Understandable Artificial Intelligence

Table of Contents

©2019 Economic Data Sciences

  1. Our Story
  2. The Problem
  3. EDS Solution
  4. EDS A.I. Implementation
  5. EDS Infrastructure
  6. The Market
  7. Financials and Unit Economics
  8. Funding Goals

Founders & Our Story

©2019 Economic Data Sciences

Revolutionizing Decision Making

©2019 Economic Data Sciences

Arising from inability to incorporate all inputs, simultaneously

EDS's success will enhance the business process across financial services -- leading to improved, personalized outcomes

EDS Software Solution - First of its Kind

EDS developed the first software to solve these problems

©2019 Economic Data Sciences

EDS A.I. Implementation

Machine Learning

Human + Machine Insight

Best Trade-offs In A Complex World

Broader, Deeper, More Detailed

Extending research from Stanford our modelling process flexibly incorporates data and checks for robustness

Combining the power of distributed computing, our A.I. solves problems simultaneously  by combining optimization with an approach inspired by genetic algorithms

Our custom built data infrastructure combines many of the latest open source foundations which we have extended to meet today's needs

©2019 Economic Data Sciences

Artificial Intelligence

Big And Fast Data

EDS A.I. Implementation

Software which incorporates more data and simultaneous decision making

©2019 Economic Data Sciences

On The Shoulders Of Giants

EDS's mathematical insights extend current best practices

\small Utility = \underbrace{\small \mu}_{\text{\tiny Return}} - \underbrace{\small \lambda \tiny 1 \small \cdot \sigma}_{\text{\tiny Variance}} - \underbrace{\small \lambda \tiny 2 \small \cdot TEV}_{\text{\tiny Tracking Error}} - \underbrace{\small \lambda \tiny 3 \small \cdot TFS}_{\text{\tiny Factor Sensitivity}} ...
  • Building on current methods adds to interpretability and transparency
  • Extendable frameworks allow inclusions from other models and insights
    • Tail-Risk, Diversification Ratio, Black-Litterman, etc.

1950s

1980s

2010s

An Extendable

Framework

EDS Tech - Best Possible Mix

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Cutting edge technology with focus on ease of interpretation

Where EDS Differs

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EDS improves results across dozens of metrics at the same time

The EDS Infrastructure

The EDS Infrastructure - Big Picture

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Cloud Resources

Seamlessly combine physical & cloud resources which can be dynamically managed

EDS Data Center

Combined Computing Power

Client Interface

EDS Technology Stack

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Scala

Zookeeper

Principals

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  • Asynchronous

    • Actions occuring at the same time, in any order, without waiting on each other

    • Current solutions are sequence oriented

  • Distributed

    • Connecting many smaller systems to work together

    • Current solutions grow by sequencing faster

  • Modular

    • This principal means components can be easily repurposed

    • As opposed to current monolithic designs

EDS infrastructure design is driven by 6 key principles

Principals (cont)

©2018 Economic Data Sciences

  • Scalable and Redundant

    • Built expecting failure and can easily 'drop-in' new resources live

    • Current solutions would need to be shut down and migrated

  • Analysis Focused not Storage Focused

    • We store data multiple times, in multiple forms, focused on insight

    • Current solutions minimize storage cost, but make insight costly

  • Flexible Data Consumption

    • Takes all data comers, SQL, NoSQL -- structure, unstructure

    • Current solutions have strict data structure requirements

These principals represent a revolution in solution design

The EDS Infrastructure - Components

©2018 Economic Data Sciences

Each component of the technology stack leads towards our infrastructure principals and a more effective solution

Cassandra Database Meets All Puts Data of Any Kind Closer To Analysis For Speed and Flexibility
Hadoop Distributed Disk Meets All Quickly Consumes Any Type of Data
Mesos Scheduler Meets All Coordinates Tools for 'Drop-in' Flexibility
Zookeeper Failover Coordinator Meets All Coordinates Redistribution of 'Duties' During Failures Or When Connections Are Dropped
Spark, Scala, and R Analytics Combined -- Meets All When Combined, Tools Provide Analytics In Ways That Align With Principals
Play Front-End Meets All Visualizes to client and collects client information
Component Purpose Principal What It Does

The EDS Infrastructure - Security

©2018 Economic Data Sciences

  • Point to point encryption over the network
    • Data is never exposed without encryption
  • Client to server encryption
    • In line with best practices at banks. Each client session is encrypted regardless of where the client is connecting from
  • Cloud compute not cloud storage
    • We use the cloud for compute power not storage, we control the physical location of each data point
  • Multi-tiered security layers
    • Designed with multiple layers of security so each must be compromised separately

Always at the top of our list

EDS Infrastructure - Summary

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  • Scale and speed
    • Add nearly an unlimited number of 'factors' - qualitative/quantitative
    • Can run in seconds/minutes and so could be done 'live' for clients
    • Each analysis is applied for all
  • Data communication - goodbye to silos
    • Each part of the business process can share information
  • More in depth client feedback
    • Better understanding of client interest

These features make possible real advances 'on the ground'

Current Industry Focus: Finance

Portfolio optimization, white labeling new investing products, risk management, manager selection, scenario analysis

>$100B Addressable Market Spend

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In finance - just one of the potential applicable industries

EDS Operates High Margin SaaS Model

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High ACV clients - all profitable

Tangible Benefits to Working With EDS

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EDS analysis has made immediate improvements in 100% of cases

*Past performance is not a reliable indicator of future results, yearly performance breakout in the appendix

Completed 21 pilot projects

Appeal Across Geographies and Types

>3 years of institutional use of math and process Wide adoption by a $1.4T AUA firm

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Financial Projections

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Significant revenue growth expected over the next 2-3 years

Funds Raising

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Ready to go-to-market product following 7 years of R&D

Examples

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Click to learn more

Appendix

Case Study 1

Portfolio Optimization

Overview

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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 following preferences were deducted:

  1. Main focus was return
  2. Fees and volatility were less critical

Solution Using Client Universe

Universe included c.40 funds

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EDS is expected to outperform existing allocation:

  • Higher expected return

  • Higher Sharpe Ratio

  • Lower volatility

Opening to External Funds

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  • 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

Results - Risk/Return

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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.

Results Overview - Funds

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EDS recommended the following holdings

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

Results Overview

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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

Results Overview - Risk

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Where do these gains come from?

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

Actual Results Since Recommendation

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EDS recommendation delivered superior results

*Past performance is not a reliable indicator of future results

Case Study 2

Exposure to China vs. downside risk

Overview

©2019 Economic Data Sciences

Goal: Modify a 60-40 equity-fixed income portfolio to adjust risk

We show how the software iterates through portfolios and provides different options, depending on Client preferences

The Client had several simultaneous goals:

  1. Increase exposure to China
  2. Maintain all other factors relatively stable
  3. We assume Chinese benchmark has 0 tail risk

Results - Key Takeaways

©2019 Economic Data Sciences

  1. The analysis can be done in reverse as well:
    • We tested by how much tail risk would rise
    • With a less "generic" initial portfolio, we could do the opposite
  2. If desired, EDS software can increase hedge to China
  3. Exposure to China and tail risk are clearly related
  4. Each solution represents the best trade offs given these parameters
  5. Results were analyzed using monthly data going back to 2005

Results

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Increasing exposure to China increase tail risk by 2.7%

List of Funds for 3 Select Portfolios

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Portfolio 11 had the highest expected return and standard deviation

Number of funds or desired minimum/maximum fund weight can be modified

List of Funds for 3 Select Portfolios

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Portfolio 7 had the highest expected Sharpe Ratio

List of Funds for 3 Select Portfolios

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Portfolio 1 had the highest exposure to China

Recommendations

©2019 Economic Data Sciences

  1. Each portfolio represents optimal solution in its own right
  2. The "best" portfolio depends entirely on client preferences
  3. EDS believes that maximizing Sharpe Ratio is preferable, so would recommend portfolio #7

EDS advice based on the data:

info@EconomicDataSciences.com

FT March 2019

By Economic Data Sciences

FT March 2019

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