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Ldi for retail investors using monte carlo


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In this presentation we explain how MonteCarlo method can be used to decide on strategic asset allocation. We also explain MoneyShastra's unique approach to asset allocation that focuses on optimizing the investor's chances of meeting his/her goal without losing focus on increasing wealth

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Ldi for retail investors using monte carlo

  1. 1. Tools and insights LDI strategy for retail investors using Monte Carlo method Viswanathan M B, FCA, CFA, FRM Tools and insights
  2. 2. 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. 3. 3 About  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. 4. The story behind our approach
  5. 5. 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. 6. 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. 7. 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. 8. 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. 9. 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. 10. 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. 11. 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. 12. Our approach
  13. 13. 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. 14. 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. 15. 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. 16. 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. 17. 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. 18. 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. 19. 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. 20. 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. 21. 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. 22. How does @Risk help us in this
  23. 23. 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. 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. 25. 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