Adaptive Fundamental Quant Model
Score fundamentals, retest factors, and adapt as market behavior changes
- Difficulty
- Expert
- Time to result
- ~ongoing to results
- Steps
- 6
- Confidence
- 98%
Navellier combines behavioral finance with fundamental analysis rather than building a model around order-flow prediction. The process first evaluates risk-adjusted behavior, then scores stocks on criteria such as sales growth, margin expansion, earnings stability, momentum, cash flow, return on equity, revisions, and surprises. The weights depend on the universe: surprises and momentum matter more in small caps, while stability matters more in large caps. Because markets mutate and widely adopted factors can stop working, the team stress-tests one- and three-year factor behavior after each earnings season. Factors that break down or become more volatile are trimmed, and the revised model is run each weekend to guide the following week's portfolio decisions. Human overrides are reserved for defined merger news or liquidity events rather than discretionary second-guessing.
Origin
Navellier developed the approach after a college index-tracking portfolio unexpectedly beat the S&P 500, prompting him to document market anomalies.
Core principles
- 01Risk-adjusted performance matters more than tracking order flow.
- 02Fundamental criteria should reflect what currently influences market demand.
- 03Different market-cap neighborhoods require different factor weights.
- 04Factors lose power as markets mutate and other investors adopt them.
- 05Models require recurring stress tests rather than permanent rules.
How to run it
- 1
Measure risk-adjusted behavior
Compare candidates on price behavior and risk rather than assuming a familiar low-valuation stock is safer. Use the result as the first quantitative screen.
Pro tip Check whether observed buying pressure supports or contradicts intuitive risk judgments.
Watch out Do not confuse a low valuation multiple with low market risk.
- 2
Score the fundamentals
Grade each candidate on the fundamental criteria currently influencing market demand. Include growth, margins, earnings quality, cash flow, returns, revisions, and surprises where relevant.
Pro tip Keep the scorecard broad enough to distinguish growth quality from price momentum alone.
Watch out A model based only on order flow omits the business evidence Navellier uses.
- 3
Fit the market neighborhood
Change the importance of factors for the relevant capitalization universe. Emphasize surprises and momentum more in small caps and stability more in large caps.
Pro tip Test universes separately before combining their scores.
Watch out Applying identical factor weights everywhere ignores the different rules Navellier observes across market caps.
- 4
Stress-test after earnings
At the end of each earnings season, test factor performance over one- and three-year periods. Identify criteria whose returns have weakened or whose volatility has increased.
Pro tip Use the earnings-season cadence to incorporate newly reported fundamentals consistently.
Watch out A factor that worked historically may decay once the market adopts it.
- 5
Trim and update factors
Reduce or remove factors that are breaking down, then strengthen the model's exposure to evidence that is still working. Preserve the goal of fundamentally superior holdings.
Pro tip Treat rising factor volatility as an early warning rather than waiting for complete failure.
Watch out Do not chase a temporary pattern without testing its stability.
- 6
Run and execute weekly
Run the revised model each weekend and provide its recommendations to portfolio managers for the next week. Follow the model except when predefined merger or liquidity events invalidate its assumptions.
Pro tip Define override conditions before they occur.
Watch out Routine discretionary overrides undermine the consistency of the process.
In the wild
Navellier explains that earnings surprises and momentum carry more weight in small caps, while stability matters more in large caps. His team scores stocks within those different neighborhoods, stress-tests the factors after earnings season, and updates the model before running it each weekend.
→ The model adapts its evidence weights to both company size and changing market behavior.
Common mistakes
Using one rule set everywhere
Small-cap and large-cap stocks respond differently, so identical factor weights can hide the evidence that matters in each universe.
Freezing successful factors
Market factors can lose power as conditions change and more investors adopt the same approach.
Overriding every surprise
Navellier largely follows the model and limits overrides to merger news or liquidity events that change the decision context.
Is it for you?
Best for
It is best for systematic equity managers with reliable price, accounting, earnings, and portfolio-risk data.
Not ideal for
It is not ideal for investors who lack the data, testing discipline, or capacity to maintain a recurring quantitative process.
From the transcript
“We lock in UM what it's working on Wall Street, and we score the stocks in that criteria and then we tweak our models quarterly.”
“When you get too small cap, surprises are a bigger deal, and so is momentum. When you get to large cap, stability is a bigger…”
“as the factors start to break down and get more volatile, they get trimmed from our models and then we suttress test everything.”
From the episode
Interview With Louis Navellier: Masters in Business (Audio)
Louis Navellier