1. Quantitative Strategies
Market Risk, Asset Allocation, Security
Selection
MTA Educational Web Series
February 14, 2012
Kevin G. Hockert, CMT
Kevin@AskProspero.com

2. Quantitative Research
• formal systematic process in which numerical
data are used to obtain information about
securities and financial markets
• describe variables
• examine relationships among variables
• assess probabilities associated with cause and
effect interactions between variables

3. strategy development considerations
• know thyself
• time frame- frequency of trades
• absolute or relative returns
• drawdowns
• simplicity
• practicality
• sweetspot and limitations

4. Testing
• Hypothesis
• Test – in sample, out of sample
• Indicators
• Strategy

5. Quantitative Strategy
• quantitative process can improve ability to
collect, assimilate and apply information
• scientific repeatable process with definable
prospects
• methodical, systematic approaches to
managing market risk, allocating portfolio
capital and security selection
• reduce emotional and subjective components
of investment decision making process

6. Quantitative strategies
• Weekly Market Models:
• Trend and momentum
• Breadth (Nasdaq ADVOL, S&P 500 Weekly Adv/Dec., S&P Price Volume)
• Market Relationship Models (Intermarket)
• Event models – monthly reversals, historical overbought/oversold
readings, AAII sentiment
• Weekly and Monthly Portfolio Models:
• ETF style, sector, country rotation (relative strength & trend rank, score)
• Dedicated High Yield
• Dividend Aristocrats
• Nasdaq 100

7. Monthly Reversals
• Definition =
• Low of current month < prior monthly low
• Close of current month > close of prior month

8. Monthly Reversals
S&P 500
Monthly Candles
1400
1350
1300
1250
1200
1150
1100
1050
1000
950
900
9/1/2011 10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012

9. Results of 2 month hold
• Average Gain = 5.64%
• Average Loss = -2.69%
• Max Gain = 15.20%
• Max Loss = -10.57%
• Winning Periods = 58
• Losing Periods = 26
• Win % = 69.05%

10. 50
100
150
200
250
0
11/1/1950
11/1/1952
11/1/1954
11/1/1956
11/1/1958
11/1/1960
11/1/1962
11/1/1964
11/1/1966
11/1/1968
11/1/1970
11/1/1972
11/1/1974
11/1/1976
11/1/1978
11/1/1980
11/1/1982
11/1/1984
11/1/1986
1951-current
11/1/1988
11/1/1990
S&P 500 2 Month Hold
11/1/1992
11/1/1994
11/1/1996
11/1/1998
11/1/2000
11/1/2002
11/1/2004
11/1/2006
11/1/2008
11/1/2010

11. The magnitude of the October rally has made the history books as the 11th largest since 1950. Needless to say, it is likely
that U.S. equities will digest their recent gains over the next several days before attempting a move higher. Large rallies such as this
are rare and typically occur in prolonged downtrends. Results are mixed 1 to 3 months out unless we look at periods where trailing
12 month trailing returns are positive (like now) in which case returns are generally higher 2 to 3 months out.
12M 12M
Date Lo-Hi% ROC 1 M Out 2 M Out 3 M Out Date Lo-Hi% ROC 1 M Out 2 M Out 3 M Out
11/2/1987 51.96% 3.20% -8.53% -1.87% 2.10% 6/1/1970 19.98% -26.01% -5.00% 1.96% 6.49%
11/3/2008 38.97% -37.47% -7.48% -6.76% -14.75% 11/2/1998 19.54% 20.12% 5.91% 11.88% 16.47%
12/1/2008 35.96% -39.49% 0.78% -7.85% -17.98% 3/2/2009 19.13% -44.76% 8.54% 18.74% 25.04%
8/1/2002 28.20% -24.74% 0.49% -10.57% -2.84% 9/1/2011 18.69% 16.16% -7.18% 2.82% ?
6/1/1962 25.97% -10.41% -8.18% -2.35% -0.86% 11/1/2002 18.06% -16.42% 5.71% -0.67% -3.39%
4/1/2009 24.92% -39.68% 9.39% 15.20% 15.22% 10/1/2008 17.77% -23.61% -16.94% -23.16% -22.56%
11/1/1974 24.51% -31.76% -5.32% -7.23% 4.17% 9/1/1982 17.67% -2.67% 0.76% 11.89% 15.91%
10/1/2001 22.30% -27.54% 1.81% 9.46% 10.29% 2/2/2009 17.35% -40.09% -10.99% -3.39% 5.68%
4/1/1980 21.34% 0.49% 4.11% 8.96% 11.90% 4/2/2001 17.22% -22.57% 7.68% 8.23% 5.52%
9/3/1974 20.41% -30.79% -11.93% 2.43% -3.02% 9/1/1998 17.19% 6.43% 6.24% 14.77% 21.56%
11/1/2011 20.27% 5.92% ? ? ? 11/1/1982 16.85% 9.71% 3.60% 5.17% 8.66%

12. November 1, 2011
• Many strategists, have drawn comparisons to the May
2008 high which was also preceded by a 5 month
decline.
• There are several differences however: May of 2008
was not an upside monthly reversal
• The recent rally off of the October 4th, 1074 low has
retraced more than 61.8% of the decline from the 1370
high earlier this year while the rally off of the March
2008 low did not.
• Market internals (acceleration of advance vs. declines
and up volume vs. down volume) have achieved levels
typically associated with intermediate term advances

13. Monthly Reversals
• Simple pattern
• Rare occurrence (84 of past 720 months)
• Little or no interpretation

14. Market Risk
• Market trend and momentum
• Market Internals (breadth adv/dec., Hi-Low, %
of 10 and 30 Week)
• Market externals (intermarket relationships –
stocks vs. bonds, large vs. small, commodities
vs. bonds, high yield vs. treasury)

15. Advance Decline
• represents the amount of liquidity in the
markets
• one stock – one vote
• Nasdaq preferred over NYSE
• advance decline line – moves too slow
• acceleration/deceleration in advancing vs.
declining issues tends to expand and contract
representing “liquidity waves”

16. McClellan
• Sherman and Marion McClellan created the
McClellan Oscillator and Summation Index (son
Tom continues on tradition of innovative
indicator development)
• Oscillator – difference of the 10% and 5% trend of
advances minus declines divided by advances
plus declines
• Summation Index – addition of the daily oscillator
values

17. Nasdaq Summation Index
at a 20 day high and Nasdaq 50 day MA positive slope
Test Period Nasdaq Average 21 Day Return Strategy Average 21 Day Return
1978-2000 1.55% 1.88%
2000-current 0.09% 0.49%
1978 - current 1.03% 1.43%

18. Nasdaq Summation Index
800
600
400
200
0
-200
-400
-600
-800
-1000
-1200

19. Summation Index Observations
• indicator Peaks 4/26/2010, 1/18/2011
• indicator Bottoms 7/7/2010, 8/23/2011
• direction and level are important (short and
intermediate term signals)
• daily data has become more volatile
• consult weekly advance decline
• include volume

20. Asset Allocation
• investment strategy that attempts to balance risk and
reward by adjusting % of each asset in an investment
portfolio
• common asset classes – equities (large, mid, small) bonds
(government, corporate, high yield), REITS, commodities
(industrial and precious metals, grains, currencies)
• Core
• Tactical
• Satellite

21. Tactical Asset Allocation
• Active approach to positioning assets in a
portfolio
• Systematic allocation based on definable
metrics and rules
• Example: S&P 500, EAFE, REITS,10 Year
Bond, Commodity Index
• Considerations: Intermediate and long term
trend, also relative strength of each asset class
vs. S&P 500 and 10 year bond

22. Intermarket Anaysis
• John Murphy
• branch of technical analysis that examines
relationships between
stocks, bonds, commodities and currencies
• relationships contain important information
about the business cycle and hold important
implications for asset allocation

23. Relationship of Stocks and Interest Rates
Weekly returns - S&P 500 & 10 Year Yields
• Correlations • In most of history, rising
• 1960s -.050 rates have been
• 1970s -.288 associated with
declining stock prices
• 1980s -.278
• Structural changes in
• 1990s -.268 economy (borrowing
• 2000s .374 and demand for goods)
have caused
relationship to invert

24. S&P 500 and 10 Year Yields

25. Hypothetical results: Long SPY when intermediate term trend is positive AND
10 Year Yield trend is negative (1962-2011)
13000000
11000000
9000000
7000000
5000000
3000000
1000000

26. S&P 500 Trend up, yield trend up (green)
S&P 500 trend up, yield trend negative (blue)
14000000
13000000
12000000
11000000
10000000
9000000
8000000

27. Stocks vs. bonds
Relative Strength Line
SPY/IEF
1.50
1.45
1.40
1.35
1.30
1.25
1.20
1.15
1.10
1.05
1.00

28. Relationship of Stocks to Bonds
• began to tell us something in late spring/early
summer of 2011
• Rising bond prices (falling rates) indicated:
• Slowdown in economy
• Market participants shift from risk seeking to
risk avoidance or reduction

29. High Yield vs Treasury
Relative Strength Line
JNK/IEF
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15

30. High Yield bond characteristics
• a distinct asset class:
• due to higher coupon rates and shorter
maturities, high yield bond prices are less impacted
by interest rate changes than investment grade
corporate or treasury bonds
• low correlation to other financial asset classes
• sensitivity to business cycle and credit conditions
• tend to appreciate in economic expansions and
decline in periods of economic contractions

31. High Yield default rates
• According to Moody’s Investor Service
• 1933 = 15%
• 1990 = 10.1%
• 1991 = 10.4%
• 2001 = 10.6%
• 2009 = 13.7%

32. Advantages of High Yield
• high yield bonds have historically provided
investors with high current income
• capital appreciation potential
• low volatility relative to equities
• low correlation to other asset classes
• tends to exhibit excellent trend characteristics

33. Core and Satellite
• Core - dedicated allocation to core asset
classes
• Satellite - rotating allocation to leading asset
classes based on trend, trend momentum and
relative strength

34. Security Selection
• Trend
• Trend Momentum
• Relative Strength

35. Trend
• Moving averages –
simple, exponential, weighted
• Considerations - position of
short, intermediate, long term moving
averages

36. Trend Momentum
• acceleration/deceleration of price trend
• momentum tends to lead price
• best – improving trend, improving momentum
• divergence analysis

37. S&P 500 weekly price – blue
S&P 500 - trend momentum green
140 5
4
135
3
2
130
1
125 0
-1
120
-2
-3
115
-4
110 -5

38. Relative Strength
• performance of a security relative to:
• index or benchmark
• similar asset or group of similar assets
• different asset (stocks versus bonds)

39. Scoring System
• uniform way to objectively assess the merits
of buying, selling or holding a security at any
point in time
• score weights are based on trend, trend
momentum and relative strength
• metric weighting = 1 point if positive
• maximum score =7, minimum score = 0
• max score for SPY = 5, cannot achieve 2
additional from RS vs. itself

40. Scoring system
• Short term trend
• Intermediate term trend
• Long term trend
• Intermediate term momentum
• Long term momentum
• Intermediate term relative strength
• Long term relative strength

41. characteristics of scoring system
• Score = ideally 4 or higher (move from below 4
to above 4 is generally important
• Score direction – improving trend in score
tends to correlate with improving price trend
• Turnaround – a score of 0 that improves to 1
or 2 is indicative of improving trend
momentum (momentum precedes price)
• high score represents positive relative
strength

42. EFA price – green
EFA score - black
65 6
5
60
4
55
3
50
2
45
1
40 0

43. Equity Scores
• generally declining in May –July
• on 8.1.2011 equity ETFs lost a total of 7 points
combined
• began to improve in October - December

44. Bond ETF Scoring
• similar to equity ETF in trend and trend
momentum
• exception is relative strength: i.e. one metric
compares a bond ETF to an equity benchmark
(SPY)
• the other compares each bond ETF to a fixed
income benchmark – (IEF) iShares 7-10 year
Treasury
• max score = 7, exception is SPY and IEF as Max =
6, (each cannot score a point on RS vs. itself)

45. Bond ETF Universe
Date SPY IEF SHY AGG TLT TBT TIP LQD JNK PCY
5/31/2011 4 5 5 5 6 0 6 6 4 6
6/6/2011 3 5 5 5 6 0 6 6 4 6
6/13/2011 2 6 5 5 7 0 7 6 2 6
6/20/2011 2 6 5 6 7 0 6 6 3 6
6/27/2011 2 6 6 6 7 0 6 6 3 6
7/5/2011 2 6 5 6 7 0 7 5 4 6
7/11/2011 3 6 5 5 7 0 7 5 3 6
7/18/2011 3 6 5 5 5 1 7 5 3 6
7/25/2011 4 6 4 4 5 1 7 5 3 6
8/1/2011 4 6 4 5 6 1 7 6 5 6
8/8/2011 1 6 5 6 7 0 7 6 3 6

46. Bond ETF Universe
• generally rising in score in June and July (IEF
moved to a buy signal on 5.2.2011)
• scores of Bond ETFs higher than SPY (IEF’s max
score of 6 from 6.13.2011 through 10.17.2011
carried an important message)
• strategy metrics called for a tactical shift in
satellite allocations from equity to fixed income

47. Equity ETF Scores
SPY DJP EEM EFA GLD ICF IJH IJJ IJK IJR IJS IJT IVE IVW JJG QQQ SH UUP
11/21/2011 4 1 2 2 5 5 3 3 3 3 3 3 3 7 0 7 0 4
11/28/2011 2 1 2 2 4 3 2 2 2 2 2 2 2 5 0 6 1 6
12/5/2011 3 1 2 2 6 3 3 3 2 3 3 4 3 7 0 6 0 7
12/12/2011 4 1 2 2 5 3 3 3 3 5 5 7 3 7 0 6 0 7
12/19/2011 4 0 2 1 4 5 3 3 2 6 5 7 3 7 0 5 0 7
12/26/2011 5 0 2 0 2 7 3 4 3 7 6 7 4 6 0 3 0 7
1/2/2012 5 0 2 1 2 7 3 4 3 7 7 7 4 7 1 4 0 7
1/9/2012 5 1 1 1 2 7 4 4 3 7 7 7 6 6 2 5 0 7
1/16/2012 5 1 3 2 1 7 5 5 4 7 7 7 6 6 1 5 0 5
1/23/2012 5 1 3 3 1 7 5 6 5 7 7 7 6 6 2 6 0 5
1/30/2012 5 3 4 3 2 7 5 6 5 7 7 7 6 6 3 6 0 3
2/6/2012 5 3 4 4 4 7 6 6 5 7 7 7 6 5 3 7 0 3
2/13/2012 5 3 4 4 4 7 6 7 5 7 7 7 7 5 3 7 0 3

48. Equity ETF scores
• examples of a few recent buy signals
(crossover from below 4 to 4 or higher)
• 10.17.2011 – IVW
• 11.14.2011 “false signal”, 12.12.2011 SPY
• 12.5.2011 IJT
• 12.12.2011 IJR, IJS
• 1.30.2012 EEM, 2.6.2012 EFA

49. IJH (S&P 400 Mid Cap)
green >= 4, black <=3
120
100
80
60
40
20
0

50. Prospero Market and Portfolio
Strategy Report
• quantitative approaches to technical analysis
• market models
• portfolio models
• bridge gap between hiring 3rd party money
managers and allocating portfolios on your
own
• save time, money and improve risk adjusted
performance

51. Biography
• Kevin Hockert, CMT, is the Director of Strategies for Prospero Institute, Inc. an investment advisory
firm founded in 2005. Prospero delivers quantitative investment solutions to RIAs and portfolio
managers ranging from Barron’ Top 100 advisors to HNW wealth management firms. Several
proprietary rules based strategies have been developed that are designed to help financial advisors
and portfolio managers systematically allocate portfolios into and out of various asset classes. By
providing a broad spectrum of solutions ranging from market breadth models, broad asset
allocation, sector rotation, dedicated high yield, fixed income, alternative asset classes and
individual security selection, Prospero’s strategies are designed to help financial advisors bridge the
gap between hiring third party money managers and tackling the monumental task of managing
client assets on their own.
• Kevin is a 21 year veteran of the financial markets. He was awarded the CMT designation in 2008
and the culmination of his research at that time included his CMT 3 paper titled “Intermarket
Analysis and Dynamic Asset Allocation”. Kevin also serves as a Co-Chair of the Minnesota Chapter of
the Market Technicians Association.