A.I. In Action:

A Tax Sensitivity Example

Sample Investment Goals

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For this example we use the following assumptions and goals:

 

• Mixed allocation of U.K. reporting funds
  • Mutual funds, UCIT, ETFs 5%
 
• Lower tax drag vs. average Mixed Allocation Fund
   
• Improve expected return and lower standard deviation
   
• Income and growth are equally important

Examine The Universe: Risk/Return

Our A.I. evaluated a universe of 2,743 funds

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• We highlight the Risk/Return trade-off with a focus on Mixed Allocation
  • The average Mixed Allocation fund expects a 6.1% return with 13.8% SD

Dividend/Growth

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• There is a negative relationship between income and growth
  • The average Mixed Allocation fund has a slight value tilt and ~1% dividend

Tax/Offshore Bond

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• There is a strong relationship between yield from offshore bonds and tax
  • The average Mixed Allocation fund enjoys a benefit of only 25bps from offshore bonds and a tax drag of close to 1%

Base Case Results Overview

We focus on 12 metrics in our base case

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• EDS base portfolio easily outperforms
 
• Metrics of interest
  • Each of these metrics is likely to be an important trade-off
  • We will highlight each and show how different preferences can be implemented

Results Review by Fund

We can break down portfolio decisions by any metric/group

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Results Review by Sector

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Get In Touch

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Back to the Main Presentation

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Appendix

Additional Details:

Explore Trade-offs

Using our example goals we have set out a neutral 'base' portfolio

We compare that to the average mixed allocation fund

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• Detailed and flexible inputs
  • The inputs are a result of our machine learning process
  • Can flexibly include any inputs you prefer
• Traditional foundations: Return and Standard Deviation
  • Return needs and risk tolerances will be specific to the investor
  • In this example, we have improved both metrics and so will use these preferences going forward
• Our first trade-off: The Sharpe Ratio
  • This is a traditional metric which measures how much return expectation the investor gets per unit of uncertainty they accept
  • This type of trade-off process is the same intuition we use across all metrics

Foundations To Frontiers

Building on traditional foundations we use the same trade-off methodology across all investor preferences to 'get the most' from each

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Mapping Example: Tax Drag

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• Offshore Bonds
  • 5% of the original investment can be taken without tax implications
• Capital Gains vs. Investment Income
  • Which type is more efficient will heavily depend on circumstance
  • For this example, we assume both income and capital growth are priorities
• Importance of turnover
  • Turnover causes gains to be realised and taxable
  • High turnover and some vehicles (mutual funds) can lead to unexpected gains
• Tax loss harvesting
  • When investments realise losses, they can be used to offset gains
  • This is a valuable tax efficient strategy
  • Tax loss harvesting can be carried out through a separately managed account (SMA) or within your own investment strategy

Additional Variations:

High Income and High Growth

High Income Preference

Exploring trade-offs allows us to express individual preferences

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• A High Income Preference
  • We can customize preferences to meet client needs
 
• Find the most efficient 'costs' to gain income
  • In this example we gain 1.2% in dividend yield
  • The 'costs' we incurred to achieve these are the mathematically defined minimum

High Growth Preference

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It is possible to express any type of preference among these 12, or any other combination that you desire

Overviewing Risk Metrics:

Risks Breakdown

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Risks by Geography

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More Detailed Exposure Breakdown

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