Pricing the Unpriced Asset: A Standards-Based Method for Valuing Enterprise Data under IAS 38 and IAS 2

Adam Myer; Conor Blycha; Dean Arden; James Myint; Michael Small; Natasha E. Blycha; Rachael Johnson; Ryan Feng; Schellie-Jayne Price; Steve Bailey

arxiv: 2606.22760 · v1 · pith:7HG2FWQM · submitted 2026-06-22 · cs.CE

Pricing the Unpriced Asset: A Standards-Based Method for Valuing Enterprise Data under IAS 38 and IAS 2

Natasha E. Blycha , James Myint , Dean Arden , Michael Small , Conor Blycha , Schellie-Jayne Price , Steve Bailey , Ryan Feng

show 2 more authors

Rachael Johnson Adam Myer

This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-26 06:39 UTCgrok-4.3pith:7HG2FWQM record.json open to challenge →

classification cs.CE

keywords data assetsIAS 38intangible assetsvaluation methodenterprise dataaccounting standardsD-ValA-Val

0 comments

The pith

Authenticated data assets meeting IAS 38 criteria receive a two-layer valuation of cost-based D-Val followed by commercial A-Val.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Data assets often fail to qualify for recognition under IAS 38 because reliable measurement of future benefits is difficult. The paper sets out a two-layer progression that applies once legal provenance and contractual boundaries are established. D-Val records auditable production costs multiplied by a time factor that stays at or below one without an active market. A-Val then layers in scarcity, rivalry, completeness, accuracy, and premia for provenance and audit to supply a commercial estimate. The method is presented as a bridge until markets for authenticated data mature and allow iterative refinement.

Core claim

For authenticated data assets that satisfy IAS 38 recognition through established legal provenance and contractual boundaries, valuation occurs in two layers. D-Val equals reliably measurable production cost multiplied by an appreciation or depreciation factor over time and remains a cost-less-amortisation figure under current IAS 38 interpretations. A-Val adds explicit adjustments for scarcity, rivalry, completeness, accuracy, and premia for provenance authentication and independent audit to produce a theoretically grounded commercial valuation that is not yet auditable as fair value but serves as a defensible commercial figure before active markets exist.

What carries the argument

The two-layer valuation progression: D-Val (auditable cost basis) and A-Val (commercial factors for scarcity, rivalry, completeness, accuracy, provenance, and audit).

If this is right

Data assets that meet IAS 38 criteria become measurable at D-Val for financial reporting.
A-Val supplies a commercial benchmark usable in transactions while markets remain immature.
Parameter assumptions for A-Val improve as authenticated data markets develop, enabling model refinement.
Mispricing and allocative distortions in data and AI markets decrease as valuation becomes more reliable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Firms could adopt A-Val internally to guide data acquisition and retention decisions even if it is not recorded on balance sheets.
The approach may create initial pricing signals that help data marketplaces form by reducing information asymmetry.
Similar two-layer structures could be tested for other intangible assets that lack liquid markets, such as proprietary algorithms.

Load-bearing premise

The additional factors in A-Val can be determined in a defensible, non-arbitrary manner before active markets exist.

What would settle it

Consistent inability of independent valuers to produce materially similar A-Val figures for the same authenticated dataset without active market benchmarks.

Figures

Figures reproduced from arXiv: 2606.22760 by Adam Myer, Conor Blycha, Dean Arden, James Myint, Michael Small, Natasha E. Blycha, Rachael Johnson, Ryan Feng, Schellie-Jayne Price, Steve Bailey.

read the original abstract

The recognition and measurement of data assets under current accounting standards presents significant challenges. While International Accounting Standard 38 (IAS 38) provides a framework for intangible asset recognition, data assets frequently fail to meet capitalisation criteria due to difficulties in demonstrating separability, establishing reliable cost measurement, and proving probable future economic benefits. The widespread failure to easily and reliably value data causes mispricing and allocative distortions across data and artificial intelligence markets. This paper introduces a two-layer valuation progression for authenticated data assets, that is, datasets that have met IAS 38 recognition criteria through established legal provenance and contractual boundaries. The first layer, D-Val, is the auditable cost-basis valuation consistent with IAS 38. D-Val is defined as D-Val = Cp * Avt, where Cp is the reliably measurable production cost and Avt is the appreciation or depreciation factor applied over time. Under prevailing interpretations of IAS 38, Av is constrained to values less than or equal to one absent an active market revaluation, rendering D-Val a strictly cost-less-amortisation figure. The second layer, A-Val, is a theoretically grounded commercial valuation that incorporates scarcity, rivalry, completeness, accuracy, and explicit premia for provenance authentication and independent audit. A-Val is not auditable as fair value under current practice but serves as a defensible commercial valuation during the period before active markets for authenticated data assets mature. As authenticated data markets mature parameter assumptions improve providing a foundation for iterative refinement of the model.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper names D-Val and A-Val layers for data assets under IAS 38 but gives no measurement rules or examples for the A-Val adjustments.

read the letter

The main point to know is that this is a conceptual proposal that splits data valuation into a cost-based D-Val and a commercial A-Val, but the commercial layer has no workable procedure attached to it.

D-Val is defined as Cp times Avt with the usual IAS 38 constraint that Avt stays at or below one without an active market. That part is straightforward and stays inside existing rules. The paper also correctly flags the recognition problems data assets face around separability, cost measurement, and probable benefits.

The A-Val part lists scarcity, rivalry, completeness, accuracy, and premia for provenance and audit as the adjustments that turn the cost figure into something closer to commercial value. The text stops there. No equations, no observable proxies, no worked numerical example, and no discussion of how an accountant would set those values before markets exist. The stress-test note is accurate on this: the factors function as free parameters.

Because the full manuscript is not reproduced here, I cannot check whether later sections add the missing steps. On the supplied abstract and stress-test, the central claim that A-Val supplies a defensible bridge valuation does not yet have supporting machinery.

This is aimed at accounting teams in data-heavy companies or people who follow IAS 38 interpretations. A reader who wants an immediately applicable method will not find it. The work shows clear engagement with the standards but does not yet deliver a falsifiable or reproducible procedure.

Recommendation: desk reject in its current form; it would need explicit quantification rules or case data before a referee should spend time on it.

Referee Report

1 major / 1 minor

Summary. The paper claims to introduce a two-layer valuation progression for authenticated data assets that meet IAS 38 recognition criteria via legal provenance. The first layer, D-Val, is an auditable cost-basis valuation defined explicitly as D-Val = Cp * Avt, where Cp is production cost and Avt is an appreciation/depreciation factor constrained to ≤1 under current IAS 38 interpretations. The second layer, A-Val, is presented as a theoretically grounded commercial valuation incorporating scarcity, rivalry, completeness, accuracy, and premia for provenance authentication and independent audit, intended to serve as a defensible bridge valuation before active markets for such assets mature.

Significance. If operationalized with non-arbitrary quantification rules, the approach could help address recognition and measurement challenges for data assets under IAS 38 and IAS 2, potentially mitigating allocative distortions in data and AI markets. The explicit formula and IAS 38 grounding for D-Val represent a clear strength in providing an auditable baseline; however, the absence of comparable rigor for A-Val limits the proposal's immediate utility as a standards-based method.

major comments (1)

[Abstract / A-Val definition] Abstract and the section introducing A-Val: unlike the explicit equation D-Val = Cp * Avt with IAS 38 grounding, A-Val is defined only as a list of named factors (scarcity, rivalry, completeness, accuracy, and premia) with no equations, measurement procedures, observable proxies, or data sources supplied. This is load-bearing for the central claim that A-Val functions as a 'defensible commercial valuation' prior to active markets, since the factors (including the noted free parameters Avt and premia) directly shape the output without independent derivation or falsifiability.

minor comments (1)

[Abstract] The abstract states that 'parameter assumptions improve' as markets mature but provides no details on how iterative refinement, validation, or convergence to observable market values would occur.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the strengths of the D-Val layer. We address the single major comment below and commit to revisions that enhance the formalization of A-Val while preserving the manuscript's positioning of it as a pre-market bridge valuation.

read point-by-point responses

Referee: [Abstract / A-Val definition] Abstract and the section introducing A-Val: unlike the explicit equation D-Val = Cp * Avt with IAS 38 grounding, A-Val is defined only as a list of named factors (scarcity, rivalry, completeness, accuracy, and premia) with no equations, measurement procedures, observable proxies, or data sources supplied. This is load-bearing for the central claim that A-Val functions as a 'defensible commercial valuation' prior to active markets, since the factors (including the noted free parameters Avt and premia) directly shape the output without independent derivation or falsifiability.

Authors: We agree that the A-Val layer is presented at a higher level of abstraction than D-Val and lacks an explicit functional form, measurement procedures, or observable proxies, which weakens its claim to serve as a defensible commercial valuation. In the revised manuscript we will add a proposed equation A-Val = D-Val × (1 + p_s + p_r + p_c + p_a + p_p) where the p terms are premia for scarcity, rivalry, completeness, accuracy, and provenance/authentication, respectively, together with guidance on initial proxies (e.g., uniqueness metrics for scarcity, data-quality audit scores for completeness/accuracy, and contractual premium benchmarks for provenance). We will explicitly state that these parameters are illustrative and subject to iterative refinement as authenticated-data markets develop, consistent with the paper's framing of A-Val as a transitional construct. This change supplies the requested structure and falsifiability without claiming current market observability. revision: yes

Circularity Check

1 steps flagged

A-Val valuation defined by unquantified factors without independent measurement rules

specific steps

self definitional [Abstract]
"The second layer, A-Val, is a theoretically grounded commercial valuation that incorporates scarcity, rivalry, completeness, accuracy, and explicit premia for provenance authentication and independent audit. A-Val is not auditable as fair value under current practice but serves as a defensible commercial valuation during the period before active markets for authenticated data assets mature."

A-Val is asserted to be the commercial valuation, yet it is defined solely by incorporating the listed factors. No measurement procedures, data sources, or equations are given for quantifying scarcity, rivalry, completeness, accuracy, or the premia. The valuation output is therefore shaped directly by the choice of those input values by construction, rather than derived from observables or first principles independent of the desired result.

full rationale

The paper supplies an explicit, IAS 38-grounded formula for the first layer (D-Val = Cp * Avt). The second layer A-Val is introduced as the key commercial valuation but is described only as incorporating a list of named factors with no equations, proxies, or derivation supplied for determining their values. This makes the output valuation equivalent to the (arbitrary) selection of those inputs by construction. No self-citations, uniqueness theorems, or other patterns appear in the provided text. The central claim that A-Val provides a defensible bridge valuation therefore reduces to the definitional incorporation of the factors rather than an independent derivation, producing partial circularity confined to A-Val.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 2 invented entities

The model rests on the premise that authenticated data can be separated and costed, plus several judgment-based factors in A-Val that lack external anchors.

free parameters (2)

Avt
Appreciation or depreciation factor applied over time; constrained to ≤1 under current IAS 38 interpretations.
premia for provenance authentication and independent audit
Explicit additive premia in A-Val whose magnitudes are not derived from data or markets.

axioms (1)

domain assumption Authenticated data assets meet IAS 38 recognition criteria through legal provenance and contractual boundaries.
Stated directly in the abstract as the precondition for applying the two-layer model.

invented entities (2)

D-Val no independent evidence
purpose: Auditable cost-basis valuation layer
Defined as Cp * Avt; no independent evidence supplied.
A-Val no independent evidence
purpose: Commercial valuation layer incorporating scarcity, rivalry and premia
Presented as theoretically grounded but without derivation or external validation.

pith-pipeline@v0.9.1-grok · 5836 in / 1520 out tokens · 29416 ms · 2026-06-26T06:39:15.173097+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 1 canonical work pages

[1]

Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500. Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223–249. Bar-Isaac, H., Jewitt, I., & Leaver, C. (2021). Adverse selection, eﬀiciency and the structur...

work page doi:10.1007/s00199-020- 1970
[2]

Ullah, B. (2020). Signaling value of quality certification: Financing under asymmetric information. Journal of Multinational Financial Management, 55, 100629. Xu, Z., et al. (2024). Private data enhancement in large language models. Machine Learning Research, 156, 234–251. 30

2020

[1] [1]

Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500. Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), 223–249. Bar-Isaac, H., Jewitt, I., & Leaver, C. (2021). Adverse selection, eﬀiciency and the structur...

work page doi:10.1007/s00199-020- 1970

[2] [2]

Ullah, B. (2020). Signaling value of quality certification: Financing under asymmetric information. Journal of Multinational Financial Management, 55, 100629. Xu, Z., et al. (2024). Private data enhancement in large language models. Machine Learning Research, 156, 234–251. 30

2020