DLOM Real Talk: Discounting the Wait, Not the Worth.

that leverage public equity market data: empirical studies and theoretical models. Empirical studies rely on real marketplace transactions, providing tangible data for analysis. On the other hand, theoretical models are grounded in economic formulas, offering a theoretical perspective. Additional sources for estimating DLOM include insights from academic studies and the application of the “Mandelbaum Factors”.

Part One

In the upcoming segment, we delve into the fascinating world of empirical studies, academic exploration, and the significance of the Mandelbaum Factors. It's a journey that goes beyond theoretical concepts, bringing you insights from the real world and academic spheres.

?EMPIRICAL STUDIES

(1)?? Restricted Stock Studies

?Restricted stocks are nearly identical to freely traded stocks, differing only in their temporary restriction from open market trading. The sole distinction lies in marketability. Studies analyse potential price differences between restricted stock transactions and open market transactions for the same stock on the same date.

?Pre-1990: Discounts on restricted stocks were 13-35.6%. Post-1990: Averages dropped to the 20s, coinciding with SEC deregistration and halving the holding period. Pre-1990 studies are preferred for reliable lack of marketability discounts in closely held companies.

Challenges in restricted stock studies:

• Analysed shares differ from closely held companies in holding period, transparency, and market liquidity.
• Identified discounts vary widely (e.g., Management Planning study ranged from zero to 57.6%).
• Lack of detailed transaction information in many studies.
• Some studies are over 30 years old.

(1)?? Pre-IPO studies

Pre-IPO studies, analyse private transactions before a company goes public and compare them with its market price post-IPO. Pre-IPO transactions take place for reasons such as sales to investors, grants to stakeholders, acquisitions, service-related grants, and debt exchanges.

Challenges in Pre- IPO studies

• Critics argue pre-IPO study discounts are overstated due to the lack of time value of money adjustments.
• No published analysis on time value of money and DLOM currently exists.
• Analysis indicates that as the time between transactions and IPO increases, the discount size also rises. ?

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(3)?? Flotation cost mode

The flotation cost model estimates a Discount for Lack of Marketability (DLOM) by factoring in the expenses of going public, covering both fixed (audit, legal, and printing) and variable (underwriter’s fees) costs. Mean discounts range from 6% to 17%.

Challenges to Flotation Cost model

• A critical consideration in assessing flotation costs revolves around the profile of companies incorporated in the models.
• The companies are notably larger and exhibit a higher probability of achieving success in the public arena.

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Academic Studies

There are three academic studies on marketability of discounts. Karen Wruck's "Equity Ownership Concentration and Firm Value, Michael Hertzel and Richard Smith's "Market Discounts and Shareholder Gains for Placing Private Equity, and Mukesh Bajaj, David Denis, Stephen Ferris, and Atulya Sarin's "Firm Value and Marketability Discounts" (private placement study).

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• In Wruck's study, the difference in price between unregistered and registered shares was used to determine a Discount for Lack of Marketability (DLOM). The study found a mean discount of 17.6%, and private transactions were associated with significant positive abnormal returns.
• Hertzel and Smith's study, focusing on private placements from 1980 to 1987, identified a mean discount of 20.1%. A multivariate regression was conducted to control for non-marketability factors, revealing a calculated difference of 13.5% between registered and unregistered shares.
• The private placement study, covering 88 transactions from 1990 to 1995, highlighted companies mainly trading over-the-counter with an average placement size of $13 million and an average market capitalization of $118 million. The study found a mean discount, though specific details are not provided. Marketability discounts in the studied transactions varied widely, ranging from -14.3% to 68%, with an average of 22.2%. Specifically, registered shares had a mean discount of 14%, while unregistered (restricted) shares had a higher mean discount of 28.1%.

The academic studies face two challenges.

• Firstly, the lack of publication of transaction data renders it unverifiable, hindering the ability to compare specific companies with those in the study.
• Secondly, the studies encounter wide range of discounts, raising concerns about the consistency and reliability of the findings.

The Mandelbaum Factors

• Emerging from the Bernard Mandelbaum v. Commissioner case are not derived from an observed study or model. The Tax Court employed nine factors to ascertain a Discount for Lack of Marketability (DLOM), now recognized as the Mandelbaum Factors.
• The Factors:

1.???? ?Financial statement analysis ?

2.???? Company’s dividend policy ?

3.???? The nature of the company, its history, its position in the industry, and its economic outlook ?

4.???? Company’s management ?

5.???? Amount of control in transferred shares ?

6.???? Restrictions on transferability of stock ?

7.???? Holding period for stock ?

8.???? Company’s redemption policy

9.???? Costs associated with making a public offering

Two issues arise with the Mandelbaum Factors.

• Factors like financial statement analysis, nature of the company, and management, often included in discount rate selection, may lead to potential double counting.
• Certain factors are subjective, lacking clear empirical ranges or directions for discounts, allowing for individual interpretation.

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Hope you enjoyed the read and built a solid foundation! Stay tuned for the sequel! But that's not all – in the upcoming part two, we'll take a deep dive into, unveiling the intricacies and hidden insights behind DLOM's Theoretical Models.?And here's the thrilling part – stay tuned for some hands-on action! In part two, we'll roll up our sleeves and explore real-life calculative examples using the theoretical models.

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