Market-based evidence for performance evaluation and defensible DLOM analysis.
AbbottModelDLOM is an analytical decision-support platform for valuation professionals. It connects subject-company information with quarter-specific public-market evidence.
The platform forms an identified public-company reference cohort. It then uses that same cohort throughout performance evaluation, liquidity analysis, volatility measurement, DLOM and blockage analysis, and Mandelbaum review.
This connected structure creates an auditable and reproducible path from the user’s inputs to the resulting analytical evidence.
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More Than a Stand-Alone Calculator
A stand-alone DLOM calculator applies user-selected inputs to a formula. AbbottModelDLOM provides the market evidence needed to evaluate and support those inputs.
The platform helps the analyst answer several connected questions:
- Which public companies provide the most relevant market evidence?
- How does the subject company compare with those companies?
- What do their trading patterns indicate about liquidity?
- How long might an orderly liquidation require?
- What volatility is observed for the selected valuation quarter?
- Which information conditions apply to the transaction?
- What DLOM indications result from the supported inputs?
- How do financial performance and Mandelbaum considerations inform the conclusion?
- Has the same economic effect been counted more than once?
The platform does not answer these questions through a single mechanical score. It organizes the evidence so the analyst can develop and explain a supportable conclusion.
One Cohort Used Throughout the Analysis
The user selects the relevant public market, valuation quarter, industry classification, and subject-company size information.
The platform assigns and locks the applicable size decile. It then forms a quarter-specific public-company reference cohort.
Cohort formation begins with the requested industry. If the minimum cohort size is not met, the platform adds incremental companies from the related industry group and then the sector. Companies already selected at a more specific classification level remain in the cohort.
A market-value subgroup refinement may be applied when the initial cohort is sufficiently large and the refined group remains adequately populated.
The cohort is identified once. The same companies are then used for:
- Reference-cohort display
- Financial and operating comparison
- Liquidity and block-liquidation evidence
- Volatility measurement
- DLOM and blockage analysis
- Mandelbaum review
This prevents one module from using a different peer group without explanation. It also allows the analyst or reviewer to reconcile the results with the companies that produced them.
Quarter-Specific Public-Market Evidence
Financial condition, trading activity, volatility, market value, and investor participation change over time. AbbottModelDLOM therefore aligns the analysis with the selected valuation quarter.
The validated historical baseline currently covers:
- U.S. exchange-traded companies
- U.S. OTC-traded companies
- Canadian public companies
- United Kingdom public companies
The four market datasets contain 469,810 issuer-quarter observations through 2025Q4. The underlying daily market files contain more than 34.5 million assigned security-date observations.
Each market is maintained as a separate analytical population. Companies from another country or market are not introduced automatically to increase the selected cohort size.
Detailed market descriptions, periods, firm counts, and observation counts appear on the Data Coverage page.
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Connected Analytical Workflow
1. Define the Assignment Context
The user identifies the relevant valuation conditions.
Inputs may include:
- Valuation date
- Selected public market
- Industry and sector classification
- Subject-company size information
- Ownership percentage or block size
- Legal or contractual restriction period
- Available financial information
- Expected disposition circumstances
- Information and due-diligence conditions
The analyst remains responsible for determining whether each input is supportable for the assignment.
2. Form the Reference Cohort
The platform applies the quarter-specific market, classification, size, and cohort-formation rules.
The resulting display identifies:
- Requested industry
- Related industry group and sector
- Locked size decile
- Cohort-formation method
- Minimum cohort-size criterion
- Accepted classification level
- Selected cohort count
- Market-value subgroup treatment
- Individual companies included in the cohort
These elements provide an audit trail for the cohort-selection decision.
3. Evaluate Financial and Operating Performance
The user enters the available subject-company financial information. The platform compares those measures with corresponding statistics for the selected public-company cohort.
Available comparisons may include:
- Revenue
- EBITDA
- Net income
- Net margin
- Total assets
- Total liabilities
- Total debt
- Book value of equity
- Working capital
- Return on assets
- Return on equity
- Leverage measures
- Financial-condition measures
- Other available operating indicators
The platform reports peer medians, quartiles, and valid observation counts. It may also identify the subject company’s position within the peer distribution.
Financial performance provides context for the analysis. It does not create an automatic increase or decrease in DLOM.
4. Evaluate Liquidity and Liquidation Time
Daily price, volume, and share information provide market-based evidence about trading liquidity and the potential time required to dispose of an ownership block.
The platform evaluates connected measures that may include:
- Share turnover
- Lambda
- Half-life
- Volume-weighted average liquidation period
- Estimated block-liquidation period
- Legal or contractual restriction period
- Total delay
Total delay recognizes that full liquidity may require two phases:
Total delay = restriction period + post-restriction block-liquidation period
The restriction period applies before unrestricted disposition is permitted. The block-liquidation period estimates the additional time required for an orderly disposition after that restriction ends.
For blockage analysis of an unrestricted public block, the relevant horizon is the block-liquidation period rather than total delay.
Lambda, liquidation-period evidence, and the estimated block-liquidation period describe connected aspects of the same liquidity process. They should not be treated as independent discounts.
5. Measure Quarter-Specific Volatility
The platform estimates volatility from daily continuously compounded, or logarithmic, returns.
The applicable lookback period is 365 calendar days, ordinarily representing approximately 252 trading days. Daily volatility is annualized using the square root of 252.
The platform reports the available cohort distribution and valid observation count. The analyst can evaluate the median, quartiles, dispersion, and company-level evidence.
Volatility is a direct option-model input. It should not be converted into an additional separate adjustment after it has entered the model.
6. Develop DLOM Model Indications
The supported volatility and delay inputs enter the applicable exchange-option models.
The platform focuses on the progression between two information conditions:
- Margrabe reflects a setting in which public information is available to both parties while relevant private information may remain asymmetrically held.
- Asian Average reflects movement toward fuller information symmetry when due diligence provides the buyer with relevant private information.
The selected public-company references are publicly traded securities. Their prices and trading history provide the observable public-market conditions required for the exchange-option analysis.
The costly due-diligence framework helps the analyst evaluate whether the economic benefit of improved information supports movement 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 to the option-model result.
Displaying multiple model indications does not require the analyst to average them mechanically. Model selection should reflect the information conditions and facts of the assignment.
7. Organize Mandelbaum Considerations
The Mandelbaum analysis helps organize the qualitative and quantitative factors that may affect marketability.
The subject company is evaluated relative to the same identified reference cohort used elsewhere in the platform.
Mandelbaum considerations may help the analyst:
- Identify relevant marketability factors
- Evaluate differences between the subject company and public references
- Explain the direction of professional judgment
- Assess the reasonableness of model indications
- Document assignment-specific circumstances
The factors are not independent numerical discounts. They should not be added mechanically to the option-model result.
8. Reconcile the Evidence
The final step is not a platform-generated percentage. It is the analyst’s reconciliation of the evidence.
The analyst should consider:
- Relevance of the selected cohort
- Quality and completeness of available observations
- Subject-company financial and operating differences
- Supported restriction and liquidation periods
- Reliability of the volatility evidence
- Applicable information conditions
- Sensitivity to alternative assumptions
- Potential overlapping adjustments
- Company-specific and transaction-specific facts
- Applicable professional standards
The resulting DLOM conclusion may be a point estimate or a range. It should be documented and independently supportable.
Auditable Analytical Evidence
AbbottModelDLOM distinguishes among:
- Observed public-market data
- Platform-derived measures
- User-supplied assumptions
- Model-generated indications
- Analyst-selected interpretations
- Final professional judgment
The platform reports the selected cohort, formation basis, member companies, valid observation counts, distributional statistics, model inputs, and model results.
This structure allows a qualified reviewer to trace the principal analytical path. The reviewer can identify where the evidence originated, how the measures were calculated, and where professional judgment entered the conclusion.
Auditability does not mean that AbbottModelDLOM provides an independent audit or assurance opinion. It means that the material inputs, calculations, and analytical decisions can be examined and reconciled.
Valid Counts and Missing Information
Information availability varies by company, market, quarter, and measure.
The platform distinguishes missing information from an observed zero. It does not automatically substitute zero for an unavailable amount.
A company may remain in the selected reference cohort while lacking the data required for a particular calculation. Valid observation counts may therefore differ across financial, liquidity, and volatility measures.
A change in the valid count does not mean that the selected cohort has changed. It identifies how many cohort companies support the applicable statistic.
Avoiding Double Counting
Connected measures should not be converted into multiple adjustments for the same economic effect.
Examples include:
- Liquidity evidence used to estimate the liquidation horizon
- Lambda and liquidation-period measures describing the same trading process
- Volatility already incorporated into an option model
- Financial risk already reflected in the underlying company valuation
- Information asymmetry used to select the applicable model
- Due diligence used to evaluate information conditions
- Mandelbaum factors used to interpret the model result
The analyst should identify how each consideration enters the analysis. The same economic effect should not be counted more than once.
Platform and Calculator Access
AbbottModelDLOM uses one account and authentication process.
New users may request trial access. Existing users sign in with their registered email address and password.
The functions displayed after sign-in depend on the products authorized for the account. An account may provide access to:
- The DLOM Calculator
- The Analytical Platform
- Both products
Separate accounts or passwords are not required for calculator and platform access.
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Appropriate Use
AbbottModelDLOM is designed to support valuation professionals and other qualified analysts.
It may assist with:
- DLOM analysis
- Blockage analysis
- Public-company benchmarking
- Financial and operating comparison
- Liquidity and liquidation-horizon assessment
- Volatility benchmarking
- Information-asymmetry evaluation
- Due-diligence analysis
- Sensitivity and scenario testing
- Valuation review and reasonableness testing
- Research and professional education
The platform does not replace a complete valuation analysis. It does not determine the applicable standard of value, premise of value, legal rights, accounting treatment, or final valuation conclusion.
Users remain responsible for applying the appropriate valuation standards and assignment requirements.
Professional Judgment Remains Essential
AbbottModelDLOM provides the analytical structure. The valuation professional provides the assignment-specific interpretation.
The analyst remains responsible for:
- Selecting supportable inputs
- Evaluating cohort relevance
- Choosing the applicable model
- Considering legal and transaction facts
- Avoiding double counting
- Interpreting the model indications
- Documenting the analysis
- Developing the final conclusion
The platform supports an auditable, reproducible, and explainable process. It helps the analyst develop a conclusion that is independently defensible.
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