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IN THE FIELD OF TECHNOLOGY DEGREE PROJECT

MECHANICAL ENGINEERING AND THE MAIN FIELD OF STUDY INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2020,

Managing future

uncertainties through scenario analysis:

A case study on European financial markets from the perspective of a stock exchange

PHILIP BÖRJESSON PATRIK LARSSON

KTH ROYAL INSTITUTE OF TECHNOLOGY

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Managing future uncertainties through scenario analysis:

A case study on European financial markets from the perspective of a stock exchange

by

Philip Börjesson Patrik Larsson

Examensarbete TRITA-ITM-EX 2020:352 KTH Industriell teknik och management

Industriell ekonomi och organisation SE-100 44 STOCKHOLM

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Hantering av framtida osäkerheter genom scenarioanalys:

En fallstudie på europeiska finansiella marknader från ett börsperspektiv.

av

Philip Börjesson Patrik Larsson

Master of Science Thesis TRITA-ITM-EX 2020:352 KTH Industrial Engineering and Management

Industrial Management SE-100 44 STOCKHOLM

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Master of Science Thesis TRITA-ITM-EX 2020:352 Managing future uncertainties through scenario analysis:

A case study on European financial markets from the perspective of a stock exchange.

Philip Börjesson Patrik Larsson

Approved

2020-06-10

Examiner

Lars Uppvall

Supervisor

Kent Thorén

Commissioner

Nasdaq

Contact person

Andreas Gustavsson

Abstract

The purpose of this study is to investigate how scenario analysis can be used to strategically prepare for the future during uncertain times. The purpose is also to identify uncertainties and trends that could shape the future European financial markets and to develop projections of alternative futures on which a stock exchange could base their strategic decisions.

A literature review was conducted which provides insights and tools in order to perform a scenario analysis. This study then investigates how scenario analysis can be applied to European financial markets with a time horizon of 10 years, from the perspective of the case company Nasdaq. The investigation is conducted as an instrumental case study where 12 respondents within Europe’s financial markets have been interviewed. Five of the respondents are case company employees and seven of the respondents are external that cover a wide range of specialties within financial markets.

The results show that there are eight key drivers that were considered important and uncertain for the development of European financial markets by year 2030. Four interpretations of these drivers that the case company considered the most interesting were combined into six scenario matrices which resulted in 24 unique scenarios. Three of these were described in detail where future business environments were evaluated through the use of the theoretical framework Porter’s five forces. The scenarios presented in this study are:

• Scenario 1: Further globalization and increased competition from alternative marketplaces.

• Scenario 2: There has been a further consolidation of stock exchanges and exchanges have remained the dominant marketplace for financial products.

• Scenario 3: Brexit will have a major impact on financial markets and more regional economies.

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The scenarios show how some of the identified drivers could shape the future business environment. These projections of alternative futures can be used by a stock exchange in their strategic decision making and enables the multi-level strategic conversation within the organization to develop and continue. Such a conversation helps establish a shared mental model of the external environment and the organization itself. Since the business environment is ever changing, the aim is to continuously use scenario analysis to evaluate and improve strategic discussions. It also allows organizations to learn and change from its own experiences to identify new opportunities. And since there can be no learning without action, the ultimate aim of scenario analysis is to make an impact on strategic decisions by taking reflection-based action.

Keywords

Scenario analysis, European financial markets, Business environment.

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Examensarbete TRITA-ITM-EX 2020:352

Hantering av framtida osäkerheter genom scenarioanalys:

En fallstudie på Europeiska finansiella marknader från ett börsperspektiv.

Philip Börjesson Patrik Larsson

Godkänt

2020-06-10

Examinator

Lars Uppvall

Handledare

Kent Thorén

Uppdragsgivare

Nasdaq

Kontaktperson

Andreas Gustavsson

Sammanfattning

Syftet med denna studie är att undersöka hur scenarioanalys kan tillämpas för att på ett strategiskt sätt förbereda sig på en möjligen osäker framtid. Syftet är också att identifiera osäkerheter och trender som skulle kunna forma finansiella marknader i Europa och utifrån dessa, utveckla projektioner av alternativa framtider som en börs kan använda sig av för att basera strategiska beslut på.

En litteraturstudie genomfördes vilket gav insikter och verktyg för utförandet av en scenario analys. Denna studie undersökte sedan hur scenario analys kan appliceras på de europeiska finansiella marknaderna från ett börs-perspektiv med en tidshorisont på 10 år. Undersökningen genomfördes genom en instrumental fallstudie där 12 respondenter inom Europas finansiella marknader intervjuades. Fem av respondenterna var från fallföretaget och sju respondenter var externa som tillsammans täcker ett brett spektrum av specialistkunskaper inom finansiella marknader.

Resultatet av studien visar att det finns åtta nyckelfaktorer som ansågs vara både viktiga och osäkra för utvecklingen av Europas finansiella marknader till år 2030. Fyra tolkningar av dessa faktorer som fallföretaget ansåg vara de mest intressanta kombinerades och bildade sex scenariomatriser som resulterade i 24 unika scenarion. Tre av dessa är beskrivna i detalj där det framtida affärsmiljöerna är utvärderade utifrån det teoretiska ramverket Porter’s five forces. De scenarion som presenteras i studien är:

• Scenario 1: Ökad konkurrens från alternativa marknadsplatser och fortsatt globalisering.

• Scenario 2: Börser är fortsatt den dominanta marknadsplatsen för finansiella produkter och det har varit en fortsatt konsolidering av börser.

• Scenario 3: Mer regionala ekonomier och Brexit har haft en stor påverkan på finansiella marknader.

Scenariona visar hur några av de identifierade nyckelfaktorerna kan komma att forma den framtida affärsmiljön. Dessa projektioner av alternativa framtider kan fungera som underlag för börser i strategiskt beslutsfattande och de möjliggör den strategiska konversationen inom

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organisationen att utvecklas och fortsätta. Sådan typ av konversation hjälper till att skapa en delad mental modell av den externa miljön och organisationen i sig. Eftersom affärsmiljön är i ständig förändring är målet att kontinuerligt använda scenarioanalys för att utvärdera och förbättra strategiska beslut. Det tillåter också organisationer att lära sig och genomföra förändringar baserat på egna erfarenheter för att identifiera nya möjligheter. Eftersom det inte kan finnas något lärande utan åtgärder, är det ultimata målet med scenarioanalys att påverka strategiska beslut genom att vidta reflektionsbaserade åtgärder.

Nyckelord

Scenarioanalys, europeiska finansiella marknader, Affärsmiljö.

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Table of content

1. Introduction ... 1

1.1 Background ... 1

1.2 Problem formulation ... 1

1.3 Purpose ... 2

1.4 Research questions ... 2

1.5 Case company ... 2

1.6 European financial markets ... 2

European equity markets ... 3

Trading life cycle ... 4

1.7 Delimitations ... 5

2. Literature review ... 6

2.1 Scenario analysis ... 6

2.2 Approaches to scenario analysis ... 7

Intuitive logics ... 8

Probabilistic modified trends (PMT) school ... 10

French school – La prospective ... 10

Critique of scenario analysis ... 11

2.3 Purposes of scenario analysis ... 12

Making sense ... 12

Developing an optimal strategy ... 13

Anticipation ... 13

Adaptive organizational learning ... 13

3. Theoretical framework ... 14

3.1 Porter’s five forces ... 14

Threat of New Entrants ... 14

Bargaining Power of Suppliers ... 14

Bargaining Power of Buyers ... 14

Threat of substitute products or services ... 15

Rivalry among Existing Competitors ... 15

4. Method ... 16

4.1 Research design ... 16

4.2 Data collection ... 17

Selection of respondents ... 17

Data collection procedure ... 19

4.3 Data analysis ... 19

4.4 Research quality ... 21

Reliability ... 21

Validity ... 22

Ethics ... 22

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5. Empirics ... 22

5.1 Political ... 22

5.2 Economic ... 23

5.3 Social ... 26

5.4 Technological ... 28

5.5 Environmental ... 30

5.6 Legislative ... 31

6. Results and discussion ... 32

6.1 Key drivers and uncertainties ... 32

6.2 Scenario combinations ... 34

6.3 Scenario descriptions ... 37

Future impact of important and certain drivers ... 37

Scenario 1: Further globalization and Increased competition from alternative marketplaces ... 38

Scenario 2: There has been a further consolidation of stock exchanges and exchanges have remained the dominant marketplace for financial products. ... 40

Scenario 3: Brexit will have a major impact on financial markets and more regional economies ... 42

6.4 Scenario analysis as a tool ... 43

6.5 Sustainability ... 45

7. Conclusion ... 46

7.1 Practical implications ... 46

7.3 Limitations and further research ... 47

8. References ... 48

Appendix 1 ... 1

Interview questions (Nasdaq employees) ... 1

Interview questions (external) ... 2

Appendix 2 ... 3

Version 1 (Based on Shell): ... 3

Version 2: ... 3

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List of figures

Figure 1. Trading life cycle ... 4

Figure 2. Purposes of scenario analysis ... 12

Figure 3. Schematic view of the report structure ... 16

Figure 4. Driver matrix ... 20

Figure 5. Axis of uncertainty ... 20

Figure 6. Categorization of key drivers and uncertainties ... 33

Figure 7. Long list ... 33

Figure 8. Short list ... 34

List of tables Table 1. Market share of group owners (CBOE, 2020) ... 3

Table 2. Table of respondents ... 18

Table 3. Key drivers and uncertainties ... 32

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Acknowledgements

We would like to pay the highest gratitude to the internal and external respondents that despite the current covid-19 crises took their time to provide us with valuable insights. We would also like to thank our examiner who also is our program director. Thank you for two inspiring years at the master’s program. Also, many thanks to our supervisor who guided us through this process and provided us with expertise knowledge on how to conduct research within this field.

A special thanks to Johan Persson at innovation 360 group who guided us in the right direction and provided us with expertise knowledge within this field. Last but not least, we would like to thank our case company for letting us conduct our research at Nasdaq and also many thanks to our supervisor at Nasdaq who connected us with great respondents.

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List of abbreviations

AI: Artificial intelligence

CCP: Central clearing counterparty CSD: Central securities depository

CSDR: Central Securities Depositories Regulation EMIR: European Market Infrastructure Regulation ESG: Environmental, Social and Governance ETP: Exchange traded product

ETF: Exchange traded fund M&A: Mergers and acquisitions MAR: Market Abuse Regulation ML: Machine learning

MM: Market maker

MiFID: Markets in Financial Instruments Directive RM: Regulated market

SME: Small and medium-sized enterprises SI: Systematic internaliser

SRD II: Shareholder Rights Directive 2

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1. Introduction

In this section, a brief background to the study is presented followed by a problem formulation, purpose and research question. A brief introduction to European financial markets is also presented. The study is conducted on behalf of Nasdaq.

1.1 Background

During the last two decades, the exchange industry has experienced several challenges and major transformations. The continuous development of new technology and regulatory reforms are examples of key issues that have affected stock exchanges around the world (Geranio, 2016). In Europe, the legislative frameworks Markets in Financial Instruments Directive (MiFID) and MiFID II led to a reduction of economic and financial barriers as well as increased competition and transparency in the European Union’s financial markets (ESMA, 2020). This partially resulted in the emergence of new industry participants and a shift of liquidity towards new trading platforms. Therefore, several stock exchanges have modified their strategies in order to achieve competitiveness in this changing financial environment. New products were introduced for trading such as Exchange traded products (ETPs) and commodities in order to make the exchanges less reliable on revenue generated from traditional equity trading (Geranio, 2016). In addition, business areas were expanded to include services such as IT services and data vending. There are also some cases where exchanges have adopted an acquisition strategy as a way of consolidating liquidity to obtain economy of scale. For example, the Baltic and Nordic exchanges merged and formed OMX which was later on acquired by Nasdaq. Another example is the merger of exchanges from four European countries that resulted in the creation of Euronext which was then acquired by NYSE (Faten, 2012). Contemporary examples of consolidations are Euronext’s acquisitions of Oslo Bors in June 2019 and VP Securities, the Danish Central Securities Depository, in April 2020.

The significant changes in the structure of how exchanges operate are all results of different trends that shape the financial markets. As previously mentioned, these trends can be advancements within technology, globalization of world capital markets, and implementation of new regulations. However, in an increasingly dynamic environment with several uncertainties, these trends can be difficult to forecast (Gausmeier, et al., 1998).

In order to achieve competitiveness, organizations need to identify potential business opportunities at an early stage and develop them on time (Gausmeier, et al., 1998). Therefore, organizations could benefit from using scenario analysis as a way of projecting alternative futures to develop a robust strategy (Beinhocker, 1999). Creating future scenarios is not about forecasting nor predicting the future, it is instead about describing plausible futures where major uncertainties in the business environment are accounted for. Generally, most organizations do not have a method for planning the long term future, current trends are simply progressed and the future is assumed to be an interpolation of the current state (Gausmeier, et al., 1998).

However, little is certain, and nothing is predetermined, and future events unfold in unexpected ways (Saffo, 2007). With the help of scenario analysis, organizations will be in a better position to benefit from unexpected opportunities that will most likely appear (Schoemaker, 1997).

1.2 Problem formulation

The financial industry involves a significant number of uncertainties. During the last five years, events and trends such as Brexit, MiFID II, crypto currencies and negative interest rates have had a large impact on financial markets. Similar trends are likely to continue to come along and although market participants invest resources into predicting the future, the outcome is almost

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always different. In order to strategically consider such trends, organizations could use scenario analysis to project several alternative futures.

1.3 Purpose

The purpose of this study is to investigate how scenario analysis can be used to strategically prepare for the future during uncertain times. The purpose is also to identify uncertainties and trends that could shape the future European financial markets and to develop projections of alternative futures on which a stock exchange could base their strategic decisions.

1.4 Research questions

RQ1: How can scenario analysis be used as a tool in order to prepare for uncertain times?

SQ1: What are plausible future states in European financial markets from the perspective of a stock exchange?

SQ2: How does the business environment look like, from the perspective of a stock exchange, in these plausible scenarios?

1.5 Case company

Nasdaq Inc. is a global technology company based in the US that is one of the largest exchange operators in the world (Desjardins, 2017). Nasdaq serves the capital markets and other industries with data, analytics, software and services which is divided into four business segments: Market services, Corporate Services, Information Services and Market Technology (Nasdaq, 2020). Nasdaq was founded in 1971 and was up until year 2000 entirely owned by the Financial Industry Regulatory Authority (FINRA) (Nasdaq, 2020). Nasdaq became fully independent in 2006 and in 2008, Nasdaq entered the European market by acquiring the leading Nordic exchange OMX AB (Nasdaq, 2020). There are around 3900 companies listed on Nasdaq´s all exchanges (Nasdaq, 2020). In Europe, Nasdaq operates exchanges in Stockholm, Helsinki, Copenhagen, Iceland, Tallinn, Riga, and Vilnius as well as a commodity derivatives exchange in Oslo. These exchanges are often referred to as Nasdaq Nordic (Nasdaq, 2020).

Nasdaq Nordic has 1037 companies listed and 177 unique members trading excluding the commodity derivatives exchange (Nasdaq, 2020). Nasdaq also operates a central counterparty (CCP) where it offers clearing services for Nasdaq commodity derivatives and Nasdaq derivative markets (Nasdaq, 2020).

1.6 European financial markets

A financial market can be defined as a market where participants issue and trade financial instruments (de Haan, et al., 2009). The primary role of financial markets is to connect those with a surplus of financial resources who buy securities, with those that have a shortage, who either issue new securities or sell existing securities (Vernimmen, et al., 2017). According to Bailey (2005), there are three main functions that any financial market must perform: 1) Trading mechanism; the market must provide means by which participants who wish to buy or sell can communicate and trade with each other, 2) Price discovery; the market must enable market participants to access information on what prices financial instruments can be bought and sold at, 3) Clearing and settlement; the market must ensure that that the terms of each agreement are fulfilled.

The European financial markets are part of the whole structure of the euro area economy which also includes economic policy, fiscal policies, external trade and labor market to mention a few (European Central Bank, 2020). The European financial markets can be divided into three segments: money markets, debt markets and equity markets (European Central Bank, 2020).

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The money market consist of unsecured lending and deposit transactions, secured transactions such as repos and reverse repos, foreign exchange swaps and overnight index swaps (European Central Bank, 2019). The debt market is where debt securities such as short- and long-term bonds are traded. Debt securities can be issued by monetary financial institutions, Non- monetary financial corporations, Non-financial corporations, public sector and other issuers of the private sector (European Central Bank, 2020). The equity markets are the market were companies raise money in order to finance future growth without incurring debt (Lamont, 2018). The investors who invest money in these companies gain an ownership by holding shares issued by the companies. In this way, the investors have the right to vote on shareholder meetings as well as claim dividend of future earnings (FRBSF, 2006)

European equity markets

European equity markets have been exposed to new and updated regulatory changes the last 10 years. For instance implementation of MiFID II, Market Abuse Regulation (MAR) and Central Securities Depositories Regulation (CSDR) which have the purpose to make the European financial markets more transparent, fair, integrated and efficient in order to strengthen the investor protection (European Commission, 2018). These regulations are decided on at an EU level which involves the European Parliament and Council as co-legislators (European Comission, 2017). A regulatory framework that ended the quasi-monopoly of traditional stock exchanges was MiFID in its first form (Salger, 2020). MiFID I was implemented in 2007 and enabled alternative platforms to compete for the liquidity with the traditional exchanges. New platforms such as multilateral trading facilities (MTF) have therefore been established by investment firms and market operators (Lahet & Vaubourg, 2017). MTFs do not have any listing processes, instead MTFs offer secondary trading in securities that are already listed on traditional exchanges. What increased the competition further was the Systematic Internaliser (SI) regime, which also was introduced under MiFID I (Glowack, 2020). A systematic internalizer is an investment firm which trades on own account by executing client orders outside of traditional stock exchanges or MTFs (Glowack, 2020). According to ESMA there are over 130 regulated markets (RM), more than 200 MTFs and more than 200 SIs (ESMA, 2020)

However, the market share of trading in securities in European equity markets belongs to a few operators. The five largest group owners stand for around 80% of the liquidity in the European markets (CBOE, 2020).

Table 1. Market share of group owners (CBOE, 2020) Group Share May 1 -May 28th

(Daily average) Euronext 19.52% €6,381,840,576 LSE Group 19.45% €6,360,008,478

Xetra 16.36% €5,350,342,896

Cboe Europe 15.88% €5,192,452,450 Nasdaq OMX 8.65% €2,828,203,946

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Trading life cycle

The processes that occur within an equity market can broadly be divided into three segments;

Pre-Trade, Trade and Post Trade.

Figure 1. Trading life cycle

Figure 1 describes the trading lifecycle that starts with Pre-trade which includes all activities before a specific order is sent to a marketplace. Active participants within in pre-trade are for instance financial institutions, investment funds and retail investors. Pre-trade could for example start with gathering data, news and other information in order to conduct a research of a market or specific security. The research together with portfolio management can then be used to take informed decisions. In order to buy or sell a security the participants need to be connected to a marketplace through a marketplace member firm. There are several trading software tools available offered by marketplaces member firms and third-party providers. When connected to a marketplace participant can be fed with market data. In this stage the investor can send an order through a marketplace member firm. Before the order reaches the market, it is routed. The order routing is the start of the trade process. This is an automated process of handling orders and member firms must take all sufficient steps to obtain best execution (FCA, 2020). Best execution is the process of finding the best possible price for a given order and time on available RMs and MTFs. The time it takes for the order to reach the market is often referred to as latency. In the trading process other market participants are also involved, such as high frequency traders. High frequency traders are participants specialized in developing algorithms in order to reach the market as fast as possible in order to capture price opportunities such as arbitrage or prompt responses to market news (Hasbrouck & Saar, 2013). Other participants within the trading process can involve market makers (MM). Market makers are participants that place bids and offers in an order book on a continuous basis and dealing on own account (ESMA, 2014). A market maker helps keeping a market in a particular security in this way

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(ESMA, 2014). High frequency traders, MM, financial institutions, investment funds and retail investors all contribute with liquidity to the markets in order for a price discovery to occur. The price discovery occurs when the buyer and seller agree on a specific price.

The post-trade process includes the clearing and settlement of transactions. After the execution of buy and sell orders, transactions are processed in preparation for the transfer of ownership of the traded products and the fulfillment of all agreements. This is called the clearing process and depending on which institution that is providing this service, other services can also be performed. For example, the agreements are netted in order to minimize the number of processes and cash flows, and all relevant sources of risk are evaluated and managed in order to reduce the probability of failure in meeting obligations. Usually, the clearing process is performed on different levels. Firstly, by the clients trading parties, secondly by Central Counterparty Clearing houses (CCP Clearing) and thirdly by Central Security Depositories (CSD clearing) or banking institutions (for internal transactions). The clearing performed by a CCP differs from the one performed by a CSD. CCP clearing is focused on trade, position, risk, and delivery management while the CSD clearing is focused on confirming and matching the delivery instructions which is then forwarded to settlement. After the settling of obligations, the ownership of the product is transferred as well as the related cash. Netting can be performed in this process as well to reduce the number of settlement transactions if it has not already been performed by a CCP in the clearing process. Instead of keeping physical products at the premises of the beneficiaries, financial products are localized at central but national custodians and these fall under the functional definition of CSDs. The purpose of this is that the CSDs are close to the issuer and allows for an efficient processing of corporate transactions while taking the relevant law and tax requirements into account. (ESMA, 2020).

Within the trading lifecycle, compliance partly concerns trading surveillance. Trading surveillance monitors real-time trading in financial instruments in order to detect anomalies and identifying potential market manipulation. If potential abuse is discovered, referrals are sent to financial conduct authorities for further investigation.

1.7 Delimitations

This study is delimited to investigate the future of European financial markets through a case study. The decision to delimit the study to Europe was made given the geographical location of the case company, but also since the market is regulated by governmental agencies on a European level. It was therefore not considered relevant to delimit the scope further to investigate the future of Swedish financial markets. The case study is also delimited to investigate possible future scenarios by the year 2030. A time frame of 10 years was considered reasonable since it allowed the analysis to generate a reasonable number of scenarios.

Extending the time frame drastically increases the number and uncertainty of possible change drivers which reduces the relevance of the analysis. The year 2030 was also chosen since it coincides with some of the climate goals established in the Paris agreement.

The study is also delimited to investigate future European financial markets from the perspective of a stock exchange. The study does however not exclude other actors in the value chain, but the analysis is focused on investigating possible future scenarios that are especially relevant to a stock exchange.

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2. Literature review

The literature review is structured as follows: First, an introduction to the concept of scenario analysis is presented. Secondly, different scenario analysis methods are presented as well as different purposes of conducting them.

2.1 Scenario analysis

Understanding what might happen in the future is most commonly done by examining what has happened in the past. Past and present day trends are often assumed to have an equal importance in the future and are therefore used as a basis for predictions (van der Merwe, 2008). However, since the future is never exactly the same as the past, extrapolating trends and patterns from the past to the future will never achieve accurate predictions (Makridakis, et al., 2010). There are several statistical models that are very accurate in modelling and understanding past data, but these complex models are not as accurate in predicting the future (Makridakis, et al., 2010).

Still, many organizations use these types of predictions in their strategy planning. According to Porter (1998) committing to this predicted future could have fatal impacts to the organization since it prevents people from detecting emerging trends and discontinuities that could lead to significant changes to the business environment. Reactions to these changes that deviate from the established version of the future are therefore often slow and delayed (Porter, 1998).

Therefore, companies and organizations should not make strategic decisions based on one single vision of the future. Instead, they should try to describe scenarios that encompasses several different views on how the future might develop in order to cope with growing uncertainties (Gausmeier, et al., 1998). Companies that manage to envision a wide range of potential future states are better equipped to take advantage of unexpected opportunities and achieve competitiveness (Schoemaker, 1997).

The use of scenario analysis in strategy planning is an approach that involves diverse thinking and discussion as a way of changing and challenging how the external environment is perceived (Chermack, 2011). Chermack & Lynham (2002) defines scenario analysis as “a process of positioning several informed, plausible and imagined alternative future environments in which decisions about the future may be played out, for the purpose of changing current thinking, improving decision making, enhancing human and organization learning and improving performance”. Furthermore, Godet (2000) states that a scenario is not an image of a future reality, but instead a method of envisioning the future with potential discontinuities and uncertainties incorporated. These alternative futures can then help prepare individuals and organizations for unforeseen events and trends (Chermack, 2011).

According to Schoemaker (1997), it is important to understand the difference between scenario analysis and other planning approaches. For instance, contingency planning is an approach that explores the future in relation to one single key uncertainty while scenarios explore the combined impact of several key uncertainties that are equally considered. Sensitivity analysis is an approach that explores the outcome of altering one variable while keeping the remaining constant. According to Schoemaker (1997), this is an effective method when exploring small changes but when looking at larger changes through a systems perspective, other variables cannot be assumed to remain constant. Scenario analysis, however, alters multiple variables at the same time without keeping others constant. The difference is therefore that scenarios can cover possible future states where multiple uncertainties and trends are accounted for (Schoemaker, 1997). According to Gausmeier, et al. (1998), this is important since organizations must view their environment as a complex and interconnected network of factors that can either be influenced or not. As most business environments become more complex with the rapid development of new technologies, globalization, and increasing number of policies

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and regulations, traditional strategy planning methods become less relevant (Gausmeier, et al., 1998).

The use of scenario analysis in the business area first emerged after the second world war when the RAND corporation was established to research new forms of weapons technology (Ringland, 1998). Hermann Kahn is said to have founded a technique called “future-now”

thinking where the aim was to combine detailed analysis with imagination to produce a report as if it had been written by someone from the future. These reports were described as scenarios and were not regarded as forecasts, but rather as stories or myths. During the mid-1960s, Kahn founded the Hudson Institute where he specialized in creating stories about the future as a way of helping people in challenging their views about the future and imagine the “unthinkable”.

His most well-known idea was that the best way to prevent a nuclear war from happening, was to thoroughly think about what the consequences would be if it became a reality, and then publish the results. (Ringland, 1998).

The Hudson Institute began seeking corporate sponsors which engaged companies such as Shell, Corning, IBM and General Motors to this way of thinking about the future (Ringland, 1998). In the following years after World War II, Shell had focused on physical planning since the company had to develop its production capacity and coordinate the scheduling of new facilities. A few years later, Shell started incorporating scenarios as a method for planning their chain of activity. The method, called Unified Planning Machinery (UPM), was developed to look six years into the future where the first year was described in detail and the following years were more broadly portrayed (Ringland, 1998). However, they discovered that six years were not enough given the long lead times for new projects in an oil company and therefore decided to explore how the future might look like in year 2000 (Wack, 1985). This initiative showed that a potential shift in power from oil companies to the oil producers in the Middle East for several reasons could result in a significant increase in the oil price. Additionally, in 1969, Shell initiated a planning exercise that involved a dozen of the largest Shell companies around the world with the purpose of looking forward to 1985, further developing alternative scenarios.

When the Yom Kippur war broke out in the Middle East, the oil price did indeed significantly increase as a result of the oil embargo by the producing countries which had depressing consequences on the world economy. Given Shell’s extensive scenario planning efforts, they were able to act quickly and gain a leading role in the oil industry (Ringland, 1998). Their success motivated other companies to begin thinking about the future with a scenario-based approach. Because the oil embargo had such a shocking effect on how people viewed the future and its many uncertainties, by the late 1970s the majority of the Fortune 100 companies had adopted the method of using scenarios in their strategy planning (Wack, 1985).

2.2 Approaches to scenario analysis

There are several different methods for conducting a scenario analysis. Available scenario approaches have many common characteristics and they are generally practitioner driven (Jetter, et al., 2013). Some methods use a qualitative approach to input data and analysis while other methods use a more quantitative approach with the use of software tools (Jetter, et al., 2013). Primarily, there are three categories of scenario analysis approaches that incorporates a range of sub-techniques according to Jetter, et al. (2013). These approaches are:

• Intuitive logics

• Probabilistic modified trends (PMT) methodology

• The French approach of La prospective

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Intuitive logics

The intuitive logics approach to scenario analysis has been developed over many decades by a number of different researchers (e.g. Schoemaker (1997), Wack (1985)) and organizations (e.g.

Government Office for Science; SRI International). This methodology has received the most attention in the scenario analysis literature (Van Der Heijdena, et al., 2005). According to Cairns, et al. (2013) the approach focuses on how the future could develop up until the year defined in the specific scenario project by analyzing the relationships between:

• Critical uncertainties

• Important predetermined trends

• The behavior of stakeholders who are active in the projected future

The approach aims at including political, economic, social, technological, environmental, and legislative factors (PESTEL) in the analysis (Cairns, et al., 2013). The majority of these factors are generally external to the organization but are essential in understanding possible change drivers that could have an effect on the business environment (Honton & Huss, 1987). Some of these factors are specific, quantitative and predictable such as demographics etc., while many other factors are less specific, qualitative and difficult to predict such as customer behavior, politics, and technological development (Honton & Huss, 1987).

One example of an Intuitive logics approach is “The futures toolkit” which is a practical framework that can be used by professionals to apply long term strategic thinking in their policy and strategy processes (Government Office for Science, 2018). The framework uses the term

“futures thinking” and defines it as “an approach to identifying the long-term issues and challenges shaping the future development of a policy area and to exploring their implications for policy development.” (Government Office for Science, 2018). The framework contains several tools that can be utilized to support the development of a strategy that is robust to a range of possible future states. The method consists of four steps:

Step 1 – Gathering intelligence about the future Step 2 – Exploring the dynamics of change Step 3 – Describing what the future might be like

Step 4 – Developing and testing policy and strategic responses

Gathering of information about the future can be done by applying one of four different tools.

Each tool involves interviews or discussions with a wide group of people that can include both internal and external stakeholders. Government Office for Science uses an own version of 7 questions, first developed by Shell, in their interview process. The questions are displayed in appendix 2. The aim is to then identify, categorize and evaluate key drivers that could potentially shape the future. This can be done by mapping the most important drivers using the PESTEL framework and then categorize them by how certain or uncertain they are. Based on these key drivers, different scenarios can be created that encompasses the most important uncertainties and challenges that emerged from the interviews. According to Government Office for Science (2018), the scenarios should not be regarded as predictions as the intention is not to envision the most probable future but rather to offer stimulating versions of the future to build an understanding of how strategic decisions could pan out under different market

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conditions. When constructing the scenarios, one should also consider how key actors in the business environment might behave under different conditions. Finally, developing and testing policy and strategic responses can be done by using one of three tools described in the method.

The tools aim at identifying which objectives are robust across all potential scenarios and which objectives that require modification if conditions change in the future. This step also includes identifying what needs to be done today in order to prepare for these alternative futures.

(Government Office for Science, 2018).

A version of the “Futures toolkit” has been developed by a consultancy firm called Innovation360 which is modified to also include the purpose of developing innovation and business strategy, rather than just policy development (Innovation 360 Group, 2020). Similar to the “Futures toolkit”, the aim is to combine certainty with an analysis of the uncertain.

Macrotrends that are considered to be important and certain to continue in the future should be addressed in the planning of an innovation strategy. Furthermore, the macrotrends that are considered important and uncertain are used to develop scenarios and by investigating these scenarios, common needs for change can be identified. By answering to the common needs in the innovation strategy the organization can prepare for uncertainty. The method consists of five steps:

Step 1 – Collect Step 2 – Categorize Step 3 – Stretch Step 4 – Combine Step 5 – Develop

The first step is to collect macrotrends and drivers by using an own version of the 7 interview questions described in the “Futures toolkit”. The data gathered from the interviews are mapped according to the PESTLED framework which also includes demographical factors unlike the PESTEL framework. Step two is performed by letting each driver be categorized into a matrix, which consist of two axes, importance and uncertainty. Each driver is considered to have a level of uncertainty and importance and will be categorized by its level of importance and level of uncertainty. Depending on where in the matrix a driver is categorized, different actions are applied, for instance: Prioritize and act for drivers that are considered certain but important, building scenarios for drivers that are considered as uncertain and important, monitor and track drivers that are uncertain but of less importance and park all drivers that are certain and less important. In step 3, axes of uncertainty are developed by describing alternative ways that a critical uncertainty might play out. For example, if global security is identified as a driver and a critical uncertainty, the potential driver outcomes could be “the world is insecure and unstable” and “the world is secure and stable”. These opposite outcomes would then constitute one axis of uncertainty and in the next step (combine), the axes of uncertainty are combined to generate scenario matrices. A combination of two uncertainty axes constitutes one scenario matrix that contains four different scenarios, one for each quadrant. After further analysis and prioritization, this step in a scenario project could for example result in four axes of uncertainty which amounts to six scenario matrices and twenty-four different scenarios. The matrices are then evaluated to determine which combinations are most interesting to look at and develop scenarios from. The previously identified trends that were considered certain and important are

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in each scenario. This can be done by utilizing several different strategy tools such as: Porter’s six forces, business model canvas, value proposition canvas and blue ocean strategy. Innovation 360 uses an extended version of Porter’s five forces that also includes collaboration as a force, hence the name Porter’s six forces. Lastly, recommendations and issues for the development of a strategy is identified and a scenario description is created that captures the essence of the analysis. The description is used to set the stage and communicate to the rest of the organization and the world around the possible future that has been projected. The analysis can be iterated to examine a number of scenarios in order to gain a broader view of the future. Also, the organization can perform an innovation assessment to analyze the internal innovation capabilities related to the alternative futures. (Innovation 360 Group, 2020).

Probabilistic modified trends (PMT) school

The second approach called the probabilistic modified trends methodology includes two different matrix-based methodologies: trend impact analysis (TIA) and cross impact analysis (CIA) (Jetter, et al., 2013). Both methodologies stem from the notion that traditional quantitative forecasting methods that are based on historical data are insufficient. Traditional methods assume that past trends will continue in the future and that future events will not have a significant impact which results in surprise-free projections that will most likely be incorrect (Gordon, 1994). TIA is a forecasting method that allows extrapolated historical trends to be modified by incorporating effects of unprecedented future events. Quantitative methods such as time series analysis are combined with qualitative factors that are considered important (Gordon, 1994). According to Van Der Heijdena, et al. (2005) the four main steps in a TIA are:

Step 1 – Historical data is collected regarding the specific area of interest.

Step 2 – A curve is fitted to this data and extrapolated to generate the “surprise-free” future trend.

Step 3 – A set of unprecedented events that could potentially cause deviations from the extrapolated trend are identified.

Step 4 – The future trend is adjusted by assessing the probability of the unprecedented events occurring as a function of time and their expected impact.

The events are treated as independent of one another and their combined impact on the future trend is calculated by summing the probabilities for each year in which the events could potentially occur (Gordon, 1994). If one event is considered likely to influence the probability of another one, a cross-impact analysis is more suitable for projecting alternative futures (Gordon, 1994). This approach is used to include the interrelationship between different factors in the analysis. The purpose is to understand these interrelationships in order to move from a system of unprocessed initial probabilities to a set of corrected probabilities (Van Der Heijdena, et al., 2005).

French school – La prospective

The La prospective method was developed in France during the 1970s and was mostly used for public sector planning rather than for corporate purposes (Van Der Heijdena, et al., 2005). The approach was developed on the notion that the future is not part of a progressive continuity and cannot be accurately predicted consistently (Jetter, et al., 2013). The future develops as a result of relationships between different dynamic forces and interactions between various determinant factors and since there are multiple possible outcomes, the future is uncertain. According to Godet (1986), the main objectives with the La prospective method are:

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• To identify the relationships between the variables of the specific system through systemic analysis, in order to detect the most important issues for study.

• To determine the main actors in the system environment and their strategies related to key variables.

• To describe, with scenarios, the development of the specific system by analyzing the potential outcomes of key variables and the behavior of main actors.

A system consists of a set of interrelated variables, and the relationship between them is essential in understanding the system structure. The purpose of conducting a systemic analysis is to highlight this structure by identifying the different variables and to describe the network of relationships between them. First, a list of variables that relate to the issue that is being studied is created. This can be done by using some sort of intuitive method such as brainstorming or interviews with actors that are involved in the system. Several different viewpoints should be adopted when doing this in order to cover factors such as political, economic, technological and social. When the data has been collected, a homogenous list of variables should be created with a detailed explanation of each. The variables can also be categorized as either internal or external where internal variables characterize the sub-system that is being studied and the external variables define its environment. (Godet, 1986).

Secondly, the interrelationships between all variables are evaluated by answering the following three questions: (1) Does variable i causally affect variable j, or is it the other way around? (2) Does i have an impact on j, or is there a third variable k that has an impact on both i and j? (3) Is the relationship between i and j direct, or is it dependent on another listed variable?

After the evaluation of the interrelationship between all variables and the creation of an extensive list of the variables to be taken into account, the key variables that should be studied first need to be identified (Godet, 1986). The MICMAC (Cross Impact Matrices-Multiplication Applied to Classification) method can be used to identify the key variables by classifying them as either direct or indirect.

According to the La prospective approach, a scenario includes the following three components:

1) the base which is represented by a presentation of the current reality and the dynamics of the system that is being studied; 2) different paths created by looking at the system in relation to a time scale with specified conditions in each; 3) final images of possible future states that represent the different paths (De Jouvenel, 2000).

Critique of scenario analysis

According to Wright, et al. (2019), scenario analysis as a strategic tool has been subject to critique over the years. Some of the criticism is towards the lack of theoretical underpinning and the method have been described as an imperfect tool by some researchers (Wright, et al., 2019). The use of scenarios has also been described as the result of mere speculations where the practitioners are dealing with opinions rather than facts. It is important to emphasize that despite the fact that scenarios often are based on prognostic knowledge, scenarios should not be viewed as “hard and fast” predictions (Kosow & Gaßner, 2008). Scenarios should be seen as a range of possible futures which implies that the prognostic value should not be overestimated (Kosow & Gaßner, 2008).

Cairns, et al. (2013) states that although scenario analysis is an effective method for

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conventional thinking and reframe perceptions of the future is not clear. By having individuals, as in the intuitive logics approach, evaluate the occurrence of a set of possible events might lead to a scenario being perceived as more likely to occur than the normative probability computed for the combination of these events would suggest (Cairns, et al., 2013). Therefore, the development of scenarios could result in too high confidence in the likelihood of a certain scenario becoming the reality. In addition, Wright, et al. (2019) states that there is an issue related to the differences in status and power between the scenario analysis participants that the method does not account for. Participants that are in the lower part of the corporate hierarchy may be hesitant towards expressing views and opinions that might not be in favor of the company and are likely to be unpopular with more senior participants (Cairns, et al., 2013).

Another criticism against scenario analysis lies in the cognitive limitations in thinking of uncertainties and the unknown (Kosow & Gaßner, 2008). This inability could potentially lead to thoughts that are limited in the visualization of the future and instead, existing trends are extrapolated (Kosow & Gaßner, 2008). Scenarios are not a product, instead they are a process that in order to become truly effective needs to be refined over time (Maack, 2001)

2.3 Purposes of scenario analysis

According to van der Heijden (2004), the purpose of initiating a scenario analysis can be categorized along two dimensions: The distinction between “content and “process”, and the distinction between “thinking” and “action”. This is displayed in figure 2.

Figure 2. Purposes of scenario analysis

Making sense

The box that is constituted by the “thinking-content” combination is aimed at understanding certain aspects of the environment that an organization might find interesting to examine. The use of a scenario analysis in this context can help in defining the specific areas and questions that requires further research, or in exploring the external environment in general (van der Heijden, 2004). One example of where scenario analysis was effective in defining the important questions is the previously mentioned case of Shell during the 1960s. Shell managed to identify future uncertainties and how they relate to each other which allowed them to gain a deeper understanding of the situation in order to generate better and more accurate questions (Wack, 1985). Many of the important questions that organizations might want to define are of multi- disciplinary characteristics and multiple scenarios are therefore an effective way of explicitly describing the research issues. Studying the different scenarios and testing their internal consistency shows in which areas knowledge is inadequate and where further research should

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be focused. When conducted successfully, a scenario analysis is an iterative process in which the results from defining critical questions and research areas establishes the basis for the next scenario development round. By doing this, the problem analysis become focused and relevant (van der Heijden, 2004).

Developing an optimal strategy

The purpose of using scenario analysis in the “content-action” combination is to test a strategy already in place by trying out scenarios as a one-time project. This is considered to be difficult since single stand-alone scenario analyses rarely result in extraordinary insights. The strategic options generated from these types of projects are often perceived as unsurprising and it is not very often that they are considered worth putting current success at risk. Generally, when business is doing well, people and organizations are less motivated to put efforts into finding new strategies. On the other hand, when things are not going as well, the focus is generally towards finding immediate solutions rather than experimenting with strategy. (van der Heijden, 2004).

Anticipation

The “thinking-process” represents scenario projects that aim at increasing the quality of the ongoing strategic work in an organization. The use of scenario analysis enables the multi-level strategic conversation within the organization to develop and continue. Such a conversation helps establish a shared mental model of the external environment and the organization itself.

When successfully using scenario thinking as a base for strategic discussions, organizations are able to avoid groupthink as scenarios are built on encouraging multiple perspectives. They are also able to avoid fragmentation by ensuring participation and building a common understanding of the environment. (van der Heijden, 2004).

Adaptive organizational learning

The “action-process” combination is the last scenario purpose where the aim is to continuously use scenario analysis to achieve an understanding of the external environment, hold internal strategic discussions, and leverage strategic opportunities by taking actions. There can be no learning without action and the use of scenario analysis allows an organization to learn and change from its own experiences to identify opportunities in a complex and ever-changing business environment. Therefore, the ultimate aim of scenario analysis is to make an impact on strategic decisions by taking reflection-based action. (van der Heijden, 2004).

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3. Theoretical framework

This section gives insight to the theoretical framework that has been used in in this report. The framework is Porter´s five forces.

3.1 Porter’s five forces

In 1979, Michel E. Porter published “How Competitive Forces Shape Strategy” in Harvard Business Review. Porter’s (1979) work has shaped a generation of academic research and business practice with a competitive strategy framework for corporations within several different industries. In Essence, the framework is about understanding and cope with competition. Inconsequential of what industry a corporation operates within, the five forces shape all industries. However, the configuration of the forces can differ. According to Porter (1979), the five forces are Threat of New Entrants, Bargaining Power of Suppliers, Bargaining Power Buyers, Threat of Substitute Products or service and Rivalry among Existing Competitors. The five forces are described below in more detail.

Threat of New Entrants

Every industry with new entrants trying to capture a market share, is under pressure in terms of price, cost and the rate of investment in order to compete. Especially when new entrants are competitors that already operate in other markets, due to leveraging from existing capabilities.

If a new competitor enters the market, there will be a cap on the profit for that industry. There are seven major sources from where an incumbent firm has an advantage relative to the new entrants. The seven sources are: supply-side economy of scale, demand-side benefit of scale (Network effects), customer switching cost, capital requirements, incumbency advantages independent of size, unequal access to distribution channels and restrictive government policies.

What also matters from the perspective of new entrants is expected retaliation, is how existing incumbents will react. New entrants will fear expected retaliation if the incumbent recently has responded forcefully to newcomers and has extraordinary resources as excess cash or large influence in distribution channels and customers. Fear of that the incumbent will protect its market share by cutting prices or slow industry growth so the new entrants need to gain volume by taking it from the incumbent are two other expected retaliations that newcomers fear.

Bargaining Power of Suppliers

Suppliers that are powerful can charge higher prices, shifting costs to industry participants and limiting quality or services in order to capture more value. By doing this, they can “squeeze profitability out of an industry”. A powerful supplier is also a supplier that is operating in an industry that is more concentrated than it sells to and does not depend on a specific industry to earn profit. Bargaining power of suppliers increases if an industry participant wants to shift supplier, but it is difficult for reasons such as high switching cost or that no substitute alternative exists. Furthermore, if an industry participant has a profit that is large in comparison to the supplier, it could motivate for a supplier to enter that particular market.

Bargaining Power of Buyers

Buyers can capture more value if they are powerful enough to force prices down and demand higher quality or more service. A powerful customer can also play industry participants against each other. Negotiating leverage is typical for powerful buyers which occurs if there are few buyers in an industry or if a buyer is large in comparison to a single vendor’s order stock. If substitute products are accessible, buyers will use that to its advantage if it not comes with a larger switching cost. As with powerful suppliers, powerful buyers can also threaten existing suppliers’ markets if the suppliers are too profitable. The buyers are price sensitive if the goods

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they buy from an industry represents a large share of its cost structure, earns low profit or in some form are under cost pressure.

Threat of substitute products or services

Threat of substitute products are goods or services that threaten the existing market. High threat from substitute products makes industry profitability suffers as well as disrupt the growth potential. The threat is high if a substitute product offers a tempting price-performance trade- off and that the switching cost is low. Substitutes are easy to neglect due to that they appear to be dissimilar from the original product, but they are always present. Substitute products’

constant presence limit profits both in normal times and in good times when incumbent is supposed to reap larger profits. Additionally, changes in other industries are something to be particular observant of since improvement a product or service could develop to a substitute offering.

Rivalry among Existing Competitors

The forms of rivalry among existing competitors includes price discounting, advertising campaigns, new product introductions and service improvements. The profitability of a sector decreases if high rivalry exists and especially price competition transfers profits to the customer at the expense of the industry. Two factors that drive down an industry´s profitability are the intensity of competition and the basis of rivalry. The intensity is high if there are several participants that are equal in size and power, the industry growth is slow or that the exit barriers are high. Slow industry growth can trigger fight for market share as well as if a rival is committed to take a leading position in the market. If the basis of the competition is price, it is likely that the products or services are identical and related with low switching costs. High fixed costs and low marginal costs can also lead to price competition as well as if a company finds itself in the position that the only way to expand and be efficient, is to buy in larger increments.

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4. Method

This section describes the method used in this study including research design, data collection, data analysis, and research quality.

4.1 Research design

This study will use a qualitative method and inductively identify how scenario analysis can be used by organizations to meet future uncertainties and from the perspective of stock exchanges, discover plausible future states in Europe’s financial markets. The use of a qualitative method is chosen since the data collection entirely will be based on interviews where the answers are supposed to evolve during discussions in a semi structured manner and contain soft, rich data (Blomkvist & Hallin, 2015). The qualitative method is also chosen in order to understand complex interrelationship and seeking patterns rather than seeking explanation and control which is the case for quantitative studies (Stake, 1995). To be able to answer the research questions, an instrumental case study is conducted where methodologies from literature within scenario analysis are used to discover trends and patterns from the collected data. This follows an inductive logic in research for qualitative studies (Creswell, 2003). Also, by discovering patterns emerged from raw data and then categorize it into different themes, fulfils the purpose of an inductive style (Thomas, 2006). In regards of the case study approach, the researchers are influenced by Stakes (1995) constructivist epistemological commitment which allows the researchers to use a flexible research design to be able to make changes throughout the study from design to research as well as that the researchers wants to obtain interpretations and descriptions of others. According Creswell (2003) a constructivist perspective and qualitative studies are associated with case studies. The reason for decide on the instrumental case study approach, except that the focus of the study was known in advance (Grandy, 2010), is that it provides insights to an issue and a general understanding of something else (Stake, 1995). The particular case is also looked at in depth and the context is scrutinized as well as that the activities are detailed which helps the researcher drive the external interest (Baxter & Jack, 2008)

Since the researchers are influenced by Robert Stakes, the intrinsic case study approach has been considered. The reason for abandoning this approach is due to that it is seen as the no choice at all, which can be used when the case investigating does not fit any other approach and that the particular case is of interest to investigate (Stake, 1995). Yin´s (2012) approaches have also been considered. Especially the exploratory approach which is used when the outcome can consist of multiple set of solutions, which indeed applies to scenario analysis. However, Yin´s positivism does not reflect the researcher´s view on this study and is more applicable when conducting a quantitative study (Baxter & Jack, 2008).

Figure 3. Schematic view of the report structure

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The structure of this report is displayed in figure 3. A literature study is conducted within the field of scenario analysis and implementation. Tools discovered in the literature review are used in order to methodically build scenarios. The interviews are analyzed thematically and forms the Empirics chapter where uncertainties and trends are input data in the scenario building process in order to discover plausible future states. The business environments within these plausible future states are then presented after applying a theoretical framework.

4.2 Data collection

Since the researchers want to discover the description and interpretations of others, interviews have been conducted in order to gather data. This is in line with Stakes’ (1995) view that interviews are the best source of evidence when multiple realities are desired.

The data collection methodology has followed Stakes’ (1995) approach of categorical aggregation. Yin (2012) also describes that case studies can begin by systematically aggregate the data into bundles. The bundles in this case will be categorized according to specific themes, but ahead of this categorization, this study will identify key phrases that are associated with trends and uncertainties within the interviews. Below follows a detailed description of the data collection process.

Selection of respondents

According to Stakes (1995), the selection of data sources may involve randomness. In order to avoid this and to cover many different aspects within the case, this study has selected 12 respondents that cover a wide range of specialties within financial markets. For instance, CEO of a Swedish bank, founder of a high frequency trading firm, founder of a financial technology company, SVPs and Senior Directors. Together they cover areas such as, Trading financial instruments, Technology, Technological shifts & implementation Regulations, Economics, market models as well as environmental factors and resource requirements. As Yin (2012) mentions, the respondents’ construction of reality is important insights within a case study, which is especially true if the participants are key persons within organizations or groups that are being investigated. The Primary sources are therefore key persons within a Stock exchange and market participants as previously mentioned. The secondary sources are the literature that builds up the background and methodology for this case. The number of respondents were not decided on beforehand. Answers from the interviews guided the researchers of when a saturation point was established (Guest, et al., 2006). Table 2 displays the respondents.

References

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