Simulation in Financial Analysis


The success of any organization is dependent on its financial status. The financial statements prepared by financial managers and analysts reflect the financial position of an organization. This implies then the accuracy of various statements is a significant aspect. However, financial analysts are faced with difficulties in analyzing the relevant financial documents with utmost accuracy. This is due to the fact that some of them are prepared on predetermined aspects rather than real ones.

This involves forecasting and predictions of situations, which are in most cases uncertain. In such circumstances, simulation techniques are applied in order to generate the desired results. The following report highlights how simulation methods and techniques are applied in determining such predictions. It covers Monte Carlos techniques and their applicability, simulation and cash flow forecasting, financial simulation analysis and modeling, decision trees in financial modeling and investment management simulation. The report will also cover the applicability of these techniques in real life experience and the possible impacts they may have in various organizations.


The word simulation has been used for a long time. It means an imitation or assumption of something predefined to enable someone carry out some activities. It is usually used when the real thing cannot be applied, it is inaccessible or maybe it is dangerous to apply in the preferred context. Simulation is used in many contexts among them in the field of finance. In finance, computer simulations are usually preferred for financial planning (Mayes & Shank, 2007).

Risk-adjusted Net Present Value for instance is calculated from well-defined figures but not always known ones. Simulation in finance is also applied in financial mentoring. Under Financial simulations, we have investment simulations, risk management simulations, investment simulations, and cash-forecasting simulations among others. This report tries to analyze how simulation can be used in financial analysis, use of financial models in financial management and their significance to financial analysts (Berg, 2004).

Monte Carlos Methods in Finance

These techniques are applicable in the fields of economics, mathematical economics and finance. This aids in evaluation of various financial securities including bonds and debentures. Simulation of uncertainty originalities that may have influence on their value and determination of their mean value in a wide range of payoffs is always done. The benefit of applying Monte Carlo techniques significantly expands with the widening of the problem unlike other statistical techniques. In simple terms, Monte Carlos methods encompass all sampling statistical techniques applied to approximate solutions to various quantitative problems (Taimre & Botev, 2011). They solve a problem by simulating underlying physical procedure and subsequently computing the mean outcome of the procedure.

Applicable Areas in Finance for Monte Carlos Techniques

  1. Corporate finance: Financial analysts use these techniques to formulate stochastic or economic models unlike other traditional static models. The features of a project’s NPV are critically analyzed and the components of cash flow that are influenced by uncertainty are formulated incorporating any correlation (Metropolis & Ulam, 1949). Then the results are posted to a NPV histogram where the evaluation of the real NPV’s investment takes place. The distribution facilitates an estimation of the probability that the project’s NPV exceeds zero.
  2. Portfolio evaluation: The factors that seem to be highly correlated in every sample and they are affecting a particular financial security or instrument is simulated and the final results computed together with portfolio’s value. After subjecting portfolio values to a histogram, the portfolio’s statistical characteristics are appropriately analyzed.

Applicability of Monte Carlos Methods (Finance)

  • More complex situations: There are problems in mathematical finance that involves the computation of a given integral. Valuation of integrals can be done analytically or using partial differential equation (PDE). However, in the event that the degree of freedom is large, Monte Carlos techniques can yield more accurate results than other statistical techniques. In circumstances whereby more than three variables are involved, some formulae like Black Scholes and finite difference techniques are not practical. This calls for the use of Monte Carlos methods (Taimre & Botev, 2011).
  • Mathematically: The theory of Arbitrage-free pricing asserts that the value of a derivative equals discounted value of the anticipated pay-off with the assumption of a risk-neutral measure mechanism. Monte Carlos is suitable to use while evaluating such difficult integrals.

Monte Carlos simulation provides a very useful tool in that, it can be used to analyze more complex financial data that cannot be worked on by other techniques and give out the most impressive and accurate results. Besides finance, Monte Carlos techniques can be used in other occupations to offer lasting solutions to more sophisticated and difficult situations for instance oil exploration.

Simulation and Cash Flow Forecasting

Cash flow forecasting is the planning of a company’s future flow of cash or income over a specific period based on some assumptions. The financial manager predicts the amount the company is likely to generate using the resources at hand. The use of simulation here becomes important since we are not dealing with actual figures but rather presumed figures that are predictable (Hadley & Whitin, 1963).

Take for instance a scenario whereby the financial manager intends to calculate cash flow based on the predicted sales for the coming year. This is merely a forecast of the anticipated cash inflow and expenditure over that specific time-something quite different might just happen. Once the manager has done the required forecasting, he can go ahead and make adjustments that are necessary for the continuation of the business’s operations.

Simulation Techniques in Cash Flow Forecasting

The commonly used technique is the direct method of cash flow forecasting. It analyzes organization’s cash inflows and outflows and is also referred to as receipts and disbursements (R&D). Receipts are basically the incomes of accounts receivable from recent sales though they may also incorporate sales of other assets. Disbursements are payroll, and all the accounts payable of recent purchases (Roger, 1999).

The R&D technique is most preferred for short-term forecasting horizon. The other method is adjusted net income (ANI) method. This one begins with Earnings before Interest and Taxation (EBIT) and then changes in balance sheet account (payables, depreciation, and receivables) are added or subtracted to project the cash flow (Aldrich, 2003). The third one is the Pro-forma balance sheet method (PBS). It is based on a direct focus on the predicted book cash account. Under this method there is an assumption that the forecasting accuracy in all other accounts should match the accuracies in the cash balances.

Simulation in cash forecasting aids companies and individuals in estimating the future expenditures and incomes thus facilitating decision-making process in a company. Carrying out cash flow in a spreadsheet can easily facilitate the determination of effects behind the changing components. The other applicability of Cash flow forecasts is when doing the actual analysis of personal finances in an individual level. This is especially when one is faced with complex and difficult financial decisions.

Financial Simulation Analysis and Modeling

Behavioral and simulation analysis is mostly used in financial modeling and analysis to explain the tradeoffs between the returns of a company and the risks that can be quantified. This is solved using a systematic methodology that changes for economic and business variables to come into a number of outcomes that are used to make critical economic decisions. The increasing use of sophisticated financial modeling software has greatly influenced the use of behavioral analysis in financial modeling and analysis (Maness & John, 1998).

Different Behavioral and Simulation and Analysis Techniques

There are three commonly used behavioral and simulation techniques. The techniques are:

Sensitivity and Scenario Analysis in Financial Modeling

Analysts, in order to get a reason for the return variability with regard to adjustments in a key variable, use this modeling technique. Estimating NPV taking into consideration different income estimates is one of the approaches advocated for by analysts in financial and economic modeling. The income variation estimates may vary from best to expected and finally to the pessimistic which is the worst case (Hadley & Whitin, 1963). Having obtained NPV range, a financial analyst is in a position to make a comprehensive recommendation highlighting any possible risks prevalence and the possibility of business growth.

This can further aid business managers make the appropriate decisions. There is a similarity between Scenario analysis and Sensitivity analysis but their scope ranges distinguish them; Scenario analysis is a little bit wider. Scenario analysis does the evaluation of the effects of simultaneous adjustments in various variables such as cost of capital, cash inflows, etc. The joint effects of changes in such variables are then applied to assess how they have affected company’s returns. Financial analysts can use NPV estimates to evaluate whether the business is vulnerable to any risk under the prevailing interest rate environment.

Decision trees in Financial Modeling

They aid managers in coming up with a wide range of investment decision options and their pay-offs as well as their chances of happenings. The diagrams resemble the branches of a tree and are dependent on the probabilities’ estimates linked to the outcomes of the several alternative actions available. The outcomes for each alternative are assessed by the associated probability (Hadley & Whitin, 1963). The summation of all the weighted outcomes is then obtained and the anticipated value for each alternative is then determined. The largest values are then chosen which stems from the expected value on the diagram.

Using Financial Simulations in Financial Modeling

These are statistical-based models that rely on already determined probability distributions to estimate those results that are suspected to more risk. Cash flow factors are subjected into an economic or financial model and then to a mathematical model. Going through the process repeatedly yields probability distribution with varying project returns.

In addition, complex and advanced financial simulations may be opted to while handling independent cash inflow constituents that may include manufacturing costs, purchase price, labor costs among others. Using the returns distribution, a financial analyst is in a position to determine the chances of attaining what the company expects in terms of value returns. Financial simulations and in particular Monte Carlos simulations are so much superior to other analysis techniques applied to financial modeling and analysis.

Inventory Management Simulation (IMS)

This program helps students to manage and maintain a simulated inventory interactively. The computer using this software is capable of generating random results and maintains the inventory records for the students who choose the appropriate inventory policy (Hadley & Whitin, 1963). In order to use IMS more appropriately, basic inventory concepts such as lead times, Economic Order Quality (EOQ) need to be reinforced. Also of important, to be reinforced is the cost trade-offs through the generation of cost curves. Just like all other simulations, the instructor must have the first conditions and factors to guide the simulation dynamics.

The parameters are set in the program in such a way that the instructor can change them at will. However, changing the parameters is not advisable (Maness & John, 1998). Care is always exercised in setting up the simulation so as not to mess up the entire process. For instance, when the ordering costs are too high and holding costs are very low, there might occur problems in ordering during simulation. An example of conditions and parameters is as follows:

  • Units at the beginning at of the period (100 units)
  • Cost per unit ($15/unit)
  • Ordering cost ($Warder)
  • Holding cost ($0.60/ unit/period)
  • Expedite cost ($0.20/unit/period) (4 periods maximum)
  • Average ending period and maximum deviation (10, 6 periods)
  • Stock out penalty cost ($2/unit/period)

Once the parameters have been set, the next step is running the simulation. If it is a student, he can use his policy of inventory management while relying on the instructor’s directions. Whatever the policy is adopted, three important decisions must be made;

  • Whether or not to order
  • How much to be ordered
  • How much to be expedited

The simulation software will generate all the required demand for all the periods.

Simulation Software for Forecasting

There are number of software program simulators considered to be best for forecasting, i.e. regression tasks. Most software is best suited for classification tasks. Every software has a different focus on single variable, multi-variable or even intervention modeling in time series forecasting. New-network software programs have been suggested that can be best used in simulating Neuro-Networks for predicting applications in regression and classification to facilitate dissemination of forecasting using the latest neuro-networks (Maness & John, 1998). The CD-Rom provides several freeware in which the leading software commercial software experts and modifiers can access them to enhance smooth neural forecasting.

Financial Analysis Simulation

Simulation is also useful in financial analysis. Many financial consultants are relying on this software to test their knowledge while analyzing the final accounts including balance sheets, cash flow statements and profit and loss account. They are used to measure the financial strength and profitability of the business. After the relevant data is fed to the software, duration of about four hours is allowed after which the results are generated.

Simulations are mostly used by middle-level to senior management level personnel. The simulation explains how managerial activities can influence financial performance of a business. The participants can decide the range of business ideas to be implemented (Hadley & Whitin, 1963). In addition, simulation gives impressive and accurate results after processing financial data and presenting final financial statements that are very useful to business managers in making financial decisions.


Monte Carlos methods of stimulation are important techniques useful in varying fields without which statisticians and financial analysts would be facing so many obstacles while undertaking data analysis especially in more sophisticated and complex situations. This is because other statistical techniques may be inapplicable or inappropriate thus not able to give comprehensive and accurate results. Therefore, simulation is widely being used as a problem-solving tool and therefore justifiable to continue improvising these techniques together with their associated software. Financial analysis has been made relatively easier by the adoption of simulation techniques hence improving the management of many organizations because of relying on more information that is accurate (Maness & John, 1998).

More importantly, simulation has enhanced accurate predictions in financial situations where forecasting is required and in particular, cash flow forecasting. However, simulation is complicated and requires high skills to manage, operate and maintain for it to generate impressive results. With the growing and advancement of business enterprises, the complexity and high costs of simulation techniques and procedures have to be ignored and see the tangible benefits associated with the use of these programs. Proper use of these techniques can impact positively on the organization’s performance through improved decision-making processes. Successful organizations are believed to be continuously relying on these statistical tools and subsequently attained substantial progress.


Aldrich, C. (2003). Learning by doing: A comprehensive guide to simulations, computer games, and pedagogy in E-learning and other educational experiences. San Francisco, SF: Pfeifer -John Wiley & Sons.

Berg, B. A. (2004). Markov Chain Monte Carlo simulations and their statistical analysis (With Web-Based FORTRAN Code). Hackensack, NJ: World Scientific.

Hadley, G. & Whitin, T. M. (1963). Analysis of inventory systems. Englewood Cliffs, NJ: Prentice-Hall.

Maness, S. & John T. Z. (1998). Short-term financial management. Fort Worth, FW: Dryden Press.

Mayes, T. & Shank, T. (2007). Financial analysis with Microsoft® Excel. 5th Edn. New York, NY: Cengage Learning.

Metropolis, N. & Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association, 44 (247): 335-341.

Roger, D. S. (1999). Simulation: The Engine behind the Virtual World. The Simulation 2000 Series, 1, 1-24.

Taimre, D. P. & Botev, Z.I. (2011). Handbook of Monte Carlo methods. New York, NY: John Wiley & Sons.

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