Ldi for retail investors using monte carlo

Tools and insights

LDI strategy for retail investors using Monte Carlo method

Viswanathan M B, FCA, CFA, FRM

Tools and insights

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.


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

www.moneyshastra.com

The story behind our approach


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


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


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


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


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


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

Asset Allocation

Equity

Gold

Debt

Goal

Retirement

Home purchase

World Tour

Year

2015

2016

2017

2018

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

IDEAL ASSET ALLOCATION PROBABILITY OF SUCCESS


(1,000.0)

(500.0)

-

500.0

1,000.0

1,500.0

2,000.0

2,500.0

3,000.020

15

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


Our approach


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


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


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


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


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


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


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


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…


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


How does @Risk help us in this


@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

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


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

Date post:	15-Apr-2017
Category:	Economy & Finance
Upload:	viswanathan-baskaran
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Ldi for retail investors using monte carlo

Economy & Finance