AbbottModelDLOM

Professional Judgment and Appropriate Usage

Professional Judgment

Market-based evidence and quantitative models support the valuation process, but they do not replace informed professional judgment.

AbbottModelDLOM provides an auditable framework for evaluating the marketability of a closely held interest using quarter-specific public-market evidence. The platform identifies a reference cohort, evaluates financial and operating performance, estimates liquidity and volatility measures, and develops model-based DLOM indications.

These outputs are analytical evidence rather than an automatic valuation conclusion. The analyst remains responsible for determining whether the evidence is relevant, how it should be interpreted, and how it supports the final opinion.

Role of Professional Judgment

Professional judgment enters the analysis at several stages, including:

  • Defining the subject interest and applicable valuation date
  • Selecting the relevant market and industry classification
  • Evaluating the comparability of the public-company reference cohort
  • Assessing the subject company’s financial and operating characteristics
  • Determining the applicable restriction and liquidation periods
  • Evaluating the information available to prospective buyers and sellers
  • Selecting the appropriate DLOM model or range of model indications
  • Considering Mandelbaum factors and other assignment-specific evidence
  • Identifying potentially overlapping adjustments
  • Reconciling the quantitative results with the facts of the assignment
  • Developing and documenting the final DLOM conclusion

The platform organizes the evidence and applies the stated calculations consistently. It does not determine the relative importance of every fact in a particular assignment.

Appropriate Use

AbbottModelDLOM is designed to assist valuation professionals and other qualified analysts in developing and documenting DLOM analyses for closely held interests.

Appropriate uses may include:

  • Developing market-based evidence for a DLOM analysis
  • Identifying a quarter-specific public-company reference cohort
  • Comparing a subject company with the selected cohort
  • Evaluating observed volatility and trading liquidity
  • Estimating a supported liquidation horizon
  • Evaluating alternative information conditions
  • Developing option-model DLOM indications
  • Organizing Mandelbaum-factor considerations
  • Testing the sensitivity of results to supportable assumptions
  • Documenting the relationship among inputs, calculations, and conclusions
  • Supporting an independently reasoned valuation opinion

The platform should be used as part of a broader valuation process that considers the subject interest, assignment purpose, applicable standard of value, valuation date, legal rights, transaction circumstances, and all other relevant information.

Evaluating the Reference Cohort

The platform forms a public-company reference cohort using the selected market, valuation quarter, industry classification, and locked size decile.

The resulting cohort provides a market-based frame of reference. It does not establish that every selected public company is directly comparable to the subject company in all respects.

The analyst should consider whether the cohort is relevant in light of factors such as:

  • Business activities
  • Industry exposure
  • Company size
  • Profitability
  • Growth characteristics
  • Financial condition
  • Capital structure
  • Operating risk
  • Geographic exposure
  • Stage of development
  • Public float
  • Trading characteristics
  • Extraordinary company or market events

Additive cohort expansion improves the number of observations when a narrowly defined industry cohort is insufficient. It may also introduce companies with broader operating characteristics. The analyst should evaluate whether that broader evidence remains informative for the subject interest.

A statistically sufficient cohort is not necessarily an economically persuasive cohort. Conversely, a cohort containing meaningful variation may still provide useful distributional evidence when the differences are recognized and explained.

Subject-Interest Considerations

Public-market evidence describes the selected reference companies. The final DLOM conclusion applies to a specific subject interest.

The analyst should evaluate characteristics of that interest that may not be captured fully by the public-company data, including:

  • Legal or contractual transfer restrictions
  • Expected duration of the restriction
  • Size of the ownership block
  • Voting and governance rights
  • Distribution rights
  • Redemption or repurchase provisions
  • Shareholder agreements
  • Rights of first refusal
  • Registration rights
  • Access to company information
  • Concentration of ownership
  • Expected holding period
  • Prospects for a negotiated sale
  • Availability of potential buyers
  • Anticipated liquidity events
  • Seller execution strategy

The relevance and effect of these characteristics depend on the facts of the assignment. They should not be converted automatically into standardized premiums or discounts.

Selecting the Liquidation Horizon

The estimated liquidation horizon is a model input, not a predetermined fact.

The platform provides market-based liquidity and block-liquidation evidence from the selected public-company cohort. The analyst should determine whether that evidence reasonably represents the expected disposition of the subject interest.

For a restricted interest, total delay may include:

  • The period before unrestricted disposition is legally or contractually permitted
  • The additional period required to liquidate the block after the restriction ends

For an unrestricted block, the relevant horizon may consist only of the estimated block-liquidation period.

The analyst should consider whether an orderly market sale is a reasonable disposition assumption or whether the facts suggest a negotiated transaction, issuer repurchase, strategic sale, staged disposition, or another liquidity path.

A longer estimated horizon generally increases exposure to price uncertainty and delayed access to cash. It does not, by itself, prescribe a particular discount.

Model Selection and Information Conditions

The appropriate model depends partly on the information conditions applicable to the transaction.

The selected reference companies are publicly traded securities. Their prices and trading activity therefore provide observable public-market evidence consistent with the public-information conditions underlying the Margrabe framework.

The Margrabe model may be relevant when public information is available to both parties but material private information remains asymmetrically held.

The Asian Average model reflects a progression toward fuller information symmetry when sufficient due diligence provides the buyer with relevant private information.

The analyst should consider:

  • Information available to the seller
  • Public information available to market participants
  • Private information available to the buyer
  • Scope and quality of due diligence
  • Reliability of management information
  • Cost of obtaining and validating additional information
  • Whether the transaction process reduces material information asymmetry

The costly due-diligence framework can help the analyst evaluate whether the economic benefit of improved information supports moving from the Margrabe condition toward the Asian Average condition.

Due-diligence cost is a model-selection consideration. It is not an additional discount to be added mechanically to the selected option-model result.

Displaying more than one model indication does not require the analyst to average them. The selected model, range, or weighting should be supported by the information conditions and facts of the assignment.

Use of Financial Performance Evidence

The performance analysis compares the subject company with the same quarter-specific public-company cohort used elsewhere in the platform.

Profitability, financial condition, capital structure, and operating performance may affect investor perceptions and the marketability of the subject interest. These measures provide context for professional judgment.

They do not produce an automatic increase or decrease in DLOM.

The analyst should consider whether an observed difference:

  • Reflects a persistent operating characteristic
  • Results from temporary or cyclical conditions
  • Is typical for the industry
  • Reflects financial distress
  • Results from investment in future growth
  • Is already incorporated into the subject-company valuation
  • Affects marketability independently of enterprise value

A factor already reflected in the underlying valuation should not be counted again automatically through DLOM.

Use of Mandelbaum Factors

The Mandelbaum analysis provides a structured framework for considering qualitative and quantitative factors associated with marketability.

The factors should be evaluated in relation to the subject interest and the supporting evidence. They should not be treated as independent numerical discounts or combined through an unsupported scoring formula.

The Mandelbaum analysis may assist the analyst in:

  • Identifying relevant marketability considerations
  • Comparing the subject company with public-market evidence
  • Explaining the direction of professional judgment
  • Evaluating whether the model indications are reasonable
  • Documenting assignment-specific considerations

The factors inform the conclusion. They do not replace the option-model analysis or prescribe a particular percentage.

Avoiding Mechanical Adjustments

A DLOM conclusion should not be constructed by adding every unfavorable observation as a separate discount.

Several platform measures describe connected aspects of the same underlying economic process. For example:

  • Lambda, the volume-weighted average liquidation period, and the estimated block-liquidation period describe related liquidity evidence.
  • Liquidity evidence may already be incorporated through the liquidation horizon used in the DLOM model.
  • Volatility is already a direct option-model input.
  • Financial distress may already affect the underlying enterprise or equity value.
  • Information asymmetry informs model selection.
  • Due diligence may reduce information asymmetry rather than create a separate discount.
  • Mandelbaum factors may explain or contextualize the model result without constituting additive adjustments.

The analyst should identify how each factor enters the analysis and confirm that the same economic effect has not been counted more than once.

Interpretation of Model Results

Model-generated DLOM indications are conditional estimates. They depend on the selected inputs, measurement conventions, information assumptions, and model structure.

The analyst should evaluate:

  • Quality and completeness of the underlying data
  • Number of valid cohort observations
  • Dispersion within the cohort
  • Relevance of the cohort to the subject company
  • Reliability of the volatility estimate
  • Support for the restriction and liquidation periods
  • Applicability of the model’s information conditions
  • Sensitivity of the result to reasonable alternative assumptions
  • Consistency with other assignment evidence

A cohort median is not automatically the correct subject-company input. A model result is not automatically the final DLOM. The final conclusion may be a point estimate or a range, depending on the evidence and requirements of the assignment.

Any departure from the central cohort evidence should be supported and documented.

Data and Model Limitations

Public-market data are historical observations. They may be affected by limited trading, unusual market events, corporate actions, reporting differences, missing information, or data-provider revisions.

Historical trading evidence does not guarantee:

  • The timing of an actual sale
  • The volume available at quoted prices
  • The execution price for a particular block
  • The availability of a specific buyer
  • The success of a negotiated transaction
  • Future volatility or liquidity
  • The occurrence of a future liquidity event

Models necessarily simplify economic conditions. Their results should be interpreted as structured estimates rather than transaction guarantees.

The analyst should investigate unusual observations, material data limitations, and results that appear inconsistent with the subject interest or known market conditions.

Documentation of the Conclusion

An auditable DLOM analysis should distinguish among:

  • Observed market data
  • Platform-derived measures
  • User-supplied assumptions
  • Model-generated indications
  • Analyst-selected inputs
  • Qualitative considerations
  • Professional-judgment adjustments
  • Final conclusion

The valuation report should explain, as applicable:

  • Why the selected cohort is relevant
  • How the restriction and liquidation periods were determined
  • Which volatility evidence was used
  • Which model was selected and why
  • How information asymmetry and due diligence were considered
  • How financial performance and Mandelbaum factors informed the analysis
  • Whether alternative assumptions were evaluated
  • How potential double counting was avoided
  • How the final DLOM was reconciled with the supporting evidence

Documentation should be sufficient for another qualified reviewer to trace the material inputs, calculations, assumptions, and judgments supporting the conclusion.

Inappropriate Use

AbbottModelDLOM should not be used:

  • As a substitute for a complete valuation analysis
  • To produce a DLOM without evaluating the subject interest
  • To accept a cohort median or model result mechanically
  • To combine related liquidity measures as separate discounts
  • To add Mandelbaum factors as unsupported numerical adjustments
  • To treat due-diligence cost as an additional DLOM item
  • To assume that historical liquidity guarantees an actual sale timetable
  • To apply evidence from one valuation quarter without considering the applicable valuation date
  • To rely on unavailable information as though it were an observed zero
  • To support a conclusion that the analyst cannot independently explain and defend
  • To provide legal, tax, accounting, or investment advice

The platform should not be used beyond the user’s professional competence or without considering the applicable valuation standards, professional requirements, and assignment conditions.

Analyst Responsibility

AbbottModelDLOM supports a disciplined, market-informed, transparent, and auditable valuation process.

The platform provides the analytical structure. The analyst provides the assignment-specific interpretation.

Responsibility for the selection of inputs, evaluation of comparability, choice of model, consideration of relevant facts, avoidance of double counting, and final DLOM conclusion remains with the valuation professional.Additional Valuation Applications

Although DLOM analysis is the platform’s principal application, its quarter-specific public-company evidence can support other aspects of a valuation assignment.

Potential applications include:

Public-Company Benchmarking

The selected reference cohort can provide a market-based context for comparing the subject company’s:

  • Size
  • Revenue
  • Profitability
  • Capital structure
  • Financial condition
  • Operating performance
  • Market-value and enterprise-value relationships

These comparisons can assist with company analysis and the evaluation of potential guideline public companies. They do not, by themselves, constitute a complete guideline public-company valuation or establish the appropriate valuation multiple.

Relative Financial and Operating Assessment

Peer medians, quartiles, valid observation counts, and the subject company’s position within the cohort can help identify:

  • Relative financial strengths and weaknesses
  • Unusual operating results
  • Potential financial distress
  • Differences in leverage or liquidity
  • Measures requiring further investigation
  • Areas in which the subject company differs materially from public-market references

The analysis may assist the valuation practitioner in developing financial projections, evaluating risk, and explaining the subject company’s relative position. Any effect on value must be analyzed separately and should not be counted again through DLOM.

Market-Liquidity and Blockage Analysis

Liquidity, trading-volume, and estimated liquidation-period evidence may assist in evaluating:

  • The orderly disposition of a large block of publicly traded securities
  • Potential blockage considerations
  • Expected market absorption
  • Staged-disposition assumptions
  • The difference between an immediate sale and an orderly liquidation strategy
  • Whether quoted market prices are representative for the subject block

For an unrestricted publicly traded block, the relevant horizon generally consists of the estimated block-liquidation period. A legal or contractual restriction should be included only when it applies to the subject interest.

Holding-Period and Liquidity-Horizon Assessment

The platform’s restriction-period and liquidation-period framework can help practitioners distinguish among:

  • A legally required holding period
  • A contractually required holding period
  • A post-restriction liquidation period
  • An expected economic holding period
  • The time required for an orderly market disposition

This distinction may be useful whenever the timing of access to liquidity affects valuation, damages, transaction analysis, or the assessment of an investment interest.

Risk and Volatility Benchmarking

Quarter-specific volatility evidence can provide context for:

  • Evaluating the subject company’s operating and market risk
  • Comparing risk across public-company cohorts
  • Assessing the reasonableness of volatility assumptions used in option-based models
  • Evaluating changes in market conditions across valuation dates
  • Supporting sensitivity and scenario analyses
  • Identifying unusual dispersion within a selected industry or size group

Volatility should be used consistently with the theory and measurement conventions of the applicable model. It should not be converted automatically into a separate valuation adjustment.

Information-Asymmetry and Due-Diligence Assessment

The progression from the Margrabe information condition toward the Asian Average condition can help practitioners evaluate:

  • The information available to prospective buyers and sellers
  • The potential value of expanded due diligence
  • Whether private information remains asymmetrically held
  • Whether a transaction process has reduced material information uncertainty
  • Whether the selected DLOM model is consistent with the expected information environment
  • Whether the cost of additional due diligence is economically supportable

This framework may also assist in planning the scope of due diligence or explaining why different transaction processes could produce different marketability indications.

Sensitivity and Scenario Analysis

The platform can assist practitioners in evaluating how analytical results respond to supportable changes in:

  • Volatility
  • Restriction period
  • Block-liquidation period
  • Total delay
  • Information conditions
  • Reference-cohort selection
  • Valuation quarter
  • Subject-company assumptions

Sensitivity analysis can identify which assumptions materially influence the result and whether the final conclusion remains reasonable across a supportable range.

A sensitivity case is not an alternative fact. Each scenario should be identified clearly and supported according to its intended analytical purpose.

Valuation Review and Reasonableness Testing

The platform’s traceable workflow may assist a reviewer in determining whether another analysis:

  • Uses a relevant valuation date
  • Employs a supportable reference cohort
  • Applies consistent time units
  • Uses an appropriate volatility convention
  • Distinguishes restriction from liquidation
  • Reconciles model inputs with supporting evidence
  • Applies an appropriate information condition
  • Avoids overlapping adjustments
  • Explains departures from market-based evidence
  • Provides sufficient documentation for the conclusion

The platform can support an independent reasonableness assessment. It does not provide an audit, certification, or assurance opinion concerning another valuation.

Litigation and Expert-Support Applications

The platform’s auditable analytical structure may assist in assignments involving:

  • Shareholder disputes
  • Marital-dissolution valuation
  • Estate and gift-tax valuation
  • Damages analysis
  • Fair-value and fair-market-value disputes
  • Challenges to the marketability or blockage assumptions used in another report
  • Preparation of demonstrative or supporting analytical exhibits

The practitioner remains responsible for applying the relevant legal standard, jurisdictional requirements, evidentiary rules, and professional valuation standards.

Financial Reporting and Transaction Support

Where appropriate, the platform’s evidence may provide supporting context for:

  • Fair-value measurement
  • Equity-compensation analysis
  • Portfolio valuation
  • Transaction planning
  • Shareholder redemptions
  • Buy-sell arrangements
  • Internal valuation review
  • Evaluation of a proposed liquidity event

The platform does not determine the applicable accounting treatment, standard of value, premise of value, or transaction terms.

Research, Education, and Methodology Testing

The quarter-specific data and consistent analytical framework may also support:

  • Valuation-methodology research
  • Comparison of model behavior
  • Analysis of liquidity and volatility across markets or periods
  • Professional education and training
  • Development of case studies
  • Testing of alternative assumptions
  • Evaluation of changes in market conditions over time

Research or educational results should not be applied directly to a valuation assignment without evaluating their relevance to the subject company, interest, and valuation date.

Limits on Broader Use

These additional applications use selected components of the platform as supporting evidence. They do not transform every platform output into a valuation adjustment or replace the procedures required for a complete assignment.

For each use, the practitioner should identify:

  • The question being addressed
  • The platform evidence relevant to that question
  • The limitations of the evidence
  • Any additional information required
  • The professional reasoning connecting the evidence to the conclusion

The same evidence should not be used more than once to support overlapping adjustments. The practitioner remains responsible for ensuring that the application is consistent with the assignment’s purpose, standard of value, valuation date, applicable professional standards, and governing legal or accounting requirements.