AbbottModelDLOM

Analytical Transparency

A transparent, traceable, and auditable path from the selected valuation inputs to the resulting marketability evidence.

AbbottModelDLOM is designed to make the analytical path from the selected valuation inputs to the resulting DLOM evidence transparent, traceable, and auditable.

An auditable analysis allows the user or reviewer to identify the relevant data, follow the formation of the reference cohort, verify the measurement conventions, reconcile the model inputs with the supporting evidence, and distinguish calculated results from professional judgment.

In this context, “auditable” describes the traceability and reproducibility of the analytical process. It does not mean that the platform provides an independent audit or assurance opinion.

Auditable Analytical Workflow

The analysis follows a consistent sequence:

  1. The user selects the applicable market, valuation quarter, industry classification, and subject-company size information.
  2. The platform assigns and locks the applicable size decile.
  3. A quarter-specific public-company reference cohort is formed using the additive closest-match expansion methodology.
  4. The same identified cohort is used for performance evaluation, liquidity and block-liquidation analysis, volatility measurement, DLOM modeling, and Mandelbaum analysis.
  5. Cohort statistics, model inputs, and model results are presented for evaluation by the analyst.

This sequence creates an analytical audit trail from the original selection criteria through the intermediate market evidence to the resulting model indications.

Reference-Cohort Audit Trail

The platform reports information describing how the public-company reference cohort was formed, including, as applicable:

  • Selected market
  • Valuation quarter
  • Requested industry classification
  • Locked size decile
  • Cohort-formation method
  • Minimum cohort-size criterion
  • Accepted classification level
  • Selected cohort count
  • Market-value subgroup refinement
  • Individual public companies included in the cohort

Cohort expansion is additive. Companies retained at a more specific classification level remain in the cohort when incremental companies are added from the related industry group or sector.

These reported elements allow a reviewer to determine why particular public companies were included and to reconcile the selected cohort with the applicable formation rules.

The cohort is identified once and is not independently reconstructed for different analytical modules. The same selected companies therefore support the performance, liquidity, volatility, DLOM, Mandelbaum, and cohort-display results.

Maintaining a single cohort across the analysis prevents differences in reported results from being caused by undocumented changes in the underlying reference companies.

Source and Calculation Traceability

The platform distinguishes information obtained from the underlying public-company data from measures calculated from that information.

Observed information may include:

  • Market prices
  • Trading volume
  • Shares outstanding
  • Financial-statement amounts
  • Industry classifications
  • Valuation-quarter identifiers
  • Other available market and operating information

Derived measures may include:

  • Market value of equity
  • Financial ratios
  • Daily logarithmic returns
  • Annualized volatility
  • Lambda
  • Volume-weighted average liquidation period
  • Estimated block-liquidation period
  • Total delay
  • Peer quartiles and medians
  • DLOM model indications
  • Mandelbaum factor comparisons

This distinction allows the reviewer to identify which values originate in the underlying data and which result from the platform’s calculations.

A derived measure is not a separate source of evidence merely because it appears as an additional output. Related measures must be interpreted according to their common data source and underlying economic relationship.

Auditable Measurement Conventions

Time units, return conventions, lookback periods, and annualization assumptions must be identified because inconsistent measurement can materially affect a model result.

Volatility is estimated from daily continuously compounded, or logarithmic, returns over the applicable 365-calendar-day lookback period, ordinarily representing approximately 252 trading days. Daily volatility is annualized using the square root of 252.

Liquidity and liquidation periods are converted to consistent time units before they are combined or entered into a DLOM model.

Total delay is defined as:

Total delay = restriction period + post-restriction block-liquidation period

The restriction period represents the time during which disposition is legally or contractually constrained. The block-liquidation period represents the estimated additional time required to dispose of the interest after that restriction ends.

For blockage analysis addressing only the orderly disposition of an unrestricted block, the applicable horizon is the post-restriction block-liquidation period rather than total delay.

Stating these conventions allows a reviewer to reconcile the time horizon and volatility used in each model with the supporting calculations.

Auditable Distributional Evidence

A single cohort average can conceal important differences among the selected public companies. AbbottModelDLOM therefore presents distributional evidence that can be reconciled with the valid cohort observations.

Depending on the measure, the platform may report:

  • First quartile
  • Median
  • Third quartile
  • Valid observation count
  • Subject-company position within the peer distribution
  • Company-level supporting observations

The median generally provides the central cohort indication, while the quartiles show the range containing the middle half of the valid observations.

The valid observation count identifies how many selected cohort companies support a particular statistic. This allows the reviewer to distinguish a result supported by most of the cohort from one based on a smaller number of available observations.

Differences in valid counts across measures do not mean that the reference cohort has changed. A company may remain a member of the selected cohort while lacking the information required for a particular calculation.

Missing Information and Zero Values

Unavailable information is treated differently from an observed zero.

A zero may be an economically meaningful reported amount. Missing information means that the required observation is unavailable. The platform does not automatically replace missing values with zero.

A ratio or derived measure is unavailable when:

  • A required numerator is missing
  • A required denominator is missing
  • The denominator is zero
  • The calculation is otherwise mathematically invalid

Summary statistics are calculated from the valid observations available for the applicable measure.

Reporting the valid observation count provides an auditable basis for understanding why the number of observations may differ across measures without implying that the selected cohort itself has changed.

Model-Calculation Transparency

The DLOM Models page identifies the equations, assumptions, and principal inputs used for the applicable option-based models. Model outputs can therefore be traced to the reported volatility, delay, and other required inputs.

The models represent different information conditions:

  • The Margrabe framework reflects publicly traded securities for which public information is available to both parties while relevant private information may remain asymmetrically held.
  • The Asian Average framework reflects the progression toward fuller information symmetry when due diligence provides the buyer with relevant private information.

The costly due-diligence framework assists the analyst in evaluating which information condition and model may be applicable. It is not presented as an additional discount to be added to an option-model result.

Displaying multiple model indications does not imply that the results should be averaged mechanically. The analyst should document the model selection or weighting according to the information conditions, transaction structure, and facts of the assignment.

An auditable conclusion should permit a reviewer to identify both the quantitative model result and the professional reasoning used to select or interpret it.

Auditing for Double Counting

Analytical transparency requires identifying when multiple outputs describe the same underlying economic effect.

Lambda, the volume-weighted average liquidation period, and the estimated block-liquidation period are connected measures of liquidity and the disposition process. They should not be added as separate premiums.

Similarly, when liquidity evidence has already been used to estimate the liquidation horizon entering a DLOM model, adding another adjustment for the same liquidity limitation may count the effect twice.

Financial performance, Mandelbaum factors, information asymmetry, and costly due diligence may inform model selection and professional judgment. They do not automatically create separate additive discounts.

A reviewer should be able to reconcile each material consideration with its role in the analysis and determine whether it:

  • Provides an underlying data input
  • Supports an estimated model horizon
  • Informs model selection
  • Assists in interpreting the resulting range
  • Supports a documented professional-judgment conclusion

This reconciliation helps prevent the same economic factor from appearing more than once in the final DLOM conclusion.

Quarter-Specific Auditability

Market conditions, trading activity, volatility, public float, and company fundamentals can change over time. The analytical evidence is therefore associated with the selected valuation quarter.

A result from one quarter should not be assumed to apply unchanged to another valuation date.

Reproducing or reviewing an analysis requires identification of:

  • Selected market
  • Valuation quarter
  • Subject-company inputs
  • Requested classification
  • Locked size decile
  • Cohort-formation result
  • Selected cohort members
  • Valid observations for each measure
  • Applicable measurement conventions
  • Restriction and liquidation assumptions
  • Model inputs
  • Model-selection rationale

Historical market evidence provides an auditable analytical basis for the estimates. It does not guarantee the price, timing, or execution of an actual transaction.

Reconciliation Checks

A reviewer should be able to perform the following principal checks:

  • The selected cohort agrees across all analytical modules.
  • The reported cohort count agrees with the displayed member companies.
  • The cohort was formed using the reported industry, size, and additive-expansion rules.
  • Measure-specific valid counts reflect available observations rather than a change in cohort membership.
  • Derived financial ratios reconcile with the available underlying amounts.
  • Volatility uses the stated daily logarithmic-return, lookback-period, and annualization conventions.
  • Restriction and liquidation periods use consistent time units.
  • Total delay includes both the restriction and post-restriction liquidation components only when both are applicable.
  • Blockage analysis excludes the restriction period when measuring only post-restriction disposition.
  • DLOM model inputs agree with the supporting cohort evidence and assignment assumptions.
  • Model results reconcile with the equations and reported inputs.
  • Related liquidity measures are not treated as independent adjustments.
  • Professional-judgment considerations are distinguished from calculated model outputs.

These checks help identify whether a difference arises from the underlying data, cohort formation, measurement conventions, model calculations, or professional interpretation.

Documentation of Professional Judgment

Auditability does not eliminate the need for professional judgment. It requires that material judgments be identified and explained.

The platform provides market-based evidence, consistent calculations, and an organized analytical framework. The analyst remains responsible for determining:

  • Whether the selected public-company cohort is relevant
  • Whether the subject interest differs materially from the public-company references
  • Whether the estimated restriction and liquidation periods are supportable
  • Which information conditions best describe the assignment
  • Which model or range of model indications is most appropriate
  • Whether company-specific, contractual, legal, or transaction facts support an alternative interpretation
  • Whether the analysis contains overlapping or duplicative adjustments
  • How the market evidence and qualitative considerations support the final conclusion

A well-documented conclusion should distinguish:

  • Observed market evidence
  • Platform-derived measures
  • User-supplied assumptions
  • Model-generated indications
  • Analyst-selected interpretations
  • Final professional judgment

AbbottModelDLOM supports an auditable and reviewable valuation process by preserving a traceable relationship among the selected inputs, reference cohort, supporting evidence, calculations, and model results.

The platform does not replace the analyst’s responsibility to develop, document, and explain a supportable DLOM conclusion.