Both investment variations can be considered profitable based on the inner rate of return as well as based on the netpresentvalue. However, it is interesting that the first version is more favourable based on the inner rate of return while the second variation is more favourable based on the netpresentvalue. In this case the different results of the two methods can be explained by the significant difference in the cash-flows of the two investment versions (Incidentally we have to note that between the investment versions excluding each other mutually there are hardly any big differences in size in practice so the decision maker rarely faces this problem).
Abstract. Project scheduling to maximize the netpresentvalue of the cash flows has been a topic of recent research. These researches assume that activities cost or price for materials and service in the marketplace remain relatively unchanged over the lifecycle of the project. Unfortunately, due to the increasing prices of goods in most countries during the project, it is not generally a realistic assumption. In this paper, we consider project scheduling problem with discounted cash flows under inflationary conditions. We propose two different situations due to the type of contract between contractor and client as fixed-price contract and cost-reimbursement contract. To interpret the situation, we use a lemma which shows the importance of entering inflation for both sides of a project clients and contractor. Finally, an example is solved and the proposed procedure is applied to interpret the solution.
C ~ and DC F ~ W heuristics are different which make differences on project’s fuzzy netpresentvalue. Also the ranking method chosen for the ranking step of the algorithm could change the schedule, fuzzy netpresentvalue, and realization time of the project. The interpretation the decision maker gets from these algorithms is which activities interpretation the decision maker gets from these algorithms is which activities are critical for fuzzy netpresentvalue of the project and cannot be moved and which activities are dependent on his/her attitude. It is also worth mentioning that in our case the whole project duration is planned to be 9 or 10 time units and in one of the cases it is equal to the shortest possible project duration (which is 9) and in the other case that it is more advantageous to prolong the project realization by 1 time unit to achieve higher fuzzy netpresentvalue. As a further research the proposed models could be expanded for different ranking methods to determine the best suitable ranking method for fuzzy critical path method and maximizing NPV.
Although the practical importance of investment analysis in long-term energy investments is well understood, choosing the proper method has always been a dilemma. In this regard, classic evaluation methods, with a history of almost a century, are mostly favored, but using them in the valuation of long-lasting energy projects has particular shortcomings, nevertheless. The drawbacks mainly stem from two structural problems: a) reflecting risk in rate of return instead of cash flow thus summing up risk and time value of money in a single parameter, b) generalizing the predefined rate of return to all project life time regardless of changing nature of risk. To overcome such drawbacks, a new easy-to-implement method termed Modified- Decoupled NetPresentValue (M-DNPV) is proposed that intercepts coupling of risk and time value of money by deducting the risky portion of expected cash flows. To cover the dynamic nature of risk and as a buffer against uncertainty, it is suggested to attribute measured risks to investment lifespan using an "uncertainty coefficient”. Finally, the ability of the new method is shown through a complicated energy investment: an Iranian Petroleum Contract (IPC). Keywords: Energy investment, Decoupled NPV, Investment decision analysis, Project valuation, Netpresentvalue, Iranian Petroleum Contract.
The idea of the NPV Profile, which shows how the netpresentvalue of a project changes over the life of the project, can be used in applied settings. For example, it can be used in situations where significant changes are made to the life-of-mine plan for a gold mine. This paper presents such an example with a description of engineering changes required to achieve the change in the mine plan based on the current situation facing Anaconda Mining, a publicly-traded gold mining company in Canada.
This article is a case study analyzing the valuation process and the timing choice of a corporate acquisition project using a comparison between the netpresentvalue rule and the real option method. It seems that when a corporate acquisition project is done under the right circumstances and well executed, this could mean a huge profits and a win-win for shareholders of both the acquiring and the target company. However, in many cases the results of acquisitions turn out to be negative for one, or both of the parties. Actually, this type of investment is characterized by the risk of paying sunk costs especially for the acquirer. For reaching a win-win result from corporate acquisitions, valuation issues and the optimal timing choice play a major role. In this article, the netpresentvalue criterion and real options were used to calculate the project's value and optimal timing of investment. We started by calculating the average and the simulated netpresentvalue. Then, we used real options approach. The acquisition option was evaluated with the Black and Scholes model (1973). The timing option was evaluated with the binomial model of Cox, Ross and Rubinstein (1979). Our case study suggested that real options can produce more sensible recommendations regarding the acquisition project value and the investment timing than the traditional netpresentvalue rule. It also showed the benefits of real options beyond valuation aspects. In particular, the optional way of thinking can help structure discussions between the managers of the targets companies and the acquirers to establish a roll-out plan of the closing process.
Strategic capital investment decisions are crucial and require careful analysis and consideration. This is due to the characteristics of infrastructure projects that are vulnerable to risks and uncertainties. NetPresentValue (NPV)-at-Risk model developed by Ye and Tiong (2000) is a tool for investment evaluation under uncertainties. This paper presents an extension of the model to determine NPV at risk proposed by Ye and Tiong (2000). NPV at risk has been determined using three discount methods, cash flow after payment of tax, interest and principal debt, and the results were compared to choose the best one. NPV at risk was also determined using normal distribution and Monte Carlo simulation method with varying debt equity ratio. The evaluation of the road project shows that the NPV-at-risk method can provide a better decision for risk evaluation and investment in privately financed road projects. This paper presents NPV at risk and return at this NPV with a real case study.
of capital. As in McDonald (2000), the value of waiting to invest is underlined to explain this finding: the authors affirm that by “using high hurdle rates, companies in fact indirectly account for the existence of timing options” (p. 15). Resting on Poterba and Summers’ (1995) data, they show that “a relatively high hurdle rate of 12.2.% is successful in capturing most of the option value as long as the uncertainty of projects’ cash flows is high” (J&M, 2002, p. 18) and “If we take into account the option to delay the project, the financial decision is no longer crucially dependent on an exact figure of the discount rate” (p. 19). However, there are other explanations, which suggest that the use of hurdle rates is consistent with established theories in business and finance. For example, a project may absorb managerial skills and thus prevent firm from undertaking other profitable projects in the future. Thus, “If managerial time of a skilful manager is limited, she must decide when it is optimal to take a project. It may pay off to wait and not take the next best positive netpresentvalue project” (p. 21), which explains why “the use of a hurdle rate that is higher than the cost of capital … is likely to lead to near optimal decisions” (ibidem). This also explains the large variation that Poterba and Summers (1995) find in the hurdle rate of different companies: firms in the same sector face similar systematic risks and, therefore, if they complied with the CAPM-derived discount rate (based on systematic risk) they would apply similar discount rates. It is appropriate to use higher discount rates because managerial resources are
Using a plausible range of assumptions, the author demonstrates how the netpresentvalue of homeownership varies with changes in the holding period. If there is modest inflation and a holding period greater than five years, but real rental growth rates are zero and the imputed rental yield is the same or slightly less than the homeowner’s cost of capital, home purchases are likely to be financially beneficial. This is consistent with the argument that ownership provides an inflation hedge for future housing demand (Sinai and Souleles 2005). Even when inflation is zero, a modest positive spread between the imputed rent and the cost of capital tips the balance in favour of ownership if holding periods are long enough. Deflation tilts the balance in favour of renting unless it is offset by a sufficiently high spread of imputed rent over the cost of capital. At the margins, differences in risk preferences and marginal tax rates result in different costs of capital and hence, different answers to the question, is it better to buy or to rent across households. However, when macro-economic conditions result in positive/negative netpresent values for homeowners at the aggregate level it implies the existence of wealth transfers from renters and mortgage providers to homeowners and vice versa during periods of inflation/deflation.
This decision rule boils down “to accepting the project with the highest netpresentvalue’’ (footnote 14, p. 174). It is worth noting that such a NPV is a disequilibrium (cost-based) NPV. Its use is recommended in popular finance textbooks (e.g. Copeland & Weston, 1983, 1988; Weston & Copeland, 1988; Bøssaerts & Odegaard, 2001) and it is so widespread that Ang and Lewellen (1982) consider it “the standard discounting approach” (p. 9) in finance. Magni (2007a) shows that the procedure suggested by Rubinstein violates an accepted standard of rationality: value additivity (warnings against the use of this NPV may also be found in Ang & Lewellen, 1982; Grinblatt & Titman, 1998; Ekern, 2006). In particular, the author shows that the NPV of a portfolio of projects is different from the sum of the NPVs of the projects (Magni, 2007a, Proposition 4.1). This implies that the disequilibrium NPV provides different evaluations (and different decisions) depending on to the way the course of action is depicted. Loosely speaking, to receive a 200€ banknote or to receive two 100€ banknotes is financially equivalent: to employ the disequilibrium NPV means to be trapped in a sort of mental accounting (Thaler, 1985, 1999) because evaluations differ depending on whether outcomes are seen as aggregate or disaggregate quantities. This amounts to saying that the disequilibrium NPV is inconsistent with the principle of description invariance, which prescribes that valuations and decisions must be invariant under changes in description of the same alternative. Violations of this principle are known in the heuristics-and-biases tradition as framing effects (Tversky & Kahneman, 1981; Kahneman & Tversky, 1984; Soman, 2004). 8 As opposed to the disequilibrium NPV,
Taking the financial aspects of the project in the RCPSP into account has been less explored. The netpresentvalue (NPV) objective was introduced by Russell for the first time . The MRCPSP Considering the Discounted Cash Flows (MRCPSPDCF) is a generalized problem of the MRCPSP in which financial flows (positive/negative) occur during the implementation of the project. The MRCPSPDCF objective function maximizes the NPV of all project cash flows. Sung and Lim  studied the issue of positive and negative financial flows with constraints of fund availability and renewable resources. Mika et al.  considered the MRCPSPDCF in which the project was represented by an activity-on-node (AoN) network and a positive cash flow was associated with each activity. They used simulated annealing and tabu search algorithms to solve the problem. Optimizing netpresentvalue is taken into consideration in further studies including [16-19].
This study aims to assess the feasibility of remote sensing activities in Indonesia by using the netpresentvalue (NPV) approach. Although these remote sensing activities have been going on for a long time in Indonesia, the economic assessment of the use of remote sensing data has never been done. The research object is remote sensing data of very high, high, medium, and low resolution which has been distributed by The Indonesian National Institute of Aeronautics and Space (LAPAN) to government institution users. The data used for cash inflow is secondary data with a period of 2015–2017, while cash outflows are data on investment values during 2013–2017. Data analysis uses a qualitative descriptive analysis method based on the calculation of the NPV value generated. The results showed that the remote sensing activity had a positive NPV both in total and by type of resolution. Remote sensing activities in Indonesia deserve to be continued and developed.
Using the PVFs shown above, the future cost savings for each year can be converted to their presentvalue. These values are then added together to estimate the project's NetPresentValue. The initial investment (which is already in present-day dollars) is subtracted from the sum. The result is the NetPresentValue of the project.
The so-called anomalies that arise in the computation and interpretation of the NetPresentValue (NPV) and the In- ternal Rate of Return (IRR) can be easily overcome if the properties of the NPV function are taken into account and it is clearly defined what is an investment and what is a credit. All the roots of the NPV function have economic mean- ing and, when there is at least one IRR, the NPV and the IRR criteria agree.
In presentvalue terms, Ian must save an additional $314,491.52 in order to meet his objectives. To determine the amount he must save from year 11 through year 30, set the PV of his savings over this time period equal to the difference of $314,491.52. Since the annual savings will begin 11 years from today, discount the annuity back 10 periods. Solve for the amount Ian needs to save each year, C.
An error in an initial NPV Test could be identified by the servicer itself, by the borrower upon receipt of a Dodd-Frank Denial Letter, or by MHA-C during an onsite audit. In any of these three situations, the servicer is required to re-evaluate the borrower using the initial run-of-record, changing only the input or inputs that are found to be incorrect. The servicer is also required to change any fields that are dependent on the incorrect input(s); for example, if the borrower disputes Property Value, the servicer should re-calculate MTMLTV and Standard Waterfall terms, along with PRA Waterfall and Max Months Past Due in Past 12 Months if the loan becomes PRA-eligible or loses PRA eligibility due to the change in MTMLTV. All other fields must be held constant, including NPV Date (as this field is used to pull the appropriate PMMS rate and for NPV code versioning).
In this study, each chromosome i is a vector consisting n genes, where n denotes the number of the project activities. The value of each gene in a chromosome denotes an activity number and the value of gene states the position of that activity at generating the project schedule. Note that, each activity can appear in the vector at any position after all its predecessors. To generate the schedule of an individual, we select the activities one by one from the vector according to their priorities and schedules it as soon as possible in the schedule, considering precedence and resource feasibility. Because all of the variables and parameters in this research are considered as fuzzy numbers, for comparison between the fuzzy numbers, a fuzzy com- parison method should be applied. In the next part, a brief explanation of this procedure for comparing the fuzzy numbers is given. For decoding the procedure, let CA be the completed activities, PA be activities in progress, and ~t be the present time. As mentioned, activities in lower position have priority for scheduling; thus, activities are chosen based on their position and if there are enough resources, the activities will be sched- uled. Available resource can be computed by summing up the resource consumption of activities in progress and subtracting this resource consumption from the available resource. This process is continued until all of the activities are scheduled; then, by computing nish time of the activity in the sequence, the tness function of solution can be obtained. Decoding of the algorithm can be summarized as follows:
The numbers in the columns are transaction prices, excluding VAT and including other taxes. The VAT is excluded because the electricity price is also without VAT. (A Dutch company does almost always calculate their yearly numbers without the VAT.) To be able to use these numbers, the total power of a plant must be known, to determine which scale the plant ends up. Stork expects to sell the hybrid boiler to big plants and agreed with the assumption that they hypothetically sell a hybrid boiler to a plant with a total consumption of more than 1000 TJ. Table 2 shows the converted gas prices from €/GJ to €/MW. At the moment of this research, the gas prices for the last quarter of 2018 is missing. Therefore an estimation is made for the price in Q4. Since the gas price is dependent on the season, the ratio of Q4 in comparison to the other three quarters is determined and averaged over the three years. This value, together with the gas prices of the other three quarters gives the average price of 2018.