Technology, AI, and the Future of Valuation

Introduction

Valuation has always combined financial theory, judgment, evidence, and uncertainty. Technology is changing each of these elements. Cloud platforms allow geographically dispersed teams to work on one model; financial databases provide increasingly granular market and company information; Python and business-intelligence tools automate analysis and visualization; and generative artificial intelligence can assist with research, documentation, coding, and scenario construction. At the same time, technology introduces new risks. A valuation may become faster without becoming more accurate, and a sophisticated model may conceal rather than reduce uncertainty.

The contemporary valuation professional therefore needs two capabilities. The first is technical fluency: the ability to obtain, clean, analyze, visualize, and communicate financial information. The second is governance judgment: the ability to assess data quality, explainability, privacy, bias, cybersecurity, regulatory obligations, and the appropriate role of human review.

This chapter examines technology-enabled valuation and several developments that are expanding the scope of valuation practice: ESG and climate risk, alternative data, digital assets, automated modeling, and generative AI.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Explain how databases, cloud collaboration, Python, and BI tools support valuation.
2. Design a controlled technology-enabled valuation workflow.
3. Evaluate the usefulness and limitations of generative AI in financial analysis.
4. Assess explainability, privacy, bias, cybersecurity, and human-oversight risks.
5. Incorporate ESG and climate-related factors without double counting or unsupported assumptions.
6. Describe valuation issues associated with alternative data and digital assets.
7. Apply a disciplined framework to emerging valuation practices and communicate results responsibly.

1. The Technology-Enabled Valuation Workflow

A traditional valuation workflow consists of defining the purpose, collecting information, normalizing historical data, forecasting performance, selecting a method, estimating the cost of capital, conducting sensitivity analysis, and communicating a conclusion. Technology does not eliminate these steps; it changes their speed, scale, traceability, and possible complexity.

A robust workflow has six layers:

The distinction between data, assumptions, calculations, and judgment is essential. A historical revenue figure may be data; a forecast growth rate is an assumption; a present-value calculation is a transformation; and the decision to treat a restructuring cost as non-recurring is judgment. Good systems preserve these distinctions.

1.1 Financial databases and data governance

Financial databases can provide financial statements, security prices, analyst estimates, industry classifications, transaction multiples, interest rates, foreign-exchange rates, ownership data, and regulatory filings. Their value depends on more than breadth. A professional should examine:

• Authority: Is the source an audited filing, an exchange, a regulator, a commercial provider, or an unverified publication?
• Definition: Does “operating income,” “net debt,” or “active customer” have a consistent meaning across firms and periods?
• Timing: Was the information available at the valuation date, or is it affected by look-ahead bias?
• Survivorship: Does the database include failed, delisted, or acquired companies?
• Corporate actions: Have splits, dividends, mergers, and restatements been correctly treated?
• Currency and units: Are figures consistently expressed in the same currency, scale, and price basis?
• Licensing: Does the organization have the right to store, process, and redistribute the data?

A data dictionary should define each field, unit, frequency, source, transformation, and owner. A source register should record when data were downloaded and the exact query or file version used. These practices make a valuation reproducible and reduce accidental inconsistency.

1.2 Cloud collaboration

Cloud collaboration can permit simultaneous access to models, documents, code, and dashboards. It is particularly useful when investment, accounting, tax, operations, and sustainability specialists contribute to one engagement. However, convenience must not replace control.

Important controls include role-based access, multifactor authentication, encryption, activity logs, restricted sharing links, retention schedules, and separate development, review, and production environments. A model should not be changed silently after review. Version control should identify the author, date, change, reason, and effect on the conclusion.

A “single source of truth” is valuable only when definitions and ownership are clear. A centralized database with inconsistent field definitions merely centralizes error.

2. Automation, Python, and Business Intelligence

Automation is most useful for repetitive, testable processes. Examples include importing financial statements, mapping chart-of-account categories, recalculating historical ratios, generating peer sets, refreshing market data, running sensitivities, and checking model integrity.

Python is commonly used for data extraction, transformation, statistical analysis, forecasting, optimization, and report generation. A typical architecture separates:

1. Input layer: raw files and API responses retained unchanged.
2. Cleaning layer: missing values, duplicate records, units, currencies, and dates addressed.
3. Transformation layer: standardized measures and calculated variables created.
4. Model layer: forecasts, discounting, scenarios, and valuation methods applied.
5. Output layer: tables, charts, dashboards, and audit files produced.

The code should be modular and tested. For example, a function that calculates enterprise value from operating forecasts should be tested with known inputs, zero-growth cases, extreme discount rates, and missing values. Automated testing does not prove that a model is economically appropriate; it helps establish that the implemented logic behaves as intended.

2.1 Core valuation equations in an automated model

For a discounted cash-flow valuation based on free cash flow to the firm (FCFF), enterprise value is generally:

Here, EV is enterprise value; FCFF_t is free cash flow to the firm in period t; WACC is the weighted average cost of capital; n is the explicit forecast horizon; and TV_n is terminal value at the end of period n. Cash flows and terminal value must use consistent currency, timing, and nominal or real price assumptions. The formula assumes that the discount rate reflects the risk of the cash flows and that the terminal value is economically justified.

Under a perpetual-growth terminal-value assumption:

where g is the perpetual growth rate and FCFF_n+1 = FCFF_n × (1 + g). The condition WACC > g is necessary for a finite positive terminal value in this form. The growth rate should be sustainable for the mature business and should not exceed the long-run growth capacity of the relevant economy without a compelling justification.

A common FCFF construction is:

where EBIT is operating profit before interest and tax, T is the tax rate applicable to operating profit, D&A is depreciation and amortization, Capex is capital expenditure, and ∆NWC is the increase in operating net working capital. All components must refer to the same period and operating perimeter.

Automation makes it easy to produce thousands of outputs. It does not determine whether a forecast is sensible. A model can calculate an incorrect assumption with perfect precision.

2.2 Visualization and BI tools

Business-intelligence tools can display revenue bridges, margin trends, working-capital cycles, peer distributions, scenario outcomes, geographic exposure, and climate-risk indicators. Effective visualization should answer a decision-relevant question. A chart showing a valuation range is more useful when it identifies the assumptions driving that range.

Useful practices include:

• label units, currencies, dates, and sources;
• distinguish historical values from forecasts;
• show uncertainty rather than only a point estimate;
• avoid truncated axes that exaggerate differences;
• provide accessible colors and textual alternatives;
• enable a user to trace a dashboard number to its source.

A dashboard is a communication layer, not an independent analytical method. Its apparent objectivity can be misleading if the underlying definitions or exclusions are hidden.

3. Artificial Intelligence in Valuation

Artificial intelligence includes systems that identify patterns, classify information, generate text or code, and make predictions. In valuation, machine-learning models may support revenue forecasting, credit analysis, peer selection, fraud detection, customer retention analysis, and volatility estimation. Generative AI can summarize filings, draft code, explain spreadsheet formulas, identify inconsistencies, and produce first drafts of valuation narratives.

Generative AI should be treated as an assistant rather than an authority. It may produce plausible but false statements, cite nonexistent sources, misread tables, omit qualifications, or generate code with security defects. Its output is not evidence merely because it is fluent.

3.1 Appropriate and inappropriate uses

Appropriate uses include:

• creating a preliminary list of risks from a set of filings;
• converting clearly specified formulas into draft code;
• suggesting data-validation tests;
• comparing a report with an approved model for inconsistencies;
• summarizing documents that have been verified by a professional;
• generating alternative wording for a transparent explanation.

High-risk uses include:

• allowing an AI system to select a valuation conclusion without review;
• uploading confidential deal information to an uncontrolled public service;
• accepting an AI-generated peer group or discount rate without evidence;
• relying on unverified summaries instead of reading material disclosures;
• using predictions trained on biased or non-representative data.

3.2 Prompting and retrieval controls

A professional workflow should specify the task, provide authorized source material, identify the valuation date, require citations or document references, and demand an explicit statement of uncertainty. Retrieval-augmented systems can restrict responses to an approved document collection, but they still require testing for incomplete retrieval and incorrect interpretation.

Every material AI-assisted output should have a human owner who verifies facts, calculations, sources, confidentiality, and consistency with the engagement scope. The use of AI should be documented where it affects analysis or reporting.

4. Explainability, Bias, Privacy, and Cybersecurity

4.1 Explainability

Explainability means that a qualified user can understand why a system produced an output and how the output should be interpreted. In valuation, an explainable model identifies inputs, transformations, assumptions, limitations, and sensitivity to important variables.

There is a difference between technical interpretability and business explanation. A model may identify that a variable is statistically influential without explaining a causal economic relationship. Feature importance is not proof of causation. A black-box prediction may be useful as a challenge to a base case, but it should not automatically replace a transparent forecast.

4.2 Bias and model risk

Bias can arise from incomplete samples, historical discrimination, survivorship, measurement error, proxy variables, or an objective function that rewards the wrong outcome. For example, a peer-selection algorithm trained on current listed firms may systematically exclude private, failed, or recently listed firms. A credit model may use geographic variables that indirectly reproduce prohibited characteristics.

Model-risk management should include:

1. documented purpose and population;
2. data-quality and representativeness testing;
3. performance testing across relevant subgroups and periods;
4. stability testing under changed economic conditions;
5. independent validation;
6. monitoring for drift;
7. a process for overrides and complaints.

4.3 Privacy and cybersecurity

Valuation work may contain nonpublic financial information, employee data, customer behavior, strategic plans, and transaction terms. Data minimization means collecting and retaining only what is necessary. Access should follow least privilege: users receive the minimum permissions needed for their role.

Cybersecurity risks include credential theft, ransomware, malicious data alteration, unauthorized model access, prompt injection, and leakage through third-party AI services. Controls include multifactor authentication, encryption, backups, endpoint protection, secure APIs, vendor due diligence, penetration testing, and incident-response plans. A valuation model should be protected not only from theft but also from undetected alteration.

4.4 Human oversight

Human oversight is not satisfied by asking a person to click “approve.” The reviewer must have adequate expertise, time, authority, and access to underlying evidence. High-impact judgments—such as the valuation date, forecast architecture, terminal assumptions, discount rate, and treatment of extraordinary events—should remain explicitly accountable to named professionals.

5. ESG and Climate Risk in Valuation

Environmental, social, and governance factors affect valuation when they influence cash flows, risk, competitive position, asset lives, financing access, or terminal assumptions. ESG should not be added as a vague premium or discount without identifying the economic mechanism.

Possible transmission channels include:

• carbon prices, regulation, and compliance costs;
• energy efficiency and input-cost exposure;
• physical damage from heat, floods, storms, or water scarcity;
• changing customer preferences and product demand;
• litigation, labor disruption, or supply-chain failure;
• governance quality and the probability of capital misallocation;
• access to capital and cost of insurance.

A disciplined climate analysis distinguishes transition risk from physical risk. Transition risk arises from movement toward a lower-emission economy; physical risk arises from climate-related hazards. Both can affect revenues, costs, capital expenditure, working capital, asset impairment, and the cost of capital.

Climate scenarios are not forecasts. They are internally coherent descriptions of possible futures. A scenario analysis may alter assumptions such as volume, price, operating margin, carbon cost, useful life, or capital expenditure. The analyst should state which assumptions changed and avoid presenting one scenario as a probability-weighted prediction unless probability weights are defensible.

Double counting is a major danger. If expected regulatory costs are already included in cash flows, the analyst should not also apply an arbitrary climate discount to the discount rate for the same risk. One defensible approach is to reflect risks that are reasonably estimable in cash flows and use the discount rate only for systematic, non-diversifiable risk consistent with the chosen valuation framework.

6. Alternative Data

Alternative data are information sources not traditionally used in financial analysis, such as web traffic, satellite observations, shipping activity, app usage, footfall, card transactions, online prices, hiring advertisements, and sensor readings. Such data can provide more timely indicators of demand or operations than quarterly statements.

The analytical process should address:

• lawful acquisition and consent;
• whether individuals can be identified;
• representativeness and sample selection;
• stability of the relationship with financial outcomes;
• revisions, outages, and changes in data-collection methodology;
• potential market abuse or material nonpublic information;
• correlation, causation, and spurious relationships.

A useful validation design separates development and test periods, avoids look-ahead bias, and evaluates performance during different regimes. If a retailer’s web traffic predicts sales in one period but the website redesign changes the measurement process, historical relationships may no longer apply.

7. Digital Assets and Emerging Valuation Practices

Digital assets include cryptocurrencies, tokens, stablecoins, tokenized securities, and certain digital claims recorded using distributed-ledger technology. Their valuation depends on the rights attached to the asset, the reliability of the network, market liquidity, custody arrangements, regulation, and the economic use of the token.

Different assets require different approaches. A token representing a contractual claim may be analyzed using conventional income or market methods. A digital asset with no contractual cash flow may require analysis of market prices, network activity, scarcity, utility, and risk. A stablecoin requires examination of reserve assets, redemption rights, governance, and operational resilience; its label does not guarantee stability.

Market prices may be fragmented across venues and affected by outages, manipulation, thin liquidity, or wash trading. The analyst should define the pricing source, observation time, treatment of forks and airdrops, custody assumptions, and restrictions on transfer. Valuation should not confuse technological novelty with economic value.

Emerging practices also include real-time valuation dashboards, probabilistic scenario engines, automated peer selection, and digital twins of business operations. These tools can improve monitoring but may create false confidence when users mistake frequent updates for greater accuracy.

8. Worked Application: Technology-Enabled Valuation of Northstar Mobility

The following is a fictional illustrative case. Northstar Mobility is a listed manufacturer of electric delivery vehicles. An analyst is valuing the operating business at the end of 2025 using a five-year FCFF model. The company is exposed to battery prices, charging infrastructure, supply-chain disruption, and changing emissions regulation.

Background and problem

The finance team has a spreadsheet model, a cloud data warehouse, a Python forecasting script, and a BI dashboard. Management asks whether an AI-generated forecast and a climate scenario should replace the existing base case.

Analysis

The analyst first defines the scope: enterprise value as of 31 December 2025, using nominal currency units and a five-year explicit forecast. Historical financial statements are obtained from filings; market inputs are timestamped; operational data are checked against audited revenue. The AI system is permitted to summarize documents but is not permitted to access confidential customer-level data or determine the final discount rate.

The base-case FCFF forecasts are:

Assume WACC = 10% and perpetual growth g = 3%. The terminal cash flow is 140 × 1.03 = 144.2, so:

The present values are approximately:

• 2026: 80 ÷ 1.10 = 72.7
• 2027: 95 ÷ 1.10² = 78.5
• 2028: 110 ÷ 1.10³ = 82.6
• 2029: 125 ÷ 1.10⁴ = 85.3
• 2030: 140 ÷ 1.10⁵ = 86.9
• Terminal value: 2,060 ÷ 1.10⁵ = 1,279.5

Therefore, enterprise value is approximately:

The calculation is illustrative and assumes that the forecasts, discount rate, and terminal growth rate are internally consistent. It is not a recommendation or a complete valuation of Northstar.

The climate scenario reduces long-run operating cash flow through higher battery input costs and additional charging infrastructure expenditure. Rather than inserting an unexplained “ESG discount,” the analyst models the specific effects in FCFF. A separate downside case uses WACC = 11% and g = 2% to test sensitivity. The team reports a range and explains that terminal value is the dominant contributor to enterprise value.

Approach and solution

The AI-generated forecast is compared with management’s forecast, historical relationships, industry data, and the analyst’s operating model. The AI result is retained as a challenge case because its source variables and training process are not sufficiently transparent for primary reliance. Python independently recalculates the DCF, while the BI dashboard displays the value range, key assumptions, and source links.

Controls include a locked raw-data layer, documented transformations, code review, restricted access, and an approval record. The final report distinguishes observed facts, management assumptions, analyst assumptions, model outputs, and professional judgment.

Outcome and learning points

The technology stack improves speed and traceability, but it does not remove uncertainty. The principal conclusion is not a single precise number; it is a valuation range supported by explicit assumptions and sensitivity analysis. Climate risk is economically integrated rather than treated as a symbolic adjustment. AI assists with comparison and documentation but does not replace human accountability.

9. Professional Practice and Governance Framework

A technology-enabled valuation policy should address five questions:

1. What is the purpose? Define the decision, valuation standard, date, materiality, and users.
2. What evidence is permitted? Specify approved sources, data rights, retention, and treatment of estimates.
3. What can be automated? Automate repeatable calculations while reserving consequential judgments for accountable professionals.
4. How is quality tested? Use reconciliations, unit tests, independent review, back-testing where possible, sensitivity analysis, and exception reporting.
5. How is the conclusion explained? Provide a clear audit trail from source data to assumptions, calculations, outputs, and limitations.

A model-risk inventory should record the model owner, purpose, inputs, methodology, validation status, known limitations, materiality, and retirement date. A change-control process should assess whether a data-source change, code revision, or AI-system update could alter the valuation.

Regulatory obligations vary by jurisdiction and purpose. Professionals should consider securities laws, financial reporting requirements, privacy and data-protection rules, intellectual-property rights, market-abuse restrictions, cybersecurity expectations, and applicable AI governance requirements. Ethical compliance is broader than minimum legal compliance: a legally obtainable dataset may still be inappropriate if its use violates reasonable expectations of privacy or creates unfair outcomes.

Challenges and Limitations

Technology-enabled valuation faces several persistent limitations:

• Data may be abundant but poorly defined, inconsistent, or biased.
• Automated systems can amplify errors at high speed and scale.
• Machine-learning relationships may fail when economic regimes change.
• Generative AI can produce confident fabrications and obscure provenance.
• Cloud and vendor dependence create operational and concentration risk.
• Frequent dashboard updates may encourage short-term decision-making.
• ESG and climate scenarios contain substantial uncertainty and can be double counted.
• Digital-asset markets may lack reliable liquidity and consistent price discovery.
• Cybersecurity failures can compromise both confidentiality and model integrity.
• Human reviewers may over-trust outputs that appear quantitative or technologically sophisticated.

The correct response is not to reject technology but to match the level of automation to the consequence of error. A low-risk formatting task may be fully automated; a valuation used in a major transaction requires layered controls and accountable review.

Contemporary Direction of the Profession

The future valuation professional will increasingly work with data engineers, sustainability specialists, cybersecurity teams, and model validators. Valuation files may become living systems that update as new information arrives, while formal reports preserve the information set and assumptions applicable at the valuation date.

Three principles are likely to remain durable. First, economic reasoning matters more than computational complexity. Second, transparency and provenance are competitive advantages, not administrative burdens. Third, uncertainty should be communicated rather than hidden behind a highly precise point estimate.

Technology can widen the evidence base and improve consistency. It cannot decide what an asset is worth without a theory of value, a definition of risk, and professional judgment about the future.

Chapter Summary

Technology is transforming valuation through financial databases, cloud collaboration, automation, Python, BI tools, alternative data, and artificial intelligence. These tools improve speed, scale, reproducibility, and visualization, but they do not guarantee accuracy. A sound workflow separates source data, transformations, assumptions, calculations, and judgment.

Generative AI is useful for document review, coding assistance, validation, and drafting, but its outputs require verification and human ownership. Explainability, bias testing, privacy, cybersecurity, access control, and change management are essential elements of valuation governance.

ESG and climate factors should enter valuation through identifiable effects on cash flows, risk, asset lives, financing, and terminal assumptions. Alternative data can improve timeliness but requires legal, statistical, and methodological validation. Digital assets require analysis of rights, utility, liquidity, custody, governance, and market integrity rather than reliance on technological labels.

The future of valuation is therefore not fully automated valuation. It is controlled, transparent, evidence-based augmentation of professional judgment.

Key Terms

Alternative data: Non-traditional information used to supplement financial and market data.

Artificial intelligence: Computational systems capable of tasks such as prediction, classification, or generation.

Bias: A systematic distortion that causes outputs or decisions to differ from an appropriate reference.

Cloud collaboration: Shared access to data, models, code, or documents through networked platforms.

Data dictionary: A record defining data fields, units, meanings, sources, and transformations.

Digital asset: A digitally represented asset, right, or claim, often supported by distributed-ledger technology.

Explainability: The ability to understand and communicate how a system produces an output.

FCFF: Free cash flow available to all providers of capital after operating taxes and necessary investment.

Generative AI: AI that creates text, code, images, or other content in response to instructions.

Human oversight: Responsible review and control by qualified people over consequential automated outputs.

Model risk: The possibility that a model is unsuitable, incorrectly implemented, or misused.

Physical climate risk: Financial risk arising from climate-related physical hazards.

Prompt injection: An attempt to manipulate an AI system through instructions embedded in input material.

Reproducibility: The ability to recreate an analysis from recorded data, code, assumptions, and procedures.

Scenario analysis: Evaluation of outcomes under coherent alternative assumptions about the future.

Survivorship bias: Distortion caused by analyzing only entities that remain successful or observable.

Terminal value: The value assigned to cash flows beyond the explicit forecast period.

Transition risk: Financial risk arising from changes toward a lower-emission economy.

Weighted average cost of capital: A discount rate reflecting the required returns of debt and equity capital, weighted by their financing proportions.

Review Questions

Short Answer Questions

1. Why should a valuation workflow distinguish data, assumptions, calculations, and judgment?
2. State two appropriate and two high-risk uses of generative AI in valuation.
3. What is the difference between transition risk and physical climate risk?
4. Why can a centralized database still produce unreliable analysis?
5. What does explainability mean in a valuation model?

Long Answer Questions

1. Explain how financial databases, cloud collaboration, Python, and BI tools can be integrated into a controlled valuation workflow.
2. Discuss the principal privacy, bias, cybersecurity, and human-oversight risks associated with AI-enabled valuation.
3. Explain how ESG and climate factors can be incorporated into cash flows without double counting.
4. Compare the opportunities and limitations of alternative data in corporate valuation.
5. Discuss why digital assets require analysis of rights, utility, liquidity, custody, and governance.

Analytical / Application Questions

1. A machine-learning system selects comparable companies using current listed firms only. Identify possible biases and design validation tests before using its output.
2. A company claims that climate risk is already reflected in its cash flows but also applies a large unexplained discount to its cost of capital. Analyze the potential double-counting problem and propose a better approach.
3. Using the Northstar Mobility assumptions in this chapter, explain why changing WACC or g can materially affect enterprise value, and identify governance controls needed before publishing the result.

Multiple Choice Questions with Answers

Question 1

Which practice best supports reproducibility in a technology-enabled valuation?

A. Saving only the final presentation

B. Recording source files, transformations, assumptions, code versions, and outputs

C. Allowing unrestricted edits to the production model

D. Replacing all judgment with automated formulas

Correct Answer: B. Recording source files, transformations, assumptions, code versions, and outputs

Question 2

Under the perpetual-growth terminal-value model, which condition is required for a finite value in the standard formulation?

A. g > WACC

B. g = WACC

C. WACC > g

D. WACC = 0

Correct Answer: C. WACC > g

Question 3

Which is the most appropriate role for generative AI in a high-stakes valuation?

A. Selecting the final discount rate without review

B. Providing an unverified valuation conclusion

C. Assisting with document comparison while a professional verifies the output

D. Receiving confidential information through an uncontrolled public tool

Correct Answer: C. Assisting with document comparison while a professional verifies the output

Question 4

What is survivorship bias in financial data?

A. Using too many historical observations

B. Excluding entities that failed, delisted, or disappeared from the sample

C. Adjusting prices for stock splits

D. Using data from more than one country

Correct Answer: B. Excluding entities that failed, delisted, or disappeared from the sample

Question 5

Which statement best describes physical climate risk?

A. Risk from changes in emissions regulation only

B. Risk from climate-related hazards such as floods, heat, and storms

C. Risk from errors in a company’s accounting system

D. Risk caused only by changes in interest rates

Correct Answer: B. Risk from climate-related hazards such as floods, heat, and storms

Question 6

If WACC = 10% and g = 3%, what is the terminal value when next-period FCFF is 144.2?

A. 1,030

B. 1,442

C. 2,060

D. 4,807

Correct Answer: C. 2,060

Question 7

Which control most directly limits unauthorized access to a confidential valuation model?

A. A more colorful dashboard

B. Role-based access and multifactor authentication

C. Increasing the terminal growth rate

D. Using a longer forecast period

Correct Answer: B. Role-based access and multifactor authentication

Question 8

Why should alternative-data relationships be tested across different periods?

A. To guarantee that correlation proves causation

B. To identify instability when measurement methods or economic regimes change

C. To eliminate the need for data licensing

D. To ensure that all data are historical

Correct Answer: B. To identify instability when measurement methods or economic regimes change

Question 9

What is a principal danger of adding an unexplained ESG discount to the discount rate?

A. It necessarily reduces data storage costs

B. It may double count risks already reflected in forecast cash flows

C. It makes all scenarios identical

D. It removes the need for sensitivity analysis

Correct Answer: B. It may double count risks already reflected in forecast cash flows

Question 10

Which statement about digital-asset valuation is most accurate?

A. All digital assets should be valued using the same method

B. Technological novelty automatically creates economic value

C. Valuation should consider rights, utility, liquidity, custody, governance, and market integrity

D. A stablecoin label guarantees stable value

Correct Answer: C. Valuation should consider rights, utility, liquidity, custody, governance, and market integrity