means is that our model concentrates on the small and medium capitalization stocks in the US equity markets.
To summarize our approach, the four main factor categories we use in the Cassandra Stock Selection ModelTM are:
- Size Orientation – Small-cap and Mid-Cap
- Investment Style – Value
- Persistence – Momentum
- Corporate Health Factors: Quality, Growth, and Capital Discipline
Track Record
Our Cassandra Stock Selection ModelTM has produced excellent performance results over time. We will discuss these results in more detail in a future post. For now, you can access our track record at our Track Record study.
Summary/Conclusion
The Cassandra Stock Selection Model™ reflects our firm’s long-standing commitment to disciplined, research-driven, multifactor investing. Since its development in 1994, the model has combined quantitative screening with fundamental investment judgment to identify undervalued, high-quality companies with attractive growth, momentum, and capital discipline characteristics. While the model has evolved beyond its original five-factor framework, its core philosophy remains the same: use a systematic, risk-managed process to uncover potential Alpha opportunities, with a particular emphasis on small- and mid-cap stocks. As we bring the Cassandra Stock Selection ModelTM back into the Intrinsic Value Wealth Report Newsletter and apply it to our current investment selection process, we believe it provides a powerful foundation for identifying stocks worthy of deeper research and analysis.
References
Fama, E. F., & French, K. R. (1992). The cross-section of exptected stck returns. The Journal of Finance, 47(2), 427-465.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1-22.