Foundations of Business Value

Abstract#

This chapter is the conceptual foundation of the whole book. It examines a fundamental problem: what business value is and what its nature is. The question is far from trivial — although the notion of “value” is used freely in everyday financial language, its precise content depends on the discipline, the context and the purpose of the valuation.

What depends on the answer is not theoretical tidiness but practical decisions: which method the valuer selects, which data are treated as material, and how the resulting figure is justified before an investor, a court or a tax authority. The first chapter therefore does not define value in a single formulation; it shows why a single formulation is not enough.

Learning outcomes#

After reading this chapter and completing the modelling exercises of section 1.5 and the review exercises of section 1.6, the student will be able to:

1. Explain the development of the concept of value from the labour theory of value (Smith, Ricardo) through the marginalist revolution to contemporary multi-layered models, and identify the question each paradigm set out to answer.
2. Recognise the five ontological layers of business value — core, risk, perception, strategy and intangible capital — and assign a particular value driver to the appropriate layer.
3. Calculate the value of a company by the discounted cash flow method with a terminal value (formulas 1.1–1.2), economic value added (1.3) and the weighted average cost of capital (WACC; 1.4).
4. Analyse the divergence between fundamental and market value: explain why the limits of arbitrage may prevent price from returning to fundamental value for a long time even where the deviation is evident.
5. Assess the epistemological limits of the discounted cash flow model by carrying out a two-dimensional sensitivity analysis (WACC × growth rate), and justify why a valuation result is presented as a range rather than as a single number.
6. Build a basic discounted cash flow model in a spreadsheet with a sensitivity table and an assumptions block (section 1.5).
7. Critically appraise when a one-dimensional measure of value is sufficient and when the task requires several perspectives to be combined.

1.1 Theoretical part#

1.1.1 Historical and conceptual context#

The universality of the value problem#

The question “what is value?” is one of the oldest and most contested in economics. It runs through the entire history of economic thought — from Aristotle’s division into value in use and value in exchange to present-day debates on the measurement of intangible capital and the valuation of digital platforms. The theory of business value is not an isolated branch of finance; it is an offshoot of a broader philosophy of economic value, and it inherits all of that philosophy’s conceptual tensions and unresolved questions.

The purpose of this section is to trace how the concept of value changed over the past three centuries and how that development shaped the present multi-layered understanding of business value. The historical context here is not ornament — it reveals why different valuation schools coexist and why their synthesis is not merely possible but necessary.

Classical political economy: the labour theory of value#

Modern value theory begins with classical political economy. In The Wealth of Nations (1776) Adam Smith distinguished two senses of value: value in use — the usefulness of a thing — and value in exchange — the ability to trade it for other things. Smith observed what became known as the water and diamonds paradox: water has enormous value in use but little value in exchange, and diamonds the reverse. The paradox remained unresolved until the marginalist revolution.

David Ricardo, and later Karl Marx, developed the labour theory of value, which holds that the value of a commodity depends on the quantity of labour required to produce it. In Ricardo’s formulation (1817), value is proportional to “the comparative quantity of labour necessary for the production” of the commodity. The theory had a strong influence on nineteenth-century economic thought, but it ran into fundamental difficulties: it could not explain why labour of differing quality creates differing value, or why scarcity affects price independently of labour input.

The marginalist revolution: the theory of subjective utility#

The break came around 1870, when three scholars — William Stanley Jevons, Carl Menger and Léon Walras — formulated the theory of marginal utility almost simultaneously and independently of one another. On this account, value is determined not by the cost of production but by the subjective utility that an additional unit of a good confers on the consumer. The water and diamonds paradox was at last resolved: the marginal utility of water is low (because it is abundant), that of diamonds high (because they are scarce).

The marginalist revolution was of fundamental importance for the theory of business value, because it:

1. Moved the source of value from the producer to the valuer. Value is no longer an objective property of a good — it depends on the valuer’s perception, expectations and alternatives.
2. Introduced marginal analysis. Each additional element — a cash flow, a client, an investment — is assessed not in absolute terms but by its marginal contribution.
3. Opened the way to the subjectivist tradition. The Austrian school (C. Menger, L. von Mises, F. Hayek) extended this logic into radical subjectivism: economic value does not exist “objectively” — it arises only in the mind of the actor, as a relation between ends and the means available (Mises, 1949).

This philosophical position has direct consequences for business valuation: if value is subjective, then the “correct” value of a company depends on who is valuing (investor, manager, regulator), for what purpose and on what assumptions. It explains why different valuation standards — fair market value, investment value, fair value under IFRS 13 — can yield different results for the same asset (International Valuation Standards Council [IVSC], 2022).

The neoclassical synthesis and the formation of financial theory#

In the early twentieth century Alfred Marshall combined the supply (cost of production) and demand (marginal utility) perspectives into a single equilibrium theory in which value, or price, is determined “as by scissors” — through the interaction of both blades. This neoclassical synthesis became the dominant paradigm and created the intellectual basis for modern financial theory.

An important step towards business value was taken by Irving Fisher (1906, 1930), who formulated the theory of capital and income: the value of an asset is the discounted present value of future income streams. Fisher’s work directly inspired John Burr Williams, who in The Theory of Investment Value (1938) first applied discounted cash flow (DCF) logic systematically to the valuation of shares. Williams’s formulation — that the value of a share equals the present value of all future dividends — became the cornerstone of modern fundamental valuation.

David Friday (1922) was among the first to analyse academically an extension of the concept of value beyond exchange value alone, arguing that economic value spans a wider range than market price and calls for a multi-dimensional approach.

The revolution in modern finance (1950s–1970s)#

The transformation of value theory was completed by three fundamental works of the mid-twentieth century:

1. Harry Markowitz (1952) — portfolio theory. He showed that investors choose not individual assets but portfolios, optimising the relation between return and risk. This formalised the notion of risk in valuation.
2. Franco Modigliani and Merton Miller (1958) — the capital structure theorems. Under ideal conditions the value of a company does not depend on its capital structure; it depends only on operating cash flows and their risk. This counter-intuitive proposition forced financial theory to separate operating value clearly from financing decisions.
3. William Sharpe (1964), John Lintner (1965), Jan Mossin (1966) — the Capital Asset Pricing Model (CAPM). It provided a formal way to calculate the required rate of return, that is, the discount rate, in the light of systematic risk (the beta coefficient).

Together these works shaped what may be called the classical business valuation paradigm: the value of a company is the present value of expected future free cash flows, discounted at a rate reflecting systematic risk. This paradigm remains dominant in practice and in the academic literature (Damodaran, 2012).

Extensions of the paradigm: strategy, behaviour and intangible capital#

Since the 1980s the classical paradigm has been extended consistently along three lines.

The strategic perspective. Michael Porter’s value chain analysis (1985) and Jay Barney’s resource-based view (RBV, 1991) showed that cash flows are not an exogenous phenomenon — they arise from strategic choices, competitive positioning and unique resources. The distinction between value creation and value capture (Lepak et al., 2007) became an essential analytical tool: a company may create a great deal of value, through a technological innovation for instance, yet capture only a part of it if the innovation is easily imitated.

Behavioural finance. Prospect theory, advanced by Daniel Kahneman and Amos Tversky (1979), Robert Shiller’s work on speculative price dynamics (1981, 2000) and Richard Thaler’s studies of market anomalies together dismantled the assumption that market prices always reflect fundamental value. Behavioural finance showed that perception — expectations, sentiment, narratives — is an independent layer in the formation of value, not merely “noise” around the fundamental.

Intangible capital. In the industrial era value was “visible”: factories, equipment, inventory. Since the late twentieth century an increasing share of value has been created through intangible sources — brands, patents, data, organisational routines and human capital. Crouzet and co-authors (2022) summarise the evidence that intangible capital has become the dominant form of value in contemporary economies, even though traditional accounting systems reflect it inadequately. This gap between economic reality and accounting measurement is one of the most pressing problems in present-day valuation theory.

From a one-dimensional number to a multi-layered ontology#

The historical perspective reveals a clear tendency: the concept of value expanded steadily from a one-dimensional, “objective” magnitude (quantity of labour, cost of production) towards a multi-dimensional, contextual and dynamic construct. Each theoretical school added a new dimension:

This evolution justifies the five-layer ontological decomposition set out below (see section 1.1.2): each layer corresponds to a historical “stratum” of value theory which did not abolish its predecessor but added to it and complicated it.

As Phelan (1997) argues, the “illusion of precision” in financial analysis arises precisely when numerical exactness masks fragile assumptions. The history of the concept of value teaches that any one-dimensional measure of value — whether DCF, a market multiple or accounting profit — is inevitably partial. The first step towards serious business valuation is therefore an honest acknowledgement of that partiality.

1.1.2 The concept of business value and its ontological structure#

The problem of definition#

Despite how widespread the notion of “business value” is in the academic and professional literature, there is no single generally accepted definition. This is neither accidental nor a matter of terminological carelessness — it reflects the multi-dimensional character of the concept of value itself. As shown in section 1.1.1, each theoretical tradition “sees” value through its own prism: financial economics stresses cash flows and risk, strategic management competitive advantage and resources, behavioural finance expectations and perception.

In this textbook business value is defined as:

The risk-adjusted present value, at a given date $t$, of the future economic benefits — cash flows above all — that a company is able to generate for its capital providers, taking into account (i) expected cash flows, (ii) the risk and uncertainty attaching to them, (iii) the strategic operating context that shapes those flows and that risk, and (iv) the intangible resources that determine the long-run potential for value creation.

This definition takes classical DCF logic as its basis but deliberately extends it along strategic and intangible dimensions. Formally, the basic expression of value is as follows (for the enterprise case, see formula 1.1 in section 1.2):

where:

• $V_0$ — value at the valuation date,
• $CF_t$ — expected free cash flow in period $t$,
• $r$ — the required rate of return (the discount rate), reflecting risk,
• $TV_n$ — the terminal (continuing) value beyond the forecast period,
• $n$ — the forecast period in years.

Fundamental though it is, this formula describes only the core layer. It does not explain where the cash flows come from, why different valuers apply different discount rates, or why a market price may differ drastically from a value calculated in this way. That calls for an ontological approach.

Essential distinctions: value, price, performance, profitability#

Before an ontological model can be constructed, four frequently conflated notions must be clearly separated.

Value and price. Price is the actual outcome of a transaction or a quotation in the market; value is an estimate made on a chosen basis of value and a set of assumptions. Aswath Damodaran draws a clear line between valuation and pricing: valuation rests on cash flows and risk, whereas price may also be driven by liquidity, herd behaviour and shifts in expectations (Damodaran, 2012). The international standards codify the same distinction: IFRS 13 defines fair value as the exit price in an orderly transaction between market participants at the measurement date (IFRS Foundation, 2013), while the International Valuation Standards (IVS) define market value as the estimated amount for which an asset would exchange between a willing and informed buyer and seller acting without compulsion (IVSC, 2022). Benjamin Graham’s aphorism — “price is what you pay; value is what you get” — may be read as an ontological thesis: price exists in market reality, value in analytical reality.

Value and performance. Performance is a set of financial and non-financial indicators already achieved, oriented to a shorter horizon: revenue, return on assets, market share. Value is the present expression of the entire future economic potential. The Balanced Scorecard (Kaplan & Norton, 1992) emerged precisely because financial indicators alone can give a misleading picture of the real capacity to create value over the long run.

Value and profitability. Profitability is the ratio of an accounting result (profit or loss) to a base such as revenue, assets or equity. A company may be profitable in accounting terms yet destroy economic value if its return falls short of the cost of capital. This distinction is formalised in the concept of economic value added (EVA; formula 1.3 in section 1.2):

where:

• $NOPAT$ — net operating profit after taxes,
• $IC$ — invested capital,
• $WACC$ — the weighted average cost of capital.

A positive EVA shows that the company is creating value above the cost of capital; a negative EVA shows that it is destroying value, whatever its accounting profitability may be.

The ontological approach: why layers are needed#

If business value were nothing but the present value of cash flows, one formula (1.1) and a few technical assumptions would suffice. In practice, however, the valuer meets questions that this formula does not cover:

• Where do the cash flows come from? — They are generated by strategic decisions, competitive positioning and organisational capabilities.
• Why can two companies with identical cash flows be valued differently? — Because their risk profiles, growth prospects and intangible capital differ.
• Why does market price deviate from fundamental value? — Because the perception layer is at work: expectations, narratives, information asymmetry.
• Why are companies with negative cash flows sometimes valued in the billions? — Because the value lies in the strategic and intangible layers: real options, data holdings, network effects.

The ontological approach — asking what exists and what the fundamental components of the value system are — allows these questions to be structured.

A five-layer ontological decomposition#

Drawing on the analyses available in the literature (Groth et al., 1996; Walters, 1997; Lepak et al., 2007; Crouzet et al., 2022), this textbook proposes that business value be decomposed into five interrelated layers. Each layer is characterised along three dimensions: objects (what exists), attributes (what properties they have) and function (what role they play in the value system).

Figure 1.1. A five-layer ontological decomposition of business value

graph TD
    S5["5 · Intangible layer<br/><i>Brand, Data, IP, Human capital</i>"]
    S4["4 · Strategic layer<br/><i>Competitive advantage, Real options, Dynamic capabilities</i>"]
    S3["3 · Perception layer<br/><i>Expectations, Sentiment, Information asymmetry, Narratives</i>"]
    S2["2 · Risk layer<br/><i>Discount rate, Systematic risk, Uncertainty</i>"]
    S1["1 · Core<br/><i>Cash flows, Assets, Capital structure</i>"]

    S5 --> S4
    S4 --> S3
    S3 --> S2
    S2 --> S1

    S1 -.->|"Feedback"| S5

    style S5 fill:#4a90d9,color:#fff
    style S4 fill:#50b5a9,color:#fff
    style S3 fill:#f5a623,color:#fff
    style S2 fill:#e8705a,color:#fff
    style S1 fill:#7b8a8e,color:#fff

Layer 1. The core: cash flows, assets and capital structure#

The foundational layer of value covers the tangible elements observable in accounting systems:

• Cash flows. These are the free cash flows generated by operations, both to the firm as a whole (free cash flow to firm, FCFF) and to equity (free cash flow to equity, FCFE). Cash flows are the principal carrier of value under the income approach.
• Tangible assets. Real estate, equipment, inventory, financial assets. The asset-based approach sums these elements and deducts liabilities, establishing a minimum (liquidation) value.
• Capital structure. The mix of debt and equity, which determines the weighted average cost of capital (WACC) and thereby the discount rate used in valuation. The Modigliani and Miller (1958) paradox — that under ideal conditions capital structure is irrelevant — becomes, in the practical world, an important value driver through the tax shield, bankruptcy costs and agency conflicts.

The core provides the quantitative base on which all the other layers are built. Its objects are, in principle, observable and measurable with the standard instruments of accounting and financial analysis.

Layer 2. The risk layer: uncertainty and discounting#

The second layer transforms future cash flows into present value by pricing uncertainty:

• Systematic risk. Unavoidable market risk that cannot be diversified away, measured by the beta coefficient in the CAPM (Sharpe, 1964).
• Specific (idiosyncratic) risk. Risk factors particular to the individual company: technological, regulatory and operating risk.
• Discount rate. The rate at which future cash flows are restated to present value, reflecting both the time value of money and a risk premium.
• Knightian uncertainty. Frank Knight (1921) introduced the fundamental distinction between risk, where probabilities are known or can be reasonably assessed, and uncertainty, where the structure of probabilities is unknown or unstable. Standard DCF and CAPM models assume risk; yet many strategic and technological environments are marked by uncertainty. The distinction is ontologically essential: in the one case it is possible to calculate, in the other only to reason through scenarios (Alfaro et al., 2018).

The risk layer is where valuation first becomes distinctly subjective: the choice of discount rate involves a substantial element of expert judgement.

Layer 3. The perception layer: expectations, information and sentiment#

The third layer covers the cognitive and informational mechanisms of value formation — how market participants perceive, interpret and price fundamental data:

• Expectations. Forward-looking conjectures about future earnings, growth and risk, which drive investment decisions and market prices.
• Information asymmetry. The uneven distribution of information between managers (insiders) and outside investors. George Akerlof’s (1970) “lemons” model revealed the mechanism of adverse selection: where the seller knows the quality of an asset and the buyer does not, the buyer rationally offers a lower price in anticipation of lower quality. High-quality sellers therefore withdraw from the market, the average quality of what remains falls, and the spiral may end in market collapse. In valuation practice this appears as liquidity discounts, wider bid-ask spreads and an information risk premium that investors demand in return for insufficient transparency.
• Market sentiment. The collective disposition of investors, which can pull price away from the fundamental in the direction of either optimism or panic.
• Narratives. Robert Shiller’s (2017) concept of “narrative economics” holds that popular stories about technologies, markets or leaders influence economic decisions no less than quantitative data. A narrative may become a price driver in its own right.
• Dispersion of analysts’ forecasts. Disagreement among analysts about future results is a quantitative indicator of perceptual uncertainty — studies associate greater forecast dispersion with higher uncertainty premia and a wider range of price fluctuation [PATIKRINTI: source].

The perception layer explains why identical fundamental data can lead to different valuations depending on the informational and psychological context. It is here that behavioural finance intersects with traditional valuation theory.

Layer 4. The strategic layer: competitive advantage and positioning#

The fourth layer links the financial dimensions of value to competitive dynamics:

• Competitive advantage. The company’s ability to generate returns above the cost of capital, sustained through entry barriers, switching costs, network effects or proprietary resources (Porter, 1985; Barney, 1991).
• Strategic positioning. The company’s place in the value chain and its strategic choices: differentiation, cost leadership, focus on a niche.
• Real options. Strategic flexibility — the right, but not the obligation, to expand, contract, defer or abandon investments in response to changing conditions. This matters particularly in innovative and technology-driven fields, where a standard DCF undervalues flexibility (Phelan, 1997). Conceptually, equity itself may be read as a call option on the assets of the company with an exercise price equal to the value of its debt; Merton’s (1974) model thereby links the valuation of equity to option pricing (for detail see Chapter 14).
• Dynamic capabilities (Teece, 2007). The organisational processes through which a company senses opportunities, seizes them and reconfigures its resource base.

The strategic layer acts on value through five channels (Walters, 1997; Kim, 2004):

1. A level effect — higher margins or volumes.
2. A growth effect — an expansion of future opportunities.
3. A sustainability effect — competitive rents retained for longer.
4. A risk effect — altered volatility or probability of loss.
5. An option effect — the creation of strategic flexibility.

Value-based management cannot therefore be reduced to short-term financial indicators: strategic variables may determine value even where their short-term accounting effect is weak.

Layer 5. The intangible layer: data, brand and human capital#

The fifth layer, increasingly dominant in the contemporary economy, covers non-physical assets:

• Intellectual property (IP). Patents, copyrights, trade secrets, proprietary technologies.
• Brand equity. Consumers’ willingness to pay a premium for a known brand — accumulated capital of trust and recognition.
• Human capital. The knowledge, skills, experience and creativity of employees. Gamerschlag (2013) showed that the disclosure of human capital is value-relevant even where it produces no direct market price reaction.
• Data assets. Data as a distinct subclass of intangible capital, whose value depends on exclusivity, quality, granularity, legal rights and interoperability with analytical algorithms (Hughes-Cromwick & Coronado, 2019). The OECD (2022) proposes that data be conceptualised as an intangible asset and is developing measurement frameworks for “data as an asset”.
• Organisational capital. The specific organisational routines, processes, culture and management systems that make coordination and value creation possible.
• Customer capital. The value of accumulated customer relationships, loyalty and network. Unlike brand equity, customer capital covers concrete economic indicators: the retention rate, customer lifetime value (CLV) and switching costs. In the digital economy, selling and marketing expenditure often accounts for 20 % or more of revenue — not operating costs but investment in customer capital, which explains why subscription and SaaS (software as a service) business models are valued at higher multiples than traditional companies with comparable revenue.

The intangible layer raises the most serious measurement problems, because many of these assets are not recognised on a traditional balance sheet: IAS 38 recognises an intangible asset only where it is identifiable (separable, or arising from legal or contractual rights) and can be measured reliably. Yet the economically most important intangibles — organisational culture, network effects, accumulated data holdings — frequently fail those criteria. By Ocean Tomo’s (2025) calculations, intangible assets account for some 90 % of the market capitalisation of the S&P 500 — a single provider’s estimate, obtained by deducting balance-sheet tangible assets from market capitalisation [PATIKRINTI: Ocean Tomo, 2025 — exact figure and methodology]. The figure indicates how wide the gap between accounting and economic value has become.

Interaction of the layers and a formal synthesis#

The five layers are not independent “boxes” — they interact through complex causal and constitutive relations:

• Intangible capital (layer 5) generates competitive advantage (layer 4).
• Strategic positioning (layer 4) shapes market expectations (layer 3) about the ability to manage risk (layer 2) and to generate cash flows (layer 1).
• Strong cash flow generation (layer 1) enables investment in intangible capital (layer 5), creating a virtuous cycle.
• Disruption in any layer — a technological shock, a shift in market mood, a capital structure crisis — transmits through the whole system.

Combining the theoretical foundations discussed above, this interaction may be expressed formally as a state vector covering all five layers of value in a single structure (formula 1.5 in section 1.2):

where:

• $CF_t^e$ — expected cash flows,
• $A_t$ — tangible assets,
• $I_t$ — intangible capital,
• $K_t$ — capital structure,
• $S_t$ — strategic position,
• $B_t$ — the behavioural or perceptual state (expectations, sentiment),
• $R_t$ — risk and uncertainty parameters.

This vector is not static — it evolves over time under managerial decisions and external shocks (formula 1.6 in section 1.2):

where $a_t$ denotes managerial actions (investments, strategic decisions) and $\varepsilon_{t+1}$ exogenous shocks (market changes, regulatory changes, technological breaks). The equation formalises three essential properties: (a) value is path-dependent — the same current indicators may signify different values if they were reached along different trajectories; (b) managerial actions are endogenous to the value system; (c) exogenous shocks transmit through every layer.

Business value as a whole is then a structural function of these layers rather than their simple sum (formula 1.7 in section 1.2):

where $\Phi_t$ denotes the institutional and informational context, and the operator $\Omega(\cdot)$ indicates that business value is an organised relation between the layers rather than a mechanical sum.

This formalisation carries three essential implications:

1. Value is path-dependent: the same current profits may signify different values if they were produced along different strategic and intangible trajectories.
2. Management is endogenous to valuation: managers’ decisions form part of the value transition mechanism rather than being an external factor.
3. Market signals feed back into fundamentals: financing conditions, takeover threats and reputational effects alter the strategic actions available and, through them, future cash flows (Walters, 1997; Kim, 2004).

1.1.3 Fundamental and market value: a dualism#

One of the most important and longest-standing tensions in the theory of business value is the dualism between fundamental (intrinsic) value and market value. This tension is not merely a technical nuance of valuation — it reflects a deep philosophical disagreement: is value an objective property of an asset, revealed by analysis, or a social construct arising from the interaction of market participants?

Fundamental value: the analytical ideal#

Fundamental value, or intrinsic value, deeply rooted in firm foundation theory, denotes the present value of expected future cash flows discounted at a rate that accurately reflects the systematic risk of the asset. The tradition was begun by Benjamin Graham and David Dodd in the classic Security Analysis (1934) and formalised by John Burr Williams (1938).

Fundamental value is treated as economic gravity — the true, long-run capacity of an asset to generate economic benefit, independent of short-term market fluctuations. It is an analytical judgment grounded in the properties of the asset rather than in the moods or behaviour of particular investors (Hardin et al., 2025).

Formally, fundamental value is expressed by the DCF formula (1.1; see section 1.2), but calculating it in practice requires a great many judgments: cash flow forecasts, the choice of discount rate, growth rates and terminal value assumptions. Each of these parameters carries substantial uncertainty.

Market value: empirical reality#

Market value is the price at which an asset is actually sold, or could be sold, in secondary markets. It is set at the intersection of supply and demand through the price mechanism — the collective judgment of heterogeneous, boundedly rational market participants.

The IVS define market value as “the estimated amount for which an asset or liability should exchange on the valuation date between a willing buyer and a willing seller in an arm’s length transaction, after proper marketing and where the parties had each acted knowledgeably, prudently and without compulsion” (IVSC, 2022). IFRS 13 defines fair value as the exit price in an orderly transaction between market participants (IFRS Foundation, 2013).

It is important to grasp that market value is not “wrong” or “unreal” — it is empirical reality, reflecting the actual space of transactions. It need not, however, coincide with fundamental value, because market participants act under real-world rather than perfect conditions.

The efficient market hypothesis: the convergence thesis#

The efficient market hypothesis (EMH), formulated by Eugene Fama (1970), provides the theoretical grounds for the claim that market price is the best available estimate of fundamental value. The EMH holds that market prices reflect all available information fully and immediately, so that systematically “beating the market” on a risk-adjusted basis is impossible.

The EMH rests on several principal assumptions:

• Investors are rational, or their irrationalities are independent and cancel one another out.
• Information is freely and rapidly available.
• Arbitrage mechanisms promptly eliminate any mispricing.

Under these conditions the dualism of fundamental and market value effectively disappears: market price is fundamental value, or at least the best unbiased estimate of it.

Even within the EMH literature, however, it is accepted that the “extreme” version of efficiency is too strong as an empirical description of reality — information and trading are costly, so complete efficiency is an ideal benchmark rather than a guarantee (Grossman & Stiglitz, 1980). Fama (1991) treats market efficiency as a benchmark against which real deviations are measured.

Behavioural finance: structural mechanisms of divergence#

Behavioural finance, pioneered by Daniel Kahneman, Amos Tversky, Robert Shiller and Richard Thaler, supplies the structural mechanisms that explain why market price may deviate systematically from fundamental value.

The principal cognitive biases with direct significance for valuation are these:

These biases do not operate in isolation; they interact and produce cumulative effects. Overconfidence combined with herding and a compelling narrative can, for example, create a speculative bubble in which market price exceeds fundamental value many times over.

The limits of arbitrage: why divergence persists#

Classical financial theory implies that even if some investors are irrational, rational arbitrageurs will exploit price deviations quickly and return prices to fundamentals. Shleifer and Vishny (1997) showed, however, that arbitrage in reality is risky, costly and limited:

• Short-selling constraints: it is not always possible to bet against an overvalued asset.
• Margin requirements: an arbitrageur may be forced to close a position before prices converge.
• Model risk: the estimate of fundamental value may itself be wrong.
• Noise trader risk: irrational investors may keep pushing the price in the “wrong” direction for longer than the arbitrageur’s capital allows them to wait.

John Maynard Keynes’s classic phrase — “markets can remain irrational longer than you can remain solvent” — captures the situation exactly. The limits of arbitrage explain why divergence between fundamental and market value may be structural rather than merely temporary.

Factors of convergence and divergence#

The relation between fundamental and market value is not static — it shifts continuously with institutional, informational and psychological conditions:

Figure 1.2. Factors of convergence and divergence

graph LR
    subgraph Convergence["Convergence (P ≈ V)"]
        K1["High information<br/>transparency"]
        K2["Strong liquidity<br/>and competitive arbitrage"]
        K3["Stable expectations<br/>and institutional trust"]
        K4["Long time horizon"]
    end

    subgraph Divergence["Divergence (P ≠ V)"]
        D1["Information asymmetry<br/>and unreliable signals"]
        D2["Arbitrage constraints<br/>and shortage of capital"]
        D3["Spread of narratives<br/>and emotion"]
        D4["Regime shift:<br/>speculation > fundamentals"]
    end

    Convergence ---|"Long-run<br/>gravitation"| FV["Fundamental<br/>value"]
    Divergence ---|"Short-run<br/>deviations"| MV["Market<br/>price"]

    style Convergence fill:#e8f5e9,color:#1b5e20
    style Divergence fill:#fbe9e7,color:#bf360c
    style FV fill:#4a90d9,color:#fff
    style MV fill:#f5a623,color:#fff

Empirical studies observe that over long horizons fundamental value acts as a “gravitational force” on market prices — the effect known as mean reversion. Over short horizons, however, sentiment and momentum may dominate (Albuquerque et al., 2016; Hardin et al., 2025).

Reflexivity: when market price changes the fundamentals#

An analysis of the dualism would be incomplete without George Soros’s concept of reflexivity (Soros, 1987). Soros argues that the expectations of market participants not only reflect reality but also change it, creating a feedback loop:

If the market values a company optimistically → its share price rises → it can borrow capital more cheaply → it can invest more → real results improve → the initial optimism is “confirmed”.

The converse holds too: a pessimistic valuation may become a self-fulfilling forecast through more expensive access to capital, declining employee motivation and supplier mistrust.

The implication is ontologically significant: market value is not a passive “reflection” of fundamental value — it is an active agent that alters the fundamentals themselves through feedback. Valuation is at once descriptive, in that it describes a present state, and performative, in that it acts on a future one.

Soros (2014) formalises this feedback as the interaction of two functions operating simultaneously:

1. The cognitive function — market participants try to understand reality: their expectations passively reflect the fundamental properties of a business (cash flows, risk, growth).
2. The manipulative function — market participants try to change reality: decisions grounded in those expectations actively alter the fundamentals themselves (a rising share price, for instance, permits cheaper borrowing and greater investment).

When both functions operate at once, the dependent variable of one becomes the independent variable of the other, and a circular, self-referring system arises. It is because of this structure that value is not determined by a one-way relation running from fundamentals to price, but takes shape as a recursive loop.

Formally, this recursive loop may be described as follows (formula 1.8 in section 1.2):

where $V_t$ is value, $E_t$ expectations and $D_t$ decisions.

In more detail:

1. Strategy changes expected cash flows and risk.
2. Expectations about those changes move market prices.
3. Market prices affect financing capacity and governance pressure.
4. Financing capacity and governance constrain or enable further strategic action.
5. Those actions reshape future fundamentals.

This recursive structure matters especially in financially constrained environments, where uncertainty shocks and financing frictions can affect investment and long-run value materially (Alfaro et al., 2018; Hartman-Glaser et al., 2025).

Implications for standards of value#

The dualism of fundamental and market value has direct practical consequences: different situations call for different standards of value, and the choice between them is not neutral:

Each standard takes a different position on the dualism: FMV is closer to the market perspective, investment value to the fundamental one (with assumptions specific to the buyer), while stakeholder value extends the definition of value itself beyond financial boundaries.

Summary of the section#

The dualism of fundamental and market value is permanent rather than accidental: convergence is possible, but divergence is structurally explained by information asymmetry, uncertainty, narratives and the limits of arbitrage. The mechanism of reflexivity shows that the relation between these two forms of value is not one-way (fundamentals → price) but two-way (fundamentals ↔ price). This has essential consequences for valuation practice and for business strategy alike: value management must take account not only of “true” value but also of how market perception acts on the fundamentals themselves.

1.1.4 Extensions of the concept of value in the contemporary economy#

The five-layer ontology and the fundamental/market dualism presented in the preceding sections provide the analytical basis. The economy of the twenty-first century has, however, raised new challenges that the classical models were not designed to meet. This section examines four structural transformations that extend the boundaries of the concept of business value: the dominance of intangible capital, the emergence of data as an asset, the economy of platforms and ecosystems, and the ESG and societal value dimension.

Intangible capital as the dominant driver of value#

The rise of intangible capital is one of the most important structural transformations in valuation theory. The valuation models of the industrial era were designed for a world in which the principal carriers of value — factories, land, equipment — are physically tangible, reflected in accounting systems and traded in markets. In the contemporary economy that assumption fits ever less well.

Intangible assets differ fundamentally from tangible ones in several respects (Haskel & Westlake, 2018):

• Scalability. An intangible asset — software, a brand, an algorithm — can be used in many markets at once with no additional marginal cost. A factory serves one location; an algorithm serves the world.
• Non-rivalry. One person’s use of an intangible asset does not reduce its availability to others. This creates the potential for exponential growth in value.
• Partial non-excludability. It is difficult to protect an intangible asset completely against imitation or “leakage” — knowledge spreads through employee mobility, reverse engineering and academic publication.
• Difficulty of collateralisation. Banks are less willing to accept intangible assets as security for a loan, so companies intensive in intangible capital face financing constraints of their own.
• Complementarity. Intangible assets often create value not in isolation but in interaction with other intangible and human resources. A patent without a competent team is of little worth; so is a database without analytical algorithms.

These properties have essential consequences for valuation. Companies may exhibit a high market value relative to the book value of their assets, and this need not mean “overvaluation” — traditional accounting systems simply do not reflect economically productive stocks of intangible capital (Liu et al., 2009; Crouzet et al., 2022). Cedergren and co-authors (2018) showed that the value of unrecognised assets has implications for the relation between market valuation and debt valuation — in other words, accounting “blindness” to intangibles distorts the valuation not only of equity but of debt as well.

Research on intellectual capital confirms that innovation and human capital carry explanatory power for company value over and above traditional financial capital (Liu et al., 2009; Tseng et al., 2015). Gamerschlag (2013) found that the disclosure of human capital is value-relevant even where it produces no direct market price reaction — evidence of a delayed and mediated transmission from intangible information to valuation.

Data as a value-generating asset#

Data are best treated as a distinct subclass of intangible capital, an asset class in their own right, rather than as simply one more intangible asset. The value of data depends on specific properties (Hughes-Cromwick & Coronado, 2019):

• Exclusivity — are the data available only to this company, or publicly?
• Quality — accuracy, completeness, currency.
• Granularity — do the data permit individualised analysis?
• Legal rights — ownership, licences to use, privacy regulation (the General Data Protection Regulation, GDPR).
• Interoperability — compatibility with other databases and analytical systems.
• Complementarity with algorithms — data create value not directly but through possibilities for forecasting, personalisation, coordination and experimentation.

The OECD (2022) proposes that data be formally conceptualised as an intangible asset and is developing measurement frameworks for “data as an asset”. It is worth noting that some data have no clear market price yet are economically valuable for decision-making — further confirmation that value cannot be equated with an observed exchange price alone.

The data economy also gives rise to new models of value creation: data network effects, where each new user generates data that improve the algorithm for all users, creating a dynamic of increasing returns. Knorr and co-authors (2025) showed that data network effects shape the strategies of platform complementors and the creation of user value.

The value of platforms and ecosystems#

In platform business models, value is created not within the company alone but within an ecosystem — in the interaction of users, complementors (providers of additional services), standards and data. The locus of value therefore shifts from the individual company to the architecture of the ecosystem. The theoretical foundation of this logic comes from two classical lines of work. The theory of network externalities (Katz & Shapiro, 1985) explains why expectations and coordination become fundamental variables for value. The theory of two-sided markets (Rochet & Tirole, 2003) shows that in platforms value is shaped not only by the level of prices but by their structure across the sides of the market — how the platform allocates charges between sellers and buyers.

The value of a platform depends on:

• Cross-side network effects. The more sellers there are on a platform, the more valuable it becomes to buyers, and conversely. In some markets these effects create a winner-takes-all dynamic.
• Complementor participation. Application developers in the Apple ecosystem, drivers on the Uber platform — their number and quality bear directly on the value of the platform.
• Governance rules. How does the platform distribute value among participants? What are the principles of access, pricing and data use?
• Data network effects. Each user interaction generates data that refine the platform’s algorithms and increase the value of the service for everyone.
• Ownership and access rights. Do users own their data and content, or does the platform?

Reuschl and co-authors (2021) showed that in sharing-economy business models the configurations of value depend on the control of shared resources and on the structure of ownership rights. Valuing a platform therefore requires more than a forecast of the company’s cash flows: the ecosystem’s rents, its governance power and the durability of participation externalities must all be assessed.

Figure 1.3. The value ecosystem of a platform

graph TD
    P["Platform<br/><i>Governance rules, Algorithms, Data</i>"]

    V["Users<br/><i>Demand, Data, Content</i>"]
    K["Complementors<br/><i>Applications, Services</i>"]
    R["Regulators<br/><i>Competition, Privacy</i>"]

    V -->|"Use,<br/>data generation"| P
    P -->|"Services,<br/>personalisation"| V
    K -->|"Additional<br/>services"| P
    P -->|"Access to the<br/>user base"| K
    R -->|"Regulation,<br/>standards"| P

    V ---|"Cross-side<br/>network effects"| K

    style P fill:#4a90d9,color:#fff
    style V fill:#50b5a9,color:#fff
    style K fill:#f5a623,color:#fff
    style R fill:#e8705a,color:#fff

The valuation of platforms is among the most pressing areas of contemporary valuation theory, because traditional DCF and multiples methods struggle to capture the value of network effects, ecosystem rents and winner-takes-all dynamics.

ESG and societal value#

The classical shareholder value paradigm does not cover the whole field of valuation. Environmental, social and governance (ESG) factors act on business value through several transmission channels:

• Regulation. Tightening environmental standards — the EU taxonomy, the Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards applied under it, and the international IFRS S1/S2 standards — bear directly on operating costs, investment needs and permissible activity.
• Reputation. ESG incidents such as environmental disasters or breaches of labour rights can reduce market value sharply and materially through the perception layer.
• Financing conditions. Institutional investors increasingly apply ESG filters, and banks “green” lending criteria. Yadav (2025) showed that a high ESG rating can support a company’s valuation particularly in times of crisis, by lowering the cost of capital and investor mistrust.
• Shifts in demand. Consumer preferences are increasingly oriented towards sustainable goods and services.

A systematic literature review by Dang and co-authors (2025) revealed that ecological and biodiversity risks are entering financial valuation to an increasing extent, through transmission channels that connect environmental degradation with corporate exposure, disclosure and governance.

An analytical distinction must be stressed: private value and societal value are not the same. A company may create great private value for its shareholders while generating negative externalities such as pollution or the depletion of resources. Conversely, activity that creates great societal value may be financially loss-making. Contemporary value theory must nonetheless allow for crossings between private and societal value: regulation, reputation, consumer behaviour and investor preferences increasingly turn “external” ESG factors into internal determinants of value.

Figure 1.4. ESG transmission channels into business value

flowchart LR
    ESG["ESG factors<br/><i>Environment, Social responsibility, Governance</i>"]

    ESG --> R["Regulation<br/><i>Taxonomy, CSRD and sustainability standards</i>"]
    ESG --> Rep["Reputation<br/><i>Brand, Trust</i>"]
    ESG --> Fin["Financing<br/><i>Cost of capital, ESG filters</i>"]
    ESG --> Pak["Demand<br/><i>Consumer preferences</i>"]

    R --> VV["Business value"]
    Rep --> VV
    Fin --> VV
    Pak --> VV

    style ESG fill:#50b5a9,color:#fff
    style VV fill:#4a90d9,color:#fff

Sustainability value as a dimension of long-run value creation#

Within the ESG perspective two views must be distinguished:

1. ESG as a matter of compliance. On this view ESG is a cost centre — regulatory requirements that must be satisfied in order to operate at all.
2. ESG as a dimension of value creation. This view holds that sustainable operations create long-run value: they reduce risk, strengthen the brand, attract talent, open new markets and lower the cost of capital.

This book takes the second view: sustainability is not an addition to value but an integral part of it, acting through all five ontological layers — from cash flows (the effect of regulation) to intangible capital (reputation, human capital). The IFRS S1 and S2 sustainability disclosure standards adopted in 2023 (ISSB; IFRS Foundation, 2023a, 2023b) formalise this view by requiring disclosure of sustainability-related financial effects, opportunities and risks. In the European Union, and therefore in Lithuania, sustainability information is disclosed under the CSRD by applying the European Sustainability Reporting Standards; IFRS S1 and S2 are not mandatory in the EU, although their logic is close.

Summary of the section#

Four structural transformations — the dominance of intangible capital, the data economy, platform business models and ESG integration — extend the boundaries of the concept of business value beyond the classical DCF frame. These transformations are not peripheral: they change the nature of value itself. Value is increasingly created not in tangible assets but in relationships, data, ecosystems and institutional context. The ontological model presented in section 1.1.2 integrates these transformations through the strategic and intangible capital layers, but operationalising them — measurement, forecasting, valuation — remains an open methodological problem (for more detail see section 1.1.5 and part 1.2).

1.1.5 Critical analysis and discussion#

The preceding sections presented the ontology of business value, the structure of the dualism and the contemporary transformations of the concept. This section changes the perspective: it examines the limits of valuation methods, compares different approaches and identifies unresolved academic problems. The purpose of a critical assessment is not to discredit existing methods but to understand the bounds of their application — when a given approach is appropriate, when it is insufficient, and why.

The first dispute — is the discounted cash flow model enough?#

Is DCF the foundation of valuation to which other methods merely contribute, or one instrument among equals, suited only to businesses of a certain kind? On the answer depends whether alternative methods count as a supplement or as a substitute.

The position defending the primacy of DCF rests on the fact that it alone expresses the definition of value directly: value is the present value of future economic flows (Fisher, 1930; Williams, 1938). Other methods lack this connection — multiples import the pricing of other transactions, and asset-based valuation measures historical outlays. In practice the position also rests on the visibility of DCF assumptions: they are written into the model and can therefore be tested (Damodaran, 2012). The choice of a multiple permits no such review.

The opposing position holds that this primacy is conditional and applies only where the model’s assumptions are satisfied. The discounted cash flow (DCF) method remains the cornerstone of fundamental valuation, because it preserves the central intuition that value must relate to future economic flows. Its limits, however, are serious and well documented (Phelan, 1997; Crouzet et al., 2022).

First, parameter sensitivity. Small changes in growth rates, terminal value or the discount rate can generate large changes in the valuation. No universal magnitude can be stated here: the size of the effect is governed by the gap between the discount rate and the growth rate, by the share of terminal value in total value, and by the forecast horizon. In this chapter’s sensitivity table (see section 1.2), a change in the terminal growth rate from 2 % to 3 % — one percentage point — alters total value by between 8.1 % (at a WACC of 12 %) and 16.5 % (at a WACC of 8 %). Terminal value itself responds more strongly than total DCF value, because the present value of the flows in the explicit forecast period does not change; where the gap between the discount rate and the growth rate is small, the change may exceed 50 %. A DCF result is therefore less an “answer” than a “sensitivity range”.

Second, insufficiency of the model. DCF suits the valuation of stable, predictable businesses more naturally than it suits strategic flexibility, synergies or option-like payoffs. Real options theory was developed to fill precisely this gap, but calibrating it in practice is demanding and depends on modelling choices.

Third, the opacity of intangible capital. Where the principal drivers of value are data, talent, platform governance or organisational routines, short-term cash flows may be a poor reflection of the real generation of value. An innovative company may invest aggressively, and so report negative cash flows, while creating enormous long-run value through intangible assets.

Fourth, the dominance of terminal value. It is often said in practice that 60–80 % of total DCF value consists of terminal value — a practitioners’ rule of thumb rather than an empirical law — that is, value attributed to the period beyond the explicit forecast. Terminal value rests on highly simplified assumptions (constant growth in perpetuity), so the greater part of value is essentially an extrapolation rather than a forecast.

Why the positions differ. The dispute arises not from conflicting data but from a different question. The first position asks which method best expresses the concept of value, and answers reasonably: DCF. The second asks which method yields the most reliable answer for a particular object, and answers that it depends on the object. Both answers are correct for their own question, which is why the dispute is often left unresolved — the parties are arguing about different things.

Epistemic status. This is uneven across the four limits. Parameter sensitivity is the most rigorously grounded: it can be demonstrated in any model and verified by calculation (see the sensitivity table in section 1.2). The opacity of intangible capital rests on an accumulating but still uneven empirical base. Least well grounded is the extent of terminal value dominance: the proportions depend on the forecast horizon and on growth assumptions, so a general figure is a point of reference here, not a regularity.

Practical implication. What follows for the valuer is not the abandonment of the method but a test of its assumptions before choosing it: can the flows be forecast, do comparable transactions exist, does value reside in the tangible core? Where the answers are affirmative, DCF is the first choice. Where they are not, it remains a point of reference against which other methods are calibrated, and the centre of gravity shifts to those layers (see section 1.1.2) in which value is actually concentrated.

DCF is therefore irreplaceable as a baseline reference but insufficient as a comprehensive ontology of value.

A comparison of valuation approaches: an integrative table#

Each valuation approach illuminates a particular ontological layer while remaining “blind” to others. The integrative table below synthesises the strengths and limits of the valuation methods:

The table shows that no single method covers all five ontological layers. Methodological pluralism — combining several approaches — is therefore not a compromise but a methodologically grounded principle. The conclusion rests on an assumption worth naming: that the particular valuation task really does span all five layers. A narrower question — setting a tariff for regulated infrastructure, say — may be answered within a single layer; pluralism is obligatory only so far as the question itself is broad.

Unresolved academic problems#

Although the theory of business value is a mature field, several fundamental questions remain unresolved.

1. Measuring intangible capital. How are data, ecosystem position and human capital to be measured reliably and comparably? Indicators of intangible assets improve valuation and the analysis of financial health, yet such measures remain partial and sensitive to context (Green & Ryan, 2005; Sriram, 2008; Russell, 2016). Four specific problems arise:

• The recognition problem: many intangible assets are not capitalised in the accounts.
• The boundary problem: it is unclear whether value belongs to the asset itself or to a complementary system.
• The depreciation problem: rates of obsolescence are unstable and specific to context.
• The transferability problem: an asset valuable in one organisational environment may be worthless in another.

A further complication arises after acquisitions: IFRS 3 requires the transaction premium to be allocated between identifiable intangible assets (patents, customer relationships, brands) and residual goodwill, whose subsequent impairment test becomes a subjective act of valuation — so that accounting “recognition” not only reflects but also shapes the market’s perception of asset value.

In practice, three specialised methods are used to value intangible assets: relief from royalty (RRM), which values an asset through the hypothetical royalty payments saved; multi-period excess earnings (MPEEM), which deducts contributory asset charges from DCF cash flows, isolating the value created by the intangible; and the with and without method, which compares the value of the business with and without the specific intangible. These methods are applied in the case studies of later chapters (see Chapters 13 and 14).

2. Uncertainty and risk. Standard valuation models (DCF, CAPM) assume that uncertainty can be reduced to risk — that is, that the probability distribution is known or can be reasonably estimated. In many strategic and technological environments this is untrue: what is met is Knightian uncertainty, where the distribution of probabilities is itself unknown or unstable. How should valuation models reflect deep uncertainty rather than stochastic risk alone? Scenario analysis, robust control and reasoning in real options become more appropriate than a point estimate (Phelan, 1997; Alfaro et al., 2018).

3. Formalising narratives and behavioural factors. Behavioural finance has documented cognitive biases and the effect of narratives on prices convincingly. But how should these factors be integrated formally with strategic and cash-flow-based models? Shiller’s (2017) narrative economics offers a qualitative perspective, yet a quantitative synthesis with DCF or multiples methods remains an open problem.

4. The relation between private and societal value. How should the private value of a business be related to societal and ecological value where externalities are material? Regulation (the EU taxonomy, the CSRD, the ISSB standards) is formalising the connection to an increasing extent, but academic theory still lacks a unified model that would integrate private and societal value without “crushing” either dimension into the other.

5. The temporality of value: the life-cycle and sustainability dilemma. The standard DCF model implies that value is created by the largest cash flows as early as possible. This ignores the question of the value-lifespan equilibrium: does a product designed to fail after two years and be replaced by a new one (planned obsolescence) create more value than a durable product with smaller periodic flows? The problem is at once economic, ethical and ecological — it cuts across the boundary between private value (shareholder return) and societal value (resource use, the cost of waste). As circular economy regulation and ESG standards develop, the question of the balance between value and lifespan grows steadily more pressing for valuation practice: maximising short-term return may destroy value over the long run through reputational, regulatory and environmental risks.

The second dispute — is value a state or a process?#

Is business value a magnitude that can be fixed at a point in time, or a process of which a snapshot is misleading in principle? The question is not merely academic: on the answer depends how long a valuation conclusion is held to remain valid.

The state position derives from the assumption of Fisher (1930) and Williams (1938) that the value of an asset is the present value of its future cash flows. If the flows and the discount rate are defined, value is an unambiguous number, and time enters the model only as the order of discounting. The whole practical apparatus rests on this logic — DCF (1.1), multiples, asset-based valuation. Value here is a “snapshot”: a fixed magnitude at a particular point in time, calculated from present assumptions and projections.

The process position holds that a company’s capabilities and market relations reconfigure themselves continuously, so that value is not a noun but a process — a continuous transformation, a dynamic “becoming”. Teece’s (2007) notion of dynamic capabilities formalises the idea: competitive advantage is created not by the set of resources held but by the ability to reconfigure it. The state vector presented in this chapter (formula 1.5), the dynamics equation (formula 1.6) and the recursive loop (formula 1.8) belong to this camp.

Why the positions differ. Not because of the data — both sides agree on the facts. The difference lies in the assumptions: the state view treats the structure of the model as fixed and admits only the parameters as variable, whereas the process view holds that the structure changes too. No empirical study will therefore settle the dispute — it concerns what question the model is permitted to ask at all. The practical tension remains open: this chapter’s apparatus belongs to the process camp, while everyday valuation tools belong to the state camp.

Epistemic status. The dynamics of value have an epistemological dimension: not everything is “risk” in the sense of the model. Under Knightian (1921) uncertainty (see problem 2 above), what changes is not only the values of the parameters but the applicability of the model itself — we no longer know which sets of scenarios to define. The value system therefore has two distinct regimes: (a) a regime of stable probabilistic assessment, in which DCF and CAPM work as reliable tools of approximation, and (b) a regime of structural uncertainty, in which what changes is not the parameters but the logic of valuation itself — the earlier risk matrix no longer fits, because the economic structure has reconfigured. The transition between these regimes is usually abrupt and hard to predict; this is precisely what Taleb (2007) calls a “black swan”.

Practical implication. What follows for the manager is not a choice of camp but the recognition of a regime. While the structure of a sector is stable, the state apparatus is appropriate and cheaper; but the period for which a valuation remains valid must be stated as an assumption rather than inferred from the date on the document. Signals of a regime break — a regulatory change, a technological substitution, a reconfiguration of business models across the sector — are grounds for valuing afresh rather than for adjusting the parameters of the old model.

Methodological recommendations#

In the light of the limits discussed, a rigorous valuation methodology should be pluralistic:

Baseline tools:

• DCF — the cash flow “anchor”.
• Market multiples — the benchmark for market comparison.

Necessary supplements:

• The logic of real options — for flexibility.
• Strategic analysis — for rents and barriers to imitation.
• Models of intangible capital — for non-physical assets.
• Ecosystem analysis — for platforms and network businesses.
• Scenario methods — for deep uncertainty.
• ESG integration — for long-horizon risks and opportunities.

This pluralist principle is developed in detail in part 1.2 (analytical models and formulas) and applied in practice throughout the book.

Epistemological calibration#

Both disputes discussed in this section remain open, and that is not a defect of exposition. Neither is settled by data: the first rests on a different question, the second on a different assumption about what is held constant in the model. There simply is no consensus that could be handed on here as a conclusion.

This textbook takes a restrained position: DCF is a baseline reference rather than a definition of value, and the valuation apparatus is chosen according to the layer in which value resides. The position is not neutral — it belongs to the process camp. The reader has grounds to disagree with it, particularly in valuing stable, predictable businesses, where the state view is well founded and cheaper.

The differing status of the two disputes matters in practice. In the first there is something to test: whether the assumptions hold can be assessed before the work begins. In the second there is no test: a regime break is recognised only after the fact, so what remains here is not a method but vigilance.

1.1.6 The technology perspective#

The concept of business value and the methods of measuring it did not take shape in an intellectual vacuum — in every era they were determined not only by theoretical insight but also by the technological possibilities of the day. Three technological waves have fundamentally changed how value is understood, measured and created.

The era of computerisation (1970–1995): from intuition to the model#

Before that decade business valuation was largely a qualitative process. Although Irving Fisher’s discounted cash flow logic had been known since the beginning of the twentieth century (Fisher, 1906), practical application of the DCF model was limited, because manual calculation was far too laborious. The break came with the release of VisiCalc (1979), the first electronic spreadsheet. According to D. Bricklin, VisiCalc’s creator, the spreadsheet “allowed the computer to do the iterative ‘what if’ work in minutes rather than days, making financial planning dynamic” (Power, 2004). The analyst gained the ability to model cash flows interactively and to change assumptions in real time. It was this digital what-if logic that turned valuation into an iterative process resilient to assumptions, extending the bounds of modelling from a single scenario to unlimited matrices.

Lotus 1-2-3 (1983) and Microsoft Excel (1985) democratised financial modelling — DCF valuation ceased to be the privilege of elite investment banks and became a standard competence of the analyst. At the same time Markowitz’s portfolio theory (1952), which had remained an academic curiosity for decades because of the complexity of the matrix computations, became practically applicable: computers made it possible to calculate covariance matrices for hundreds of assets. The CAPM (Sharpe, 1964) was transformed from a theoretical model into an everyday tool for setting the discount rate.

This era changed the epistemology of value itself: value became something calculated rather than merely judged.

The era of the internet and data (1995–2020): from the model to information#

The internet created an unprecedented availability of information. The Bloomberg terminal (in operation since 1982, though widely adopted from the 1990s) provided real-time market data; the EDGAR system (US SEC, 1996) free access to public financial statements; Damodaran’s open data (from the 2000s) systematic databases of risk premia and beta coefficients. This infrastructure was later supplemented by standards for structured financial data such as XBRL (eXtensible Business Reporting Language), and by commercial platforms such as Capital IQ, FactSet and Refinitiv, which made it possible to base valuation not on individual statements alone but on continuously updated data ecosystems. These changes reduced information asymmetry substantially between professionals, academics and a growing proportion of practitioners.

The era also created new challenges for the concept of value. The first dot-com wave (1995–2000) showed that traditional valuation models struggle to capture network effects, growth in the user base and the logic of the platform economy. Amazon’s shares were valued in 1999 at very high revenue multiples in the absence of profit — a value that the classical DCF model could not justify without extreme growth assumptions. This encouraged the development of new valuation models: CLV, the valuation of network effects, platform economics and ecosystem analysis (Parker et al., 2016).

The data revolution also transformed the conception of intangible capital (see 1.1.4). Long-run studies by Ocean Tomo revealed a radical break. In 1975 tangible assets accounted for as much as 83 % of the total value of US S&P 500 companies; by 2025 the position had reversed entirely, and some 90 % of company value already resided in intangible assets (Ocean Tomo, 2025; on the methodology see section 1.1.2). This meant that the ontology of value could no longer be confined to buildings or equipment: it had to encompass data, algorithms, software and organisational knowledge, even where the classical accounting balance sheet fails to measure them properly. The gap explains why the explanatory power of standard multiples has diminished considerably in valuing technology companies.

In the Lithuanian and Baltic context this last transformation became particularly evident in the growth of the region’s “unicorns” (Vinted, Nord Security), where the value created is largely bound up with network effects and intangible intellectual property. Nasdaq Vilnius, in operation since 1993 as the then National Stock Exchange, provided a transparent price-setting infrastructure without which the valuation of public companies would have remained speculative.

The modernisation of technology also opened new possibilities for the availability of financial data. Open data and APIs from the Centre of Registers (Registrų centras, RC) made it possible to automate the import of annual statements into analytical modelling. The integration of XBRL into EU reporting formats changed the nature of the valuation analyst’s work — from manual data entry to algorithmic processing.

The era of artificial intelligence (AI) and AI agents (2020→): from information to automated valuation#

The integration of artificial intelligence (AI) into business valuation opens a third fundamental break. Large language models (LLMs) and AI agents are already changing valuation practice at several levels.

The analytical level. AI models are able to process and synthesise large quantities of financial statements, market data and textual information, performing an initial analysis in minutes rather than days. Automated financial due diligence is no longer a mere vision — it is becoming working practice in investment banks, consultancies and private equity funds.

The forecasting level. Machine learning algorithms forecast cash flows, probabilities of bankruptcy and market multiples. The modern asset pricing literature shows that deep learning can capture vast non-linear interactions where traditional linear regression models stall (Gu et al., 2020). This meets, however, the dilemma of the limited transparency of the “black box problem” — as investors delegate complex valuations to third-party artificial intelligence platforms, a critical epistemological question arises: can we accept responsibility for a model’s value when we are unable to decompose its causal logic rationally in the accounts?

The ontological level. AI is changing the structure of value itself — patents in the field of AI, databases and algorithmic advantages are becoming value drivers in their own right. At the same time AI is democratising valuation competence: what once required a team of investment bank analysts can increasingly be done by a single specialist with AI assistants.

The era raises serious questions too. The “garbage in, garbage out” principle becomes stronger, because AI models can create an illusion of false precision — a complex model fed with poor assumptions generates results that are not better, merely more plausible in appearance (on false precision see Chapter 2). Systemic fragility — where many market participants use the same algorithms — can amplify correlated valuation distortions. Valuation practice also becomes dependent on third-party data infrastructure, model licences and platform standards: if a data source is wrong or a model opaque, the error can be replicated across the whole market. Finally, the question of responsibility — who answers for an AI-generated valuation? — remains legally and ethically unresolved.

A deeper analysis of the relation between technology and value, synthesising all the themes of the book and looking to future prospects, is given in Chapter 19.

1.1.7 The Lithuanian context#

The argument of this chapter has so far relied on the international literature, and almost all of that literature was formulated for deep markets. In Lithuania some of its assumptions do not hold — not because the theory is poor, but because the assumptions are simply not satisfied here.

Market depth as a condition of valuation#

In the fourth quarter of 2025 the main list of Nasdaq Vilnius contained 22 companies, with three more on the First North list (Nasdaq Europe, 2025). By way of comparison, the efficient market hypothesis (see section 1.1.3) presupposes a multitude of independent participants whose transactions incorporate information into price rapidly.

From this follows a conclusion more important than the figure itself: the divergence between fundamental and market value in Lithuania is structural rather than anomalous. In the Western literature divergence is often explained as an investor error that arbitrage corrects in due course. In a thin market the opposite logic applies — the limits of arbitrage (see section 1.1.3) are tighter here, so a deviation may persist for a long time without any “error” at all. For the valuer this means that a market price in Lithuania is weaker evidence of fundamental value than the same price in Frankfurt or New York.

Two value profiles: Ignitis and Vinted#

The five-layer cross-section (see section 1.1.2) becomes evident when two well-known Lithuanian companies are compared, their value concentrated in opposite layers:

The comparison explains why one method will not serve both. For valuing Ignitis the discounted cash flow model (formula 1.1) is natural — its assumptions are satisfied. In the case of Vinted the same model would require forecasting flows that do not yet exist, so practice relies on other tools (see the start-up valuation methods in Chapter 14). The difference arises not from the analyst’s choice but from the layer in which value resides.

The second lesson is subtler. Vinted has no market price, so for it layer 3 is not “different” but invisible: there is no mechanism to turn investors’ expectations into an observable number. This also limits the dualism discussed in section 1.1.3 — that dualism supposes both values to exist simultaneously, whereas for an unlisted company market value arises only at the moment of a transaction.

What this means for the valuer in Lithuania#

The practical conclusion is not that “Lithuania is special”. It is this: before choosing a method, it is worth asking whether the assumptions of that method are satisfied — market depth, the number of comparable transactions, the availability of data. In the Lithuanian market the answer will often be “in part”, and this must be reflected not in abandoning the method but in how the result is presented: with a wider range and with assumptions clearly stated.

1.2 Analytical models and formulas#

This part sets out the principal mathematical formulas introduced in the theoretical part, with detailed explanations of the variables and numerical examples. The formulas fall into two groups: computational (1.1–1.4), which carry worked examples, and conceptual (1.5–1.8), which formalise the ontological structure of the chapter. Some of the formulas were already introduced in part 1.1 and are repeated here with fuller explanations.

Formula (1.1): The discounted cash flow (DCF) model#

The basic formula for enterprise value:

where:

• $V_0$ — the enterprise value today,
• $FCF_t$ — free cash flow in period $t$,
• $WACC$ — the weighted average cost of capital,
• $TV_n$ — the terminal (continuing) value at the end of the forecast period,
• $n$ — the length of the explicit forecast period in years.

Formula (1.2): Terminal value (the Gordon growth model)#

Terminal value is most often calculated with the Gordon growth model:

where $g$ is the constant long-run growth rate, usually close to inflation or the rate of GDP growth. The formula holds only where $g < WACC$: as $g$ approaches WACC, terminal value grows without limit.

Numerical example. Suppose a company generates the following free cash flows (EUR thousand):

WACC = 10 %, long-run growth rate $g$ = 2 %.

Step 1: the value of the explicit period.

Step 2: terminal value.

Step 3: discounted terminal value.

Step 4: total enterprise value.

It is worth noting that terminal value accounts for $1{,}155.8 / 1{,}610 = 71.8\,\%$ of total value — consistent with the frequently cited 60–80 % rule of thumb, and an illustration of the sensitivity of DCF to terminal assumptions (see the critique in section 1.1.5).

Formula (1.3): Economic value added (EVA)#

where:

• $NOPAT$ — net operating profit after taxes,
• $IC$ — invested capital,
• $WACC$ — the weighted average cost of capital.

Numerical example. A company’s NOPAT = EUR 200 thousand, invested capital IC = EUR 1,500 thousand, WACC = 10 %.

A positive EVA (EUR 50 thousand) shows that the company is creating value above the cost of capital. If NOPAT were EUR 120 thousand:

A negative EVA means the destruction of value, even though the accounting profit (EUR 120 thousand) is positive.

Formula (1.4): The weighted average cost of capital (WACC)#

where:

• $E$ — the market value of equity,
• $D$ — the market value of debt,
• $r_e$ — the cost of equity (the return required by shareholders),
• $r_d$ — the cost of debt (the interest rate),
• $T_c$ — the corporate income tax rate.

Numerical example. E = EUR 600 thousand, D = EUR 400 thousand, $r_e$ = 12 %, $r_d$ = 5 %, $T_c$ = 17 % (the standard Lithuanian corporate income tax rate in 2026).

Note: the cost of equity $r_e$ is usually established with the CAPM (for detail see Chapter 11).

Formula (1.5): The state vector of business value#

In an ontological context, the state of a company’s value at time $t$ is described by a vector:

where:

• $CF_t^e$ — expected cash flows,
• $A_t$ — tangible assets,
• $I_t$ — intangible capital,
• $K_t$ — capital structure,
• $S_t$ — strategic position,
• $B_t$ — the behavioural or perceptual state (expectations, sentiment),
• $R_t$ — risk and uncertainty parameters.

This formula is not computational but conceptual: it shows that value depends not on a single parameter but on a vector of seven dimensions. In practical valuation each dimension is operationalised with different instruments (DCF for $CF_t^e$ and $R_t$; strategic analysis for $S_t$; intellectual capital valuation for $I_t$; and so on).

Formula (1.6): The equation of value dynamics#

The state vector $X_t$ is not static — it evolves over time under managerial decisions and exogenous shocks:

where:

• $X_t$ — the business value state vector at time $t$ (see formula 1.5),
• $a_t$ — managerial actions (investments, strategic decisions),
• $\varepsilon_{t+1}$ — exogenous shocks (market changes, regulatory changes, technological breaks),
• $G(\cdot)$ — the transition function describing how the present state, actions and shocks shape the future state.

This formula formalises three essential properties of value: (a) value is path-dependent — the same current indicators may signify different values if they were reached along different trajectories; (b) managerial actions are endogenous to the value system — they not only respond to value but change it; (c) exogenous shocks transmit through every layer — a technological break affects not only cash flows but also strategic position, perception and risk parameters.

Formula (1.7): The compositional function of business value#

where:

• $V_t^{fundamental}$ — fundamental value (DCF logic),
• $V_t^{market}$ — market value (the observed price),
• $S_t$ — the strategic state,
• $I_t$ — the state of intangible capital,
• $\Phi_t$ — the institutional and informational context,
• $\Omega(\cdot)$ — a structural function (not simple addition).

This formula formalises the central claim of the chapter: business value is neither fundamental value alone nor market price alone, but an organised relation between all the ontological layers.

Formula (1.8): The recursive loop of value#

where $V_t$ is value, $E_t$ expectations and $D_t$ decisions.

This recursion shows that valuation is at once descriptive and performative: value acts on expectations, expectations act on decisions, decisions change the fundamentals, and the fundamentals reshape value.

Epistemological limits: the importance of sensitivity analysis#

Since all the preceding models depend on assumptions, the instrument of sensitivity analysis is indispensable. A typical two-dimensional sensitivity table in a DCF context looks as follows:

Note: values are in EUR thousand; calculated from the FCF of the example in part 1.2 (EUR 100–146 thousand), varying WACC and g.

The table demonstrates visually how variation in two parameters (WACC and $g$) creates a wide range of value — from about EUR 1,191 thousand to about EUR 2,527 thousand. This bears out Phelan’s (1997) “illusion of precision” argument and underlines that a valuation result should be presented as a range with assumptions rather than as a single “exact” number.

A more detailed sensitivity analysis and its implementation in Excel are given in part 1.5.

1.3 Summary#

This chapter has formulated six principal claims about business value:

1. Business value is not the same as price. Price is one manifestation of value — the outcome of a market transaction at a particular moment. Value is the broader notion, covering future benefits, risk, strategic agency and institutional context (Friday, 1922; Hardin et al., 2025). Value and price may coincide, but often, and with good reason, they do not.

2. Business value is internally layered. Cash flows and assets remain the fundamental base, but they are transformed by risk, interpreted by perception, modified by strategy and increasingly generated by intangible capital (Groth et al., 1996; Walters, 1997; Crouzet et al., 2022). The five-layer ontological decomposition (Figure 1.1) gives this multi-dimensionality its structure.

3. The dualism of fundamental and market value is permanent rather than accidental. Convergence is possible, but divergence is structurally explained by information asymmetry, uncertainty, narratives and the limits of arbitrage (Albuquerque et al., 2016; Hardin et al., 2025). The EMH is a useful benchmark but not an empirical guarantee.

4. Value is dynamic and recursive. It is shaped by expectations, which act on managerial and financial decisions that change future fundamentals (Kim, 2004; Alfaro et al., 2018). Valuation is at once descriptive and performative — it measures a company and also acts upon it.

5. Every valuation has epistemological limits. All models depend on assumptions: the discount rate, growth rates, terminal value parameters. This chapter’s sensitivity table shows the magnitude. A change of one percentage point in WACC or in the growth rate, relative to the base case, alters value by roughly −12 % to +15 %. The effect is asymmetric, because value depends on WACC and g non-linearly, through the denominator $WACC - g$. Varying both parameters across the full range of the table yields a difference of more than double (EUR 1,191–2,527 thousand). A valuation result should therefore be presented as a range with assumptions rather than as a single “exact” number — which is the substance of Phelan’s (1997) “illusion of precision” argument.

6. The contemporary concept of value encompasses intangible capital, platform ecosystems and the ESG dimension. The classical DCF model remains an irreplaceable foundation, but it is insufficient as a comprehensive ontology. Methodological pluralism — combining several valuation approaches — is a methodologically grounded principle wherever the valuation question spans several layers.

Connections with the chapters that follow#

1.4 Case studies and worked examples#

Case 1: NordTech — the challenge of valuing intangible capital#

This is a hypothetical teaching example based on the dynamics of the Baltic technology sector.

The situation#

NordTech is a SaaS company based in Vilnius that develops a business analytics platform. It has been operating for 5 years, employs 200 people and serves 1,200 clients in the Baltic states and Scandinavia. Its principal financial indicators are as follows:

The problem. Current cash flows are negative, so the DCF value depends entirely on assumptions about future growth and margin. Potential investors nevertheless value NordTech at some EUR 27–36 million. What assumptions does that valuation imply?

Analysis#

An ontological cross-section:

• Layer 1 (core). Current cash flows are negative — the company is investing in growth. Tangible assets are slight (EUR 0.5 million). On this layer alone, value is close to zero or negative.
• Layer 2 (risk). Specific risk is high (a young loss-making company dependent on a few large clients). Venture capital investors in the Baltic region apply a target return of 25–40 % to companies at this stage (see Chapter 14); stable recurring flows reduce risk, but not yet enough to justify a late-stage rate of about 15 %.
• Layer 3 (perception). Investors see the SaaS sector narrative: fast-growing, scalable companies with recurring revenue are valued at high multiples. NRR above 100 % signals that existing clients are increasing their usage — a strong quality signal.
• Layer 4 (strategy). An NRR of 115 % indicates a strong switching-cost effect and growth potential within the existing base. The regional focus (the Baltic states plus Scandinavia) creates a niche competitive advantage. The platform has the potential for data network effects.
• Layer 5 (intangible capital). The principal value lies here: (a) the software code and intellectual property, (b) a team of 120 R&D specialists, (c) accumulated client usage data, (d) client relationships and the brand in the Baltic market.

A valuation synthesis. Investors apply a SaaS multiple (of 6–8× ARR, say):

This valuation reflects not the present layer 1 (negative cash flows) but a combination of layers 3–5: growth expectations, strategic positioning and the potential of intangible capital.

A reverse DCF: what does the multiple imply? Suppose ARR grows at 35 % for eight years, from EUR 4.5 million to some EUR 49.6 million. The FCF margin rises steadily from its present −26.7 % (−1.2 / 4.5) to a target of 20 % in the eighth year, the level of a mature SaaS company, and flows then grow at 3 %. Calculating $FCF_t = ARR_t \times m_t$, where the margin $m_t = -26.7\,\% + (20\,\% + 26.7\,\%) \times t / 8$, $t = 1, \ldots, 8$; terminal value follows formula 1.2 and enterprise value formula 1.1. This gives:

On these assumptions a multiple of 7× ARR is justified only if a young loss-making company is valued almost as a late-stage one — with a substantially lower risk rate (even the lower bound of 25 % yields only some EUR 8.8 million) and with eight years of uninterrupted rapid growth.

Conclusions#

The case illustrates why a single-layer valuation — DCF alone or multiples alone — is insufficient. The ontological approach makes it possible to explain systematically where value resides and to assess critically whether investors’ expectations are well founded. The reverse DCF reveals what the multiple tacitly implies: a value of EUR 31.5 million is justified only by a late-stage risk rate and long, rapid growth — and these are precisely the assumptions the investor must substantiate.

Case 2: Divergence of fundamental and market value on the Nasdaq Vilnius exchange#

A mixed teaching example: the market dynamics are based on observations of the Nasdaq Vilnius exchange, but the company BalticProd and its figures are hypothetical.

The situation#

The Lithuanian manufacturing company BalticProd (the name is hypothetical) is listed on the Nasdaq Vilnius exchange. Its fundamentals are stable and sound:

The DCF value is calculated with the Gordon formula (1.2), taking EUR 8 million as the FCFF of the most recent year: $8 \times 1.02 / (0.09 - 0.02) \approx 116.6$ million. The company has no financial debt, so enterprise value equals the value of equity and can be compared directly with market capitalisation.

The problem. The market price is some 36 % below fundamental value. Does this mean the shares are “cheap”?

Analysis#

Market structure here is not background — it is the explanation. In the fourth quarter of 2025 the main list of Nasdaq Vilnius contained 22 companies, with three more on the First North list (Nasdaq Europe, 2025). A market of this size means that the assumptions of the efficient market hypothesis (see section 1.1.3) are only partly satisfied here: they were formulated for deep markets with a multitude of independent participants.

Analysis by the factors of convergence and divergence (Figure 1.2):

• Liquidity. Nasdaq Vilnius is a small exchange with low trading activity. Low liquidity means that a price may fail to reflect fundamental value simply because there are not enough buyers.
• Information asymmetry. Companies on a small exchange receive less coverage in institutional research — an information vacuum allows the price to drift.
• Constraints on arbitrage. Low liquidity complicates short selling and limits the activity of arbitrageurs.
• The investor base. One possible reason is a narrower base of institutional investors than in Western European markets: less “gravitational force” pulling the price towards fundamentals.

Compositional value (1.7). The simplest illustration of formula (1.7) is a weighted average: giving fundamental value a weight of 60 %, $0.6 \times 117 + 0.4 \times 75 = 100.2$ million. This is only a first approximation: in the general case the operator $\Omega$ is not linear, and the weight itself depends on liquidity, information and the institutional context ($\Phi_t$).

What rate is the market applying? The Gordon formula (1.2) can be solved in reverse: a market capitalisation of EUR 75 million corresponds to a rate of $r = 8 \times 1.02 / 75 + 0.02 \approx 12.9\,\%$. The market is applying a rate roughly 4 percentage points higher than the 9 % WACC, so part of the divergence may be not mispricing but priced illiquidity and information risk, which the 9 % rate does not cover.

Conclusions#

The divergence in this case is explained not by “market error” but by structural features of the market: low liquidity, a shortage of information and constraints on arbitrage. It is a classic instance of the effect of layer 3 (perception) and of market microstructure. For an investor it may signify an opportunity — but only if the fundamentals are sound and the 9 % rate values illiquidity risk correctly — or a risk, since if liquidity does not improve the price may remain “undervalued” for a long time.

1.5 Building the Excel model#

Objective#

To build a basic DCF valuation model with sensitivity analysis that illustrates the theoretical principles of Chapter 1 in practice. On completing the exercise the student will be able to:

• Forecast free cash flows over a 5-year period.
• Calculate WACC and terminal value.
• Perform a two-dimensional sensitivity analysis (WACC × growth rate).
• Interpret a valuation range rather than a single “exact” number.

Step-by-step instructions#

Step 1: the assumptions sheet. Create a separate sheet with the baseline input parameters:

• Opening revenue (EUR)
• Revenue growth rate (%)
• EBITDA margin (%)
• Capital expenditure (% of revenue)
• Change in working capital (% of revenue)
• Tax rate (%)
• Cost of equity $r_e$ (%)
• Cost of debt $r_d$ (%)
• Debt-to-equity ratio (D/E)
• Long-run growth rate $g$ (%)

Step 2: the FCF forecast (5 years). For each year, calculate:

• Revenue = previous year’s revenue × (1 + growth rate)
• EBITDA = revenue × EBITDA margin
• NOPAT = EBIT × (1 – tax rate) (more precisely; in a basic model EBITDA may serve as an approximation of EBIT where depreciation is unknown — see Chapter 9)
• FCF = NOPAT – capital expenditure – change in working capital

Step 3: calculating WACC.

Step 4: terminal value and discounting.

Step 5: sensitivity analysis using a data table. Build a two-dimensional sensitivity table with Excel’s DATA TABLE function:

• Rows: WACC variants (7 %, 8 %, 9 %, 10 %, 11 %, 12 %)
• Columns: variants of the growth rate $g$ (0 %, 1 %, 2 %, 3 %, 4 %)
• Values: the result — $V_0$ for each combination

Step 6: interpretation. Mark the results with colour (conditional formatting):

• Green — value exceeds current market capitalisation (a potentially “cheap” share).
• Red — value is below market capitalisation (potentially “expensive”).
• Yellow — close to the current price (a neutral zone).

List of formulas#

File: Excel/CH01/Ch01_Model_Master.xlsm Formula guide: Excel/CH01/Ch01_Formula_Guide_v2.md

1.6 Review questions and exercises#

1.6.1 Facts and concepts (recognition level)#

Question 1.1 [recognition]. List the five ontological layers of business value (see section 1.1.2) and give one value driver for each. Which layer is the only one directly visible in the financial statements?

Question 1.2 [recognition]. Name the paradigms of the concept of value discussed in section 1.1.1 and one representative of each. What question did each of them set out to answer?

1.6.2 Explanations (comprehension level)#

Question 1.3 [comprehension]. Explain why terminal value in the Gordon growth model (formula 1.2) is sensitive to the difference between the growth rate $g$ and the discount rate. What happens to the formula as $g$ approaches WACC, and what does this mean for the reliability of the forecast?

Question 1.4 [comprehension]. Explain the difference between fundamental and market value. Why can these two values fail to coincide for a long time, even though both relate to the same company?

1.6.3 Computational exercises (application level)#

Question 1.5 [application]. DCF valuation. A company’s forecast FCF (EUR thousand) is: year 1 — 80, year 2 — 92, year 3 — 106, year 4 — 122, year 5 — 140. WACC = 11 %, long-run growth $g$ = 2.5 %. Calculate: (a) the value of the explicit period, (b) terminal value, (c) total enterprise value, (d) the share of terminal value in total value.

Question 1.6 [application]. Calculating EVA. A company’s NOPAT = EUR 350 thousand, invested capital = EUR 2,800 thousand, WACC = 9 %. (a) Calculate EVA. (b) What is the minimum NOPAT at which the company would avoid a negative EVA?

Question 1.7 [application]. WACC. A company’s market value of equity = EUR 12 million, value of debt = EUR 8 million, cost of equity $r_e$ = 14 %, cost of debt $r_d$ = 4.5 %, corporate income tax rate = 17 % (2026). Calculate WACC. How would WACC change if the share of debt rose to 60 %, holding $r_e$ and $r_d$ unchanged? Explain why that assumption is unrealistic (see Chapter 11).

Question 1.8 [application]. Sensitivity analysis. Using the data of question 1.5, construct a sensitivity table: WACC (9 %, 10 %, 11 %, 12 %, 13 %) × $g$ (1 %, 2 %, 2.5 %, 3 %, 3.5 %). What is the range of value? What does this say about the “precision” of DCF?

1.6.4 Comparative analysis (analysis level)#

Question 1.9 [analysis]. Is the relation between Tesla’s market value and its fundamental value better explained by the efficient market hypothesis, by behavioural finance, or by both? Argue your case using the five-layer ontology.

Question 1.10 [analysis]. In which ontological layer does the greater part of the value of Google (Alphabet) reside? And that of a manufacturer of furniture? How does this difference affect the choice of an appropriate valuation method?

Question 1.11 [analysis]. George Soros’s concept of reflexivity holds that market price acts on fundamentals. Give an example from a Lithuanian or Baltic business where this might occur, and identify the channel through which the feedback would operate.

1.6.5 Synthesis and original design exercises (synthesis level)#

Question 1.12 [synthesis]. An ontological audit. Choose one company listed on the Nasdaq Vilnius or Nasdaq Tallinn exchange. Carry out an “ontological audit” of it: identify the layers (of the five) in which the greater part of its value is concentrated. Write an analysis of 1–2 pages with supporting arguments.

Question 1.13 [synthesis]. A study of divergence. Choose two periods in which the price of a particular company, or of a market index, diverged sharply from its fundamental indicators (P/E, P/B). Identify which factors of convergence and divergence (Figure 1.2) were active in each period.

Question 1.14 [synthesis]. Analysis of ESG impact. Choose a Lithuanian or Baltic company that publishes ESG reports. Analyse the transmission channels — regulation, reputation, financing, demand — through which ESG factors may affect its business value. Give concrete examples.

1.6.6 Open discussion questions (evaluation level)#

Question 1.15 [evaluation]. Why is Benjamin Graham’s phrase “price is what you pay; value is what you get” not merely practical advice but also an ontological thesis? How does this distinction show itself in the different standards of value (FMV, investment value, fair value)?

Question 1.16 [evaluation]. Are ESG factors a “real” source of value, or merely a short-lived fashion? How would your answer change if the European Sustainability Reporting Standards applied to all EU companies, including small ones?

Question 1.17 [evaluation]. When is a one-dimensional measure of value — a single number — sufficient for taking a decision, and when is it misleading? Formulate a criterion by which a valuer could decide this before starting work.

1.6.7 Decisions with AI#

Unlike the previous six sections, this one is not part of the taxonomy of cognitive levels. They test what the student can do alone; this one tests whether the student can formulate a problem, hand tasks over to a tool, check the result and take public responsibility for it. The procedure for the tasks is set out in Appendix A: the extended task workflow (A.1), the verification protocol (A.2), the handover record (A.3), the verdict (A.4), defence and critique (A.5), the assessment criteria (A.6), the conditions (A.7). The appendix needs to be read before starting the task.

Question 1.18 [basic task]. Situation. RytLog, a hypothetical private limited company (UAB), has no loans or other financial liabilities. Its free cash flow to the firm for the last year was EUR 5.2 million. The current owner offers to sell all the company’s shares to a potential buyer for EUR 54 million. The buyer asked an AI assistant to assess whether the purchase is worthwhile. Before reading the AI summary, the student writes down protocol steps 0a and 0b (A.2).

This is a task at level 1 of openness: the student must calculate the rate of return that corresponds to the offered price (as in Case 2 in section 1.4) and state the growth rate used. Below is the kind of summary an AI assistant produces within a few minutes. It contains no arithmetic errors.

Valuation of RytLog UAB. The company has no loans or other financial liabilities. Free cash flow to the firm for the last year was EUR 5.2 million, stable over the last four years. The Gordon growth model is applied: WACC 8.5 %, long-run growth rate 2.5 % (conservative, in line with the inflation target).

Fundamental value = 5.2 × 1.025 / (0.085 − 0.025) = EUR 88.83 million. The owner is asking EUR 54 million, so the offered price is 39 % below fundamental value. The DCF method is reliable here because it is based on cash flows rather than on the seller’s opinion. Recommendation: buy.

The student applies the verification protocol to the summary and states whether the conclusion holds after the check. Approximate calculations are enough: what is assessed is not precision but what has been noticed.

Submission: up to two pages, containing:

• records of the eight protocol steps (0a, 0b and 1–6);
• a verdict with a review condition;
• answers to three questions: (1) what rate of return the offered price corresponds to, and what explains its difference from 8.5 %; (2) why the owner might sell for less than the value calculated by the AI; (3) which statements in the summary remain unsupported.

Question 1.19 [extended task]. This task is intended for an independent analysis of a real company: the student formulates a research question, assigns part of the analytical work to AI tools, critically checks the results and, in the seminar, defends the conclusion they have reached. The general principles of working with AI and of carrying out the task are set out in Appendix A, while this section sets out the mechanics of the task that are specific to this chapter.

Task conditions. The lecturer assigns each student two essential things:

• A company. It is recommended to analyse a company listed on a stock exchange. This ensures access to observable market price data and makes it possible to avoid passing confidential organisational data to AI platforms (see section A.7).
• An analytical perspective. This is the lens through which the assigned company is assessed. In this chapter’s task, the analytical perspective is one of the five layers of value (see section 1.1.2).

The decision is addressed to an investor considering whether to buy or sell shares in the company; the research question is formulated so that the answer helps the investor decide.

Work organisation and cross-analysis. The same company is analysed by the whole group of students (or a subgroup), but each member examines it from a different analytical perspective. With a large cohort of students, the lecturer may assign several different companies — one to each subgroup.

Because the students assess different aspects of the same company, in the seminar their conclusions can be combined into an overall picture. This allows a cross-check and an assessment of whether the results of the individual perspectives are logically consistent with one another.

Example: a subgroup of students assesses one company. The first student analyses what rate of return the market applies to the company’s shares (b. Risk), the second assesses what part of the share price is explained by investors’ expectations (c. Perception), and the third examines the barriers to imitation that prevent competitors from replicating the company’s advantage (d. Strategy).

Open variant (f). If the lecturer sets the open variant of the task, the student independently chooses the company to be analysed and formulates a specific research question. In terms of the levels of openness in A.1, perspectives a–e correspond to level 3 and the open variant (f) to level 5.

The analytical perspectives and the corresponding research questions are set out in the table below. In each perspective, the factors that determine value and their causal links are identified first (qualitative assessment); quantitative assessment, where the chapter material allows it, counts in the student’s favour. Anything that cannot be calculated from the material of this or earlier chapters is not required; applying methods from other topics is the student’s own initiative and counts in the student’s favour. The questions in the table indicate the direction of the research; in stage E1 this direction is narrowed down to one specific question. The illustrative qualitative examples draw on the case of Ignitis Group (the value profile of this company is analysed in more detail in section 1.1.7), while the quantitative E2–E6 examples illustrate a hypothetical company with no significant financial debt; if the chosen company has such debt, the qualitative route is followed. Note that these examples show the direction of analytical thinking and the mechanics of the task, not absolute or final answers.

Task workflow. The task is carried out following the extended task workflow E1–E7 described in Appendix A (A.1). Examples for this chapter are given below; stage E4 needs no separate example (see A.3).

• E1. Formulating the research problem.

  Formulation to avoid: “Ignitis Group’s risk.”

  Recommended formulation (perspective b): “Do the risk factors typical of regulated activities justify a lower rate of return than analysts apply to Ignitis Group, and is it therefore worthwhile for an investor to buy its shares?”

• E2. Analytical objective and format of the result.

  Example (qualitative): “The factors that increase and reduce the company’s risk are identified, and it is assessed whether they justify a rate of return below or above the market average; the conditions under which the conclusion would change are stated.”

  Example (quantitative, for a hypothetical company with no significant financial debt): “Two intervals for the rate of return are established: the implied one, derived from the current share price, and the interval of rates that analysts apply to comparable companies. Rates of the same kind are compared (for example, both WACC). The assumptions used are stated for each interval (for example, the long-run growth rate).”

• E3. Decomposition into subtasks and assignment to AI. Examples:

  • collecting historical financial data, the share price and number of shares, and data on financial debt — assigned to AI as a source finder; the figures obtained are verified against the company’s official announcements;
  • assessing the risk factors and establishing the direction of their effect on the rate of return — not assigned to AI: this is the core of the research, and the student is responsible for its conclusion;
  • calculating the implied rate of return from the current price (if the company has no significant debt) — not assigned to AI: it is calculated independently in an Excel model to keep full control over the components of the result;
  • searching for the rates of return that analysts apply to comparable energy companies — assigned to AI as a source finder; each figure found is checked in the primary source.

• E5. Verifying the results.

  Example (protocol step 0a): “The hypothesis is that the rate of return applied by analysts will be close to the rate justified by the risk factors, because the company operates regulated assets and its cash flows are easy to forecast.”

  Example (protocol step 0b, if the rate is calculated): “If the implied rate were above 15 %, the more likely explanation would be a wrongly chosen cash flow, not a market that sees such a high risk.”

  Example (protocol step 4 — sensitivity, if the rate was calculated): “The result is sensitive to the long-run growth rate: changing it by 1 percentage point changes the implied rate of return by about the same amount. When cash flow is about 10 % of the price, a 10 % change in cash flow changes the rate by about 1 percentage point, so what matters most is which quantity is more uncertain.”

• E6. Research conclusions and review condition.

  Example (qualitative): “The risk factors (regulated revenue, low dependence on the business cycle) justify a rate of return below the market average. If analysts apply a higher rate to the company, the difference has to be explained by other factors — liquidity, expectations or the risk of the investment programme; until it is explained, the purchase decision remains uncertain. The conclusion will be reviewed if the state regulator changes the allowed rate of return for network infrastructure.”

  Example (quantitative, if the rate was calculated): “If the implied rate of return, for the chosen range of growth rates, falls within the range of rates applied by analysts, the result is consistent with that range and the chosen assumptions. If it is higher, the market either requires an additional return or expects lower cash flows than the assumption implies, and this has to be explained. If it is lower, the price may contain an expectations premium (perspective c).”

• E7. Defence, critique and reflection. The lecturer forms defender–opponent pairs within the subgroup working on the same company, deliberately setting different analytical perspectives against each other (A.8). For remote work without contact, the counterfactual can be the event in the E6 review condition (for example, the regulator changes the allowed rate of return).

  Example: a student who examined perspective c (perception) acts as opponent to the analyst of perspective b (risk): “If a significant part of the price is formed by expectations and narrative, the rate of return you stated measures more than risk alone. Does your conclusion withstand this argument?”

Submission. As in A.1: up to 4 pages (shorter for remote work, see A.5); in an annex — the handover record and the unedited prompt log; after the seminar — the defender’s reflection and the opponent’s summary (A.5).

References#

Classic and foundational works#

• Akerlof, G. A. (1970). The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), 488–500. https://doi.org/10.2307/1879431
• Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
• Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
• Fama, E. F. (1991). Efficient Capital Markets: II. The Journal of Finance, 46(5), 1575–1617. https://doi.org/10.2307/2328565
• Fisher, I. (1906). The Nature of Capital and Income. Macmillan.
• Fisher, I. (1930). The Theory of Interest. Macmillan.
• Freeman, R. E. (1984). Strategic Management: A Stakeholder Approach. Pitman.
• Friday, D. (1922). An Extension of Value Theory. The Quarterly Journal of Economics, 36(2), 197–219. https://doi.org/10.2307/1883479
• Gordon, M. J. (1959). Dividends, Earnings, and Stock Prices. The Review of Economics and Statistics, 41(2), 99–105. https://doi.org/10.2307/1927792
• Graham, B., & Dodd, D. L. (1934). Security Analysis. McGraw-Hill.
• Grossman, S. J., & Stiglitz, J. E. (1980). On the Impossibility of Informationally Efficient Markets. The American Economic Review, 70(3), 393–408.
• Groth, J., Byers, S., & Bogert, J. (1996). Capital, Economic Returns and the Creation of Value. Management Decision, 34(6), 21–30. https://doi.org/10.1108/00251749610121452
• Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
• Kaplan, R. S., & Norton, D. P. (1992). The Balanced Scorecard – Measures That Drive Performance. Harvard Business Review, 70(1), 71–79.
• Katz, M. L., & Shapiro, C. (1985). Network Externalities, Competition, and Compatibility. American Economic Review, 75(3), 424–440.
• Knight, F. H. (1921). Risk, Uncertainty, and Profit. Houghton Mifflin.
• Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics, 47(1), 13–37. https://doi.org/10.2307/1924119 [PATIKRINTI]
• Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
• Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 29(2), 449–470. https://doi.org/10.2307/2978814
• Mises, L. von. (1949). Human Action: A Treatise on Economics. Yale University Press.
• Modigliani, F., & Miller, M. H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. The American Economic Review, 48(3), 261–297.
• Mossin, J. (1966). Equilibrium in a Capital Asset Market. Econometrica, 34(4), 768–783. https://doi.org/10.2307/1910098 [PATIKRINTI]
• Phelan, S. E. (1997). Exposing the Illusion of Confidence in Financial Analysis. Management Decision, 35(2), 163–168. https://doi.org/10.1108/00251749710160322
• Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance. Free Press.
• Ricardo, D. (1817). On the Principles of Political Economy and Taxation. John Murray.
• Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.2307/2977928
• Shiller, R. J. (1981). Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends? The American Economic Review, 71(3), 421–436.
• Shleifer, A., & Vishny, R. W. (1997). The Limits of Arbitrage. The Journal of Finance, 52(1), 35–55. https://doi.org/10.1111/j.1540-6261.1997.tb03807.x
• Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. W. Strahan and T. Cadell.
• Soros, G. (1987). The Alchemy of Finance: Reading the Mind of the Market. Simon & Schuster.
• Walters, D. (1997). Developing and Implementing Value-Based Strategy. Management Decision, 35(10), 709–720. https://doi.org/10.1108/00251749710192020
• Williams, J. B. (1938). The Theory of Investment Value. Harvard University Press.

More recent works (2000–2026)#

• Albuquerque, R., Eichenbaum, M., Luo, V., & Rebelo, S. (2016). Valuation Risk and Asset Pricing. Journal of Finance, 71(6), 2861–2904. https://doi.org/10.1111/jofi.12437
• Alfaro, I., Bloom, N., & Lin, X. (2018). The Finance Uncertainty Multiplier. Journal of Political Economy, 132(2), 577–615. https://doi.org/10.1086/726230
• Cedergren, M. C., Chen, C., & Chen, K. (2018). The Implication of Unrecognized Asset Value on the Relation Between Market Valuation and Debt Valuation Adjustment. Review of Accounting Studies, 24, 426–455. https://doi.org/10.2139/ssrn.2378145
• Crouzet, N., Eberly, J. C., Eisfeldt, A. L., & Papanikolaou, D. (2022). The Economics of Intangible Capital. Journal of Economic Perspectives, 36(3), 29–52. https://doi.org/10.1257/jep.36.3.29
• Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset (3rd ed.). Wiley.
• Dang, T. N., Nandy, M., Lodh, S., & Hussainey, K. (2025). Nature at Risk, Finance at Stake: A Systematic Literature Review of Biodiversity Risk in Finance Research. Business Strategy and the Environment. https://doi.org/10.1002/bse.70398
• Gamerschlag, R. (2013). Value Relevance of Human Capital Information. Journal of Intellectual Capital, 14(2), 325–345. https://doi.org/10.1108/14691931311323913
• Green, A., & Ryan, J. J. C. H. (2005). A Framework of Intangible Valuation Areas (FIVA). Journal of Intellectual Capital, 6(1), 43–52. https://doi.org/10.1108/14691930510574654
• Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009
• Hardin, W. G., Jiang, X., Wu, Z., & Zhang, Q. (2025). Intrinsic Value, Transaction Price Movement, and Cointegration. Financial Management. https://doi.org/10.1111/fima.12496
• Hartman-Glaser, B., Mayer, S., & Milbradt, K. (2025). A Theory of Cash Flow-Based Financing with Distress Resolution. Review of Economic Studies. https://doi.org/10.1093/restud/rdaf009
• Haskel, J., & Westlake, S. (2018). Capitalism Without Capital: The Rise of the Intangible Economy. Princeton University Press.
• Hughes-Cromwick, E., & Coronado, J. (2019). The Value of US Government Data to US Business Decisions. Journal of Economic Perspectives, 33(1), 131–146. https://doi.org/10.1257/jep.33.1.131
• Kim, K. (2004). Strategic Planning for Value-Based Management. Management Decision, 42(8), 938–948. https://doi.org/10.1108/00251740410555434
• Knorr, C., Erath, M., Saesen, J., Kindermann, B., & Strese, S. (2025). What Role Do Data Network Effects Play for Multihoming Complements? Electronic Markets, 35. https://doi.org/10.1007/s12525-025-00817-4
• Lepak, D. P., Smith, K. G., & Taylor, M. S. (2007). Value Creation and Value Capture: A Multilevel Perspective. Academy of Management Review, 32(1), 180–194. https://doi.org/10.5465/amr.2007.23464011
• Liu, D., Tseng, K., & Yen, S. (2009). The Incremental Impact of Intellectual Capital on Value Creation. Journal of Intellectual Capital, 10(2), 260–276. https://doi.org/10.1108/14691930910952650
• Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy – and How to Make Them Work for You. W. W. Norton.
• Power, D. J. (2004, September 30). A brief history of spreadsheets. DSSResources.COM. https://dssresources.com/history/sshistory.html
• Reuschl, A. J., Tiberius, V., Filser, M., & Qiu, Y. (2021). Value Configurations in Sharing Economy Business Models. Review of Managerial Science, 16, 89–112. https://doi.org/10.1007/s11846-020-00433-w
• Rochet, J.-C., & Tirole, J. (2003). Platform Competition in Two-Sided Markets. Journal of the European Economic Association, 1(4), 990–1029. https://doi.org/10.1162/154247603322493212
• Russell, M. (2016). The Valuation of Pharmaceutical Intangibles. Journal of Intellectual Capital, 17(3), 484–506. https://doi.org/10.1108/JIC-10-2015-0090
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Shiller, R. J. (2017). Narrative Economics. American Economic Review, 107(4), 967–1004. https://doi.org/10.1257/aer.107.4.967
• Soros, G. (2014). Fallibility, Reflexivity, and the Human Uncertainty Principle. Journal of Economic Methodology, 20(4), 309–329. https://doi.org/10.1080/1350178X.2013.859415
• Sriram, R. (2008). Relevance of Intangible Assets to Evaluate Financial Health. Journal of Intellectual Capital, 9(3), 351–366. https://doi.org/10.1108/14691930810891974
• Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
• Teece, D. J. (2007). Explicating Dynamic Capabilities. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
• Tseng, K., Lin, C. I., & Yen, S. (2015). Contingencies of Intellectual Capitals and Financial Capital on Value Creation. Journal of Intellectual Capital, 16(1), 156–173. https://doi.org/10.1108/JIC-04-2014-0042
• Yadav, A. (2025). Financial Distress, ESG Practices and Firm Valuation: Comparing Pre- and Post-Paris Agreement Periods. Management Decision. https://doi.org/10.1108/md-05-2024-1159

Lithuanian and Baltic institutional sources#

• Nasdaq Europe. (2025). Nasdaq Vilnius Stock Exchange celebrates its 30th anniversary – 22 companies listed on Main market, 3 on First North (2025 Q4). https://www.nasdaq.com/solutions/european-markets/vilnius

Standards and institutional documents#

• IFRS Foundation. (2013). IFRS 13 Fair Value Measurement. https://www.ifrs.org/issued-standards/list-of-standards/ifrs-13-fair-value-measurement/
• IFRS Foundation. (2023a). IFRS S1 General Requirements for Disclosure of Sustainability-related Financial Information. https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/ifrs-s1-general-requirements/
• IFRS Foundation. (2023b). IFRS S2 Climate-related Disclosures. https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/ifrs-s2-climate-related-disclosures/
• International Valuation Standards Council [IVSC]. (2022). International Valuation Standards. IVSC.
• Ocean Tomo. (2025). Intangible Asset Market Value Study. Ocean Tomo, a part of J.S. Held. https://www.oceantomo.com/intangible-asset-market-value-study
• OECD. (2022). [PATIKRINTI: exact title and link of the OECD publication on measuring data as an asset].