Artificial Intelligence Valuation The Impact on Automation, BioTech, ChatBots, F.pdf
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Artificial Intelligence Valuation ——The Impact on Automation, BioTech, ChatBots,
FinTech, B2B2C, and Other Industries

The origins of AI can be traced back to ancient myths and stories of mechanical beings with human-like capabilities. However, the modern development of AI is rooted in the mid-twentieth century: a. Early Concepts (Antiquity to Nineteenth Century): The concept of creating artificial beings with human-like intelligence dates back to ancient civilizations. For instance, in Greek mythology, there are stories of automatons like Talos and Pandora. In the Middle Ages, alchemists and inventors attempted to create mechanical beings. However, these were more in the realm of myth and folklore than actual technology.
b. Early Computational Devices (Nineteenth Century): The nineteenth century saw the invention of early computational devices like Charles Babbage’s Analytical Engine, which was a mechanical general-purpose computer. These devices laid the theoretical foundation for the idea of automating computation.
c. Alan Turing and his Test (1930s–1950s): Alan Turing, a gifted British mathematician and computer scientist, is often considered one of the founding figures of AI. In his 1936 paper, he introduced the concept of a “universal machine” (now known as a Turing
machine) that could simulate any other machine.. Early AI Research (1950s–1960s): The term “artificial intelli￾gence” was coined in 1956 at the Dartmouth Workshop, which marked the beginning of AI as a formal field of study. Early AI
researchers like John McCarthy and Marvin Minsky developed the
first AI programs and explored symbolic logic and problem-solving techniques.
e. AI Winter (1970s–1980s): AI research faced significant challenges and funding cuts during the AI winter periods. Expectations had outpaced the capabilities of the technology at that time, leading to disappointment.
f. Resurgence and Machine Learning (1990s–Present): AI experi￾enced a resurgence in the 1990s, driven in part by advancements in machine learning and neural networks. The development of
powerful computers and the availability of large datasets enabled
breakthroughs in areas like computer vision, natural language
processing, and reinforcement learning.
g. Contemporary AI (Twenty-First Century): The twenty-first
century has seen remarkable progress in AI, with applications in self￾driving cars, virtual assistants, recommendation systems, healthcare,
chatbots, generative artificial intelligence, and more. Deep learning,
a subfield of machine learning, has played a crucial role in these
advancements.
AI boasts a longstanding historical background. The progression
of AI has undergone a series of transformations, shifting from rule￾based approaches to statistical methods and eventually to data-centric
techniques within domains such as computer vision, natural language
processing, and machine learning. The interdisciplinary character of AI
has resulted in its widespread implementation across various sectors,
including economics, manufacturing, healthcare, and defense tech￾nologies, thereby influencing contemporary business environments and
decision-making processes. The sustainable trajectory of AI develop￾ment underscores the significance of human–machine collaboration and
a technological trajectory focused on computational capabilities, exerting
a substantial impact on society. The historical track record of AI has
influenced future expectations in several ways, as shown in Table 1.1:
The historical origins of AI have shaped the way people approach and
perceive the field. While there have been setbacks and disappointments,
1 INTRODUCTION 3
Table 1.1 History of artificial intelligence
Cycles of Hype and Disillusionment The history of AI has shown cycles of hype
followed by periods of disappointment, often
referred to as “AI winters”. These cycles
have made people cautious about
overestimating AI’s capabilities
Realistic Expectations Modern AI researchers and practitioners have
a more realistic understanding of AI’s
current capabilities and limitations. They
focus on solving specific, practical problems
rather than aiming for general human-level
intelligence
Ethical and Societal Concerns The historical development of AI has raised
concerns about job displacement, bias in
algorithms. These concerns shape the way AI
is developed and regulated today
Continuous Progress Despite past challenges, AI has made
significant progress, and this historical track
record of innovation fuels optimism about its
future potential. It encourages ongoing
research and investment in AI technologies

1 Introduction 1
1.1 The Historical Background 1
1.2 Artificial Intelligence Surveys: Pattern Recognition,
Machine Learning, Computer Vision, and Other
Research Fields 9
Data-Centric Artificial Intelligence 9
1.3 Artificial Intelligence’s Definitions and Trends 11
1.4 Cultural Aspects and Dystopian Perspectives 26
Dystopian Perspectives 32
1.5 Outline of the Book 34
References 38
2 The Valuation of Intangible Assets: An Introduction 41
2.1 Purpose of the Firm Evaluation 41
2.2 Artificial Intelligence Business Modeling
and Planning as a Prerequisite for Valuation 49
2.3 The Balance-Sheet-Based Approach 59
2.4 The Income Approach 64
Estimated Normalized Income 64
Choice of the Capitalization Rate 66
Choice of the Capitalization Formula 67
Capitalized Earnings Method 67
2.5 The Mixed Capital-Income Approach 69
The Average Value 70
2.6 Cash Is king: The Superiority of the Financial
Approach 72
The Capital Asset Pricing Model and the Dividend
Discount Model 84
Free Cash Flow Planning 87
Adjusted Present Value 89
CAPEX, Net Working Capital, and the Firm’s
Profitability 91
2.7 Empirical Approaches (Transaction Multiples
and Market Comparables) 95
2.8 The Control Approach 102
2.9 The Accounting Value of Intangible Assets 102
2.10 Valuation Drivers, Overcoming the Accounting
Puzzle 104
2.11 Intangible Assets Valuation According to IVS 210 106
Cost Approach 110
Income/Financial Approach 112
Market Approach 114
2.12 Hard-to-Value Intangibles 116
2.13 The Intangible Roadmap: From Patents
and Trademarks to Blockchains, Big Data,
and Artificial Intelligence 120
2.14 Intangible-Driven EBITDA 123
References 128
3 Artificial Intelligence-Driven Digital Scalability
and Growth Options 131
3.1 Introduction 131
3.2 Vertical and Horizontal Scalability 135
3.3 Digital Scalability 143
3.4 Artificial Intelligence-Driven Scalability as a Real
Option 145
3.5 The Impact of Scalable Intangibles on CAPEX
and OPEX 152
3.6 The Accounting Background: Operating Leverage 157
3.7 Break-Even Analysis 161
3.8 The Impact of Scalability on the Enterprise Valuation 164
3.9 Corporate Profitability and Scalability 167
3.10 Metcalfe’s Law 170
3.11 Moore’s Law and Other Scalability Patterns 171
3.12 Exponential Growth 180
3.13 Geo-localization and Traceability 183
3.14 From Digital Scalability to Blitzscaling 185
3.15 Scalable and Digital Supply and Value Chains 187
3.16 Digital Transformation 192
3.17 Networking Digital Platforms 192
3.18 Sustainable Business Planning 195
3.19 Artificial Intelligence, Scalable Leveraging,
and the Pecking Order Theory 197
References 200
4 The Valuation of Artificial Intelligence-Driven
Know-How and Patents 205
4.1 The Uncertain Perimeter of “Know-How”, Between
Organization and Technology 205
4.2 Galilean Replicability and Industrialization
of the Experimental Scientific Method 212
4.3 Protection, Sharing, and Transfer of Know-How 215
4.4 Economic and Financial Valuation 220
4.5 Product and Process Innovation 236
4.6 The Impact of Artificial Intelligence
and Digitalization on Know-How 240
4.7 Patents: Definition and Rationale 240
4.8 From Know-How to Patents 242
4.9 Accounting as a Prerequisite for Valuation 245
4.10 License or Sale? 248
4.11 Artificial Intelligence-Driven Patent Valuation
Approaches 250
4.12 Cost-Based Approaches 257
4.13 Market Valuations and Net Present Value 259
4.14 Comparability Factors 264
4.15 Income Approach 268
4.16 Real Options 271
4.17 Quick and Dirty Valuation Techniques 274
4.18 Forecasting Patent Outcomes with Big Data
and Stochastic Estimates 277
4.19 The Impact of Digitalization on Patents 278
4.20 Artificial Intelligence-Assisted Reverse Engineering 281
4.21 The Impact of Knowledge Management on Artificial
Intelligence 283
4.22 Artificial Intelligence and General Purpose
Technologies 284
References 286
5 The Valuation of Artificial Intelligence-Driven Startups 293
5.1 Artificial Intelligence Startups 293
Adaptation of the General Valuation Approaches 298
5.2 The IPEV Valuation Guidelines 304
5.3 The Fair Value of the Investments in the Target Firms 309
5.4 The Fair Value of the Investments in the Portfolio
Companies 313
5.5 Startup Evaluation with Binomial Trees 314
The Venture Capital Method 320
5.6 The Break-Up Value of Venture-Backed Companies 324
5.7 Stock Exchange Listing and Other Exit Procedures 326
5.8 Valuation of the Investment Portfolio with a Net
Asset Value 330
5.9 Boom and Bust Cycles 332
5.10 A Practical Valuation Case 333
References 342
6 The Valuation of Software as a Prerequisite
for Artificial Intelligence 345
6.1 Definition and Main Features 345
6.2 Accounting and Fiscal Aspects 347
6.3 Legal Protection of Software: Introductory Remarks 349
6.4 Software as a Prerequisite for Artificial Intelligence 351
6.5 Economic and Financial Valuation 360
Software House Revenue Model 363
Applicability of Empirical and Analytical
Evaluation Approaches to Software 366
CO.CO.MO Method and Putnam Model 369
6.6 Artificial Intelligence-Driven Economic
and Financial Valuation of Software 375
6.7 Open-Source Software 377
6.8 Impact of Open-Source AI Software 380
6.9 Software as a Service (SaaS) 381
6.10 Databases and Artificial Intelligence 383
6.11 Legal Protection 386
6.12 Information Value Chain, Data Mining
and Interaction with Networks, Big Data,
and the Internet of Things 389
6.13 Economic Valuation of Database and Links
with Cloud Computing 392
The Cost Approach 395
The Income-Financial Approach 396
The Empirical Approach 397
6.14 Data Validating Blockchains 397
6.15 New Assessment Scenarios and Monetization
Strategies 399
6.16 Artificial Intelligence and Technical Debt
in Software Development 400
References 402
7 The Valuation of Artificial Intelligence 405
7.1 The Fundamentals of Artificial Intelligence 405
7.2 Applications and Business Models 409
Artificial Intelligence-driven Productivity Growth 419
7.3 Legal Aspects: Introductory Notes 421
7.4 Valuation Metrics 431
The Financial Method 439
The Empirical Method of Market Multiples 446
7.5 Forecasting Rational Expected Outcomes: Sales
Prediction 450
7.6 Network Theory Applications (Artificial
Intelligence-driven Nodes and Edges) 453
7.7 Risk Assessment and SWOT Analysis 458
7.8 The Valuation of Listed Artificial Intelligence Firms 464
7.9 The Valuation of Artificial Intelligence-adopting
Traditional Firms 469
7.10 Economic, Financial, and Market Benefits
and Concerns 475
7.11 Gamification and Artificial Intelligence 477
7.12 Model Collapse and Other Value Destroying Patterns 480
7.13 Artificial Intelligence-Driven Goodwill 485
The Controversial Concept of Goodwill 486
oodwill Valuation, Competitive Advantage Period,
and Monopolistic Rents 487
Goodwill Determinants 491
7.14 Residual Income and Artificial Intelligence 497
7.15 Impact of Artificial Intelligence on the Economic
Value Added and Market Value Added 498
7.16 Artificial Intelligence and the Franchise Factor Model 502
7.17 Minimizing Value Destruction and Financial
Distress with Artificial Intelligence 503
References 505
8 Chatbots and Generative Artificial Intelligence 507
8.1 Introduction 507
8.2 What are the Main Typologies of Chatbots?
A Taxonomy 513
8.3 Large Language and Multimodal Models 516
8.4 Rule-Based Chatbots 519
8.5 Artificial Intelligence-Powered Chatbots 520
8.6 Virtual Assistant Chatbots 522
8.7 Transactional Chatbots 523
8.8 Social Chatbots 525
8.9 Hybrid Chatbots 527
8.10 Business Models, Value Drivers, and Scalability 528
8.11 Economic Valuation Patterns 529
8.12 Valuation of Chatbot Platforms 534
From the Cost Approach to Cost–Benefit Analysis 536
Market Approach (Comparables) 538
Revenue Generation (Income Approach) 539
Return on Investment (ROI) 543
8.13 Customer Lifetime Value (CLV) 545
Cost per Interaction/Transaction 546
8.14 Customer Satisfaction and Net Promoter Score (NPS) 550
8.15 Ethical Concerns 551
8.16 Toxiticy in Natural Language Processing and Its
Impact on Economic, Financial, and Market
Valuation 554
8.17 Factuality and Truthfulness 561
8.18 Inaccuracy, Cybersecurity, and Intellectual Property
Infringements 563
CONTENTS xi
8.19 Conclusion 564
References 567
9 Sustainable Artificial Intelligence Issues: From ESG
Valuation to Ethical Concerns 569
9.1 ESG Valuation 569
Environmental Impact 572
Social Impact 576
Governance Implications, Gender Diversity
and Impact on Disabilities 579
9.2 Ethical Concerns 586
Artificial Intelligence, Algorithmic,
and Automation Incidents and Controversies
(AIAAIC) Repository 593
Artificial Intelligence-Based Virtual Partners 595
9.3 OECD Artificial Intelligence Principles 597
Economics, Ethics, and Artificial Intelligence 597
9.4 IEEE Ethically Aligned Design 599
9.5 Artificial Intelligence’s Perception in Public
Opinion and Social Media 601
9.6 Sustainable Development Goals 603
9.7 Artificial Intelligence and Corporate Governance 609
References 612
10 Artificial Intelligence-Driven Industry Applications 613
10.1 A Taxonomy of the Main Artificial Intelligence
Industry Applications 613
10.2 The Impact of Artificial Intelligence in the BioTech
and MedTech Business 620
10.3 Artificial Intelligence, Transportation,
and Self-Driving Cars 624
10.4 Artificial Intelligence and FinTechs 626
10.5 The Impact of Artificial Intelligence on Retail
Businesses 630
10.6 Artificial Intelligence and Manufacturing 630
10.7 Data Management, Processing, and Cloud
Computing 632
10.8 Education 636
10.9 The Metaverse 636
References 640
xii CONTENTS
11 Artificial Intelligence Valuation: Empirical Cases
and Templates 643
11.1 C3.AI 643
Synthesis of the Valuation Methods 645
Business Plan 645
Competitors 649
WACC 649
Enterprise and Equity Value 651
Dividend Discount Model 653
Capitalized Earnings 653
Trading Multiples 659
Transaction Multiples 661
EBITDA-Driven Firm Valuation Benchmarking:
Market Multiplier Discounts for Unlisted
Comparables 663
Illiquidity Discount for Lack of Marketability 664
Small Size Impact 667
Information Asymmetries 670
ESG Metrics 671
Beta Determinants 672
11.2 Unlisted Artificial Intelligence Firm: An Empirical
Case 674
Simplified DCF WACC Methodology 677
Simplified Flow to Equity Approach 677
Dividend Discount Method 679
Capitalized Earnings 679
ESG Sensitivity 679
References 685
Index 687
List of Figures
Fig. 1.1 Artificial intelligence types 4
Fig. 1.2 History of artificial intelligence 6
Fig. 1.3 Data science and machine learning applications 25
Fig. 2.1 From the valuation of the firm to the estimate
of the intangibles and digital assets 45
Fig. 2.2 Functional analysis, business planning, and firm valuation 49
Fig. 2.3 From big data-driven forecasting to augmented business
planning 60
Fig. 2.4 Value creation, from traditional to augmented business
planning 61
Fig. 2.5 Interaction of top-down and bottom-up strategies 61
Fig. 2.6 Operating and net cash flows 73
Fig. 2.7 Value of the firm and cash flows 77
Fig. 2.8 WACC determinants 82
Fig. 2.9 The financial-economic cycle 92
Fig. 2.10 The integrated equity–economic–financial–empirical
and market valuation 107
Fig. 2.11 Approaches of valuation of intangible assets 109
Fig. 2.12 Intangible interaction 121
Fig. 2.13 Information value chain 122
Fig. 3.1 Vertical and horizontal scalability 141
Fig. 3.2 Investment tree and real options 151
Fig. 3.3 Impact of scalable intangibles on OPEX and CAPEX 157
Fig. 3.4 Break-even analysis 163
Fig. 3.5 Operating leverage and cash flows 168
xiii
xiv LIST OF FIGURES
Fig. 3.6 Different types of communication networks 171
Fig. 3.7 Value for the networked user according to Metcalfe’s law 172
Fig. 3.8 Break-even point with Metcalfe’s law 172
Fig. 3.9 Linear vs Exponential Growth 181
Fig. 3.10 Key-value drivers of digital supply chains 188
Fig. 3.11 Traditional versus digital supply chain 189
Fig. 3.12 Sequential value chain 189
Fig. 3.13 The link between digital transformation and scalability 193
Fig. 3.14 Networked digital platforms 194
Fig. 4.1 Protection, sharing, and transfer of know-how 216
Fig. 4.2 Links between know-how, value chains, and external
evaluation sources 225
Fig. 4.3 Know-how valuation approaches 236
Fig. 4.4 Know-how, Artificial Intelligence, and Digitalization 241
Fig. 4.5 Patent valuation approaches 256
Fig. 5.1 Representation of the payoff 318
Fig. 6.1 Software valuation approaches 362
Fig. 6.2 Software as a digital hub 370
Fig. 6.3 Features of cloud computing 394
Fig. 6.4 As a service models 395
Fig. 6.5 Cloud computing ecosystem 396
Fig. 7.1 Turing test 406
Fig. 7.2 AI and business models 415
Fig. 7.3 Application of AI, digital value chains, and valuation
approaches 437
Fig. 7.4 Node interaction in a traditional network 461
Fig. 7.5 Node interaction in a network made scalable by AI 462
Fig. 7.6 Goodwill as a positive differential between the return
and the cost of invested capital 492
Fig. 7.7 From EVA to MVA 501
Fig. 8.1 Flowchart of the chapter 512
Fig. 8.2 From market ecosystem to business models and chatbot
platform valuation 513
Fig. 8.3 Firm valuation approaches: A taxonomy 532
Fig. 8.4 Compared valuation approaches 533
Fig. 11.1 Average valuation metrics 646
Fig. 11.2 WACC derivation 651
Fig. 11.3 EV/sales 661
Fig. 11.4 EV/EBITDA 662
Fig. 11.5 Price Earnings 662
Fig. 11.6 C3.AI stock price versus the NASDAQ and the World
Index 663
LIST OF FIGURES xv
Fig. 11.7 Valuation ranges and midpoints based on different
valuation methods 676
Fig. 11.8 WACC derivation 679
List of Tables
Table 1.1 History of artificial intelligence 3
Table 1.2 Impact of artificial intelligence on organizations
and the global business landscape 14
Table 1.3 Artificial intelligence-driven language models 17
Table 1.4 Data integration market in artificial intelligence
applications 19
Table 1.5 Environmental impacts of artificial intelligence 23
Table 1.6 Development of intelligent systems 24
Table 1.7 Culture and artificial intelligence 28
Table 1.8 Artificial intelligence’s impact on culture and creativity 31
Table 2.1 Top-down and bottom-up approaches 54
Table 2.2 Interaction of big data with traditional budgeting
patterns 57
Table 2.3 Free cash flow to equity 75
Table 2.4 Free cash flow to the firm 76
Table 2.5 Cash flow statement and link with the cost of capital 83
Table 3.1 Artificial Intelligence-driven digital scalability
and growth options 136
Table 3.2 Scalability drivers 146
Table 3.3 AI as a real option 149
Table 3.4 Real options 150
Table 3.5 Network scalability laws 174
Table 4.1 Artificial Intelligence-driven economic and financial
valuation of know-how 223
Table 4.2 Relief from royalties 228
xvii
xviii LIST OF TABLES
Table 4.3 Artificial Intelligence, know-how, and the incremental
income approach 230
Table 4.4 Reproduction costs 232
Table 4.5 Artificial Intelligence and the complex balance
sheet-based approach 234
Table 4.6 Artificial Intelligence and patents 243
Table 4.7 License valuation 270
Table 4.8 Relationship of royalty rate to magnitude
of improvement 271
Table 5.1 Valuation patterns of Artificial Intelligence-driven
versus seasoned firms 299
Table 5.2 Fair value of investments in Artificial
Intelligence-driven startups 311
Table 5.3 Payoff calculation 319
Table 5.4 Mean forecast EBITDA 334
Table 5.5 From the net cash flow to the equity value 336
Table 5.6 Listed comparables 337
Table 5.7 Net financial position 337
Table 5.8 Adjusted Multiple of the EBITDA 338
Table 5.9 Synthetic valuation 338
Table 5.10 Sensitivity analysis 339
Table 5.11 Cost of equity (ke) sensitivity 340
Table 6.1 Level of protection 350
Table 6.2 Relationship between software and artificial intelligence 354
Table 6.3 Valuing software as a prerequisite for artificial
intelligence 357
Table 6.4 Valuing software-driven artificial intelligence patterns 364
Table 6.5 Software as a service and artificial intelligence 384
Table 6.6 Databases and artificial intelligence 387
Table 7.1 Examples of the application of artificial intelligence
in the field of business solutions 412
Table 7.2 Artificial Intelligence’s impact on business models 416
Table 7.3 Artificial Intelligence’s impact on productivity 420
Table 7.4 Artificial Intelligence-related legal cases 428
Table 7.5 Economic, financial, and market valuation patterns
of artificial intelligence 432
Table 7.6 Artificial intelligence and data value 434
Table 7.7 Cash flow statement and link with the cost of capital 444
Table 7.8 EBITDA determination 444
Table 7.9 Comparable companies 448
Table 7.10 Forecasting sales with Artificial Intelligence 451
Table 7.11 Impact of data analytics on cash flow forecasting 454
LIST OF TABLES xix
Table 7.12 Network theory and digital scalability 459
Table 7.13 SWOT analysis 463
Table 7.14 Impact of Artificial Intelligence adoption on valuation
metrics 470
Table 7.15 Impact of Artificial Intelligence on the firm’s key
parameters 475
Table 8.1 Artificial Intelligence taxonomy 510
Table 8.2 Economic, financial, and market impact of Artificial
Intelligence 518
Table 8.3 Business models, value drivers, and scalability 530
Table 8.4 Performance and financial impact of the chatbot 540
Table 8.5 Impact of revenue-generation activities
on the economic and financial value of a chatbot 542
Table 8.6 CLV’s impact on the economic and financial evaluation
of a chatbot 547
Table 8.7 Customer satisfaction and net promoter score 552
Table 8.8 Factuality and truthfulness 562
Table 8.9 Inaccuracy, cybersecurity, and intellectual property
infringements 565
Table 9.1 ESG and artificial intelligence 572
Table 9.2 Artificial intelligence’s environmental impact 574
Table 9.3 Artificial intelligence’s governance implications 580
Table 9.4 Artificial intelligence’s sustainability and valuation 582
Table 9.5 Economic, financial, and market impact of artificial
intelligence’s gender diversity 584
Table 9.6 Ethical concerns of artificial intelligence 587
Table 9.7 Automated fact-checking using natural language
processing 591
Table 9.8 Impact of fairness, bias, and ethics concerns 594
Table 9.9 OECD artificial intelligence principles 598
Table 9.10 Ethically aligned design for artificial intelligence 600
Table 9.11 Social media and artificial intelligence 604
Table 9.12 Link between Sustainable Development Goals
and Artificial Intelligence 606
Table 9.13 Artificial intelligence and corporate governance 610
Table 9.14 Artificial intelligence -driven corporate governance
patterns 611
Table 10.1 Artificial intelligence’s industry applications 615
Table 10.2 Artificial intelligence integration 619
Table 10.3 Artificial intelligence, transportation, and self-driving
cars 625
Table 10.4 Artificial intelligence’s valuation impact on FinTechs 628
xx LIST OF TABLES
Table 10.5 Impact of artificial intelligence on retail businesses 631
Table 10.6 Artificial intelligence and manufacturing 633
Table 10.7 AI and education 637
Table 11.1 C3 AI - Business plan key figures 647
Table 11.2 Growth Rates and Economic Profitability Margins 648
Table 11.3 Main Competitors 650
Table 11.4 Sensitivity Analysis 652
Table 11.5 Enterprise Value and Equity Value 653
Table 11.6 WACC sensitivity 653
Table 11.7 From Free cash flow calculations to enterprise
and equity value 654
Table 11.8 Free Cash Flow to Equity and Equity Value 656
Table 11.9 Dividend Discount Model 658
Table 11.10 Equity and Enterprise Value deriving from Capitalized
Earnings 659
Table 11.11 Discount on listed trading multiples 660
Table 11.12 Transaction Multiples based on Target Companies 663
Table 11.13 Multiple Discount Rates 677
Table 11.14 Business Plan 678
Table 11.15 Simplified DCF WACC methodology 680
Table 11.16 Simplified Flow to Equity approach 681
Table 11.17 Simplified DDM approach 682
Table 11.18 Capitalized earnings 683
Table 11.19 ESG parameters and impact on the cost of capital 685