This is a guest post by Jitendra Kumar, Sr.Business Analyst- Fiserv. Mr Kumar is a certified financial planner. His additional qualifications include MBA and Certification in Investment & Portfolio Mgmt. (IIMB). He is also a certified associate of the Indian institute of bankers (CAIIB).

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Traditional Financial Planning works on certain assumptions on returns, risk, inflation, savings growth etc. In the real world these assumptions may or may not hold True. So to depict the real world uncertainty **Monte Carlo Simulations** are used by Financial Planners. Clearly, Monte Carlo represents an improvement over traditional methods of financial planning.

The Monte Carlo method is used to simulate the various sources of uncertainty that affect the value of the instrument, portfolio or investment in question, and to then calculate a representative value given these possible values of the underlying inputs. (“Covering all conceivable real world contingencies in proportion to their likelihood.”)

A Monte Carlo simulation is a mathematical tool that offers a way to evaluate a retirement portfolio to see if it will last a lifetime. With the help of computer software, a planner can simulate hundreds or thousands of market-condition scenarios and learn the probability that your portfolio would last your expected lifetime.

Now a more sophisticated alternative is working its way into financial planning. Using computer software or a Web-based program, you can calculate the probability of achieving your goals through a ”Monte Carlo” simulation (table).

When it comes to financial planning, a Monte Carlo simulation takes into account returns, volatility, correlations, and other factors, all based on historical statistical estimates. That’s similar to the traditional financial-planning approach.

If your portfolio is run through 1,000 simulations, projecting 1,000 separate retirement scenarios, and it works 800 times, it means there’s an 80 percent probability that the portfolio won’t run out of money. If 80 percent seems too risky and you’d like to increase the odds to 85 percent or 90 percent, you could tweak the portfolio by adding more money to your investments or taking out less.

Given the unpredictable nature of the stock market, the Monte Carlo method can help financial planners model how a particular portfolio will perform under various market conditions, thus helping them make more informed investment decisions. This approach is especially useful in retirement planning, in which investors try to figure out which savings rates, allocations, market returns, and spending patterns will allow them to make their nest eggs last a lifetime.

Monte Carlo method has become popular with financial planners because it takes into account real-world experiences in a way that other methods that assume a given rate of return don’t. “The reason Monte Carlo simulations are being used more frequently, is because they do a better job explaining the potential outcomes versus time-value-of-money calculations, such as future value. Future value will tell you the expected value of a portfolio given its present value, years to grow, potential cash flows, and growth rate. The problem with a future value calculation is that it treats the outcome as certain, while in reality, and especially with the markets, nothing is certain. A Monte Carlo simulation provides a more ‘colorful’ perspective of the range of potential outcomes given the expected return and volatility of a portfolio.”

In India very few Financial Planning Software actually have Monte Carlo Simulations. AdvisorVision from Fiserv is among the select few, has detailed Monte Carlo Simulation for all financial goals. For more information on AdvisorVision and request for Free Trial Access, please do visit our website www.advisorvision.fiserv.co.in or alternatively you can write to us Sales.AVIndia@fiserv.com .

You can read more about Fiserv and AdvisorVision: **Financial Advice Management **

Specific features of AdvisorVision include:

**Automated strategies:**AdvisorVision can automatically generate strategies to meet customer goals within established parameters, without manually juggling inputs to find a successful plan**Monte Carlo simulation:**Analyzes plan success probabilities based on the detailed historical data available

**Customized risk assessment:**Each organization — or individual advisor — can create their own risk assessment methodology, to help tailor advice and promote specific products

**Scenario builders:**Advisors can easily create “what-if?” and alternative scenarios off the base plan, to allow for contingencies and alternative funding strategies, with a minimum of effort**Arbitrary goal analysis:**Factor in a broad range of prioritized goals beyond retirement funding and distribution, for a holistic plan can create their own risk assessment methodology, to help tailor advice and promote specific products**Support for a complete range of financial planning functions:**Debt management, portfolio construction, estate planning, tax planning, and life insurance need analysis.

This is a guest post by Jitendra Kumar, MBA, CAIIB, CFP, Certification in Investment & Portfolio Mgmt. (IIMB), Sr.Business Analyst- Fiserv.

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**Note:** I requested Mr. Kumar to write about AdvisorVision as I think it is important for the investor and advisor alike to be aware of Monte Carlo simulations. There is no monetary gain involved for me. MC simulations are used in almost all walks of life including Physics. If you are interested in my version, check this out: **Excel Monte Carlo Retirement Calculator**

**Create a "from start to finish" financial plan with this free robo advisory software template**

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I would like to know, given the Monte Carlo results, what would help us control the results. Therefore isn’t it a better idea to start with a particular strategy and review it on a periodic basis and come closer to the actual result. From a financial planning perspective, I strongly feel this is going a little overboard with maths. I’m willing to change my opinion given a convincing answer. We can definitely build a strategy regarding the degree of aggression through a detailed cash flow analysis and revise it on the basis of actual occurrences. For example, if we take the retirement corpus to be such that it sustains till age 100 at the risk free rate, where is the need to analyse 1000 scenarios? Also what should be the scenario to be finalized after doing the MC analysis? Look forward to your inputs…

Dear Chentil,

Welcome!

My two cents while we wait for Jitendra to respond to your comment:

MC simulations has divided the FP community! There are those who swear by it and those who abhor it. It is good to see you have an open

mind towards it.

“start with a particular strategy and review it on a periodic basis and come closer to the actual result. From a financial planning perspective” is

the only real-world thing a planner can do.

MC simulations if used effectively can help the client understand the what lies ahead.

To me retirement planning is a two-stage process.

1. Convince the client why retirement planning essential and why it is top priority

2. Make him understand goal-planning cannot and should proceed like a straight line, given the underlying assumptions and what can really

happen.

To met MC simulations is the perfect way to demonstrate stage two. We introduce a fluctuating returns and state our strategy in the case of a

prolonged sideways market, bull run etc.

Many in the FP community argue that MC simulations should not be used for this because of its limitations (normal distribution primarily) and

that backtesting should be used (See Jim Otars retirementoptimizer.com).

While I like backtesting myself, I am not against MC simulations. In fact I prefer that because of Normal distributions.

I can use to convince a client that equity returns can fluctuate pretty wildy (+45% one year and -35% another) with EQUAL probability.

This equal prbability part maynot be reality reflected in backtesting but is crucial for education.

Hence I am a fan of MC calculations (it helps that I am trained to use it for other applications!)

“For example, if we take the retirement corpus to be such that it sustains till age 100 at the risk free rate, where is the need to analyse 1000 scenarios?”

Good enough for me because in a way you have done MC simualtions mentally!

Technically 1000 is not good enough. I have used 50,000(!) in my MC calculator:

http://freefincal.com/retirement-calculators-advanced/monte-carlo-calculator/

Do have a look.

“Also what should be the scenario to be finalized after doing the MC analysis? Look forward to your inputs…”

This is subject to debate as well. At the end of the day the client can only save so much. So all we can do is to come up with a conservative plan taking into account the max. possible inflation and min. possible return.

If the client is mature enought to understand he/she can be warnedthat, given the parameters used there is a X% chance of achieveing the goal.

The more important thing is for the planner to keep the probability in mind while taking course corrections as you mentioned.

I would like to know, given the Monte Carlo results, what would help us control the results. Therefore isn’t it a better idea to start with a particular strategy and review it on a periodic basis and come closer to the actual result. From a financial planning perspective, I strongly feel this is going a little overboard with maths. I’m willing to change my opinion given a convincing answer. We can definitely build a strategy regarding the degree of aggression through a detailed cash flow analysis and revise it on the basis of actual occurrences. For example, if we take the retirement corpus to be such that it sustains till age 100 at the risk free rate, where is the need to analyse 1000 scenarios? Also what should be the scenario to be finalized after doing the MC analysis? Look forward to your inputs…

Dear Chentil,

Welcome!

My two cents while we wait for Jitendra to respond to your comment:

MC simulations has divided the FP community! There are those who swear by it and those who abhor it. It is good to see you have an open

mind towards it.

“start with a particular strategy and review it on a periodic basis and come closer to the actual result. From a financial planning perspective” is

the only real-world thing a planner can do.

MC simulations if used effectively can help the client understand the what lies ahead.

To me retirement planning is a two-stage process.

1. Convince the client why retirement planning essential and why it is top priority

2. Make him understand goal-planning cannot and should proceed like a straight line, given the underlying assumptions and what can really

happen.

To met MC simulations is the perfect way to demonstrate stage two. We introduce a fluctuating returns and state our strategy in the case of a

prolonged sideways market, bull run etc.

Many in the FP community argue that MC simulations should not be used for this because of its limitations (normal distribution primarily) and

that backtesting should be used (See Jim Otars retirementoptimizer.com).

While I like backtesting myself, I am not against MC simulations. In fact I prefer that because of Normal distributions.

I can use to convince a client that equity returns can fluctuate pretty wildy (+45% one year and -35% another) with EQUAL probability.

This equal prbability part maynot be reality reflected in backtesting but is crucial for education.

Hence I am a fan of MC calculations (it helps that I am trained to use it for other applications!)

“For example, if we take the retirement corpus to be such that it sustains till age 100 at the risk free rate, where is the need to analyse 1000 scenarios?”

Good enough for me because in a way you have done MC simualtions mentally!

Technically 1000 is not good enough. I have used 50,000(!) in my MC calculator:

http://freefincal.com/retirement-calculators-advanced/monte-carlo-calculator/

Do have a look.

“Also what should be the scenario to be finalized after doing the MC analysis? Look forward to your inputs…”

This is subject to debate as well. At the end of the day the client can only save so much. So all we can do is to come up with a conservative plan taking into account the max. possible inflation and min. possible return.

If the client is mature enought to understand he/she can be warnedthat, given the parameters used there is a X% chance of achieveing the goal.

The more important thing is for the planner to keep the probability in mind while taking course corrections as you mentioned.