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Algorithm & Data Science

Education

Education

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Solution Business

Engineering

Education

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Product

PR&Mkt / Operations

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Robo-advisorPassive fund

As the “New Normal” continues, Robo-advisor is expected to become the leading force in the investment market

Source : 1) Based on Fount analysis, AT Kearney Financial Institutions report 2015, McKinsey Global Wealth Management Survey 2014

Changes in the global investment market trend

Period 1970s~2000s 2000s ~ 2014s Late 2010s ~

Market feature High economic growth Stagnant economic growth Intensified low growth market conditions

Invest. preference High return investment Low variance in investment Low variance/low cost investment

Key success factor Individual investment insights Economies of scale for cost competitiveness

Technological innovation for cost competitiveness

Leading playersIndividual investors

(e.g., Jim Rogers, Warren Buffett)Large sized invest. companies

(e.g., CAV, Vanguard)Small sized IT companies

Active fund-

As market conditions continue to diminish, stable, low cost incurring investments led by Robo-advisor will lead the investment trend

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Efficient Market Hypothesis (EMH)

Unlike technical analysis, Robo-advisor starts from the premise that the Market cannot be predicted

Technical analysis attempts to forecast the price

• Attempts to forecast the security's price by

identifying the patterns from the charts • Do not attempt to measure the intrinsic

value of the security; only use the past prices and values to find market timing

Source : 1) http://www.indexfund.co.kr/understand/back.asp

Robo advisor presupposes that market cannot be predicted

• Developed by Eugene Fama, a recipient of the Nobel Prize in Economic

Science in 2013 • Stocks always trade at the fair value, making it impossible for investors

to arbitrage • Thus, it is impossible to "beat the market" consistently on a risk-

adjusted basis

• Developed by Burton Malkiel, the CIO of Wealthfront in 1973 • Stock market prices evolve according to a random walk and thus cannot

be predicted • The correlation between the return on day t and day t+1 is very low (correlation coefficient of -0.07, based on 1984-2004 data1))

Random Walk Hypothesis

VS

11Source : 1) Gary P: Brinson, L. Randolph Hood, and Gilbert L. Beebow (1991), “Determinants of Portfolio Performance”, Financial Analysts Journal 47.3, 45,

Asset Allocationstock selectionmarket timingetc.

<Determinants of Portfolio Performance – US Pension Fund>1)

Correlation of assets

Robo-advisor enables advanced diversification of assets through real-time analysis and rebalancing

Asset Allocation is the key determinant of the portfolio performance

Robo-advisor allows advanced diversification of assets

• According to the research of Brinson, Hood&Beebow, Asset

allocation takes the most part of the portfolio performance, up

to 90% of the performance

• Market timing, stock selection ability influences less than 10%

90%

< Robo-advisor asset allocation process >

< Robo-advisor key strengths >

1. Real-time correlation analysis with vast database

2. Asset allocation rebalancing by consistent monitoring

3. Fearless, rational decision making

Asset attribute

Efficient allocation

finding

Portfolio Formation

• Instead of simply diversifying the assets, Robo-

advisor allocates them with instant, on-going

correlation and attribute analysis

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13

'30'13 '14 '15 '16 '17 '18 '19 '20 '21 '22 '23 '24 '25 '26 '27 '28 '29

'30 710B USD

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Korean economic trends intensifying low growth

Low interest rate

Low birth rate

Population ageing

High concentration on cash savings

Lack of financial advisory SVC

• Government can no longer fully cover public pension programs • Individuals are pushed to seek private wealth mgmt

Recent government actions to cope with the current economic conditions

• ISA(Individual Saving Account)

: Launching in Mar. 2016. Tax deferral effect

• Alleviating financial regulations

: Deregulation of financial Face-to-Face identification for

opening bank account

Source: Statistics Korea, Household Financial Asset Analysis(2013), Bank of Korea; Analysis on flow of funds (2014)

0.0

0.0

0.0

0.0

0.0

2016 2017 2018 2019 2020

44.0

12.9

3.7 1.0 0.3

< Korea Robo Market Size >

44.0

($B USD)

Robo Portion in WM

0.03% 0.1% 0.4% 1.3% 4.0%

• The landing of the new trend is facilitated in Korea due to the series of government actions

• Subsequently, the growth of the Robo-advisor market

is expected to be steeper in Korea than in any other Asian countries

In Korea, the market trend for Robo-advisor is expedited with its deteriorating market conditions and subsequent government support

Expected market growth for Robo-advisor in Korea

Stagnant growth

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16

17

18

0

0.5

1

1.5

2

1/2/20

06

5/30/2

006

10/30

/2006

3/28/2

007

8/24/2

007

1/25/2

008

6/27/2

008

11/21

/2008

4/21/2

009

9/14/2

009

2/9/20

10

7/8/20

10

12/2/

2010

5/2/20

11

9/29/2

011

2/24/2

012

7/24/2

012

12/18

/2012

5/21/2

013

10/21

/2013

3/19/2

014

8/18/2

014

1/19/2

015

6/18/2

015

11/13

/2015

fount KOSPI

Return rate ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 Avg.

annual

Fount 6% 5% 0% 10% 13% 8% 4% 9% 8% 4% 7%

KOSPI 11% 32% -39% 45% 21% -12% 9% -1% -3% 2% 4%

KOSPI比 -5% -27% 39% -36% -8% 20% -5% 9% 11% 2% 3%

7%

• Real-time analysis on global market data

• Automated asset allocation & diversification

With Big Data analysis & Machine learning

• Optimized allocation & diversification with

the work outs of expected rate of return, risk

of each asset, correlation of asset classes

The latest Finance Engineering Methodology

4%

1 Robust algorithm selected by proven clients

Stable growth even midst the market crisis with its technological competency

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0

0.5

1

1.5

2

2006

-1-2

2006

-5-1

6

2006

-9-2

7

2007

-2-1

2

2007

-6-2

7

2007

-11-

12

2008

-3-2

8

2008

-8-1

2

2008

-12-

23

2009

-5-1

1

2009

-9-1

6

2010

-1-2

8

2010

-6-1

4

2010

-10-

25

2011

-3-9

2011

-7-2

0

2011

-12-

1

2012

-4-1

6

2012

-8-2

8

2013

-1-1

1

2013

-5-2

7

2013

-10-

11

2014

-2-2

5

2014

-7-1

0

2014

-11-

25

2015

-4-1

0

2015

-8-2

4

Static-fee fount KOSPI

Return per annum

Volatility Sharp Ratio

Compared to high

Transaction Fee

Dynamic 6.99% 7.01% 0.997 -0.158 0.21%

Static 5.04% 6.83% 0.731 -0.177 0.13%

Automated dynamic rebalancing with real-time analysis on market changes

1 Robust algorithm selected by proven clients

Strong performance through its automated dynamic rebalancing technology

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21

22

23

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