The Clear
Alternative
CE Quantitative Models –
Exploring the Application of Counter-Trend Strategies
For Investment Professional Use Only
Agenda
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
Defining Counter-Trend models
How Counter-Trend works
Discover market environment factors that influence performance
Exploring the environments that are most effective for Counter-Trend (and least)
Application to Managed Futures
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For Investment Professional Use Only
Trend Following
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For Investment Professional Use Only
Trend Following

Trend Definition – In general, a trend following system aims to invest in the direction
of the trend

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Most often describe moving average crossover
Short term, medium term, and/or long term
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For Investment Professional Use Only
Trend Following
Characteristics
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Reactionary
Hit Ratio – 25% - 40%
Can give back gains at turning points (Whipsaw)
Performs well in long trends
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For Investment Professional Use Only
Trend Following


Crossover Model Example – 30/120 day moving average
If 30 day moving average is HIGHER than 120 day moving average, then model would
take a long position
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For Investment Professional Use Only
Trend Following
Short
Long
Past performance is not a guarantee of future results. Unlike investments, indices are unmanaged and do not incur management fees or
charges; it is not possible to invest in an index.
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For Investment Professional Use Only
Counter-Trend
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For Investment Professional Use Only
Counter-Trend (Cont.)

Definition – Majority of models are looking to sell over bought levels and buy
oversold

Mean Reversion

Shorter Term
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Counter-Trend (Cont.)
Characteristics

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Reactionary
Hit Ratio – 55% - 70%
Gains come at inflection points
Performs well in choppy, “noisy” markets
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For Investment Professional Use Only
Counter-Trend (Cont.)


Crossover Model Example – 10/30 day moving average
If 10 day moving average is HIGHER than 30 day moving average, then model
would take a short position
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For Investment Professional Use Only
How Counter-Trend Works
Case Study
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For Investment Professional Use Only
Case Study
Simple Case Study Rules (Cont.)

Two Models Examined
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Simple Trend (Momentum) Model

Buy (Long Exposure) after a ten day high is realized

Sell (Short Exposure) after ten day low is realized
Simple Counter-Trend Model

Buy (Long Exposure) after a ten day low is realized

Sell (Short Exposure) after a ten day high is realized
Holding periods are fixed for both Models
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For Investment Professional Use Only
Case Study
Simple Case Study Rules


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S&P 500 January 1, 1990 to December 31, 2011
5547 Trading Days
S&P had a total return of 468.10%
Past performance is not a guarantee of future results. Unlike investments, indices are unmanaged and do not incur management fees or
charges; it is not possible to invest in an index.
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For Investment Professional Use Only
Case Study
Table 1: Short Term Momentum Model v. Short Term Counter-Trend Model on the S&P 500
from 1/1/1990 to 12/31/2011
Past performance is not a guarantee of future results. Unlike investments, indices are unmanaged and do not incur management fees or
charges; it is not possible to invest in an index.
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For Investment Professional Use Only
Case Study
Table 2: Annual Performance Summary of 10 Day Counter-Trend Model with 1 Day Holding
Period from 1/1/1990 to 12/31/2011
Past performance is not a guarantee of future results. Unlike investments, indices are unmanaged and do not incur management fees or
charges; it is not possible to invest in an index.
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For Investment Professional Use Only
Market Environment Factors
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For Investment Professional Use Only
Volatility and Noise
Series 1 – Volatility 21%
Series 2 – Volatility 16%
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For Investment Professional Use Only
What is Noise?
𝑁
𝑖=1 𝑅𝑒𝑡𝑢𝑟𝑛[𝑖]

𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =

𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =

% 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =

𝑁𝑜𝑖𝑠𝑒 = 100% − % 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡
𝑁
𝑖=1 𝐴𝐵𝑆(𝑅𝑒𝑡𝑢𝑟𝑛
𝑖)
𝐴𝐵𝑆 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡
𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡
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For Investment Professional Use Only
What is Noise? – Numerical Example

𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =
𝑁
𝑖=1 𝑅𝑒𝑡𝑢𝑟𝑛[𝑖]
𝑁
𝑖=1 𝐴𝐵𝑆(𝑅𝑒𝑡𝑢𝑟𝑛 𝑖 )
𝐴𝐵𝑆 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡
𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =
𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡

𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =

% 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙

𝑁𝑜𝑖𝑠𝑒 = 100% − % 𝑁𝑒𝑡 𝐷𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑀𝑜𝑣𝑒𝑚𝑒𝑛𝑡
Assumptions – Market is down a total of -2% over a ten day period (sum).
Market path is as follows:
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Day 9
Day 10
- 1.0%
-1.0%
.5%
1.0%
-1.0%
1.0%
-1.0%
1.0%
-2.0%
.5%
Total Movement = 10%
% Net Directional Movement = ABS (-2%)/10% = 20%
Noise = 1 - 20% = 80%
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For Investment Professional Use Only
Exploring Environments
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For Investment Professional Use Only
What Characterizes a “Noisy” Market?
Noisy Market profile:

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Market participants have differing opinions
Market participants must be able to “vote” or express their opinion
Barriers to entry: low

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Cost of Trade
Speed of Trades
Free from centralized control
Liquidity
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For Investment Professional Use Only
The Data

Two sets of data explore the presence of “Noise”

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1926 – 1996
1997 – 2013
Data from 1997 – 2013 used for environment expectations
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Structural changes in market beginning in 1997
All projections are subject to change if adverse structural market changes exist
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For Investment Professional Use Only
Why look at data starting in 1997?
Key structural changes:

September 9, 1997 - The E-mini S&P 500 Futures Contract was introduced by the Chicago Mercantile
Exchange, greatly increasing the liquidity and activity of equities futures trading.

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Dollar volume increased 8.5x the 5 years proceeding September of 1997 compared to the 5 years preceding the
advent of the E-mini contracts
1997 to 2000 - In concert with the dot-com bubble, online trading and day trading became exponentially
more popular.
August 2000 - Regulation Fair Disclosure was put into effect by the U.S. Securities and Exchange
Commission, all but eliminating the legal information edge of large institutional investors over others.
This regulation increased trading smaller money management firms.
April 9, 2001 - Conversion to decimalization for U.S. equities was completed, which significantly
reduced trading costs and increased the liquidity of many stocks because of tighter bid/ask spreads.
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For Investment Professional Use Only
Monthly “Noise”


1926 – 1996 was 73.63%
1997 – 2013 was 78.62%
18%
16%
73.63%
78.62%
14%


Frequency
12%
The majority of the observed months
showed “Noise” ranging between
60% - 90%
10%
8%
6%
4%
Further – a two sample test of the
two time frames’ average noise yielded
a t-statistic of 3.54 at the
99.96% confidence level
2%
0%
Noise
Distribution of Monthly Noise 1997 to 2013
Avg. Noise 1997 to 2013
Avg. Noise 1928 to 1996
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For Investment Professional Use Only
Volatility and Noise Quadrants
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Quadrant 1: Low Volatility & Low Noise (Q1: LVLN)
Quadrant 2: High Volatility & Low Noise (Q2: HVLN)
Quadrant 3: Low Volatility & High Noise (Q3: LVHN)
Quadrant 4: High Volatility & High Noise (Q4: HVHN)
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For Investment Professional Use Only
Volatility and Noise Quadrants
Volatility
Low: <20%
High: >=20%
Low: <80%
Q1: LVLN
Q2: HVLN
High: >=80%
Q3: LVHN
Q4: HVHN
Noise
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For Investment Professional Use Only
Volatility and Noise Quadrants 1926 -1996
Percentage of
Time Spent
Volatility
Low
High
Total
Low
48.43%
8.94%
57.37%
High
34.90%
7.73%
42.63%
Total
83.33%
16.67%
Noise
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For Investment Professional Use Only
Volatility and Noise Quadrants 1997 - 2013
Percentage of
Time Spent
Volatility
Low
High
Total
Low
37.75%
9.80%
47.55%
High
36.27%
16.18%
52.45%
Total
74.02%
25.98%
Noise
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For Investment Professional Use Only
Volatility and Noise Quadrants 1997 - 2013
100%
90%
80%
% of Time in Quadrents
70%
60%
50%
40%
30%
20%
10%
0%
Year
Q1: LVLN
Q2: HVLN
Q3: LVHN
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Q4: HVHN
For Investment Professional Use Only
S&P Performance by Quadrant 1997 - 2013
Q1: LVLN
Q2: HVLN
Q3: LVHN
Q4: HVHN
64.56%
12.88%
71.51%
28.81%
90.12%
13.89%
89.99%
32.26%
2.28%
-1.17%
0.25%
-1.39%
4.44%
8.76%
1.57%
4.91%
31.11%
-13.20%
3.06%
-15.49%
8.92%
-9.12%
76.62%
10.93%
-14.46%
45.00%
4.00%
-3.10%
63.51%
8.76%
-16.79%
39.39%
77
1.71
7.00
20
1.33
3.00
74
1.80
6.00
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1.50
6.00
Statistic
Environmental
Avg. Noise
Avg. Volatility
Performance
Avg. Monthly Return
Std. Dev. of Monthly Returns
Annualized Return Expectation
Max Monthly Return
Min Monthly Return
% of Positive Months
Duration
# of Months
Avg. Consecutive Months
Max Consecutive Months
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For Investment Professional Use Only
Predictability of Noise 1997 - 2013
Probability of transition
From Q1 From Q2 From Q3 From Q4
To Q1: LVLN
42.11%
20.00%
43.24%
24.24%
To Q2: HVLN
2.63%
25.00%
6.76%
24.24%
To Q3: LVHN
40.79%
20.00%
44.59%
18.18%
To Q4: HVHN
14.47%
35.00%
5.41%
33.33%
Monthly Autocorrelations
1928-1996
1997-2013
Volatility
73.50%
73.55%
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Noise
-11.11%
-14.69%
For Investment Professional Use Only
Simple Counter-Trend Performance Quadrant:
1997 - 2013
Q1: LVLN
Q2: HVLN
Q3: LVHN
Q4: HVHN
Avg. S&P 500 Monthly Return
2.28%
-1.17%
0.25%
-1.39%
Avg. STCTS Monthly Return
-0.10%
-0.49%
1.19%
2.55%
Std. Dev. of Monthly Returns
1.99%
4.16%
1.48%
4.88%
Annualized Return Expectation
-1.16%
-5.77%
15.32%
35.21%
Max Monthly Return
5.62%
4.99%
4.52%
15.84%
Min Monthly Return
-4.38%
-8.97%
-2.05%
-9.08%
% of Positive Months
49.35%
50.00%
75.68%
75.76%
% of Time Spent in Quadrant
37.11%
9.80%
36.27%
16.18%
Statistic
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For Investment Professional Use Only
Application to Managed Futures
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For Investment Professional Use Only
Application to Managed Futures

These trading models can be implemented through multiple different vehicles including:




Futures are the vehicle of choice for several reasons:




Stocks
ETF’s
Mutual Funds
Liquidity
Cost
Tax treatment
Trend Models have struggled
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For Investment Professional Use Only
Summary





Defining Counter-Trend models
How Counter-Trend works
Discover market environment factors that influence performance
Exploring the environments that are most effective for Counter-Trend (and least)
Application to Managed Futures
36
For Investment Professional Use Only
Risks




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


There are risks involved with investing, including loss of principal. Past performance does not guarantee future
results, share prices will fluctuate, and you may have a gain or loss when you redeem shares.
Exposure to the commodities markets may subject a fund to greater volatility than investing in traditional
securities. The value of commodity-linked derivative instruments may be affected by changes in overall market
movements, commodity index volatility, changes in interest rates, or factors affecting a particular industry or
commodity, such as natural disasters and international economic, political and regulatory developments.
Derivative instruments involve risks different from those associated with investing directly in securities and may
cause, among other things, increased volatility and transaction costs or a fund to lose more than the amount
invested.
Investing in Exchange-Traded Funds (ETFs) will subject a fund to substantially the same risks as those
associated with the direct ownership of the securities or other property held by the ETFs.
Investing in a non-diversified fund involves the risk of greater price fluctuation than a more diversified portfolio.
Futures contracts involve additional investment risks and transaction costs, and create leverage, which can
increase the risk and volatility of a fund.
Alternative strategies typically are subject to increased risk and loss of principal. Consequently, investments
such as mutual funds which focus on alternative strategies are not suitable for all investors.
Diversification does not assure profit or protect against risk.
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For Investment Professional Use Only
Definition of Indexes

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
The S&P 500 Index is an unmanaged index of 500 common stocks chosen to reflect the industries in the U.S. economy.
The Russell 2000 Index measures the performance of the 2,000 smallest companies in the Russell 3000 Index. The
Russell 3000 Index represents approximately 98% of the investable U.S. equity market.
The NASDAQ 100 measures the 100 largest, most actively traded U.S companies listed on the Nasdaq stock exchange.
This index includes companies from a broad range of industries with the exception of those that operate in the financial
industry, such as banks and investment companies.
The NIKKEI 225 measures the largest 225 stocks of the Tokyo Stock Exchange. The index is a simple average,
unweighted.
The Euro Stoxx 50 Index provides a Blue-chip representation of supersector leaders in the Eurozone. Covers Austria,
Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain.
One cannot directly invest in an index.
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For Investment Professional Use Only
CE Credit
You will receive an email from 361 Capital following this presentation.
If you are a CFP and would like to receive CE credit for your attendance,
please respond to that email.
If you have other designations with which you would like to receive CE
credit, you will be responsible for requesting the credit.
39
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Denver, CO 80237
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