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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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'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
27