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 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


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


Counter-Trend (Cont.)

Definition – Majority of models are looking to sell over bought levels and buy oversold

Mean Reversion

Shorter Term




Characteristics Reactionary Hit Ratio – 55% - 70% Gains come at inflection points Performs well in choppy, “noisy” markets

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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



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


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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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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



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


Market Environment Factors


Volatility and Noise

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Series 1 – Volatility 21%Series 2 – Volatility 16%


What is Noise?

19For Investment Professional Use Only

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%


Exploring Environments


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


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


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.


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


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)


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


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%


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%


Volatility and Noise Quadrants 1997 - 2013

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1997

1998

1999

2000

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2002

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2011

2012

2013

0%

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Q1: LVLN Q2: HVLN Q3: LVHN Q4: HVHN

Year

% o

f Tim

e in Q

uadre

nts


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


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%


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%


Application to Managed Futures


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



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

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.


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.


CE Credit

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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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40 For Investment Professional Use Only

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CE Quantitative Models – Exploring the Application of Counter-Trend Strategies