Assessment of construction object financing solutions/Statybos objektu finansavimo sprendimu ivertinimas.
Stunguriene, Stanislava ; Urbsiene, Laima
1. Foreword
Implementation of construction projects is a complex process
frequently causing controversy for a number of reasons: 1) an enterprise
may simultaneously be implementing several projects; 2) the timing for
project implementation may be different; 3) labour, material or
financial resources required for the implementation of projects
frequently are limited. Considering that the procurement and utilisation
of labour and material resources are directly related to the financing
of projects, the present paper focuses on optimisation of the allocation
of financial resources according to construction objects and project
implementation periods. The proper allocation of funding resources
acquires a special significance in view of a shrinking economy (Rabin,
Thaler 2001; Friedman 2010; Ferguson 2009), then it becoming vitally
essential to assess the possible funding volumes taking into account the
time value of money (Damodaran 2007), and be particularly cautious and
efficient in managing borrowed funds (Kale 2008; Brealey et al. 2008b).
Issues related to long-term accumulation and use of funds should be
addressed by employing quantitative or qualitative methods. In this
respect research sources show clear preference to quantitative methods
(Leibowitz, Langetieg 1989; Bier et al. 2008; Peters 2005), since in
most cases research data are processed and the findings obtained are
assessed by means of different computer-assisted programmes (Stutzer
2004).
In practice the funds required for funding of construction objects
are most often accumulated and allocated employing heuristic methods. In
research literature the assessment of this funding process has been an
object of ardent controversy where special account needs to be taken of
peculiarities of timing, cash flow formation, funding sources and other
factors that affect the appropriation of financial resources available.
The funding process and the relevant outcomes are being assessed
following different methodologies (Garvin, Cheah 2004). Nevertheless,
each specific case requires not only the relevant knowledge and
appropriate competences, but also an ability to exercise a creative
insight into the trends of cash flow movements and value fluctuations
(Kentouris 2004; Rannou 2008; Shevchenko et al. 2008).
The present article provides an overview of the theory for the
assessment of investment project solutions applicable to alternative
choices of hypothetical corporate activities according to different
financing plans. The research covered by the present paper is limited to
three optional financing allocation plans according to four periods: the
heuristic plan is a hypothetical reference plan, while two other optimal
plans were computed with reference to the linear programming theory.
Having established, by means of mathematical methods, the
appropriation of financial resources (applying the linear mathematical
method) the financial resources allocation plans obtained are assessed
from the financial viewpoint: additionally, by reference to cash flow
discounting method and the present value of the tax shield effect, the
value theory produced serves to assess the effect of the choice of each
of the three financial resource appropriation plans upon the value of an
enterprise. A simulated situation is used to assess three scenarios of
the funding of a company depending on its liquidity: a) an enterprise
has accumulated sufficient amount of own funds to fund the project, b)
an enterprise only has accumulated own funds sufficient to pay the
interest for borrowed funds only, and c) an enterprise borrows funds and
capitalizes the interest payable to the bank.
2. Methods of the assessment of investment solutions
Any assessment of the economic viability of a project should
specifically focus on all factors and variables that potentially affect
the project value. When assessing the projects experts most often use
traditional assessment methods not infrequently failing to dedicate
sufficient attention to the analysis of preconditions, assumptions or
limitations, their identification and formulation. The choice of an
appropriate assessment model to a large extent depends upon the
peculiarity of the project, also related variables and the inherent
market risk (Garvin, Cheah 2004; Ginevicius, Zubrecovas 2009).
Specific relevance the project assessment has acquired in view of
the need to further the development of infrastructure, upgrading and
modernisation, and specifically under the conditions of economic
downturn (Platt et al. 2010; Agenor 2010; Pit 2010; Torrisi 2009). Any
assessment of long-term projects shall necessarily take into account the
ever changing situation in the capital markets (sudden increase or
decrease of interest rates, the lending policy pursued by the banks).
Although the research literature describes a number of different project
assessment methods (Karazijiene, Saboniene 2008; Parsons 2006; Pratt,
Hammond 1979), the authors of the present paper has missed any more
profound analysis of reconciliation of economic and mathematical
methods, or that of alternatives for project funding in the context of
an assessment of an enterprise performance.
Research literature has been focusing on the cash flow discounting
methods where the discount rate is defined as the key variable (Ross et
al. 2002; Galiniene 2005; Fuenzalida, Mongrut 2010; Brown, Reilly 2009).
In the opinion of most authors (Loewenstein et al. 2002; Grout 2003;
Grimsey, Lewis 2005; Florio 2006) the discount rate is the principal and
most influential factor in calculating the value of a project, assessing
its economic feasibility and taking reasonable decisions as to its
implementation.
Discounted cash flow method may be described as one of the most
popular methods in evaluating infrastructure projects (Brealey et al.
2008b). Under this method the economic viability of an investment is
most dependent upon the discount rate. However, quite a number of
authors have underestimated the significance of discount rate and chosen
to use the alternative cost rate or the arithmetic weighted average cost
of capital (WACC) instead (Garvin, Cheah 2004; Kahraman, Kaya 2010;
Brown, Reilly 2009; Berk, DeMarzo 2011):
WACC = [R.sub.e] x (E / A) + [R.sub.d] (D / A) x (1 - t), (1)
where [R.sub.e]--required return on equity, [R.sub.d]--cost of
debt, t--tax rate, D--value of debt, E--value of equity, the sum of
which represents the total assets A of an enterprise.
[R.sub.e] is computed using the long-term capital pricing model
(CAPM):
[R.sub.e] = [R.sub.f] + [[beta].sub.e] (Rm - Rf), (2)
where [R.sub.f]--risk free return, [R.sub.m]--market return,
[[beta].sub.e]--equity systemic risk (Sharpe 1964).
For infrastructure projects that, as a rule, do not have a liquid
secondary market the principal challenge was to correctly calculate
[[beta].sub.2].
Brealey and Myers (2000) propose to compute the return on assets
[R.sub.a] using the sensitivity of the assets to market fluctuations
that is in its own turn calculated by reference to the assets'
cyclicality and weight (4).
[R.sub.a] = [R.sub.f] + [[beta].sub.a] ([R.sub.m] - [R.sub.f]), (3)
[[beta].sub.[alpha]] [[beta].sub.revenue] [1 + [PV.sub.fixed cost]
/ [PV.sub.asset]], (4)
where [[beta].sub.revenue]--dependence of proceeds from the assets
on the economic cycle, [R.sub.f]--risk free return, [R.sub.m] --market
return, [[beta].sub.a]--an approximation of the asset's sensitivity
to market movements, [PV.sub.fixed_cost]--current value of fixed
liabilities, [PV.sub.asset]--current value of the assets.
With the dependence of revenues on the economic cycle,
[[beta].sub.revenue] approximates 1. The ratio [[PV.sub.(fixed_cost]) /
[PV.sub.(assets)]] may be calculated by using the fixed costs / EBIT
ratio of the same project (in case the costs still have not been
established--by analogy of a similar project in the past), where EBIT is
the earnings before interest and taxes (Garvin, Cheah 2004).
The discounted cash flow method may be used for project evaluation
by making an assumption that the risk throughout the duration of the
project is relatively constant (Luehrman 1997) and the company uses its
assets passively, i.e., without considering possibilities to expand,
postpone or terminate the project (Brealey 2008). This possibility is
neither taken into account when conducting a sensitivity analysis or
under the Monte Carlo simulation method (Muller et al. 2004). The
following features are distinguished as characteristic of infrastructure
projects: a) most often implemented in stages; b) may be implemented as
several sub-projects, c) require feasibility and environmental impact
assessment studies. These peculiarities of infrastructure projects may
possibly have an impact on the course of the preparation and
implementation of an initial project. In view of a lengthy period of a
project implementation the project risk in its individual stages tends
to change (Trigeorgis 1999: Brach 2003). The discounted cash flow method
does not provide a capability to assess all positive development that
may potentially create an added value for the project in the future
(e.g., decrease in project cost prices due to the emergence of more
efficient technologies, or an increase of sale prices due to a suddent
increase in demand).
Quite a number of infrastructure projects implemented in individual
stages may be postponed for a later period therefore such projects
should be attributed certain features characteristic of options (Ford et
al. 2002; Cox et al. 1979; Dagiliene 2008), therefore conventional
discounting methods may turn not entirely adequate for the assessment of
such projects. Research papers most often present the conventional
infrastructure project evaluation methods highlighting the factors
directly affecting the project value (Rutkauskas, Stankevicius 2006).
The option pricing model opens a possibility to define the value
that shall be created by taking advantage of the possibilities available
in the future (Damodaran 2002; Ross et al. 2002; Brach 2003; Gatev, Ross
2009). The value of the option may be calculated as a function of the
current asset value, asset price fluctuation, exercise price, period and
risk-free return. Part of these variables may be also computed applying
the discounted cash flow method, therefore the latter should be employed
in connection with the real option method this adding some flexibility
to the projects, i.e., making it possible to modify the projects having
regard to an actual situation. The real option method's
innovativeness lies in the ability it provides to determine a project
value higher than the current value of future cash flows where the value
of such flows is affected by future events. However, these conditions
are met only where the value of the underlying assets is higher (in the
case of a call option), or lower (in the case of a put option) than the
price of the underlying assets determined in advance. In evaluating a
project funded from own and borrowed funds the project owners'
equity may be assessed as a call option value, the repayable amount of
borrowed capital (nominal debt value) as the price of execution, and the
debt term may be treated as the option term (Damodaran 2005; Brealey et
al. 2008a). According to the evaluation techniques real options may be
the discrete time models and continuous-time models. Conceptually the
two models are not different; however, the two models refer to different
assumptions, therefore accordingly they employ different mathematical
calculation methods, and the application of the same in construction and
infrastructure projects (Brealey, Myers 2003; Petravicius 2009).
3. Options for the allocation of construction project funding
Since normally the implementation term of construction projects is
quite lengthy and funding of a project requires sizable financial
resources, modern funding arrangements next to conventional financing
sources (own and borrowed capital) employs various complex schemes
combining capital of private and public origin (leasing, concessions,
temporary transfer of the benefit to the private sector) (Devapriya,
Pretorius 2002; Tseng et al. 2005; Kutut et al. 2008; Kazlauskiene,
Christauskas 2008; James, Miller 2004). This leads to a further increase
in the project risk, and the cost of funding. Where a project is
implemented in stages, or where several projects are implemented
simultaneously, the need to appropriate funding resources in a most
efficient way by stages of tasks and objects acquires a special
significance (Kramarenko, Shevtshenko 2009; Luehrman 1997;
Macerinskiene, Vasiliauskaite 2007; Mackevicius et al. 2007;
Tamosiuniene et al. 2006).
The task of an efficient allocation of limited financial resources
becomes even more challenging due the money time value factor (Li, Wu
2009). Furthermore, account needs to be taken of: i) funding objectives;
ii) prioritization of objects financed and periods, iii) methods for the
assessment of alternative solutions (Bier et al. 2008; Kazlauskiene,
Christauskas 2007).
For the purpose of examining available options for construction
project funding the authors of the present paper have selected one of
the heuristic plans for financing construction objects (Table 1).
Hypothesis put forward: the application of linear programming
methods could enhance the optimization of the appropriation of financial
resources available. This hypothesis can be confirmed by comparing at
least one optimal solution obtained with the existing (heuristic) plan.
The application of linear programming method requires some
additional information: i) conditions for limitations, ii) coefficients
in the objective function, iii) interpretation of the objective function
extremum (Muller et al. 2004). The conditions for the restrictions shall
be established having regard to the limits of the absorption of funding
(Table 2). For example, an object C in Period III is to be allocated the
funding of not less than LTL 35,000 and not more than LTL 75,000. Also,
the overall funding amounts limits shall be indicated ([less than or
equal to], [greater than or equal to] or =).
The coefficients in the objective functions must reflect the
attitude of a decision-maker towards the conceptual expression of the
objective function. For example, where profit maximization is sought,
the coefficients show the yield of each solution component (variable
values to be found). In a construction organization the evaluation of
object funding in terms of individual periods poses difficulties due to
the multi-stage nature of such projects, and a significantly lengthy
time span between the project investment and its return.
The allocation of funding of construction projects is affected by
numerous factors part whereof can be assessed in terms of quantitative
(Ahern, Anadarajah 2008), and part in terms of qualitative indicators
(Turskis 2008; Gerchak, Kilgour 1999; Ginevicius, Podvezko 2008;
Ginevicius, Petraskevicius 2008). The authors of the present article
have chosen an expert evaluation of an object funding by periods. Having
thoroughly explored and examined all the peculiarities of the works
carried out in the objects the significance of funding of the projects
by periods was rated under a scale of 10 points (Table 3).
Since the significance of each period of an object is assessed in
points, the exercise seeks to ensure the best possible results of the
overall funding allocation, i.e., the objective function value must be
maximised. The interpretation of the significance of the objective
function cannot be explained by economic terms--the product of the
funding amount and the evaluation of the significance of such funding in
points does not have any logically explainable measurement unit. Thus
the objective function value is described as a criterion of the optimal
solution (the larger the value, the more efficient the solution).
According to such concept of the objective function there should be
more than one solution. The authors of the present paper have selected
two options for the problem solution: a) the limitations specify the
funding amounts by objects (Option 1), b) only the overall funding
amount is indicated (Option 2).
The data of Tables 1, 2 and 3 constitute a basis for the generation
of Option 1 linear programming problem (1):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (5)
In Option 2 the financing limitations by objects have been replaced
by the general limitations imposed upon funding of all objects (2):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (6)
The results of optimal solutions are compared with the heuristic
financing plan (Fig. 1). Under Option 1 optimal solution the value of
object function is 7,565,000, under Option 2-8,365,000.
[FIGURE 1 OMITTED]
Mathematically the Option 2 optimal financing plan is better due to
a larger value of the object function. This conclusion has been drawn up
by reference to the opinion of experts without taking into account the
effect of the time factor.
4. Assessment of funding alternatives
Having optimised the allocation of financial resources by applying
the linear programming method an expedient further action is to assess
the plans for the financial resources allocation in different time
periods (quarters) from the financial viewpoint taking into account the
profile of the enterprise, objectives and the investment project
implementation period. Where the term for an investment project
implementation is in excess of one year account shall be taken of the
interest rate risk. Where the project implementation, however, lasts for
about a year, it may be assumed that the interest rate risk does not
produce any material effect upon the funding allocation. Therefore
researchers focused upon the evaluation of financing plans from the
point of view of cash flows and interest costs.
The assessment of financing allocation plans from a financial view
point shall be carried out by means of the analysis of the effect of a
selection of one or another plan upon the enterprise value. The impact
upon the enterprise value shall be computed by two methods: a) cash flow
discounting method, and b) with reference to the theory on the present
value of tax shield (Modigliani, Miller 1958).
The enterprise value is a fundamental economic measurement of the
entire business market value representing the takeover of the enterprise
valuation under free market conditions. This criterion has been selected
because: i) the enterprise value is a measurement neutral in respect of
the corporate capital structure therefore it may be used for the purpose
of comparing enterprises of different capital structure (Modigliani,
Miller 1958; Brealey, Myers 2003; Allen et al. 2008), and ii) enterprise
value much more accurately than the owners' value reflects all
interests related to the business, as the enterprise value encompasses
the value of borrowed capital (Fig. 2).
For the purpose of determining the enterprise value the claims of
all parties concerned shall be added, and then cash is deducted from the
amount obtained since cash may be paid in the form of dividends thus
reducing the value of the enterprise as a potential purchase; or such
cash may be disbursed to creditors. It is specifically in terms of cash
amounts that the enterprise value may be negative (in the event the
amount of cash is in excess of other components constituting the
enterprise value).
Fig. 2 Components of the enterprise value
Enterprise = Market + Market + Market + Minority - Cash and
value value of value of value of interest cash
ordinary preference borrowed (if any) equivalents
shares shares capital
Since the enterprise value is affected by the choice of financing
source the calculations may be performed by assessing three enterprise
funding scenarios that are selected depending on the liquidity of the
enterprise: scenario a--the enterprise has accumulated sufficient own
funds to fund the project, b--the enterprise has accumulated own funds
to pay the interest for the borrowed funds only, c--during the project
implementation period the enterprise does not have any available
monetary resources therefore it capitalizes the interest due to the bank
(accrued interest is added to the loan amount so that in each next
interest payment period the interest is paid for an increased loan
amount).
The assessment of any alternatives for enterprise operations
employs a number of assumptions such, as: i) the selection of a
financing allocation plan does not affect the amount of investment and
cash flows that will be generated upon the completion of the project;
ii) investments are effected at the beginning of each quarter; iii) the
weighted average cost of capital (WACC) of all three enterprises is the
same, since WACC = [R.sub.A], where [R.sub.A] is return on assets
(Modigliani and Miller 1958); iv) there is no bankruptcy costs (Brealey
et al. 2008a).
The following conditions have been selected for the purpose of the
calculations: WACC--10%, loan interest rate--6%, profit before interest
and taxes of all enterprises (EBIT)--LTL 500 000, in equal shares by LTL
125 per quarter, and the corporate income tax--20%.
a) The assessment by cash flow discounting methods seeks to
determine the impact of cash flows incurred during the investment period
upon the enterprise value depending on the selection of a financing
plan. Since the value in the business of interests depends on the future
benefit that will be generated to the interests theoretically the
correct preferred model would be to project the future benefit and
discount it by translating it into the current value (Galiniene,
Butvilas 2010; Ross et al. 2002; Zaptorius 2006).
According to A. Damodaran (2009), the value of an enterprise is
equal to the value of the assets of the enterprise that may be
calculated by discounting the cash flows generated by the assets of the
enterprise (CFFA), that are composed of operating cash flows (OCF), less
net investment (NCS), and the increase in working capital ([DELTA]NWC),
where EBIT is corporate earnings before interest and taxes (Fig. 3).
[FIGURE 3 OMITTED]
Depending on the selected funding allocation plan the enterprise
value is affected by 2 factors: money time value and loan interest rate
(7).
[PV.sub.investment] = [summation] ([Investment.sub.i]) / [(1 +
WACC).sup.i] (7)
With an investment effected at a later point in time the
investment's current value is lower which alleviates the adverse
impact upon the enterprise value. According to the Optimal plan 1 the
largest share of the investment is allocated in Q4, and, as the only
influence is the money time value (discount rate), the value of the
investment is the lowest. Therefore in the case of the (financing
scenario) enterprise operation a the most acceptable is the Optimal plan
1 under which the company does not have to borrow funds, therefore its
value is not affected by the factor of the interest rate.
The interest payable for the loan does not effect the cash flows
generated by the assets (CFFA), since the interest is paid from the
funds earned by the assets of the enterprise. The cash flows generated
by an enterprise are allocated to creditors (CFTC), and to owners (CFTS)
(Galiniene 2005; Ross et al. 2002) (8):
CFFA = CFTF = CFTC + CFTS, (8)
where CFTC = interest--loan_change.
An enterprise that had invested earlier assumes larger interest
liabilities which decreases its taxable profit and increases its working
capital. An increase of the working capital reduces the cash flow
generated by the assets of the enterprise, therefore the enterprise
value shall be accordingly reduced (Table 4). This is equally confirmed
by the calculations of the impact of the financing scenarios b and c
upon the enterprise value. In the case of b and c enterprise financing
scenarios the Optimal plan 2 is more efficient, as the bulk of the
investment is allocated in Q1. In the case of the activity of the
enterprise under the scenario c the impact upon the enterprise value
shall be alleviated, as the enterprise uses larger amounts of borrowed
capital.
b) The assessment of financing plans on the basis of the theory of
the present value of tax shield (Modigliani, Miller 1958) analyses the
extent to which increases the enterprise value under each financing
plan. The theory is based on the enterprise valuation model developed by
Miller and Modigliani (M &M) which establishes that the value of an
indebted enterprise is equal to the value of a non-indebted enterprise
plus the value generated by the tax shield effect (9):
[V.sub.L] = [V.sub.U] + D x [T.sub.C], (9)
where [V.sub.L]--levered enterprise value, [V.sub.U]--unlevered
enterprise value, D--debt amount, [T.sub.C]--tax rate.
The value of unlevered enterprise is calculated by capitalizing the
cash flows before interest and taxes (EBIT) of an unlevered enterprise
by a capitalization rate [R.sub.U], that is the return required by
owners of unlevered enterprise (10) (Modigliani, Miller 1958):
[V.sub.U] = EBIT (1 - [T.sub.C])/ [R.sub.U]. (10)
Due to the inclusion of the interest into the costs reducing the
taxable profit the enterprise's interest costs will be lower than
the interest amount paid. In this particular case the saving are
achieved on the account of the taxes. The larger the amount of interest
and/or the tax rate, the larger the amount of the savings. The value
created for the enterprise by virtue of tax savings is the current value
of saved taxes (tax shield effect). For evaluation purposes an
assumption is made that the loan is of indefinite duration, therefore
the annual value of tax savings ([PV.sub.(TaxSch]) = D x RD x [T.sub.C])
is capitalized applying the loan interest rate [R.sub.D] (according to
the assumption used [R.sub.D] = 6%). The tax saving current value
([PV.sub.(TaxSch])) shall be calculated as follows:
[PV.sub.(TaxSch)] = D x [R.sub.D] x [T.sub.C] / [R.sub.D] = D x
[T.sub.C], (11)
where [T.sub.C]--corporate income tax rate, D--loan amount.
Having assessed the impact of selection of the financing plan on
the basis of the M&M theory the conclusion was arrived at that the
highest value is created where he enterprise selects the activity
alternative c. In this case the amount of borrowings for funding of the
investment project will be the largest.
In the case of selection of the operation alternative a, the
selection of the financing plan shall have no impact upon the enterprise
value since the enterprise will be financing the investment by own
funds. Likewise, no effect will be produced upon the enterprise value
where the enterprise chooses activity alternative b, however, a LTL 1 m
loan obtained to fund the investment creates for the enterprise an
additional value of LTL 200,000. Where an enterprise chooses operation
option c, it will be able to generate the largest value in the case of
the Optimal plan 2, since the largest amount of the loan shall be
accumulated at the end of the year (Fig. 4).
It should be noted that financing plans should be assessed in terms
of their impact upon the enterprise value. In case of the assessment
from the point of view of the owners (Fig. 2), part of the statements
would be quite opposite.
The results of the assessment of financing plans allow a conclusion
that no optimal plan that could equally suit all alternative options of
an enterprise activities exists. Therefore when selecting a financing
plan account should be taken not only of the peculiarities of the
enterprise's operations, but also of the projected financing
sources. Still a combination of different methods for the assessment of
the enterprise operations and the optimal resource appropriation allows
producing several alternative solutions for project financing.
5. Conclusions
1. Challenges related to the implementation of construction project
are often linked to limited resources available, and the need for the
implementing enterprises to prioritize the allocation of funding in
individual periods of project implementation. Hence the need to identify
methods to economically substantiated financing plans.
2. The application of mathematical methods such as linear
programming for evaluating construction projects turn justifiable only
in the case the value of object function is designated as the principal
evaluation criterion.
3. Having regard to the impact of the project funding upon its
value project executors select the optimal one option the implementation
whereof creates the maximum value.
4. The results of the survey related to the present paper
demonstrated the difficulties in attempting to drawn up an ideal
construction project plan that could equally well suit all alternative
modes of an enterprise activities.
5. A need has been identified to develop alternative financial
resources allocation plans by means of variety, specifically
mathematical, methods. Such plans need to be assessed from different
viewpoints with a clear priority assigned to the methods best meeting
the expectations of all practitioners.
http://dx.doi. org/10.3846/20294913.2011.580590
Received 12 October 2010; accepted 02 March 2011
References
Agenor, P.-R. 2010. A theory of infrastructure-led development,
Journal of Economic Dynamics & Control 34(5): 932-950.
doi:10.1016/j.jedc.2010.01.009
Ahern, A.; Anadarajah, G. 2008. An Optimisation Model for
Prioritising Transport Projects, Transport 161(4): 221-230.
doi:10.1680/tran.2008.161.4.221
Allen, F.; Bhattacharya, S.; Rajan, R.; Schoar, A. 2008. The
contributions of Stewart Myers to the Theory and Practice of Corporate
Finance, Journal of Applied Corporate Finance 20(4): 8-19.
doi:10.1111/j.1745-6622.2008.00200.x
Berk, J.; DeMarzo, P. 2011. Corporate Finance. Pearson.
Bier, V M.; Haphuriwat, N.; Menoyo, J.; Zimmerman, R.; Culpen, A.
M. 2008. Optimal Resource Allocation for Defence of Targets Based on
Differing Measures of Attractiveness, Risk Analysis: An International
Journal 28(3): 763-770.
Brach, M. A. 2003. Real Options in Practice. John Wiley & Sons,
Inc., Hoboken, New Jersey.
Brealey, R. A.; Myers, S. C. 2003. Capital Investment and
Valuation. McGraw-Hill, New York.
Brealey, R. A.; Myers, S. C.; Allen, F. 2008a. Principles of
Corporate Finance. McGraw-Hill/Irwin, New York.
Brealey, R. A.; Myers, S. C.; Allen, F. 2008b. Brealey, Myers, and
Allen on Real Options, Journal of Applied Corporate Finance 20(4):
58-71. doi:10.1111/j.1745-6622.2008.00204.x
Brealey, R. A. 2008. Brealley, Myers and Allen on Valuation,
Capital Structure, and Agency Issues, Journal of Applied Corporate
Finance 20(4): 8-19. doi:10.1111/j.1745-6622.2008.00203.x
Brealey, R.; Myers, S. 2000. Principles of Corporate Finance.
McGraw-Hill, New York.
Brown, K. C.; Reilly, F. K. 2009. Analysis of Investments and
Management of Portfolios. South-Western Cengage Learning.
Cox, J. C.; Ross, S. A.; Rubinstein, M. 1979. Option Pricing: A
Simplified Approach, Journal of Financial Economics 7: 229.
doi:10.1016/0304-405X(79)90015-1
Dagiliene, L. 2008. The model for the choice of financial valuation
methods, in Ekonomika ir vadyba 2008 = Economics and management--2008:
13-osios tarptautines mokslines konferencijos pranesimu mediiaga
[elektroninis isteklius]. Kauno technologijos universitetas, 17-21.
Damodaran, A. 2002. Investment Valuation. Tools and Techniques for
Determining the Value of Any Assets. John Wiley and Sons. 2nd ed.
doi:10.1561/0500000013
Damodaran, A. 2005. Valuation Approaches and Metrics: A Survey of
the Theory and Evidence, Foundations & Trends in Finance 1(8):
693-784.
Damodaran, A. 2007. Return on Capital (ROC), Return on Invested
Capital (ROIC), Return on Equity (ROE): Measurement and Implications.
Stern School of Business.
Damodaran, A. 2009. Equity Risk Premiums (ERP): Determinants,
Estimation and Implications--A Post-Crisis Update, Financial Markets,
Institutions & Instruments 18(5): 289-370.
doi:10.1111/j.1468-0416.2009.00151.x
Devapriya, K. A. K.; Pretorius, F. 2002. The Economic Implication
of Project Finance Arrangements for BOO/BOT Power Projects in Asia,
Journal of Construction Research 3(2): 285-310.
doi:10.1142/S1609945102000217
Ferguson, N. 2009. The descent of finance, Harvard Business Review
87(7/8): 44-53.
Florio, M. 2006. Cost-benefit Analysis and the European Union
Cohesion Fund: on the Social Cost of Capital and Labour, Regional
Studies 40(2): 211-224. doi:10.1080/00343400600600579
Ford, D. N.; Lander, D. M.; Voyer, J. J. 2002. A real options
approach to valuing strategic flexibility in uncertain construction
projects, Construction Management Economics 20(4): 343-351.
doi:10.1080/01446190210125572
Friedman, S. 2010. P&C Prices to Lag In Shrinking Economy,
National Underwriter/ Property & Casualty Risk & Benefits
Management 114(3): 7-31.
Fuenzalida, D.; Mongrut, S. 2010. Estimation of Discount Rates in
Latin America: Empirical evidence and Chalenges, Journal of Economics,
Finance & Administrative Science 15(28): 7-43.
Galiniene, B. 2005. Turto ir verslo vertinimo sistema. Formavimas
ir pletros koncepcija. Vilnius: Vilniaus universiteto leidykla.
Galiniene, B.; Butvilas, A. 2010. Analysis of the Capital Cost
Impact on Share Value, Technological and Economic Development of Economy
16(1): 126-142. doi:10.3846/tede.2010.08
Garvin, M. J.; Cheah, Ch. Y. J. 2004. Valuation techniques for
infrastructure investment decisions, Construction Management and
Economics 22(4): 373-383. doi:10.1080/01446190310001649010
Gatev, E.; Ross, S. A. 2009. Momentum Trading and Performance with
Wrong Return Expectations, Journal of Portfolio Management 35(3): 73-78.
doi:10.3905/JPM.2009.35.3.073
Gerchak, Y.; Kilgour, D. M. 1999. Optimal Parallel funding of
Research and Development Projects, IIE Transactions 31(2): 145-152.
doi:10.1080/07408179908969813
Ginevicius, R.; Petraskevicius, V. 2008. Some problems of
determining diversification level of enterprise activities, Verslas:
teorija ir praktika [Business: Theory and Practice] 9(3): 215-220. doi:
10.3846/1648-0627.2008.9.215-220
Ginevicius, R.; Zubrecovas, V. 2009. Selection of the Optimal Real
Estate Investment Project Basing on Multiple Criteria Evaluation Using
Stochastic Dimensions, Journal of Business Economics and Management
10(3): 261-270. doi:10.3846/1611-1699.2009.10.261-270
Ginevicius, R.; Podvezko, V. 2008. Multicriteria
graphical-analytical evaluation of the financial state of construction
enterprises, Technological and Economic Development of Economy 14(4):
452-461. doi:10.3846/1392-8619.2008.14.452-461
Grimsey, D.; Lewis, M. K. 2005. Are Public and Private Partnerships
Value for Money? Evaluating alternative approaches and comparing
academic and practitioner views, Accounting Forum (29): 345-378.
doi:10.1016/j.accfor.2005.01.001
Grout, P. A. 2003. Public and Private Sector Discount Rates in
Public-Private Partnerships, Economic Journal (113): C62-C68.
doi:10.1111/1468-0297.00109
James, R. F.; Miller, J. S. 2004. The Effects of Pro Forma Earnings
Disclosures on Analysts' and Nonprofessional Investors' Equity
Valuation Judgments, The Accounting Review 79(3): 667-686.
doi:10.2308/accr.2004.79.3.667
Kahraman, C.; Kaya, I. 2010. Investment Analyses Using Fuzzy
Probability Concept, Technological and Economic Development of Economy
16(1): 43-57. doi:10.3846/tede.2010.03
Kale, J. R. 2008. Corporate leverage and Specialized Investments by
Customers and Suppliers, Journal of Applied Corporate Finance 20(4):
8-19. doi:10.1111/j.1745-6622.2008.00207.x
Karazijiene, 2.; Saboniene, A. 2008. The Forecast of the State
Borrowing Demands, in Ekonomika ir vadyba--2008 = Economics and
management--2008: 13-osios tarptautines mokslines konferencijos
pranesimu mediiaga [elektroninis isteklius]. Kauno technologijos
universitetas, 120-121.
Kazlauskiene, V.; Christauskas, C. 2007. Risk Reflection in
Business Valuation Methodology, Inzinerine Ekonomika--Engineering
Economics (1): 7-15.
Kazlauskiene, V.; Christauskas, C. 2008. Business Valuation Model
Based on the Analysis of Business Value Drivers, Inzinerine
Ekonomika--Engineering Economics (2): 23-31.
Kentouris, Ch. 2004. Fund Valuation: Art or Science? Securities
Industry News, February 9.
Kramarenko, S.; Shevtshenko, E. 2009. Decision Support System for
Multi-Project Cash-Flow, Annals of DAAAM & Proceedings; Jan.:
1111-1112.
Kutut, I.; Ginevicius, R.; Kutut, V. 2008. Diversification of
activity in the context of companies life cycle, Verslas: teorija ir
praktika [Business: Theory and Practice] 9(3): 169-179.
doi:10.3846/1648-0627.2008.9.169-179
Leibowitz, M.; Langetieg, T. 1989. Shortfall Risk and the Asset
Allocation Decision: A Simulation Analysis of Stock and Bond Profoles,
Journal of Portfolio Management 16(1) (Fall): 61-68.
doi:10.3905/jpm.1989.409236
Loewenstein, G.; O'Donoghue, T.; Frederick, S. 2002. Time
Discounting and Time Preference: a Critical Review, Journal of Economic
Literature 40(2): 351-401. doi:10.1257/002205102320161311
Luehrman, T. 1997. What's it Worth? A general Managers Guide
to valuation, Harward Business Review 75(3): 132-142.
Macerinskiene, I.; Vasiliauskaite, J. 2007. The Model of Evaluation
of the Impact of Corporate Social Capital on the Operation of
Enterprise, Inzinerine Ekonomika--Engineering Economics (4): 53-60.
Mackevicius, J.; Moliene, O.; Poskaite, D. 2007. Methodology of
complex analysis of return on equity, Verslas: teorija ir praktika
[Business: Theory and Practice] 8(2): 73-81.
Modigliani, F.; Miller, M. 1958. The Cost of Capital, Corporation
Finance and Theory of Investment, American Economic Review 48: 261-297.
Muller, P.; Sansxo, B.; De Iorio, M. 2004. Optimal Bayesian Design
by Inhomogeneous Markov Chain Simulation, Journal of the American
Statistical Association 99(467): 788-796. doi:10.1198/016214504000001123
Parsons, V. S. 2006. Project Performance: How to Assess the Early
Stages, Engineering Management Journal 18(4): 11-15.
Peters, E. 2005. Tactical Asset Allocation Strategies, The Wall
Street Transcript October: 31. Petravicius, T. 2009. Use of Real Options
in Project Valuation, Transformations in Business & Economics 8(3):
69-89.
Pit, G. 2010. Building Boom: A much needed explosion in
infrastructure spending is already under way in both developed and
emerging markets, Global Finance 24(1): 32-34.
Platt, G.; Guerrero, A.; Iskyan, K.; Chaze, A.; Clouse, T. 2010.
Emerging Markets Roundup, Global Finance 24(5): 10-16.
Pratt, J. W.; Hammond, III, J. S. 1979. Evaluating and Comparing
Projects: Simple Detection or False Alarms, Journal of Finance 34(5):
1231-1242. doi:10.2307/2327247
Rabin, M.; Thaler, R. 2001. Anomalies: Risk Aversion, Journal of
Economic Perspectives 15(1 Winter): 219-232.
Rannou, Y. 2008. Measuring Investor Sentiment and Behaviour to
Gauge Financial Risk, Journal of Risk Management in Financial
Institutions 1(4): 416-429.
Ross, S.; Westerfield, R. W.; Jaffe, J. F. 2002. Corporate Finance.
The McGraw-Hill Companies.
Rutkauskas, A. V; Stankevicius, P. 2006. Investiciniu sprendimu
valdymas: monografija. Vilniaus pedagoginis universitetas. Vilnius.
Sharpe, W. 1964. Capital Asset Prices: A Theory of Market
Equilibrium under conditions of Risk, Journal of Finance (19): 425-442.
doi:10.2307/2977928
Shevchenko, G.; Ustinovichius, L.; Andruskevicius, A. 2008.
Multi-attribute analysis of investments risk alternatives in
construction, Technological and Economic Development of Economy 14(3):
428-443. doi:10.3846/1392-8619.2008.14.428-443
Stutzer, M. 2004. Asset Allocation without Unobservable Parameters,
Financial Analysts Journal 60(5): 38-51. doi:10.2469/faj.v60.n5.2655
Tamosiuniene, R.; Sidlauskas, S.; Trumpaite, I. 2006. The
multicriterial evaluation method ofthe effectiveness of the investment
projects, Verslas: teorija ir praktika [Business: Theory and Practice]
7(4): 203-212.
Torrisi, G. 2009. Public Infrastructure Definition, Classification
and Measurement Issues, Economics, Management & Financial Markets
4(3): 100-124.
Trigeorgis, L. 1999. Real Options: Managerial Flexibility and
Strategy in Resource Allocation. MIT Press, Cambridge, MA.
Tseng, H.-L.; Lin, K. Y.; Sundararajan, S. K. 2005. Managing Cost
Overrun Risk in Project Funding Allocation, Annals of Operations
Research 135(1-4): 127-153. doi:10.1007/s10479-005-6238-4
Turskis, Z. 2008. Multi-attribute contractors ranking method by
applying ordering of feasible alternatives of solutions in terms of
preferability technique, Technological and Economic Development of
Economy 14(2): 224-239. doi:10.3846/1392-8619.2008.14.224-239
Li, X.; Wu, Z. 2009. Corporate risk management and investment
decisions, Journal of Risk Finance 10(2): 155-168.
doi:10.1108/15265940910938233
Zaptorius, J. 2006. {mones verte: vertinimo koncepcija ir
finansavimo politikos jtaka, Ekonomika ir vadyba: aktualijos ir
perspektyvos 1(6): 190-197.
Stanislava Stunguriene (1), Laima Urbsiene (2)
International Business School at Vilnius University, Sauletekio al.
22, LT-10225 Vilnius, Lithuania E-mails: 1ststunguriene@centras.lt
(correspondingauthor); (2) laima.urbsiene@gmail.com
Stanislava STUNGURIENE. Doctor, Professor. Financial Department of
International Business School at Vilnius University. First degree and
master in economic engineering, Vilnius University (1972). Doctor
(1982). Author of about 30 scientific articles. Research interests:
information technologies, operations management, quantitative analysis
in economics and management, optimal financial decisions.
Laima URBSIENE. Lecturer of International Business School at
Vilnius University. Master in Banking Management, Exeter University
(1995). Research interests: Financial Markets, Corporate Finance,
Business Valuation, Globalization of Financial Markets.
Table 1. Allocation of construction project financing: heuristic
approach
Object Item title Q1 Q2 Q3 Q4 Total per
year
A Amounts by quarters 100 150 50 200 500
B Amounts by quarters 50 80 20 50 200
C Amounts by quarters 75 75 75 75 300
Total per quarter 225 305 145 325 1 000 000
Table 2. Limitations of object funding
Objects Limits Period I Period II Period III
Object A Upper 0 0 0
Lower 50 30 20
Object B Upper 0 80 0
Lower 40 10 20
Object C Upper 0 0 75
Lower 25 35
Total fundung amount [less than [less than 145
or equal to] or equal to]
250 000 400 000
Objects Limits Period IV Total funding amount
Object A Upper 0 [less than or equal to] 500 000
Lower 40
Object B Upper 0 [less than or equal to] 200 000
Lower 15
Object C Upper 0 [less than or equal to] 300 000
Lower 40
Total fundung amount [less than Amount to be allocated
or equal to] 1 000 000
325 000
Table 3. Expert evaluation ([c.sub.ij])
Objects Period I Period II Period III Period IV
A 6 5 10 8
B 10 8 7 7
C 6 5 4 6
Table 4. Changes in the enterprise value depending on the selection
of the funding allocation plan and the funding source
Scenarios Heuristic plan
for the
enterprise Investment Effect upon
funding related cash the enterprise
flow change value
a -1000 -941
b -883 -832
c -876 -826
Scenarios Optimal plan 1
for the
enterprise Investment Effect upon
funding related cash the enterprise
flow change value
a -1000 -932
b -902 -841
c -836
Scenarios Optimal plan 2
for the
enterprise Investment Effect upon
funding related cash the enterprise
flow change value
a -1000 -946
b -872 -827
c -863 -818
Fig. 4. Impact of the selection of the plan on the
enterprise value
Scenario b Scenario c
Heuristic plan 200 231
Optimal 1 plan 200 225,8
Optimal 2 plan 200 234,4
Note: Table made from bar graph.
