SIMULATION Application in Finance

Gurpreet Kaur Bilkhu

1/29/2018

Foundation for Information Technology; S.P. Jain School of Global Management; FinTech Program, Mumbai 2018

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Index

Sr. No. Particulars Page No. 1. Introduction to Simulation 2

2. Monte Carlo Simulation 3

3. Use Case I: Predicting Portfolio Return 4

4. Use Case II: Risk and Return of a Project 6

5. Use Case III: Predicting Share Prices 8

6. Conclusion 10

7. References 11

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Introduction to Simulation

What does Simulation mean?

Simulation is an effective technique for imitating an existing or proposed process. It is a process of

designing a Model of a real system, which through repeated trial and error helps understand the

behaviour of the system and its operation.

It helps in building a visual representation of the process similar to making a flow chart. By adding

rules for activities and processes, the simulation will mimic real life behaviour. Making changes to

the inputs will show how it will affect the process in real-life without any risk to the current

performance

It is therefore a uniquely powerful approach for making confident-evident based decisions to

improve the efficiency and profitability of organisations and/or countries at large.

Simulation is used in a diverse spectrum of fields like, financial modelling, economics, safety

engineering, project management, portfolio management, trading of currencies, commodities and

stock, game theory, etc.

Advantages and Disadvantages of Simulation:

Advantages Disadvantages Tests a process without committing resources Model building (requires special training) and

may take time to perfect

Explore possibilities & diagnose problems scientifically through a computer program

Results may be different to interpret and apply

Identify constraints and develop understanding of complex real-life variables

Modelling can be a time consuming and expensive affair

Prepare for change by investing wisely Simulations may be used inappropriately

Basic principles of building a Simulation model:

I. Conceptualising a model requires system knowledge, engineering judgement and model-

building tools.

II. The secret to be a good modeller is to remodel

III. Modelling process is evolutionary because information is revealed in piecemeal

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Monte Carlo SimulationRisk analysis is a part of every decision we make. We are constantly faced with uncertainty,

ambiguity and variability. Risk analysis is a systematic use of available information to determine how

often specified events may occur and the magnitude of their consequences. Risk analysis can be

performed qualitatively or quantitatively. Qualitative risk analysis generally involves assessing a

situation by instinct or “gut feel,” and is characterized by statements like, “That seems too risky” or

“We’ll probably get a good return on this.” Quantitative risk analysis attempts to assign numeric

values to risks, either by using empirical data or by quantifying qualitative assessments

A better way to perform quantitative risk analysis is by using Monte Carlo Simulation.

In the following we will see 3 use cases of Monte Carlo simulation model in decision-making and risk

assessment.

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Use Case I: Predicting Portfolio Return

Example:

An individual is looking to invest Rs. 10,000 per year for a period of 30 years (time to retire) in the

BSE Sensex index fund. He starts with an initial investment of Rs. 100,000. Assuming the BSE Sensex

giving a return of 11.40% p.a. with a volatility of about 18%, what could be the return an investor

would generate over a 30-year period.

A portfolio manager through a simple Monte Carlo simulation model would be in a position to advise

in a very effective manner.

The Excel model would appear as follows:

Note: More than 1050 iterations were run in the above model to arrive at a conclusion above.

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We will be in a position to arrive at the returns of each year using the NORM.INV function, where

probability could be a random number. Once random return percentages are arrived at for a period

of 30 years, the end value could be determined through simulation. The simulation is run 1050 times

to arrive at 1050 the likely figures of the portfolio.

From the above, an average of the portfolio return is arrived at, which per the above example is

around Rs. 47-48 lakhs. The median of Rs.35 lakhs indicates that the distribution is positively

skewed.

Despite having information on median and mean it still remains a challenge to predict the value at

close of the period. However, the model equips the portfolio manager to come up with probability

statements, which are computed through percentiles. In the example above, it is seen that there is a

95% probability that the portfolio will be around Rs.9.38 lakhs and there is a 75% probability that it

would be Rs.21.56 lakhs. The high standard deviation is in line with the volatility of the market.

You can see from the above example that the simulation model has enabled the portfolio manager

to advise his client on the investment by giving him quantitated inputs on the returns and risks of

the portfolio. Similarly, simulation techniques could be applied to compute, amongst other things,

portfolio value at risk (VaR), efficient portfolios, multi-asset VaRs, stock volatility, market risk, etc.

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Use Case II: Risk and Return of a Project

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In this example we can see how a Simulation Model can help assess the Net Present Value (NPV) of a

project. This will aid the investor in taking the decision of whether or not to invest. The example

above shows the assumptions of ABC Company Ltd. - in the business of trading. On the basis of the

assumptions given above, a 4-year profitability statement has been projected with the net cash

flows derived at the end of each given period. Thereafter, the NPV has been calculated which in the

current case is positive. This indicates that one may consider investing in the project, ceteris paribus.

Since investment decisions cannot be taken on a static numbers, as demand for products can be

uncertain, one will have introduce the element of uncertainty of demand in the projection.

In the above example, it is assumed that the demand will be in the range of 80-130 units for the

period under consideration. A formula is built in which will bring out random integers indicating

demand in units within the above range. Around 100 iterations were run with the what-if function to

bring out the NPV values (only 30 mentioned) above. The hypothesis is tested on a 95% confidence

interval which reveals it is 95% likely that range of possible NPV outcome will be between Rs.

716,870 – Rs 552,554 with only a 2.5% probability that it will be below Rs. 552,554 or above Rs.

716,870.

It can therefore be concluded, that based on the above analysis an investor is being able to assess

the risk of a project and also estimate with reasonable confidence the range of returns a project can

possibly generate.

As we can see from the above example, simulation can be used in a host of applications in corporate

financial planning and risk management, including but not limited to financial forecasting, valuation

analysis, financial planning and debt analysis.

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Use Case III: Predicting Share Prices

Example: To decide whether to ‘Buy’ or ‘Sell’ shares of Ashok Leyland Ltd.

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For the above example, a two year data on the movement of Ashok Leyland share price was

obtained from the BSE India’s website. Stock prices as recent as 25th of January 2018 have been

considered. In the simulation table, prediction of stock movement has being made for the next 30

days basis the historical movement. This has been evaluated by deriving the mean and standard

deviation of the two-year data. The output has been averaged.

This average is compared to the last traded value to evaluate whether the share price is above its

two-year moving average or below. Through this trend analysis one can say that since the closing

price is above the moving average it is potentially over-priced, ceteris paribus.

This analysis throws up another area where simulation could be effectively used to predict

movement of stock prices, market indices, currency movements, etc.

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ConclusionSimulation, therefore, is a very effective tool in evaluating risk and return for any given set of

variables over a given period of time. Today a lot of companies cater to their ever-growing business

needs by programming computer simulations, which are based on the same principles as discussed

above, although are more comples. This assists companies to cater to their day-to-day as well as

Long term decision making.

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References Banks, J., Ed. (1998). Handbook of simulation: Principles, methodology, advances,

applications, and practice. New York: John Wiley & Sons.

Wikipedia

Bombay Stock Exchange of India,

Tutorial lessons on developing Simulation Models in excel from YouTube.

Reference websites:

o https://simulationfinance.com/

o http://www.bseindia.com/

o http://www.globalinvestorsims.com/

o http://www.palisade.com/risk/risk_analysis.asp

o http://www.prisim.com/