Project Cash Flow Forecasting Using Value at Risk

Transcription

1 Technical Journal of Engineering and Applied Sciences Available online at TJEAS Journal21332/ ISSN TJEAS Project Cash Flow Forecasting Using Value at Risk Mohammad Reza Feylizadeh 1*, Morteza Bagherpour 2 1. 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 s. The approach is successfully implemented through a construction project. 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., 24). 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, 21). 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, 22). 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, 211; Mavrotas et al., 2). 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., 2). On the other side, VAR has been extensively applied through financial firms. That was why; market forecasting under risk condition had been focused (Berkowitz, 2; Christoffersen, 1998) and then many models has been suggested and fitted for modelling of market and financial firms under probabilistic and risk conditions (Dowd, 22; Dowd, 2). Kupiec (2) 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 s 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.

2 Tech J Engin & App Sci., 3 (2): , 213 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 s should be analysed. Through this procedure, the following inputs should be considered: Extra works resulting variation of order Changes during project execution Reworks 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, s ) Step 3 Information gathering for selected inputs Step 4 Initialize VAR model and cash flow at risk Step Run MonteCarlo 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 1 Run the above mentioned procedure while stopping condition satisfied. The stopping condition maybe includes: Set number of simulation run more than 1, 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 11 M USD. The project is achieved more than percent progress. After data gathering process, the data has been summarized in Table 1. Items phase 1 phase 2 phase 3 phase 4 phase phase 6 phase 7 phase 8 phase 9 Table1. Data gathering for cash flow forecasting Description Upper bound % Most likely % Lower bound % change change Extra work Extra work 1 Extra work RESULTS The results have been obtained after 1, simulation run software. Distributed cash flow has been then obtained as given in Figure

3 Tech J Engin & App Sci., 3 (2): , 213 Figure 1. Project value at risk using simulation study As it is indicated above, value at risk with percent level of significance, is equal to 122. It means that with a 9 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 9 percent confident is between 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. 2683

4 Tech J Engin & App Sci., 3 (2): , 213 Figure 3. Value at risk for change order Value at risk using cash flow simulation also has been studied for s which is presented in Figure 4. Figure 4. Value at risk for s 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 9 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 s have simulated to determine financial risks of the project undertaken. Different scenarios have been developed and simulated using 9 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 2684

5 Tech J Engin & App Sci., 3 (2): , 213 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. 2. Testing Density Forecasts, with Applications to Risk Management. Graduate School of Management, University of California, Irvine Bonnal P, Gourc K, Lacoste G. 24. Where do we stand with fuzzy project scheduling? Journal of Construction Engineering and Management 13(1): Cheng M, Roy A. 21. Evolutionary fuzzy decision model for cash flow prediction using timedependent support vector machines. International journal of project management 29:6 6 Christoffersen P Evaluating Interval Forecasts. International Economic Review 39: Dowd K. 22. A Bootstrap Backtest. Risk 1(1):9394 Dowd K. 2. Measuring Market Risk, 2nd edn. John Wiley and Sons, Chichester and New York Hwee N, Tiong R. 22. Model on cash flow forecasting and risk analysis for contracting firms. International journal of project management 2: Kupiec PH Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives 3(2):7384 Maravas A, Pantouvakis J Project cash flow analysis in the presence of uncertainty in activity duration and cost. International journal of project management 13(1):111 Mavrotas G, Caloghirou Y, Koune J. 2. A model on cash flow forecasting and early warning for multiproject programmes: application to the Operational Programme for the Information Society in Greece. International journal of project management 23: Park H, Han S, Russell J. 2. Cash Flow Forecasting Model for General Contractors Using Moving Weights of Cost Categories. Journal of Management and Engineering 4 (164): Value At Risk (VAR) 268