Understandable Artificial Intelligence

A.I. In Action:

Evaluating Existing Fund

Overview

EDS was given a sample portfolio by a UK pension fund. Since only the asset weights were known, EDS tool deducted the investors' preferences and proceeded to analyze the holdings

The analysis was split into 3 stages:

Asset allocation within the sample fund
Incorporating other internally available funds
Opening the universe to external funds

Step 1: Only Sample Fund

Use only the 7 funds currently in the portfolio
Conclusion:
- Good current asset allocation
- Little value add from using EDS tool

*Data from EDS, St. James's Place, & Bloomberg, courtesy of London Business School

Step 2: Other Internal Funds

Increase universe to all approved internal funds

Fees are not a focus point

In contrast to other portfolios, income, growth, and off-shore bonds are not strong preferences

Step 2: Results Overview

EDS is expected to outperform existing allocation
Given the fund universe:
- Current fund selection is solid
- Some gains with EDS method

*Data from EDS, St. James's Place, & Bloomberg, courtesy of London Business School

Step 3: Opening to External Funds

Expand possible investments beyond existing funds
- Preference remains for approved client funds
Limit each fund to 20%
Focus on same preferences as the original portfolio

Step 3: Results - Risk/Return

EDS tool reviewed 2,743 potential U.K. funds

Original portfolio allocation shows good asset selection
Broader universe allows for more targeted risk taking
The average mixed allocation fund expects a 6.1% annual return with 13.8% S.D.

*Data from EDS, & Bloomberg, courtesy of London Business School

Step 3: Results Overview

Significant projected improvement using EDS tool

Higher expected return
- With lower standard deviation
Lower management fees

Higher dividend yield

Client approved funds comprise 68% of the total

Every metric can be flexed and adjusted

depending on individual preferences

Step 3: Results Overview - Returns

Significant projected improvement using EDS tool

Step 3: Results Overview - Risk

Where do these gains come from?

Lower risk exposure across most metrics
Significantly improved factor risk exposure and risk distribution

Step 3: Results Overview - Funds

Breakdown of selected funds

Each fund is capped at 20%
8 additions to original portfolio
Full flexibility around which funds and how many to include

*Data from EDS, St. James's Place, & Bloomberg, courtesy of London Business School

Step 3: Human Factor - EDS

We combine human analysis with A.I. and M.L.:

We maintain preference for client's funds

Original fund has a high return target

Our tool showed that fund holders were not getting paid sufficiently for the additional risk

All new funds are indexes, thereby lowering total fees
- For actively managed funds more indepth research regarding the fund, the team, the manager and the process would be needed

Adding diversification to the existing portfolio

*Data from EDS, St. James's Place, & Bloomberg, courtesy of London Business School

Better Performance

Tangible benefits to working with EDS tool

Past performance is not a reliable indicator of future results

*Data from EDS, St. James's Place, & Bloomberg, courtesy of London Business School

Conclusion

Client fund: good, but can be improved

Within the current list of client funds, existing allocation is done well
Expanding the universe can provide several benefits:
- Higher projected returns
- Lower overall market risk exposure
- More even factor risk exposure across factors
- Lower fees

Disclaimers

Please remember that past performance may not be indicative of future results. Different types of investments involve varying degrees of risk, and there can be no assurance that the future performance of any specific investment, investment strategy, or product made reference to directly or indirectly in this presentation, will be profitable, equal any corresponding indicated historical performance level(s), or be suitable for an individual's portfolio.

Our projections are based on current market conditions which can vary over the coming months and weeks. Additionally, our projections are based on historical market behavior which may vary unexpectedly. Using Machine Learning, our tool should adjust to new market fluctuations but we might not be able to avoid short term volatility.

Get In Touch

Back to the Main Presentation

See A.I. In More examples

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

info@EconomicDataSciences.com

www.EconomicDataSciences.com

Appendix

Risks by Geography

Risks by Type

Disclaimers

Get In Touch

info@EconomicDataSciences.com

www.EconomicDataSciences.com

info@EconomicDataSciences.com

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