Introduction for the reader

“Numbers have an important story to tell, and it is up to us to help them tell it.” — Stephen Few (2012)

“Essentially, all models are wrong, but some are useful.” — George E. P. Box

Part A. General information about the textbook (for all readers) — the concept of the book, its target audience, its architecture and the conditions under which it was prepared.

1 The concept of the textbook: integrating planning and valuation#

In university practice, business planning and business valuation are often taught as two isolated disciplines. Business planning traditionally belongs to management programmes, which emphasise strategy, operational processes, marketing and human resources. Business valuation is part of finance programmes, which are dominated by the discounted cash flow (DCF) method, the calculation of the weighted average cost of capital (WACC), and multiples and real options methods. The two disciplines draw on different academic literature, unrelated case studies and different analytical and computational models.

The central claim of this book is that such an academic separation, although institutionally convenient, is misleading both pedagogically and in practice. Every strategic business decision (an investment in a production line, market expansion, debt refinancing, a structural reorganisation or the transfer of a business) has an inseparable valuation dimension: what the alternative is worth, how risky the assumptions are and how sensitive the result is to change. Conversely, every valuation inevitably rests on planning assumptions: which revenue growth rates are forecast, which capital structure is considered optimal and which terminal value is economically justified.

Valuation without planning becomes a sterile set of numbers that does not reflect reality, while planning without valuation remains an unvalidated theoretical vision.

This textbook joins the two disciplines into a single analytical process, with the modelling of business decisions as its axis. After studying this material, the reader is expected to be able to:

• Quantify alternatives: understand that the alternatives of every business decision can be modelled quantitatively, at least in part.
• Integrate processes: build an Excel model that directly links planning assumptions to valuation logic.
• Analyse uncertainty: assess the sensitivity of assumptions and the risk of forecasts using scenario and Monte Carlo analysis.
• Argue in ranges: justify a decision not as a single, supposedly “correct” number, but as a range of values with clear limits and an assessment of risk.

This is not merely a set of technical skills — it is an analytical and epistemological stance. The book deliberately emphasises the limits of models: where they work, under which conditions they lose their adequacy and where mathematical elegance can disguise economic nonsense. The analyst with the highest level of competence is not the one who calculates a result to the nearest decimal place, but the one who knows at which point their calculations can no longer be trusted.

The concept of the textbook combines the results of academic research, a synthesis of theoretical approaches, and experience of teaching and business consulting. For this reason, theoretical positions are not left in a vacuum: they are directly linked to management decisions and financial modelling. Some chapters also present models proposed in this textbook (the main ones are listed in section 2.2). Sections 3 and 8 explain in more detail how this concept is reflected in the structure of the chapters, while the integration of planning and valuation itself is set out in the integrative Chapter 16 (section 16.1.4).

2 Who the book is for and how to read it#

This publication has been designed around the specific needs of four different audiences. To make learning more effective, targeted reading guidelines have been developed, which allow each reader to select material according to their own goal.

The introduction itself is organised on the same principle. Part A sets out the general context, Part B is addressed directly to students and practitioners, and Part C is intended for lecturers and researchers. It follows that students need to read only Parts A and B; the methodological foundation of the book, described in Part C, is optional for them.

2.1 Undergraduate student#

Study aim — to acquire the fundamentals of business planning and valuation, to be able to build basic financial models in a spreadsheet, and to prepare for master’s studies or for the position of a junior analyst.

Reading guidelines.

• Core. The introduction, Part I (Chapters 1–2), Part II (Chapters 3–4) and Part III (Chapters 5–8). This is the foundational theoretical core that is needed to understand the rest of the material.
• Finance practice. Part IV (Chapters 9–12). This part provides the necessary practice in financial analysis and forecasting.
• Methodological depth. Chapter 13 in Part V, devoted to mastering the main business valuation methods — DCF, multiples and their triangulation.
• Synthesis of processes. Chapter 16 in Part V, which helps to combine the separate theoretical components into an integrated financial model of a business plan.
• Implementing plans (overview). Section 17.1 of Chapter 17 and the comparison of methods in the summary of that chapter — a general understanding of how a plan becomes a management system based on the Balanced Scorecard (BSC), objectives and key results (OKR) and key performance indicators (KPI). The calculation apparatus (17.2) and the case studies (17.4) — depending on the study programme, or at master’s level.
• Deferred to master’s studies. It is recommended to skip Chapter 14 (special valuation cases), Chapter 15 (multi-criteria evaluation), Chapter 18 (exit strategies) and Chapter 19 (a reflection on the technological future).

2.2 Master’s student#

Study aim — to acquire a toolkit of advanced valuation methods and to develop the ability to model complex cases independently, such as start-up valuation, mergers and acquisitions (M&A) and leveraged buyout (LBO) transactions. A further aim is to understand the methodological limitations of financial models and to prepare for research work or a professional career as a business consultant.

Reading guidelines.

• Full scope. All of the material is studied — from the introduction to the epilogue in Chapter 19.
• Analytical focus. Particular attention is given to complex analysis. This covers Chapter 4 (risk analysis methods), Chapter 13 (business valuation methods), Chapter 14 (special cases), Chapter 15 (multi-criteria decision analysis, MCDA), Chapter 17 (managing plan implementation) and Chapter 18 (exit strategies).
• Models proposed in the textbook. At master’s level the emphasis is on specialised tools. Chapter 15 examines the Condition–Trend–Variation (BTV) model in detail. Chapter 17 presents a fifth perspective of the Balanced Scorecard (BSC) for family businesses, based on socioemotional wealth (SEW).

2.3 Practitioner and business consultant#

Analytical aim — to find and apply quickly a specific method, formula, case study or Excel template in order to solve a particular business task.

Reading guidelines. For this audience, a targeted rather than a linear approach to the material is suggested, depending directly on the specific problem to be solved. In most chapters the formulas are given in the second section (for example, 13.2), the case studies in the fourth, and the description of the Excel model in the fifth.

• Appraising a new investment. The block of Chapters 10–13. It covers investment criteria — net present value (NPV), internal rate of return (IRR) and modified internal rate of return (MIRR) — the cost of capital (WACC), cash flow forecasting, terminal value and DCF valuation.
• Business acquisition or sale transactions (M&A). The group of Chapters 11, 13, 14 and 18. It covers the cost of capital, the triangulation of valuation methods, the valuation of synergies and exit routes.
• Valuing a start-up. Chapter 13 together with sections 14.1.2.1, 14.2.1 and 14.4. These present the venture capital (VC), Berkus and Scorecard methods, their formulas and a start-up valuation case.
• Leveraged buyout (LBO) or synergy analysis. Specific sections of Chapter 14 (14.1.2.3, 14.2.3, 14.4), and Chapter 18. They cover the valuation of synergies, M&A and LBO formulas, an M&A transaction case with an open outcome, and LBO as an exit route.
• Preparing a financial plan. Chapters 9, 12 and 16. They cover the diagnostics of financial statements, the preparation of forecasts and an integrated financial model of a business plan with an assumptions protocol.
• Credit risk diagnostics. Chapter 9 (bankruptcy prediction models), combined with sections 15.1 and 15.5 (multi-criteria and bank credit assessment, including a case of assessment under the internal ratings-based (IRB) approach).
• Strategic reorganisation. Chapters 5, 17 and 18. They cover the value of diversification and synergies, systems for implementing plans (BSC, OKR, KPI) and exit strategies.

Navigation note. The integrated glossary and the system of cross-references allow the reader to move in a few steps from a single term to the wider context in which it is used in the chapters (7.1).

2.4 Lecturer#

Resources. The material in the textbook — mathematical formulas, case studies and self-assessment questions — can be used to structure academic modules of different intensity and orientation. The list below shows what the lecturer will find and will be able to use directly in lectures, seminars and independent work.

• Material for discussion. The theoretical part of every chapter contains a critical analysis section with opposing views.
• Formulas and calculations. Formulas are given with numerical examples and an economic interpretation; they are suitable for practical classes.
• Case studies. Every chapter contains at least two case studies (their types are explained in section 4); some of them have an open outcome and are suitable for the case method.
• Excel models. Lecturer (Master) models with all formulas, and student (Student) versions derived from them, with empty calculation cells (7.2).
• Self-assessment and exercises. At the end of the chapters there are discussion questions, calculation exercises and tasks for independent work; in the virtual learning environment there are self-assessment tests for every chapter (7.3).
• “Decisions with AI” tasks. The general procedure, assessment criteria and advice for the lecturer are given in Appendix A.
• Course planning. An intensive module concentrates on the material of Chapters 1–13 and 16. For final theses, Chapters 12, 13 and 15 can serve as a methodological basis.
• Methodological foundation. How the chapters are structured, and what their structure is based on, is described in Part C (section 8).

3 The architecture of the book#

The publication is divided into six parts that follow the logic of forming and managing a business. This is not a linear life-cycle chronology but a purposeful analytical progression — from conceptual foundations to operational application and comprehensive valuation.

3.1 The parts and their main questions#

3.2 Principles of the structure#

The size of the parts (2, 2, 4, 4, 4 and 3 chapters respectively) is based on three methodological principles:

1. Thematic unity (ontological separation). Each part has a clearly defined epistemological object (value, risk, structure, condition, valuation, implementation). Concentrating on one fundamental theme allows the reader to organise and complete the topic mentally before moving on to the next analytical level.
2. Progressive increase in complexity (Bloom, 1956). The sequence of parts is designed as a consistent progression of cognitive levels. It starts with basic understanding (Part I) and application (Part II). It then moves on to analysis (Parts III and IV), which is finally turned into synthesis (Part V) and concluding evaluation (Part VI).
3. Managing cognitive load (Miller, 1956; Sweller, 1988). The capacity of human short-term (working) memory is limited, so too many units of information can make learning harder. For this reason the material is divided into small parts — in this book, each part consists of two to four chapters.

3.3 The sequence of Part VI and the epilogue#

The architecture of the final part of the publication reflects the natural course of processes that follow the stages of business planning and financial modelling (Chapters 1–16). It covers the implementation of the strategic plan, the alternatives for exiting a business and a reflection on fundamental methodological limitations:

• Chapter 17. Managing plan implementation. Turning a plan into operational activity and a management system (the integration of BSC, OKR, KPI and Lean).
• Chapter 18. Exit strategies. Mechanisms for realising capital: M&A, LBO, initial public offering (IPO), management buyout (MBO) and liquidation.
• Chapter 19. Technology and the future of business valuation. An epilogue and philosophical reflection.

Chapter 19 is conceived deliberately as an epilogue. It is not an additional set of technical tools but a reflective analytical closure, covering the development of technology, the ethics of artificial intelligence and the atrophy of human analytical skills. Having mastered the technical valuation methods, the reader is given room to evaluate critically the epistemological and ethical limitations of these methods.

This course of learning corresponds to the highest level of Bloom’s original taxonomy — evaluation (Bloom, 1956). At this stage the analyst is required not only to apply the tools mechanically but also to understand critically the limits of their application.

4 The Lithuanian and European Union context#

The practical situations (case studies) examined in each chapter of the textbook are placed primarily in the Lithuanian, Baltic and European Union environment. Priority here is given to the macroeconomic and regulatory context rather than to the identity of a specific economic entity. Most of the cases in the book reflect the authentic dynamics of the regional market and of individual sectors, although the company analysed often remains notional. The legal framework and the competitive environment are described on the basis of sources, while company names and internal financial indicators are mostly constructed for teaching purposes.

Such modelling is often the only option. The data that most Lithuanian small and medium-sized enterprises (SMEs) publish are too aggregated to reconstruct the circumstances of decisions accurately, and more detailed management accounting information is usually not made public.

This methodological choice reflects a deliberate pedagogical strategy aimed at different audiences:

• For students, it creates a sense of local relevance and opens up a prospect of direct application in their future workplace.
• For practitioners, it offers an opportunity to analyse real, relevant regulation — the EU Artificial Intelligence Act, the General Data Protection Regulation (GDPR), the EU Corporate Sustainability Reporting Directive and the EU taxonomy.
• For researchers, it documents gaps in empirical research at the national level, for example, the lack of research on marketing expenditure in the Lithuanian SME sector.

4.1 Types of case studies#

To ensure academic transparency and to help the reader distinguish a real company from a teaching illustration, the type of case is stated after the heading of each case, and one sentence explains which part of the case is based on real data and which was created for teaching purposes. The textbook distinguishes three categories of practical situations:

• [real] — a specific company is analysed using authentic, publicly available and verifiable data.
• [hypothetical] — an exclusively didactic construction: the company and its financial indicators are modelled, but they reflect the typical situation of a sector or region.
• [mixed] — the actual macroeconomic situation and the competitive environment are authentic (based on sources), but the name of the company and its specific data may be invented.

Mixed situations occur often in the textbook, and this is due to the information environment itself. In the Lithuanian market there are many cases where the external environment (sector dynamics, regulatory changes, price fluctuations) can be described precisely, while the internal financial data of a company are not public or it would be inappropriate to publish them. In such circumstances the context remains real, while the economic entity itself becomes notional.

4.2 Hierarchy and reliability of sources#

The analytical basis of the book follows priorities of information reliability, applied according to the nature of the claim. The sources used in the textbook are arranged hierarchically into five levels:

1. Academic literature. Peer-reviewed research articles and monographs; where possible, articles are identified by their digital object identifier (DOI).
2. European Union legal acts and documents of EU institutions. Directives and regulations, as well as documents of the European Commission, the European Central Bank (ECB), the European Banking Authority (EBA) and the European Securities and Markets Authority (ESMA).
3. National legal framework. Laws of the Seimas (Parliament) of the Republic of Lithuania, and legal acts of the Government, the Bank of Lithuania and individual ministries.
4. Professional standards and data sources. International Financial Reporting Standards (IFRS), International Valuation Standards (IVS), CFA Institute guidance, official statistics (for example, Eurostat), company financial statements, stock exchange data and A. Damodaran’s databases.
5. Business media. Publications of Bloomberg, the Financial Times and Verslo žinios (a Lithuanian business daily), which are used in the text as a supplementary argument, not the only one.

5 Artificial intelligence in the preparation and teaching of the book#

This textbook was prepared with the help of artificial intelligence (AI) technologies. This is not a disclaimer of responsibility but a declaration of academic transparency. Every chapter has been reviewed by the author, who is responsible for all the claims, conclusions and interpretations presented. In this process, artificial intelligence is treated as an instrument, not as a subject of authorship. This practice rests on the position of the Committee on Publication Ethics (COPE, 2023), the International Committee of Medical Journal Editors (ICMJE, 2023) and the Ombudsperson for Academic Ethics and Procedures of the Republic of Lithuania (2024) that an AI tool cannot be listed as an author and that the author is responsible for all the content. The guidelines of Vytautas Magnus University (VDU, 2024) likewise treat AI as an auxiliary tool whose use must be disclosed.

However, the book does not stop at formal transparency. Over the past few years AI has evolved from a technological experiment into a standard operational tool. Where building a complex financial model once took an analyst several weeks, today a working basic model can be prepared in a single working session, although it still has to be checked (the course of such a process is described in detail in section 16.7.2).

This technological turning point has fundamentally changed what makes a financial analyst valuable. Mechanical speed of calculation has lost its status as a competitive advantage. The competence of a modern expert is increasingly determined by the ability to notice that an algorithmically generated (although mathematically accurate) result rests on an assumption that is inadequate and does not hold in a particular market.

This new analytical paradigm has been transferred directly into the teaching methodology. The chapters of the textbook (starting with the first) include practical tasks in which the student is encouraged not to ignore AI but to use it purposefully. The cycle of working with AI consists of four stages:

1. Defining the problem (formulating the task).
2. Handing over to the tool (running the query).
3. Verifying the result (critical checking).
4. Public accountability (defending the decision).

The detailed method for carrying out and assessing these tasks is given in Appendix A of the book. A full reflection on how technology affects business valuation, on ethics and on the epistemological limits of AI is presented in the final Chapter 19.

Part B. Information for the reader (for students and practitioners) — methodological terminology and practical navigation in the literature and in the textbook.

6 The terminology of vertinimas and įmonė: one Lithuanian word, several English ones#

At first sight this section of the introduction looks purely linguistic, but its purpose is strictly methodological. The English term that is chosen directly determines in which literature the reader will look for an answer and what kind of problem they will believe they are solving. In Lithuanian this conceptual difference is partly hidden, because several different English terms are translated by the same word. This feature has to be discussed even in the Lithuanian text. Otherwise, on turning to the international literature, the reader will be confused and will not understand why the same phenomenon is called by three or four different names there.

6.1 The root of the confusion: “business” as an object and as a process#

The terminological ambiguity arises not only from the Lithuanian word vertinimas, but also from the word for business itself, verslas. In Lithuanian this concept encodes two different dimensions at once:

• Business as an object. An asset that can be managed, transferred and inherited, and whose value can be established in monetary terms.
• Business as a process. A dynamic activity that generates operational results and that can be judged by how well it works.

These two perspectives call for different analytical questions. When an object is analysed, the question is “What is it worth?”. When a process is examined, the question is “Is it working well?”. In Lithuanian terminology both activities are called by the same term — vertinimas.

As a first orientation, the distinction can be reduced to two basic questions. It must be stressed, however, that this is only a didactic heuristic and not an absolute linguistic rule. The term evaluation is sometimes applied to objects too, and valuation is not limited to businesses. The International Valuation Standards (IVS) define the object of valuation very broadly. According to the International Valuation Standards Council (IVSC, 2024), the General Standards apply to all assets and liabilities. The standards define an asset as the right to an economic benefit; this concept covers both tangible and intangible assets, while liabilities are defined separately.

6.2 Vertinimas: four English terms#

The Lithuanian term vertinimas covers an analytical field that English describes with four different terms: valuation, appraisal, assessment and evaluation.

It must be stressed that this distinction is not absolute, and the boundaries between the concepts cannot be derived from theoretical definitions alone. The terminological overlap is observed empirically in the dictionaries themselves. For example, Merriam-Webster explains valuation as “appraisal of property”, and appraisal as “a valuation of property by the estimate of an authorized person” (Merriam-Webster, n.d.-a, n.d.-b). One concept is defined through the other. The same can be seen in professional practice: the US organisation of valuers is called the American Society of Appraisers, while the academic journal it publishes is titled the Business Valuation Review.

It follows that the use of terms is determined not by a strict linguistic taxonomy but by professional tradition and established collocations.

The practical boundary between the two fundamental terms — valuation and evaluation — becomes clearest when the nature of the answer sought and the institutional definitions are analysed:

• Valuation. The International Valuation Standards Council defines this process as “the act or process of forming a conclusion on a value as of a valuation date that is prepared in compliance with IVS” (IVSC, 2024). The standards treat value itself as the valuer’s quantitative conclusion, and the basis of value applied must be stated. The essential answer in this case is an estimate of the value of the object in monetary terms.
• Evaluation. The Organisation for Economic Co-operation and Development (OECD) defines this term as “the systematic and objective assessment of a planned, ongoing or completed intervention, its design, implementation and results” (OECD, 2023, p. 31). Michael Scriven describes the same concept as a process of determining the merit, worth, or value of an object (Scriven, 1991, p. 139). Here the result is not a monetary amount but an analytical judgement, based on criteria, about quality, effectiveness or impact.

Although both terms are semantically related to determining significance, their epistemological object differs. Valuation is directed at economic value, and evaluation at the quality of a system or process and its conformity with the goals set.

This textbook examines the fields covered by all four terms in a comprehensive way:

Appraisal is most often found in the established collocation investment appraisal, which denotes the vertinimas of investments, to which Chapter 10 is devoted; in other fields it often refers to a formal determination of the value of an asset carried out by an expert.

This matrix shows that the publication is not limited to establishing value in money. The textbook evaluates the quality of operational processes (Chapter 6), carries out diagnostics of financial condition (Chapter 9) and examines the implementation of strategic plans (Chapter 17). These elements belong to the field of evaluation.

The literature on business analysis combines both perspectives into a single sequence. Palepu, Healy and Peek (2019) divide business analysis into four steps: strategy analysis, accounting analysis and financial analysis, followed by prospective analysis — forecasting and valuation. In other words, valuation rests on the results of evaluation: forecast cash flows, growth rates and the discount rate are only as well justified as the preceding analysis of strategy and performance. Without it, a valuation model turns into a mechanical extrapolation of historical data. This is the same logic that underlies the claim in section 1: valuation rests on planning assumptions — in this sequence, forecasting and valuation form a single step.

It is for this reason that the broader term was chosen for the official English title of the book, while the narrower term, focused on establishing value, was moved to the subtitle: “Business Planning and Evaluation: Planning, Performance and Valuation for Business Decisions”. This construction covers both methodological perspectives and keeps the term of the recognised field (valuation) visible.

Despite this integration, there is one strict dividing line in professional discourse. The process of establishing the value of a specific business is called “business valuation” in English (not “business evaluation”). It is an independent, institutionalised field with its own standards (IVS), professional associations and academic literature. Using the second term by mistake in this field would send a researcher in an entirely different direction — towards the analysis of operational performance.

6.3 Įmonė: four English terms#

A similar terminological feature applies to the Lithuanian concept įmonė, which refers universally to an economic entity of any size and legal form. In the English-speaking academic and professional world, four different terms are used to describe this concept, and their use is determined by the analytical context:

One exception needs attention. In the terminology of finance and valuation, enterprise value is an established technical term. For this reason, in this phrase the word enterprise cannot be replaced at will by the synonyms company or business.

6.4 Guidelines for practical navigation in the literature#

The terminological features discussed lead to three essential practical consequences, relevant both in studies and when searching for information for professional work:

• The term as a search key. When searching academic or professional literature, the English word acts as a direct filter. The concepts business valuation and business evaluation lead to different academic discourses. Similarly, risk assessment and risk evaluation are not synonyms: in the risk management standard of the International Organization for Standardization, risk evaluation is treated as one of the stages of risk assessment (ISO, 2018, clause 6.4).
• Calibrating expectations when analysing sources. When reading an international text, it is necessary to identify which research question it actually answers. A research article or report whose title uses the term evaluation often does not aim to establish value in monetary terms — and this is a methodological norm, not a shortcoming of the research.
• The choice of term as a substantive decision. When preparing academic publications, final theses or professional reports in English, the choice of term is a methodological rather than a stylistic decision. It is precisely for this reason that the glossary of the book gives English equivalents for Lithuanian terms and, where there are several, the limits of their application.

7 Navigation and practical use of the textbook#

Structure and numbering. The publication is divided into chapters, and chapters into sections, identified by decimal numbering (for example, 13.1, 13.1.5). For structural brevity, all lower-level units of text are called sections. Internal cross-references in the text give only the number in brackets (see 13.1.5) or, where the syntax of the sentence requires it, are worked naturally into the text (for example, analysed in section 13.1.5). This introduction is divided into numbered sections, to keep them clearly distinct from the main chapters of the book. Appendices at the end of the publication are marked with letters of the alphabet, and their internal sections with a combination of a letter and a number (for example, Appendix A, section A.2).

7.1 The glossary and the cross-reference system#

Each glossary entry ends with the chapters in which the term is used or examined, so the reader can move directly from a term to its context. The terminology of the integrated glossary is classified into four basic categories that make information quick to find:

• [FIN] — finance terms (for example, NPV — net present value, WACC — weighted average cost of capital, EBITDA — earnings before interest, taxes, depreciation and amortisation).
• [MET] — methodological terms (for example, epistemology, heuristic, pre-mortem analysis).
• [STAT] — statistical terms (for example, correlation, regression, Monte Carlo simulation).
• [TECH] — technological terms (for example, AI — artificial intelligence, API — application programming interface, BI — business intelligence, DSS — decision support system).

In the Lithuanian edition, specialised terms are introduced on first mention in Lithuanian, with the original English equivalent in brackets — “Lithuanian term (angl. English term)” — to ensure consistent handling of anglicisms.

7.2 Excel models for financial modelling#

Every analytical chapter is supplemented with a formula guide and consistent (step-by-step) instructions for building a financial model. For practical work, one comprehensive Excel model is designed for each chapter, and two different versions are produced from it:

• Lecturer version (Master). A fully built and working financial model with integrated formulas. It serves as the reference (master) file from which a modified version for students is generated.
• Student version (Student). A file derived from the lecturer’s model and adapted for independent practice, in which the calculation cells are left empty. This design is aimed directly at developing practical modelling skills.

In file names, the version is indicated by the word Master or Student.

7.3 Virtual learning environment (VLE)#

To extend the limits of static text, the textbook material is published on a digital platform at rakstys.eu/studijos/verslo-planavimas-ir-vertinimas. In this space the reader has access to additional tools:

• Interactive formula calculators (for example, models for DCF — discounted cash flow, CLV — customer lifetime value, ROMI — return on marketing investment, and BSC — Balanced Scorecard scoring).
• Self-assessment modules (knowledge tests adapted to each chapter).
• Glossary of terms (with an integrated quick search and filtering system).
• Visualisation tools (Mermaid diagrams and mathematical formulas rendered in LaTeX).

Part C. The methodological foundation of the textbook (for lecturers and researchers) — the structure of the chapters and the analytical position.

This final part of the introduction is intended for lecturers, reviewers and researchers. It sets out the methodological foundation of the publication and the structural decisions that are relevant when designing the logic of a study module and when evaluating the textbook itself.

8 The methodological foundation of the textbook#

The chapters of the textbook have been designed on the basis of a consistent methodological architecture. This is not a mechanically repeated format — it is a deliberate pedagogical discipline that eases the transition between topics and optimises the reader’s cognitive load. In the integrative Chapter 16, this structure has been deliberately adapted to the purpose of the chapter.

8.1 Six methodological layers of the chapters#

The structure of the chapters follows the principle of stratification (layering). Each layer performs a clearly defined analytical function that supports the overall cognitive progression (the section in which the layer is presented is given in brackets; N is the chapter number):

8.2 The author’s narrative and analytical position#

In this textbook, the logic of reaching a decision is as important as the final result of a calculation. The text deliberately avoids mechanical statements (for example, “NPV = X, therefore the project is acceptable”). Instead, the exposition pauses where an experienced practitioner would naturally have doubts. It reflects on whether the chosen terminal value assumption really withstands criticism, and whether the estimate obtained is artificially inflated if a real growth rate of 3.5 % is still forecast after ten years. Each analytical instrument is presented not as a theoretical given but, first of all, as an answer to the question of which problem it solves.

Such an analytical narrative is not just a stylistic choice. It is a deliberate pedagogical strategy that:

• builds a foundation for analytical thinking, providing not only a set of formulas but also a template for decision logic;
• develops critical reflexivity — it shows when the analysis should pause, when assumptions should be questioned and when the data should be cross-checked;
• instils the principle of epistemological humility. The reader becomes used to the idea, based on the famous dictum of the statistician George E. P. Box, that essentially all models are wrong, but some are useful (Box & Draper, 1987, p. 424) — and this applies to financial models too.

8.3 Epistemological awareness#

The main aim of the critical analysis section (N.1.3) of each chapter is to dispel the illusion that financial models are objective indicators of truth. The epistemological limits of models are illustrated from several angles:

• The discounted cash flow (DCF) method rests on a discount rate that is not a deterministic mathematical quantity but rather the result of an interpretive judgement. For example, the capital asset pricing model (CAPM), multi-factor models and the build-up method act here as competing approaches, while the size of the equity risk premium (ERP) is a further interpretive judgement.
• Valuation based on multiples depends directly on how the sample of comparable companies is formed. This leaves room for manipulation aimed at fitting the model to a desired result decided in advance.
• Macroeconomic and financial forecasts lose accuracy as the time horizon lengthens (as the results of the first Makridakis M-competition, which compared time-series forecasting methods, also show: Makridakis et al., 1982). Long-term forecasts should therefore be treated as scenarios rather than point estimates, although they remain necessary for investment decisions (analysed in more detail in Chapter 12).
• Monte Carlo simulation generates a probability distribution, but the distributional assumptions themselves (log-normal, triangular or beta distribution) often remain empirically untested in practice.

The need for this epistemological awareness grew especially after the global financial crisis of 2008 (see 3.1.3 and 4.1.3), and became even more pressing with the beginning of the era of large language models (LLMs). In this textbook, that era is taken to begin in 2020, when the GPT-3 model was introduced, capable of performing new tasks from only a few examples provided (Brown et al., 2020); such models became widely available at the end of 2022, with the release of ChatGPT (OpenAI, 2022). Although LLMs considerably speed up analytical tasks, responsibility for identifying the limits of algorithmic conclusions rests exclusively with humans (the stages of AI development and the limits of this responsibility are discussed in more detail in the epilogue in Chapter 19).

This situation creates a fundamental analytical dilemma: if a model does not guarantee objective truth, on what basis is a decision made? The most attractive way out, cognitively, is to appeal to authority (to rely on recognised authors or on the methods of international consulting firms), assuming that an institutionalised process in itself ensures a correct result. However, such reliance on authority is methodologically flawed. Authority points to the direction of inquiry, but it does not answer the essential questions: why a particular decision works and under which circumstances it loses its validity.

This does not mean that personal names should be abandoned in academic discourse. Scientific knowledge does not take shape in a vacuum — it has clear coordinates of time, place and authorship. For example, the work of Harry Markowitz (Markowitz, 1952) marks the turning point at which risk in finance was transformed from intuitive hunch into a quantifiable (calculable) parameter, while the system of theorems formulated by Franco Modigliani and Merton Miller (Modigliani & Miller, 1958) gave rise to the whole of modern capital structure theory. When the origins of a method are analysed, the author’s name functions not as textual decoration but as a marker of a historical turning point.

When the discussion moves from the history of a methodology to specific analytical claims, however, the role of the name changes radically. From a marker of historical context it becomes a reference (an address) that makes it possible to verify from which empirical data a conclusion was built and under which boundary conditions it holds. The validity of a claim rests not on the status of the researcher who published it but on a traceable causal link. This textbook requires exactly the same traceability of every variable in a financial model.

The essential axis of critical analysis is the understanding that every valuation method reflects the macroeconomic assumptions of the period in which it was formed — a different base interest rate environment, different data availability or a different structure of capital markets. When market conditions change, the value of a method is determined not by its historical reputation but by its empirical adequacy under the new conditions. There are no absolutely universal answers in financial analysis. It is enough for a decision to be logically justified, traceable and verifiable in the environment in which the analyst works.

It is for this reason that the choice of terms (discussed in section 6) is only an apparently linguistic question — in fact it continues the same epistemological logic. If the author of a source or the originator of a methodology acts as coordinates in the space of knowledge, then a precise term is the navigation instrument that makes it possible to reach that information. If an inaccurate concept is chosen, the analytical process is sent in the wrong direction, even if the original source was correct.

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