2. Page | 2
EXAMPLE 1
Create a portfolio of 70-100 stocks from Global developed markets, choosing from an IRP’s
(here it is MorningStar) actively researched 1700+ stocks.
Risk Profile: Target Return 12-15%, Draw-down Tolerance 6%, Vol Target: 8%-10%
Overall Structure: Market Neutral, Net Exposure +/- 10%, Gross Exposure 150-200%
Sector Limits: No GICS sector more than 40% gross , Sector Net Exposures limited -10% to +10%
Rebalance: Monthly
Constraint Table:
Optional Inputs 2: Group level Constraints
Sub Portfolio Gross and Net Limits
Category Group minNet maxNet minGross maxGross
Sector Information Technology-10.0% 10.0% 0.0% 40.0%
Sector Industrials -10.0% 10.0% 0.0% 40.0%
Sector Financials -10.0% 10.0% 0.0% 40.0%
Sector Health Care -10.0% 10.0% 0.0% 40.0%
Sector Consumer Discretionary-10.0% 10.0% 0.0% 40.0%
Sector Materials -10.0% 10.0% 0.0% 40.0%
Sector Utilities -10.0% 10.0% 0.0% 40.0%
Sector Consumer Staples -10.0% 10.0% 0.0% 40.0%
Sector Telecommunication Services-10.0% 10.0% 0.0% 40.0%
Sector Energy -10.0% 10.0% 0.0% 40.0%
Portfolio Portfolio -10.00% 10.00% 150.00% 200.00%
3. Page | 3
BACK TESTED RESULTS FOR EXAMPLE 1: BETTER SHARPE FROM OPTIMISED PORTFOLIO
ORS Set Compliance with Constraints: 100%
Original Set Compliance with Constraints: 87%
80
130
180
230
280
330
380
1-Jan-07
1-May-07
1-Sep-07
1-Jan-08
1-May-08
1-Sep-08
1-Jan-09
1-May-09
1-Sep-09
1-Jan-10
1-May-10
1-Sep-10
1-Jan-11
1-May-11
1-Sep-11
1-Jan-12
1-May-12
1-Sep-12
1-Jan-13
1-May-13
1-Sep-13
1-Jan-14
1-May-14
1-Sep-14
1-Jan-15
1-May-15
1-Sep-15
Cumulative P&L, Equally Sized and ORS sized with only
GICS sector constraints +/- 10%
ORS Sized, Sharpe = 125%
Equally Sized Portfolio,
Sharpe=95%
4. Page | 4
EXAMPLE 2
Same base case, but add constraints to ensure the portfolio is geographically spread and has
very low net Market Cap or PE Ratio Factor exposure.
Constraint Set:
Optional Inputs 2: Group level Constraints
Sub Portfolio Gross and Net Limits
Category Group minNet maxNet minGross maxGross
Sector Information Technology -10.0% 10.0% 0.0% 40.0%
Sector Industrials -10.0% 10.0% 0.0% 40.0%
Sector Financials -10.0% 10.0% 0.0% 40.0%
Sector Health Care -10.0% 10.0% 0.0% 40.0%
Sector Consumer Discretionary -10.0% 10.0% 0.0% 40.0%
Sector Materials -10.0% 10.0% 0.0% 40.0%
Sector Utilities -10.0% 10.0% 0.0% 40.0%
Sector Consumer Staples -10.0% 10.0% 0.0% 40.0%
Sector Telecommunication Services-10.0% 10.0% 0.0% 40.0%
Sector Energy -10.0% 10.0% 0.0% 40.0%
Area US -10.0% 10.0% 50.0% 125.0%
Area EU -10.0% 10.0% 0.0% 40.0%
Area CN -10.0% 10.0% 0.0% 40.0%
Area JP -10.0% 10.0% 0.0% 10.0%
Area AU -10.0% 10.0% 0.0% 10.0%
Area LA -10.0% 10.0% 0.0% 10.0%
Area IN -10.0% 10.0% 0.0% 10.0%
Area AF -10.0% 10.0% 0.0% 10.0%
Area AS -10.0% 10.0% 0.0% 10.0%
Mkt_Cap Mkt_Cap (5,000) 5,000 44,000 64,000
PE_Ratio PE_Ratio (5) 5 50 80
Portfolio Portfolio -10.00% 10.00% 150.00% 200.00%
Market Cap and PE ratio are
Factors that have ‘loadings’ for
each stock.
The portfolio is constructed to
have the weighted sum of the
loadings constrained.
In this case we have set the
Portfolio Net PE Ratio to be
centred on and close to zero,
ensuring the portfolio is not
just a play on high PE vs low PE
stocks.
5. Page | 5
RESULTS : PORTFOLIO CONSTRAINTS REDUCE RETURNS, BUT ALPHA IS PRESERVED
Note that there are some dates on which the constraints are impossible, for which ORS
minimizes the errors. The equally weighted portfolio is never compliant with the constraints.
ORS Set Compliance with Constraints: 79%
Original Set Compliance with Constraints: 0%
50
75
100
125
150
175
200
225
250
275
300
1-Jan-07
1-Apr-07
1-Jul-07
1-Oct-07
1-Jan-08
1-Apr-08
1-Jul-08
1-Oct-08
1-Jan-09
1-Apr-09
1-Jul-09
1-Oct-09
1-Jan-10
1-Apr-10
1-Jul-10
1-Oct-10
1-Jan-11
1-Apr-11
1-Jul-11
1-Oct-11
1-Jan-12
1-Apr-12
1-Jul-12
1-Oct-12
1-Jan-13
1-Apr-13
1-Jul-13
1-Oct-13
1-Jan-14
1-Apr-14
1-Jul-14
1-Oct-14
1-Jan-15
1-Apr-15
1-Jul-15
1-Oct-15
Cumulative P&L, Equally Sized and ORS sized with further constraints on
Geography, PE Ratio and Market Cap
ORS Sized, Sharpe = 121%
Equally Weighted Portfolio, Sharpe=95%
6. Page | 6
EXAMPLE 3
This is the same Geographical and Sector Constraints, but optimised to have a negative
correlation to the S&P index. This is a portfolio constructed to have a specific risk attribute.
Constraints Set:
Optional Inputs 2: Group level Constraints
Sub Portfolio Gross and Net Limits
Category Group minNet maxNet minGross maxGross
Sector Information Technology-10.0% 10.0% 0.0% 40.0%
Sector Industrials -10.0% 10.0% 0.0% 40.0%
Sector Financials -10.0% 10.0% 0.0% 40.0%
Sector Health Care -10.0% 10.0% 0.0% 40.0%
Sector Consumer Discretionary-10.0% 10.0% 0.0% 40.0%
Sector Materials -10.0% 10.0% 0.0% 40.0%
Sector Utilities -10.0% 10.0% 0.0% 40.0%
Sector Consumer Staples -10.0% 10.0% 0.0% 40.0%
Sector Telecommunication Services-10.0% 10.0% 0.0% 40.0%
Sector Energy -10.0% 10.0% 0.0% 40.0%
Area US -10.0% 10.0% 0.0% 150.0%
Area EU -10.0% 10.0% 0.0% 40.0%
Area CN -10.0% 10.0% 0.0% 40.0%
Area JP -10.0% 10.0% 0.0% 10.0%
Area AU -10.0% 10.0% 0.0% 10.0%
Area LA -10.0% 10.0% 0.0% 10.0%
Area IN -10.0% 10.0% 0.0% 10.0%
Area AF -10.0% 10.0% 0.0% 10.0%
Area AS -10.0% 10.0% 0.0% 10.0%
SPXIndex SPXIndex (40) (20) - 100
Portfolio Portfolio -20.00% 20.00% 150.00% 200.00%
We set the desired Correlation
to SPX of the whole portfolio to
be between -20% and -40%,
and pass to the optimiser the
correlation of each stock.
7. Page | 7
RESULTS: NEGATIVE CORRELATION ACHIEVED, SLIGHT COST IN RETURNS.
ORS Set Compliance with Constraints: 82%, Correl to S&P -23%
Original Set Compliance with Constraints: 0%, Correl to S&P -6%
50
75
100
125
150
175
200
225
250
275
300
Date
30-Mar-07
29-Jun-07
28-Sep-07
31-Dec-07
31-Mar-08
30-Jun-08
30-Sep-08
31-Dec-08
31-Mar-09
30-Jun-09
30-Sep-09
31-Dec-09
31-Mar-10
30-Jun-10
30-Sep-10
31-Dec-10
31-Mar-11
30-Jun-11
30-Sep-11
30-Dec-11
30-Mar-12
29-Jun-12
28-Sep-12
31-Dec-12
28-Mar-13
28-Jun-13
30-Sep-13
31-Dec-13
31-Mar-14
30-Jun-14
30-Sep-14
31-Dec-14
31-Mar-15
30-Jun-15
30-Sep-15
Cumulative P&L for Equally weighted and ORS Portfolios,
optimised to have a Negative Correlation to the S&P Index
ORSSized, Sharpe = 93% Correl to S&P = -23%
Equally Weighted Portfolio, Sharpe=95% Correl to
S&P=-6%
SPX
8. Page | 8
EXAMPLE 4
Here we do the same example, but with a required positive correlation to the S&P
Constraints Set:
Optional Inputs 2: Group level Constraints
Sub Portfolio Gross and Net Limits
Category Group minNet maxNet minGross maxGross
Sector Information Technology-20.0% 20.0% 0.0% 40.0%
Sector Industrials -20.0% 20.0% 0.0% 40.0%
Sector Financials -20.0% 20.0% 0.0% 40.0%
Sector Health Care -20.0% 20.0% 0.0% 40.0%
Sector Consumer Discretionary-20.0% 20.0% 0.0% 40.0%
Sector Materials -20.0% 20.0% 0.0% 40.0%
Sector Utilities -20.0% 20.0% 0.0% 40.0%
Sector Consumer Staples-20.0% 20.0% 0.0% 40.0%
Sector Telecommunication Services-20.0% 20.0% 0.0% 40.0%
Sector Energy -20.0% 20.0% 0.0% 40.0%
Area US -20.0% 20.0% 50.0% 150.0%
Area EU -20.0% 20.0% 0.0% 40.0%
Area CN -20.0% 20.0% 0.0% 40.0%
Area JP -20.0% 20.0% 0.0% 10.0%
Area AU -20.0% 20.0% 0.0% 10.0%
Area LA -20.0% 20.0% 0.0% 10.0%
Area IN -20.0% 20.0% 0.0% 10.0%
Area AF -20.0% 20.0% 0.0% 10.0%
Area AS -20.0% 20.0% 0.0% 10.0%
Mkt_Cap Mkt_Cap (20,000) 20,000 - 100,000
RatesRisk RatesRisk (20) 20 (200) 200
CCYRisk CCYRisk (20) 20 (200) 200
VixRisk VixRisk (20) 20 (200) 200
SPXIndex SPXIndex 10 30 (200) 200
PE_Ratio PE_Ratio (20) 20 50 80
Portfolio Portfolio -10.00% 10.00% 150.00% 200.00%
We set the desired Correlation
to SPX of the whole portfolio to
be between +10% and +30%,
and pass to the optimiser the
correlation of each stock.
9. Page | 9
RESULTS SET: POSITIVE CORRELATION WHILST MARKET NEUTRAL AS DESIRED
ORS Set Compliance with Constraints: 92%, Correl to S&P = 37%
Original Set Compliance with Constraints: 0%, Correl = 1%
50
75
100
125
150
175
200
225
250
275
300
Date
30-Mar-07
29-Jun-07
28-Sep-07
31-Dec-07
31-Mar-08
30-Jun-08
30-Sep-08
31-Dec-08
31-Mar-09
30-Jun-09
30-Sep-09
31-Dec-09
31-Mar-10
30-Jun-10
30-Sep-10
31-Dec-10
31-Mar-11
30-Jun-11
30-Sep-11
30-Dec-11
30-Mar-12
29-Jun-12
28-Sep-12
31-Dec-12
28-Mar-13
28-Jun-13
30-Sep-13
31-Dec-13
31-Mar-14
30-Jun-14
30-Sep-14
31-Dec-14
31-Mar-15
30-Jun-15
30-Sep-15
CumulativeP&L for Equally weighted and ORSPortfolios,
optimisedto have a Positive Correlationto the S&P Index
ORS Sized, Sharpe = 101% Correl to SPX = 37%
Equally Weighted Portfolio, Sharpe=95%Correl to S&P=-1%
SPX
10. Page | 10
EXAMPLE 5 JAPAN DOMESTIC SALES GROWTH STRATEGY: THEMATIC INVESTMENTS
Create a portfolio, Long/Short, correlated to easily hedgeable indices (N225/ Topix), but
uncorrelated to USDJPY. Select from stocks with high domestic sales growth.
Here we ignore the sector limits, and focus on resulting portfolio correlations.
Given the constraints, we cannot get the exact target correlations, so we find the best possible
match.
Category Group minNet maxNet minGross maxGross
MktCap MktCap (1,500) 1,500 - 2,872
USDJPY Correl USDJPY Correl 0.0% 25.0% -100.0% 500.0%
N225 Index Correl N225 Index Correl 50.0% 80.0% -100.0% 500.0%
SnP Correl SnP Correl 0.0% 100.0% -100.0% 500.0%
Topix Correl Topix Correl 50.0% 80.0% -100.0% 500.0%
Borrow Cost Borrow Cost -10.0% 10.0% -100.0% 2.5%
Portfolio Portfolio 50.0% 80.0% 100.0% 150.0%
Category Net Gross Num LongNum ShortCompliance
MktCap 435 940 25 12 TRUE
USDJPY Correl 10.3% 23.4% 25 12 TRUE
N225 Index Correl39.3% 90.1% 25 12 FALSE
SnP Correl 3.9% 9.1% 25 12 TRUE
Topix Correl 40.8% 92.7% 25 12 FALSE
Borrow Cost -0.7% 0.7% 25 12 TRUE
Portfolio 78.0% 150.0% 25 12 TRUE
11. Page | 11
RESULTS SET 5:
80.00
100.00
120.00
140.00
160.00
180.00
200.00
220.00
240.00
22-Nov-13 2-Mar-14 10-Jun-14 18-Sep-14 27-Dec-14 6-Apr-15 15-Jul-15 23-Oct-15 31-Jan-16 10-May-16
RebasedReturns, ORS Portfolio, and CorrelatedIndices
Return
NKY Index
TPX Index
USDJPY curncy
13. Page | 13
SUMMARY
You can build Indices, strategies and products where the underlying assets are selected from
independent research (or other alpha generative filter), and the portfolio characteristics are
driven by the a “CIO’s” thematic overviews and the “Client’s” risk tolerances.
You create ‘Smart-Alpha’ Portfolios.
The assets are selected using research, and the Alpha inherent in this process is
preserved, and even enhanced and then customised to fit the overview and risk.
You express a house view, define Risk, Sector, Factor, and per-Asset exposure limits and
constraints.
Any attribute that is measurable against an asset can be constrained within the strategy.
The resulting portfolio can be used to build products that match a house view, have any
desired risk/return characteristics, and be investable given inputs on per-asset constraints.
ORS CREATES PORTFOLIOS THAT BEHAVE AS YOU WANT
