ce quantitative models – exploring the application of counter-trend strategies
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CE Quantitative Models – Exploring the Application of Counter-Trend Strategies. For Investment Professional Use Only. Agenda. Defining Counter-Trend models How Counter-Trend works Discover market environment factors that influence performance - PowerPoint PPT PresentationTRANSCRIPT
The Clear Alternative
CE Quantitative Models – Exploring the Application of Counter-Trend Strategies
For Investment Professional Use Only
For Investment Professional Use Only
Agenda
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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Trend Following
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Trend Following
Trend Definition – In general, a trend following system aims to invest in the direction of the trend
Most often describe moving average crossover Short term, medium term, and/or long term
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Trend Following
Characteristics Reactionary Hit Ratio – 25% - 40% Can give back gains at turning points (Whipsaw) Performs well in long trends
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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
For Investment Professional Use Only6
For Investment Professional Use Only
Trend Following
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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.
Counter-Trend
For Investment Professional Use Only8
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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For Investment Professional Use Only
Counter-Trend (Cont.)
Characteristics Reactionary Hit Ratio – 55% - 70% Gains come at inflection points Performs well in choppy, “noisy” markets
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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
For Investment Professional Use Only11
For Investment Professional Use Only
How Counter-Trend WorksCase Study
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Case Study
Simple Case Study Rules (Cont.) Two Models Examined
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
For Investment Professional Use Only
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For Investment Professional Use Only
Case Study
Simple Case Study Rules
S&P 500 January 1, 1990 to December 31, 2011 5547 Trading Days S&P had a total return of 468.10%
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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.
For Investment Professional Use Only
Case Study
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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.
For Investment Professional Use Only
Case Study
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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.
Market Environment Factors
For Investment Professional Use Only17
Volatility and Noise
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Series 1 – Volatility 21%Series 2 – Volatility 16%
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What is Noise?
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What is Noise? – Numerical Example
Assumptions – Market is down a total of -2% over a ten day period (sum).Market path is as follows:
Total Movement = 10% % Net Directional Movement = ABS (-2%)/10% = 20%Noise = 1 - 20% = 80%
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Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9Day 10
- 1.0% -1.0% .5% 1.0% -1.0% 1.0% -1.0% 1.0% -2.0% .5%
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Exploring Environments
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What Characterizes a “Noisy” Market?
Noisy Market profile: Market participants have differing opinions Market participants must be able to “vote” or express their opinion Barriers to entry: low
Cost of Trade Speed of Trades
Free from centralized control Liquidity
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The Data
Two sets of data explore the presence of “Noise” 1926 – 1996 1997 – 2013
Data from 1997 – 2013 used for environment expectations Structural changes in market beginning in 1997 All projections are subject to change if adverse structural market changes
exist
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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.
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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Monthly “Noise”
1926 – 1996 was 73.63% 1997 – 2013 was 78.62%
The majority of the observed months showed “Noise” ranging between
60% - 90%
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
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65.00% 65.00%
Distribution of Monthly Noise 1997 to 2013Avg. Noise 1997 to 2013Avg. Noise 1928 to 1996
Noise
Fre
quency
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Volatility and Noise Quadrants
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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Volatility and Noise Quadrants
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Volatility
Low: <20% High: >=20%
Noise
Low: <80% Q1: LVLN Q2: HVLN
High: >=80% Q3: LVHN Q4: HVHN
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Volatility and Noise Quadrants 1926 -1996
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Percentage of Time Spent
Volatility
Low High Total
Noise
Low 48.43% 8.94% 57.37%
High 34.90% 7.73% 42.63%
Total 83.33% 16.67%
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Volatility and Noise Quadrants 1997 - 2013
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Percentage of Time Spent Volatility
Low High Total
Noise
Low 37.75% 9.80% 47.55%
High 36.27% 16.18% 52.45%
Total 74.02% 25.98%
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Volatility and Noise Quadrants 1997 - 2013
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1997
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Q1: LVLN Q2: HVLN Q3: LVHN Q4: HVHN
Year
% o
f Tim
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uadre
nts
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S&P Performance by Quadrant 1997 - 2013
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StatisticQ1: LVLN Q2: HVLN Q3: LVHN Q4: HVHN
Environmental
Avg. Noise 64.56% 71.51% 90.12% 89.99%
Avg. Volatility 12.88% 28.81% 13.89% 32.26%
Performance
Avg. Monthly Return 2.28% -1.17% 0.25% -1.39%
Std. Dev. of Monthly Returns4.44% 8.76% 1.57% 4.91%
Annualized Return Expectation31.11% -13.20% 3.06% -15.49%
Max Monthly Return 8.92% 10.93% 4.00% 8.76%
Min Monthly Return -9.12% -14.46% -3.10% -16.79%
% of Positive Months 76.62% 45.00% 63.51% 39.39%
Duration
# of Months 77 20 74 33
Avg. Consecutive Months 1.71 1.33 1.80 1.50
Max Consecutive Months 7.00 3.00 6.00 6.00
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Predictability of Noise 1997 - 2013
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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 Volatility Noise
1928-1996 73.50% -11.11%
1997-2013 73.55% -14.69%
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Simple Counter-Trend Performance Quadrant: 1997 - 2013
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Statistic
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%
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Application to Managed Futures
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Application to Managed Futures
These trading models can be implemented through multiple different vehicles including:
Stocks ETF’s Mutual Funds
Futures are the vehicle of choice for several reasons: Liquidity Cost Tax treatment
Trend Models have struggled
35For Investment Professional Use Only
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
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Risks
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.
For Investment Professional Use Only37
Definition of Indexes
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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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.
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