Empirical Evidence and Judgment in Practice

Role of Empirical Data

If you’ve ever chatted with a fellow analyst about private company valuation—maybe over coffee or even during a late-night spreadsheet marathon—you’ve probably heard the phrase “data is king.” But in practice, it’s more like data is a strong advisor, not an absolute monarch that gives perfect mandates.

Empirical data consists of historical transaction databases, restricted stock studies, data from observed M&A deals, and other tangible market-based evidence. By looking at patterns—say, how a range of minority stakes in private firms sold at 10–30% discounts—we can start forming a baseline for the discount or premium needed in our own valuations.

But here’s the tricky part: these data sets are rarely uniform. They can vary significantly based on company sizes, industries, and broader market cycles. For instance, a restricted stock study from the tech sector in one bullish year might not translate so well to a manufacturing firm in a slow-growth environment. Nonetheless, these real-world observations help anchor the valuation process; they give you something to compare against, rather than simply making guesses out of thin air.

When building your analysis:

• Study transaction databases (e.g., PitchBook, Pratt’s Stats) for relevant multiples and discount data.
• Look for industry consistency: a broad range from healthcare might differ from technology or consumer staples.
• Understand time-period differences: deals during expansion phases can show lower discounts due to aggressive buyer competition.

Importance of Professional Judgment

Now, let’s be honest. You can have the best, most comprehensive private transaction database, and you’ll still see wide ranges of discount percentages. That’s where your personal judgment—as a professional—kicks in. Sometimes I say, “Data can only get you so far.” It’s like reading restaurant reviews: numbers matter, but if you know the chef personally (and maybe their style), you may refine your opinion beyond the raw star rating.

Professional judgment, in this context, means understanding the story behind the data and adjusting for the specific circumstances of your target firm. Does the target have extremely concentrated clientele? Are there unusual supply chain constraints? Is there a family dynamic in governance that influences liquidity concerns? All those nuances factor into your discount or premium assessment.

A few guidelines:

• Document your thought process in a valuation memo or report.
• Cross-check if you’re double-counting or missing factors (e.g., if you’ve already reflected illiquidity in your required return, avoid layering too high a discount for marketability).
• Engage stakeholders (legal counsel, CFOs, industry experts) to gather their sense of realistic valuations.

Triangulating Discounts

Because it’s easy to get lost, think of triangulation as using multiple compasses when orienteering in the woods. One data point might suggest a 25% discount, another might say 15%. Rather than picking one figure in isolation, see if you can converge on a narrower range by combining:

1. Restricted Stock Analysis: Observing how restricted shares of public companies trade relative to their freely tradable counterparts.
2. Option Pricing Models: Applying theoretical frameworks that treat lack of marketability similar to a put option’s value.
3. Observed Private Transactions: Real deals in your target’s industry or possibly a broader set of deals if industry info is scarce.

Add a dose of qualitative color, too. For instance, if management is top-notch and there is a buy-sell agreement that partially mitigates liquidity concerns, your final discount might land at the lower end of the observed range. On the other hand, if it’s a turbulent industry with uncertain product pipelines, you might lean higher.

Below is a short conceptual illustration showing how these pieces might fit together:

    flowchart LR
	    A["Begin Valuation <br/>(Collect Data)"] --> B["Empirical Market Evidence <br/>(Restricted Stock Studies)"]
	    B --> C["Adjust for Market Conditions"]
	    C --> D["Apply Judgment <br/>(Firm-Specific Factors)"]
	    D --> E["Final Discount/Premium"]

The key is that each step can be influenced by macro factors like interest rates, sector growth, or even global events—so keep an eye on the broader context.

Economic and Market Cycles

It’s no big secret that valuations can swing widely based on whether we’re in a booming or sluggish economy. When credit is plentiful and everyone is feeling bullish, competition for deals intensifies, often compressing discounts (for instance, a minority discount that might typically be 20% in an average market could drop to 10% if buyers are eager). Conversely, in a downturn, prospective buyers demand more compensation for the risk, pushing discounts upward.

Imagine 2020–2021, when certain sectors saw frenzied deal-making at robust valuations despite global uncertainties. Some might have predicted deep discounts because of volatility. Yet liquidity was high, and frothy markets often mean discounted stakes ended up commanding more than you’d expect from historical norms.

So, your final discount can end up being as much a function of the market’s temperature as it is of pure firm fundamentals. Keep an inventory of relevant macro factors—interest rate environment, industry growth prospects, or even intangible brand value that might attract strategic buyers—and factor them into your conclusion.

Case Study Illustration

Let’s say you’re valuing a 15% minority stake in a profitable private manufacturing firm. The firm has a decent track record, moderate growth, and stable cash flows—plus strong corporate governance (e.g., audited financials, independent boards). They also have a contractual buy-sell agreement that grants partial liquidity at fair market value if certain triggers occur (like an owner’s death or retirement).

You collect data from restricted stock studies. Those studies suggest a median discount for marketability of around 18–20% in similar industries. However, your subject firm has above-average corporate governance, so you think that discount might be on the high side. You check a couple of direct private transactions in the same sector; they show discounts around 10–15%. This narrower range is consistent with the fact that the buy-sell agreement provides a fallback liquidity option. Ultimately, you decide on around 12%.

The logic:
• Start with median discount from restricted stock studies (say 18%).
• Adjust downward for strong governance and partial liquidity from the buy-sell agreement (–6%).
• Cross-check with real deals in a robust transaction database (10–15% range).
• Final concluded discount: ~12%.

As a friend once told me, “Valuation is an art supported by data, or maybe data that demands an artist to interpret.” This example shows how you use that artistry—backed up, of course, by your best professional reasoning.

Putting It All Together

At this point, a major watch-out is double-counting or layering multiple discounts for the same factor. Say you already increased the discount rate in your discounted cash flow (DCF) approach to account for some liquidity risk, but then you slapped on a 20% marketability discount. You need to confirm you’re not penalizing the company twice for the same intangible factor.

Similarly, ask yourself (and your team) if the final value aligns with real-world transactions. If your analysis produces a massive 50% discount in an industry where typical minority stakes go for earlier in the teens, you’d better have a rock-solid explanation—like extremely poor governance, a niche product with no buyer demand, or some major risk factor that justifies such an outlier result.

From an exam perspective, consider how you’d present your final figure in an item set:
• Show how each discount or premium was derived or observed.
• Explain your rationale for weighting one set of data over another.
• State any unique company-specific factors that led you away from the “typical” discount.

Glossary

• Empirical Data: Observed market data from past transactions, restricted stock, or placements.
• Judgment Overlay: The “art” component where analysts account for firm-specific nuances.
• Cross-Sectional Analysis: Examining multiple firms or deals at a single point in time to identify trends and benchmarks.
• Longitudinal Analysis: Studying how discounts or premiums evolve over time, capturing cyclical factors.
• Sensitivity Analysis: Testing how changes in assumptions (e.g., required returns, growth) might push your discount or premium up or down.
• Endogenous vs. Exogenous Factors: Internal vs. external influences that may shape valuation.
• Buy-Sell Agreements: Legal frameworks that sometimes mitigate illiquidity by offering a path to monetization.
• Transactions Databases: Tools like PitchBook or Pratt’s Stats used to glean real-world deal multiples.

References and Further Reading

• Pratt’s Stats (Business Valuation Resources) – a go-to for private transaction data.
• Mun, J. (2002). “Real Options Analysis” – helpful for understanding how to factor optionality into valuations, potentially affecting discounts.
• Duff & Phelps Valuation Handbook Series – industry-specific discount rates, risk measures, and observed valuation multiples.
• IFRS 13 and ASC 820 – guidance on measuring fair value, relevant for cross-checking completeness of your discount or premium rationale.

Conclusion and Exam Tips

When you’re knee-deep in practice vignettes for the CFA exam, remember these guiding principles:

• Start with data, but don’t end there—scrutinize its applicability to your specific valuation scenario.
• Document the rationale behind your final choice of discount or premium.
• Guard against duplication of risk adjustments.
• If your conclusion veers far from typical market evidence, make sure you can explain it thoroughly.

Finally, keep your reasoning transparent and methodical, so both you and your exam graders (or future stakeholders) can track how you derived each discount or premium. Good luck out there—and remember, behind each discount is a story. Make sure you’re telling it well!

Test Your Knowledge: Empirical Evidence & Judgment in Practice