Understandable Artificial Intelligence

A.I. In Action:

A Tax Sensitivity Example

Sample Investment Goals

©2018 Economic Data Sciences

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

©2018 Economic Data Sciences

  • 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


©2018 Economic Data Sciences

  • 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

©2018 Economic Data Sciences

  • 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

©2018 Economic Data Sciences

  • 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

See A.I. In More examples

  • Sustainable Investing (ESG)
  • Big Data Simulations And Forecasts
  • Factor Tracking Error Management




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

©2018 Economic Data Sciences

  • 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

©2018 Economic Data Sciences

Mapping Example: Tax Drag

©2018 Economic Data Sciences

  • 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

©2018 Economic Data Sciences

  • 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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Updated Tax Example

By Economic Data Sciences

Updated Tax Example

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