In this presentation, we explain Money Shastra's unique approach to strategic asset allocation using Monte Carlo method that tries to optimize the chances of an investor meet his goals without losing focus on the need to increase wealth.

1. Tools and insights
LDI strategy for retail investors
using Monte Carlo method
Viswanathan M B, FCA, CFA, FRM
Tools and insights

2. www.moneyshastra.com 2
Our two cents
Monte Carlo method might be difficult for advisors to
understand. But, we believe, the insights that a simulation
based framework offers is far more easy to convey and
understandable to end investors.
Further, its ability to factor various risk factors and investor
specific issues make it our only approach to financial
planning for individual investors.

3. www.moneyshastra.com 3
About MoneyShastra.com
 An online portal providing useful
tools and insights to retail investors
 Provide advice on strategic asset
allocation using our web interface
 Aspire to become a full fledge robot
advisory firm

4. www.moneyshastra.com
The story behind our approach

5. www.moneyshastra.com 5
Our view on traditional wealth management
 Focuses on wealth maximization
 Considers the overall risk tolerance/aversion of investors
 Decisions on strategic asset allocation is qualitative
 Forecast and risks are calculated, if at-all, using deterministic approaches using
mean-variance framework

6. www.moneyshastra.com 6
Our view on investors
 Retail investors do not decide based on mean-variance framework
 They are not risk neutral
 They hope for luck; especially, if they realize they don’t have enough money
 Sometimes, they take risk because they believe they wouldn’t actually face the
consequence
 Their risk tolerance for each investment and need varies based on their mental
bucket
 More risk averse if goals are indispensable as compared to goals that are optional

7. www.moneyshastra.com 7
Traditional approach and investors: Our view on mismatch
 It is impossible to explain lay investor the risk in their strategy using mean-
variance framework
 The math is too complicated for most of them to understand
 Qualitative “common-sense” explanation about risk may not cut ice; especially,
if investors believe they won’t face consequences of their action
 Earmarking a portfolio towards a particular goal does not mean that investors
will not use it for other purpose if they see the “need” to do that

8. www.moneyshastra.com 8
How does Monte Carlo method help?
 Monte Carlo method enables optimize an asset allocation by considering
various scenarios
 Various risk factors, especially low probability high impact risk factors, can be
incorporated and quantified
 Risk aversion or tolerance for each individual goal can be incorporated using a
suitable penalty function
 Most importantly, can help provide insight that any common investor can
appreciate

9. www.moneyshastra.com 9
The insights provided by our approach
 Our approach to financial planning can provide various insights that are far
easier and more useful to end investors
 Some of the insights include the following:
 The chances that their goals can be met using their current asset allocation
 The chances of having funds to meet an unexpected emergency
 The optimal asset allocation that enables the end investor to maximize their chances of
meeting their goals and uncertain emergencies without losing focus on wealth
maximization
 The maximum, minimum and most likely value of investments
 The chances that they would have an amount that is more than or less than a specific
amount

10. www.moneyshastra.com 10
Some of the insights that can be provided (1/2)
The approach can tell the investors the chances of meeting their goal using their asset allocation
Equity,
33.7%
Gold,
34.0%
Debt,
32.3%
96%
70%
60%
Retirement
Home purchase
World Tour
Chances of meeting goal Chances of missing a goal
IDEALASSET ALLOCATION PROBABILITY OF SUCCESS

11. www.moneyshastra.com 11
(1,000.0)
(500.0)
-
500.0
1,000.0
1,500.0
2,000.0
2,500.0
3,000.0
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
Expected Max Min
The insights Monte Carlo method provides (2/2)
The approach can tell us how much the assets are likely to be at different points in future

12. www.moneyshastra.com
Our approach

13. www.moneyshastra.com 13
What do we do?
 Identify the ideal asset allocation that maximizes the chance of meeting your
goal
 Investors have several constraints that prevents them from saving the ideal amount that
they need to meet various goals
 We identify the asset allocation that enables them to maximize their chance of meeting
goal with special focus on indispensable goals
 Our model focuses on reducing the chances of shortfall as well as the amount of
shortfall

14. www.moneyshastra.com 14
How we do it?
 Our model considers several random factors that affects an investor’s financial
life
 It includes return on markets, inflation, personal emergencies
 Several thousand trials involving several thousand scenarios are run to identify
an asset allocation that result in the least amount of expected shortfall

15. www.moneyshastra.com 15
Our model, in a nutshell (1/2)
Step 1: Identify various goals and its importance
Step 2: Identify the value of current investment and the amount of annual savings
Step 3: Identify initial asset allocation
Step 4: Generate values for key random variables using stochastic process

16. www.moneyshastra.com 16
Our model, in a nutshell (1/2)
Step 5: Calculate path dependent present value of assets and liabilities
Step 6: Levey a penalty on deficit and surplus (negative penalty on surplus)
Step 7: Run simulation involving several thousand iterations to identify the
shortfall
Step 8: Adjust the asset allocation and repeat step 6 and 7 several thousand times

17. www.moneyshastra.com 17
Fundamental premise of our model
 Every financial goal of an investor is a liability he needs to meet
 Investors can afford to miss some goals that are not important
 Investors associate priority to each goal
 Investors risk aversion/tolerance varies based on the importance of goal

18. www.moneyshastra.com 18
The risk and risk objective in our approach
 Risk represents the probability of not meeting a goal
 The objective of asset allocation is to minimize the chances and amount of
shortfall
 Reduce the likelihood of not having sufficient money to meet the goal
 And, in any case if shortfall arises, reduce the amount of shortfall
For example, if an investor wants to save Rupees six crores towards his retirement corpus,
the asset allocation should maximize the chance of having the amount. And, in cases where
the amount falls short, the asset allocation should focus on reducing the deficit as much as
possible

19. www.moneyshastra.com 19
Key assumptions in our model: Accumulation phase
 Constant asset allocation ratio through the accumulation phase:
 The asset allocation across different years would remain same
 Static asset allocation: The model assumes that asset allocation, once
determined shall remain constant
 In reality, we periodically review the market conditions and personal financial position of
the investor to change the asset allocation, if required.
 Portfolio rebalancing at the end of each year

20. www.moneyshastra.com 20
Key assumptions in our model: Consumption phase
 Assets to be distributed using a waterfall approach
 Investment would be first utilized to meet the top priority goal
 Remaining assets would be used to fund the next priority goal
 And so on…

21. www.moneyshastra.com 21
Other important random factors considered in the model
 Change in correlation between asset returns under different regimes
 Tracking error: Standard deviation of alpha
 Transaction cost
 Taxation

22. www.moneyshastra.com
How does @Risk help us in this

23. www.moneyshastra.com 23
@Risk helps generate random numbers easily
 Different variables in our model follow different type of distributions
 For instance while asset returns may be assumed to follow normal distribution,
unexpected personal emergencies may follow a binomial distribution; the expense on
account of that may follow a discrete distribution
 While MS Excel has inbuilt functions to generate normal, lognormal and
uniform random distribution, other distributions require fair amount of effort
 @Risk’s distribution functions enable generate random numbers across several
distributions, easily

24. Risk optimizer gives us the brute force we need
 A model to optimize asset allocation requires that
several thousand trials are run with several asset
allocation
 Risk optimizer provides the ability to run several
thousand trials each involving several thousand
iterations in order to arrive at optimal asset allocation
 Most importantly, we find risk optimizer keeps the
system much more stable even as it runs several Ghz
of process

25. www.moneyshastra.com 25
Remember, before you close
When it comes to planning your finance, the
journey is long and the path is treacherous.
Being too careful can exhaust you and being
too careless can destroy you; it makes sense to
know the balance
Thank you