IAMSR & INTELLIGENT IAMSR & INTELLIGENT TECHNOLOGIESTECHNOLOGIES

Christer Carlsson, Barbro Back Christer Carlsson, Barbro Back and Pirkko Waldenand Pirkko Walden

IAMSR / Åbo AkademiIAMSR / Åbo Akademi

IAMSR/Åbo Akademi UniversityIAMSR/Åbo Akademi University

• Academic research institute, funded by the Finnish industry Academic research institute, funded by the Finnish industry [mainly through Tekes, EU-IST programs][mainly through Tekes, EU-IST programs]

• NetworkNetwork: City University of Hong Kong, TU Delft. Nankiang : City University of Hong Kong, TU Delft. Nankiang Technological Univ, UD (Dallas), UPMC (Paris), Univ of the Technological Univ, UD (Dallas), UPMC (Paris), Univ of the Aegean, Univ of Trento, Univ of Vienna, …Aegean, Univ of Trento, Univ of Vienna, …

• Corporate partnersCorporate partners include Agentum, Amerpap, BTExact, include Agentum, Amerpap, BTExact, Metso, MetsäTissue, M-real, Nokia, Finnforest, Outokumpu, Metso, MetsäTissue, M-real, Nokia, Finnforest, Outokumpu, Rautaruukki, Restel, TeliaSonera, UPM, Veritas, …Rautaruukki, Restel, TeliaSonera, UPM, Veritas, …

• EUNITE Network of Excellence [EU-IST, EUNITE Network of Excellence [EU-IST, smart adaptive smart adaptive systemssystems]]

• Berkeley Initiative in Soft ComputingBerkeley Initiative in Soft Computing

IAMSR IAMSR 20042004

• Working principles:Working principles:– we build theory, conceptual frameworkswe build theory, conceptual frameworks

– we carry out interactive research processeswe carry out interactive research processes

– we do fundamental researchwe do fundamental research

– we carry out large projects and do project we carry out large projects and do project managementmanagement

– we do feasibility studieswe do feasibility studies

– we integrate systems from standard componentswe integrate systems from standard components

IAMSR/Åbo Akademi UniversityIAMSR/Åbo Akademi University

• Research staff 60+ of which 28 PhD students (8 countries)Research staff 60+ of which 28 PhD students (8 countries)

– fuzzy logic, fuzzy optimization, soft computingfuzzy logic, fuzzy optimization, soft computing

– hyperknowledge and mobile support systemshyperknowledge and mobile support systems

– intelligent software agents and approximate reasoningintelligent software agents and approximate reasoning

– e-commerce and m-commercee-commerce and m-commerce

– neural nets and self-organizing maps, data miningneural nets and self-organizing maps, data mining

– real options and fuzzy real options modelingreal options and fuzzy real options modeling

– industry foresight with scanning, scenario agentsindustry foresight with scanning, scenario agents

– strategic management and scenario planningstrategic management and scenario planning

IAMSR Structure 2004-5IAMSR Structure 2004-5

IAMSRIAMSR

ASRASR

KSRKSR MSRMSR

Data Mining Mobile Commerce

IAMSR PROJECTS IAMSR PROJECTS 20042004

STEERINGSTEERINGMCOMMERCE

2001-2004

MCOMMERCE2001-2004

OptionsPorts2004-2005,

OptionsPorts2004-2005,

Phoenix2004-2005

Phoenix2004-2005

SoftLogs2004-2006

SoftLogs2004-2006

Rhodonea, 4M2004-2005

Rhodonea, 4M2004-2005

SmartBulls2003-2005

SmartBulls2003-2005

CHIMER2002-2004

CHIMER2002-2004

CoFI Projects

CoFI Projects

IAMSR 1992IAMSR 1992--20032003

•• Doctoral theses Doctoral theses –– 11 defenses, 2003 (1), 11 defenses, 2003 (1), 2004 (32004 (3--4)4)

•• LicentiateLicentiate thesestheses–– 13 completed, 2003 (1)13 completed, 2003 (1)

•• ArticlesArticles in journals and in journals and editededited volumesvolumes–– 137 137 completedcompleted, 2003 (16), 2003 (16)

•• ArticlesArticles in in conferencesconferences–– 246 246 completedcompleted, 2003 (45), 2003 (45)

•• Research Research reportsreports–– 149 149 completedcompleted, 2003 (19), 2003 (19)

•• MonographsMonographs and and editededited volumesvolumes–– 30 30 completedcompleted, 2003 (3) , 2003 (3)

GIGA-INVESTMENTSGIGA-INVESTMENTS

Facts and observationsFacts and observations Giga-investments made in the paper- and pulp industry, in Giga-investments made in the paper- and pulp industry, in

the heavy metal industry and in other base industries, today the heavy metal industry and in other base industries, today face scenarios of slow growth (2-3 % p.a.) in their key face scenarios of slow growth (2-3 % p.a.) in their key markets and a growing over-capacity in Europe markets and a growing over-capacity in Europe

The energy sector faces growing competition with lower The energy sector faces growing competition with lower prices and cyclic variations of demand prices and cyclic variations of demand

PProductivity improvements in these industries have slowed roductivity improvements in these industries have slowed down to 1-2 % p.a down to 1-2 % p.a

GIGA-INVESTMENTSGIGA-INVESTMENTS

Facts and observationsFacts and observations Global financial markets make sure that capital cannot be Global financial markets make sure that capital cannot be

used non-productively, as its owners are offered other used non-productively, as its owners are offered other opportunities and the capital will move (often quite fast) to opportunities and the capital will move (often quite fast) to capture these opportunities. capture these opportunities.

The capital markets have learned “the American way”, i.e. The capital markets have learned “the American way”, i.e. there is a shareholder dominance among the actors, which there is a shareholder dominance among the actors, which has brought (often quite short-term) shareholder return to has brought (often quite short-term) shareholder return to the forefront as a key indicator of success, profitability and the forefront as a key indicator of success, profitability and productivity. productivity.

GIGA-INVESTMENTSGIGA-INVESTMENTS

Facts and observationsFacts and observations There are lessons learned from the Japanese industry, There are lessons learned from the Japanese industry,

which point to the importance of which point to the importance of immaterial investmentsimmaterial investments. . These lessons show that investments in buildings, These lessons show that investments in buildings, production technology and supporting technology will be production technology and supporting technology will be enhanced with immaterial investments, and that these are enhanced with immaterial investments, and that these are even more important for re-investments and for gradually even more important for re-investments and for gradually growing maintenance investments.growing maintenance investments.

GIGA-INVESTMENTSGIGA-INVESTMENTS

Facts and observationsFacts and observations The core products and services produced by giga-The core products and services produced by giga-

investments are enhanced with lifetime service, with investments are enhanced with lifetime service, with gradually more advanced maintenance and financial add-on gradually more advanced maintenance and financial add-on servicesservices..

New technology and enhanced technological innovations New technology and enhanced technological innovations will change the life cycle of a giga-investment will change the life cycle of a giga-investment

Technology providers are involved throughout the life Technology providers are involved throughout the life cycle of a giga-investment cycle of a giga-investment

GIGA-INVESTMENTSGIGA-INVESTMENTS

Facts and observationsFacts and observations Giga-investments are large enough to have an impact on Giga-investments are large enough to have an impact on

the market for which they are positioned:the market for which they are positioned:A 300 000 ton paper mill will change the relative competitive A 300 000 ton paper mill will change the relative competitive

positions; smaller units are no longer cost effectivepositions; smaller units are no longer cost effectiveA new teechnology will redefine the CSF:s for the marketA new teechnology will redefine the CSF:s for the marketCustomer needs are adjusting to the new possibilities of the giga-Customer needs are adjusting to the new possibilities of the giga-

investmentinvestment

The proposition that we can describe future cash flows as The proposition that we can describe future cash flows as stochastic processes is no longer valid; neither can the stochastic processes is no longer valid; neither can the impact be expected to be covered through the stock marketimpact be expected to be covered through the stock market

GIGA-INVESTMENTSGIGA-INVESTMENTS

The WAENO Lessons: The WAENO Lessons: Fuzzy ROVFuzzy ROV Geometric Brownian motion does not applyGeometric Brownian motion does not apply Future uncertainty [15-25 years] cannot be estimated from Future uncertainty [15-25 years] cannot be estimated from

historical time serieshistorical time series Probability theory replaced by Probability theory replaced by possibility theorypossibility theory Requires the use of fuzzy numbers in the Black-Scholes Requires the use of fuzzy numbers in the Black-Scholes

formula; needed some mathematicsformula; needed some mathematics The The dynamic decision treesdynamic decision trees work also with fuzzy numbers work also with fuzzy numbers

and the fuzzy ROV approachand the fuzzy ROV approach All models could be done in ExcelAll models could be done in Excel

EUR/USD, close daily 1.1.2001 - 16.8.2002, rates 19.6. 2001 ja 19.6.2002

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REAL OPTIONSREAL OPTIONS

Types of optionsTypes of options Option to DeferOption to Defer Time-to-Build OptionTime-to-Build Option Option to ExpandOption to Expand Growth OptionsGrowth Options Option to ContractOption to Contract Option to Shut Down/ProduceOption to Shut Down/Produce Option to AbandonOption to Abandon Option to Alter Input/Output MixOption to Alter Input/Output Mix

REAL OPTIONSREAL OPTIONS

Table of EquivalencesTable of Equivalences::

INVESTMENT INVESTMENT OPPORTUNITYOPPORTUNITY

VARIABLEVARIABLE CALL OPTIONCALL OPTION

Present value of a project’s Present value of a project’s operating cash flowsoperating cash flows

SS Stock priceStock price

Investment costsInvestment costs XX Exercise priceExercise price

Length of time the decision may Length of time the decision may be deferredbe deferred

tt Time to expiryTime to expiry

Time value of moneyTime value of money rrffRisk-free interest rateRisk-free interest rate

Risk of the projectRisk of the project σσ Standard deviation of Standard deviation of returns on stockreturns on stock

REAL OPTION VALUATION (ROV)REAL OPTION VALUATION (ROV)

The value of a real option is computed by

ROV =Se −δT N (d1) − Xe −rT N (d2)

whered1 = [ln (S0 /X )+(r −δ +σ2 /2)T] / σ√T

d2 =d1 − σ√T,

FUZZY REAL OPTION VALUATIONFUZZY REAL OPTION VALUATION

• Fuzzy numbers (Fuzzy numbers (fuzzy setsfuzzy sets) are a way to ) are a way to express the cash flow estimates in a more express the cash flow estimates in a more realistic wayrealistic way

• This means that a solution to both problems This means that a solution to both problems (accuracy and flexibility) is a real option (accuracy and flexibility) is a real option model using fuzzy setsmodel using fuzzy sets

FUZZY CASH FLOW ESTIMATESFUZZY CASH FLOW ESTIMATES

• Usually, the present value of expected cash Usually, the present value of expected cash flows cannot be characterized with a single flows cannot be characterized with a single number. We can, however, estimate the number. We can, however, estimate the present value of expected cash flows by using present value of expected cash flows by using a trapezoidal possibility distribution of the a trapezoidal possibility distribution of the formform

ŜŜ00 =(s =(s11, s, s22, α, β) , α, β)

• In the same way we model the costsIn the same way we model the costs

FUZZY REAL OPTION VALUATIONFUZZY REAL OPTION VALUATION

We suggest the use of the following formula for computing fuzzy real option values

Ĉ0 = Ŝe −δT N (d1) − Xe −rT N (d2)

where

d1 = [ln (E(Ŝ0)/ E(X))+(r −δ +σ2 /2)T] / σ√T

d2 = d1 − σ√T,

FUZZY REAL OPTION VALUATIONFUZZY REAL OPTION VALUATION

• E(Ŝ0) denotes the possibilistic mean value of the present value of expected cash flows

• E(X) stands for the possibilistic mean value of expected costs

• σ: = σ(Ŝ0) is the possibilistic variance of the present value of expected cash flows.

FUZZY REAL OPTION VALUATIONFUZZY REAL OPTION VALUATION

No need for precise forecasts, cash flows are fuzzy and converted to triangular or trapezoidal fuzzy numbers The Fuzzy Real Option Value contains the value of flexibility

FUZZY REAL OPTION VALUATIONFUZZY REAL OPTION VALUATION

SCREENSHOTS FROM MODELSSCREENSHOTS FROM MODELS

NUMERICAL AND GRAPHICAL NUMERICAL AND GRAPHICAL SENSITIVITY ANALYSESSENSITIVITY ANALYSES

FUZZY OPTIMAL TIME OF FUZZY OPTIMAL TIME OF INVESTMENTINVESTMENT

Ĉt* = max Ĉt = Ŵt e-δt N(d1) – X e-rt N (d2 ) t =0 , 1 ,...,T where

Ŵt = PV(ĉf0, ..., ĉfT, βP) - PV(ĉf0, ..., ĉft, βP) = PV(ĉft +1, ..., ĉfT, βP)

Invest when FROV is at maximum:

OPTIMAL TIME OF INVESTMENTOPTIMAL TIME OF INVESTMENT

Ct* = max Ct = Vt e-δt N(d1) – X e-rt N (d2 ) t =0 , 1 ,...,T

How long should we postpone an investment?Benaroch and Kauffman (2000) suggest:Optimal investment time = when the option value Ct* is atmaximum (ROV = Ct*)

Where

Vt = PV(cf0, ..., cfT, βP) - PV(cf0, ..., cft, βP) = PV(cft +1, ...,cfT, βP),

FUZZY OPTIMAL TIME OF FUZZY OPTIMAL TIME OF INVESTMENTINVESTMENT

We must find the maximising element from the set {Ĉ0, Ĉ1, …, ĈT}, this means that we need to rank the trapezoidal fuzzy numbers

In our computerized implementation we have employed thefollowing value function to order fuzzy real option values, Ĉt = (ct

L ,ctR ,αt, βt), of the trapezoidal form:

v (Ĉt) = (ctL + ct

R) / 2 + rA · (βt + αt) / 6

where rA > 0 denotes the degree of the investor’s risk aversion

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