ISSN 2051-0853 ©2013 TJEAS
Project Cash Flow Forecasting Using Value at Risk
Mohammad Reza Feylizadeh
1*, Morteza Bagherpour
21. Department of Industrial Engineering, Islamic Azad University, Shiraz branch, Shiraz, Iran 2. Department of industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Corresponding author: Mohammad Reza Feylizadeh
ABSTRACT: At the start of a project, project manager would like to understand trend of money receipt
and payments in future. However, accuracy of project cash flow always is an important issue since receipt and payments are probabilistically known. On the hand, value at risk (VAR) is a statistical technique used to measure and quantify the level of financial risk within a firm or investment portfolio over a specific time horizon. Although VAR is a powerful technique for measuring financial risks, it mostly applied in financial firms. In this paper, VAR is applied in project cash flow forecasting. The approach proposed in this paper, employed different probabilistic conditions of a project such as extra works, changes and re-works. The approach is successfully implemented through a construction project.
Keywords: @Risk, Forecasting, Risk Management, Simulation, statistical technique
INTRODUCTION
In project management systems, it is essential to aware of the project cash flow. An accurate prediction of cash flow leads project manager to effective monitoring of the project. The importance of cash flow forecasting comes from this fact that the required cash should be announced to sponsor. The sponsor will support the project from financial source according to cash flow forecast. Note that cash flow shall be under control all the time to ensure project profitability. However, the accuracy of project cash flow is still under investigation by many researchers. In this respect, initially fuzzy modelling of project scheduling has been considered for analysing cash flow (Bonnal et al., 2004). After that a reliable cash flow prediction in construction projects was pointed out to assists the project manager in a better position to identify problems and develop appropriate managerial strategies to overcome forthcoming issues (Cheng and Roy, 2010). Also it was pointed out that with a reliable estimation of project’s cash flow, contractor will be able to improve the financial position of projects (Hwee and Tiong, 2002). It was stated cash flow forecasting plays the role of an early warning system for program and projects and he suggested a cash flow forecasting model based on bottom estimation of contractor costs (Maravas and Pantouvakis, 2011; Mavrotas et al., 2005). It was generated a model for cash flow forecasting using weighed mean of cost categories. This model was built based on the planed value and the actual cost happened on job site (Park et al., 2005).
On the other side, VAR has been extensively applied through financial firms. That was why; market forecasting under risk condition had been focused (Berkowitz, 2000; Christoffersen, 1998) and then many models has been suggested and fitted for modelling of market and financial firms under probabilistic and risk conditions (Dowd, 2002; Dowd, 2005) . Kupiec (2005) examined verification of different financial risk management models to select the better choice.
The approaches discussed above mostly relied on information existing at job site levels or applying fuzzy logic models. None of them has been argued modelling of project cash flow where input variables are probabilistically given. Also, there are many re-works in construction projects to be covered through modelling of cash flows. Finally, it is pointed out that whole the parameters under risk should be financially measured using Monte- Carlo simulation study.
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Problem Statement
Consider a construction project is being executed. There are several phases to be completed to hand over the project. In order to recognize cash flow behaviour and its forecasting, probabilistic parameters and re-works should be analysed. Through this procedure, the following inputs should be considered:
Extra works resulting variation of order Changes during project execution
Re-works resulting employer inspection and approval process Probabilistic nature of incurred cost (happening of actual costs)
On the other hand, time schedule, as one of important input for forecasting of project cash flow, is deterministically known and during project execution is being changed.
The aim of this study is to measure financial risks of a project resulting from value at risk technique during project execution under different conditions of the project undertaken.
Modelling Procedure
The following steps should be implemented in order to forecast a project cash flow: Step 1- Initialize project time schedule
Step 2- Initialize input parameters (extra works, changes, re-works …) Step 3- Information gathering for selected inputs
Step 4- Initialize VAR model and cash flow at risk Step 5- Run Monte-Carlo simulation
Step 6- Verify simulation study
Step 7- Set different conditions of the project (develop the model) Step 8- Report outputs
Step 9- Suggest corrective actions
Step 10- Run the above mentioned procedure while stopping condition satisfied. The stopping condition maybe includes:
Set number of simulation run more than 10,000 Set error function to be less than 1 %
A mix of both strategies
The approach proposed above is an embedded system including project management and financial risk management. If both applied altogether, a project manager would be confident of the accuracy of cash flow forecasting.
case study data
Consider a construction project which cost estimation is equal to 115 M USD. The project is achieved more than 50 percent progress. After data gathering process, the data has been summarized in Table 1.
Table1. Data gathering for cash flow forecasting
Lower bound % Most likely % Upper bound % Description Items 10 15 20 change phase 1 5 - 10 rework 0 5 change phase 2 20 30 35 Extra work phase 3 5 - 15 Extra work phase 4 10 - 20 Extra work phase 5 0 - 10 rework phase 6 0 - 20 rework phase 7 0 - 10 rework phase 8 0 - 5 rework phase 9 RESULTS
The results have been obtained after 10,000 simulation run using @risk software. Distributed cash flow has been then obtained as given in Figure 1.
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Figure 1. Project value at risk using simulation study
As it is indicated above, value at risk with 5 percent level of significance, is equal to 122. It means that with a 95 percent level of confidence, cash flow is less than 122 for whole the project.
Also Figure 2 indicates cash flow for extra work with a 90 percent confident is between 1.4 – 1.9.
Figure 2. Cash flow simulation for extra works
Figure 3 illustrates the cash flow simulation for change orders. It reveals that this amount has not a significant impact on the total project cash flow.
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Figure 3. Value at risk for change order
Value at risk using cash flow simulation also has been studied for re-works which is presented in Figure 4.
Figure 4. Value at risk for re-works
The above mentioned reports indicate value at risk for extra works is higher than the other affecting factors on cash flow forecasting. This type of risk maybe transferred to the employer where the actual quantities exceed the planned one as mentioned in the contract. Thus, this type of analysis will assist both employer and contractor to finance the required amount based on 95 percent confidence and take it into budgetary estimate for further action. After running VAR the profitability index also would be automatically updated.
CONCLUSION REMARK AND FURTHER RECOMMENDATIONS
VAR has been mostly applied through financial firms. In this paper, VAR employed for project cash flow forecasting where several probabilistic parameters have been associated. The project cash flow including extra works, change orders and re-works have simulated to determine financial risks of the project undertaken. Different scenarios have been developed and simulated using 95 percent confidence interval. The approach should be periodically updated and the obtained results will forward to employer in order to finance the required cash and
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updating profitability index. Moreover, risk mitigation strategies can be applied to enhance performance of the project. This issue can be focused as a future research work. Additionally, value engineering can be embedded to this system to reduce total cash required for the project undertaken.REFERENCES
Berkowitz J. 2000. Testing Density Forecasts, with Applications to Risk Management. Graduate School of Management, University of California, Irvine
Bonnal P, Gourc K, Lacoste G. 2004. Where do we stand with fuzzy project scheduling? Journal of Construction Engineering and Management 130(1):114–123
Cheng M, Roy A. 2010. Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International journal of project management 29:56–65
Christoffersen P. 1998. Evaluating Interval Forecasts. International Economic Review 39:841-862 Dowd K. 2002. A Bootstrap Backtest. Risk 15(10):93-94
Dowd K. 2005. Measuring Market Risk, 2nd edn. John Wiley and Sons, Chichester and New York
Hwee N, Tiong R. 2002. Model on cash flow forecasting and risk analysis for contracting firms. International journal of project management 20:351–363
Kupiec PH. 1995. Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives 3(2):73-84
Maravas A, Pantouvakis J. 2011. Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management 130(1):1-11
Mavrotas G, Caloghirou Y, Koune J. 2005. A model on cash flow forecasting and early warning for multi-project programmes: application to the Operational Programme for the Information Society in Greece. International journal of project management 23:121–133.
Park H, Han S, Russell J. 2005. Cash Flow Forecasting Model for General Contractors Using Moving Weights of Cost Categories. Journal of Management and Engineering 4 (164):164–172
