ETF Trading Strategy Methodology
The following is a narrative prepared by ECAM in May, 2013 to explain the Interactive Brokers Linked Trading Account strategies to current clients. The methodology used in the ETF Trading Strategies is identical. Published charts indicated in the narrative have not been included due to their dated nature; current charts of performance are included in the description of the ETF Trading Strategy.
Emirates Capital Asset Management
15 May 2013
Most ECAM subscribers are aware I do not utilize the Provident Fund “C” account as my chosen investment vehicle in which to invest the bulk of my investment capital. I have pointed out on numerous prior occasions on the website how the limited number of funds available within the account does not allow for sufficient asset diversification. In addition, the “locked-in” nature of the funds with slow switching between funds does not allow for tactical account management as market conditions rapidly change.
As a result, probably the single most common question I receive from investors on a consistent and ongoing basis is “what do you do with your own money”. In the following installment I will detail the ECAM investment strategy.
In order to describe the strategy I utilize it will be necessary for readers to suffer through some investment jargon. I will attempt to keep it as “light” as possible but within the confines of adhering to sufficient disclosure to allow those interested in the strategy to understand the mechanics involved to allow for peer review.
I previously discussed the investment strategy I had utilized for the past number of years (along with a white paper I produced on the subject). The ECAM Managed Asset Allocation Model employed the use of a broad sweep of varied asset classes (U.S. Equities, Non-U.S. Equities, Emerging Market Equities, Bonds, Real Estate and Commodities) chosen according to fixed percentage allocations within the portfolio. These asset classes were accessed through the purchase of Exchange Traded Funds (ETFs) traded through any brokerage firm in the U.S. and held as long as their monthly closing price remained above their respective 10 month simple moving averages. When prices closed below those averages, their portfolio percentage allocation was moved into cash.
As can be seen on the below equity curve and performance statistics, utilizing this strategy resulted in a low volatility portfolio that showed a respectable long term rate of return over a complete equity market cycle (outperforming the S&P 500 Index). It does very well when compared to a given equity benchmark in times of market turbulence (limiting losses during bear market declines) but tends to struggle to “catch up” and keep pace with a given equity benchmark such as the S&P 500 Index in the good times (as has been the case since the current bull market began in 2009).
This “performance lag” is common for most hedged strategies that protect downside risk.
Given this relative under-performance during bull market advances, I was interested in developing an alternative investment strategy that would provide a similar level of safety to protect the downside during bear market declines but also provided significantly increased returns to keep up with (or exceed) equity benchmarks in bull market advances.
My search to build a “better mousetrap” started with defining the specific investment parameters I was looking for:
-First, I was looking to develop a strategy that would consistently provide relative out-performance to my chosen benchmark (the S&P 500 Index) in both bull market and bear market conditions combined with the requirement to have a volatility level and maximum draw down below that of the chosen equity benchmark during ALL defined time periods.
-Second, I was not satisfied with mere “relative” out-performance of the benchmark as having the benchmark decline “X” % and having the portfolio “outperform” on a relative basis (but still lose REAL money in a given year) was not satisfactory. The strategy would also have had to deliver back tested yearly “absolute” positive returns (not only outperforming the benchmark but also requiring positive absolute returns) for the past 10 years (which would include 2 bull markets and the brutal 2007-2008 bear market decline period; the second worst stock market decline in history after the 1930’s Great Depression).
-Third, given my insistence upon relative safety, it became obvious the chosen investment vehicle would have to be U.S. based Exchange Traded Funds (ETFs) given only these investment vehicles allow broad exposure to a variety of companies within targeted asset class (thereby lessening company-specific exposure to reduce risk).
It was only through the satisfaction of all the above parameters that I felt this type of strategy would allow it to be utilized by those looking for long term consistent portfolio returns superior to those found in traditional retirement accounts, mutual funds and hedge fund offerings.
This tall task I knew would require the identification and implementation of one of many forms of absolute return strategies predominantly utilized by the hedge fund industry. Through many years of personal experience, extensive research and back testing it became clear to me the optimal strategy I would employ would be based upon momentum.
It may seem counter intuitive to most but the dream every money manager and investor has of “buying low” and “selling high” just does not work on an ongoing and consistent basis. While it is possible (on rare occasions) to buy oversold out-of-favor asset classes near their bottoms and see them subsequently rise significantly higher, most times those that employ this sort of strategy (whether employing a “long only” or “long-short” hedged strategy) more often than not get caught trying to “catch a falling knife”. Many fingers have been lost over the years (along with billions of dollars lost) by those trying to be “heroes” and “bottom pickers” attempting to time this strategy.
Quite the opposite; what became clear to me in both my ongoing research and personal experience over the years was a “buy-high” and “sell-higher” strategy consistently delivered the superior rates of return I was seeking.
What I was dissatisfied with was this type of strategy came with the possibility of extreme downside risk due to “momentum crashes” associated with sudden and extreme short term market draw downs occurring in a given narrowly defined asset class over a relatively short period of several weeks/months (as occurred during 1929-1932 crash and the 2007-2008 crash) so I knew a degree of safety had to be built into the strategy for it to be suitable as a long term investment program.
It was at this point I began to develop a “risk adjusted” momentum strategy utilizing volatility. It was clear to me in my research and personal investment experience that many times the market begins to “telegraph” possible turbulent, unpredictable, or inconsistent market conditions through increased volatility in advance of a subsequent serious decline in a chosen asset class. Given this behavior, the use of a “volatility skew” inputted into a strictly defined momentum strategy resulted in a much less volatile portfolio with greater returns, very strong Sharpe Ratio increases and significantly reduced draw downs over all time periods. At this point I knew I was on to something.
Late in 2010 I began researching and developing the various parameters associated with my chosen volatility adjusted scaled momentum strategy.
I began by defining my “investment universe” which would be composed of the ETFs I would use within my strategy. My choice of ETFs was to be based upon the following parameters:
1) The chosen ETFs must be highly liquid to allow ease of entry/exit at any given moment during the trading day. Utilizing this liquidity also minimizes the bid/ask spread to allow for minimal slippage associated with the trade.
2) The chosen ETFs must define a broad part of its universe without too much exposure to a single entity (for example, a broad based Emerging Market ETF is suitable; an ETF that targets a specific emerging market country such as Brazil, Russia, India, China, etc would not be suitable).
3) The chosen ETFs would provide varied correlations relative to the S&P 500 Index from strongly correlated to strongly inverse-correlated. The intent was to have an entire “universe” with an overall combined positive correlation relative to the S&P 500 (to allow the portfolio to match its performance during equity advances) but an overall correlation less than 0.40 (which was determined via back testing as optimal to allow rotation into alternate defensive non-correlated asset classes during equity declines).
The result of this extensive research produced a universe of 23 ETFs which broadly defines the entire investment universe. They include the following:
1) U.S. Equity
2) International Equity
3) Emerging Markets Equity
4) Quasi-Equity
5) Commodities
6) Government Bonds
7) Corporate Bonds
8) Currencies
9) Volatility Derivatives
Having defined my investment universe, I then had to define both my momentum investment look-back periods and percentage allocation to the investment categories described above. Through hundreds of hours of back testing (literally; this was a very laborious task), I determined the optimal “look-back periods”, added an associated “volatility skew” and applied these parameters to my investment universe. I then compared this optimal setting to my ETF universe to define the number of ETFs to be invested in during each investment period. Once this was accomplished I was required to determine the specific period upon which this risk-adjusted momentum strategy would need to be reviewed and adjusted to optimize absolute rates of return.
Having gone through all the above, all I really care about is results. The results have been impressive. Utilizing the facilities of a third party data provider for which I do not have any professional or personal interests or association (ETFreplay.com); their software database produced the following independent back tested results for the ECAM Risk Adjusted Momentum Strategy for the period 01 January, 2003 to 15 May 2013 inclusive:
-Year to date (to 15 May 2013) +6.8%
-1 year Compound Annual Growth Rate (2012) +34.2%
-3 year Compound Annual Growth Rate (2010-2012) +37.0%
-5 year Compound Annual Growth Rate (2008-2012) +32.4%
-10 year Compound Annual Growth Rate (2003-2012) +28.1%
When selecting a back test period, I deemed this “look-back” period from 01 Jan 2003-to-present as particularly relevant as this period included 2 strong bull market advances (2003-2007) and (2009-Present) along with the worst bear market decline since the Great Depression (2007-2009). As such, the past 10 years have provided an investment climate as challenging as any in history and, in my opinion, any investment strategy considered must be bench-marked against this period to truly validate the validity and robustness of the chosen investment strategies.
As can be seen on the following chart for the period 01 Jan 2003-31 Dec 2012, the strategy has produced a very impressive rising and steady equity curve (green line vs. blue line for SPY; the ETF that matches the performance of the S&P 500 index) with significant out-performance (a total return of +1084% the past 10 years):
ECAM Risk Adjusted Momentum Strategy vs. S&P 500 Index (SPY):
-Total Return +1084% vs. +96%
-Volatility 16.3% vs. 20.7%
-CAGR* +28.1% vs. +7.0%
-Sharpe Ratio** 1.43 vs. 0.30
-Maximum Draw Down -15.8% vs. -55.2%
-Years with Positive Absolute Returns 10 (all years to end 2012)
-Years with Negative Absolute Returns 0 (none)
-Periods Outperforming Benchmark 9 (all years except 2009 which returned +17.3% vs. +26.4%)
*CAGR is the Cumulative Annual Growth Rate of the portfolio. It is a common measure used to compare the cumulative percentage performance return of Hedge/Mutual Funds over various periods of time
**Sharpe Ratio is a common measure used to compare the performance of Hedge/Mutual Funds to help investors determine how much return they're getting in exchange for the level of risk they're taking on (risk vs. return). Created by Nobel laureate William Sharpe, a Stanford University finance professor, the ratio is intended to be a measure of what an investment returned for each "unit" of risk it carried relative to a risk-free investment, typically defined as three-month Treasury bills, over that same period. The higher the ratio, the more an investor is compensated for the risk he takes on.
The results speak for themselves. I am very comfortable and satisfied with the consistent rates of return delivered over multiple market cycles (2 bull market advances and 1 severe bear market decline). A +1084% cumulative rate of return over 10 years is stellar absolute return performance given the +96% overall return of the S&P 500 Index over the same time frame.
Even more so, I am very satisfied by the low relative volatility within the portfolio (16.3% vs. 20.7% for the S&P 500 Index), strong Sharpe Ratio and reduced maximum draw downs. In other words, this is the type of portfolio that delivers consistent superior returns (in the top 5% of all equity/hedge strategies currently available anywhere) and still allows you to sleep at night.
The most impressive feature of the strategy is how it performs during transitions into poor equity market conditions. It attempts to stay with the equity uptrend while times are good (which is very difficult for a hedged strategy to do) but when things begin to decline it rotates fairly rapidly into defensive asset classes. As such, it is able to deliver positive absolute rates of return irrespective of equity market conditions.
Having developed such a strategy, I was interested to see how my risk adjusted momentum strategy would measure up against the other offerings in the hedge fund industry. The nearest benchmark is the HFRI Hedge Fund Index. From their website:
“The HFRI Monthly Indices (HFRI) are equally weighted performance indexes, utilized by numerous hedge fund managers as a benchmark for their own hedge funds. The HFRI are broken down into 4 main strategies, each with multiple sub strategies. All single-manager HFRI Index constituents are included in the HFRI Fund Weighted Composite, which accounts for over 2200 funds listed on the internal HFR Database”.
Below is the return of the HFRI Weighted Hedge Fund Index for the period 2003-2011 (in this case bench-marked against the MSCI World Index which has a 98% correlation with the S&P 500 Index but gives you an idea of the average hedge fund return over the period).
As can be seen on the graph, during the period 2003-2011 the average total cumulative hedge fund returns approached +95% (for comparison my strategy during the same period returned +784%). It is also interesting to note during the 2008 decline the average hedge fund LOST approximately 20% (in a year where my hedge strategy returned +35.2%).
In all cases the strategy I employ significantly outperforms literally anything currently offered in the hedge fund community.
Conclusion:
I went “live” with this strategy in early 2011 (February 01, 2011) and have achieved similarly superior results to previous back testing. I have enhanced the strategy from a purely “mechanical strategy” (which is what the algorithm is based upon) to include daily analysis of price action with defined stops via technical analysis. The result has been even better than the back test results indicate (year to date my return is currently + 17.0% vs. +6.8% as shown on the chart). It has done so with significantly reduced volatility and appears to be on track to deliver another consistent superior positive annual rate of return for 2013.
I have been highly involved in equity investments for well over 30 years. In that time I have gained extensive experience in just about every form of risk asset investment and technique. Having said this, I am comfortable to declare the above described strategy is the single best long term consistent, low volatility, high return strategy I have encountered. My confidence in this strategy is reflected in the fact 100% of my own investment capital and those of my investors is now invested in this strategy.
I invite any investment adviser (of which a number are subscribers to this website) to match or compare the validity of the data presented or propose ANY alternative investment strategy that delivers comparable statistical rates of return tempered with the low volatility offered by this strategy.
Should you have any questions please feel free to contact me.
15 May 2013
Most ECAM subscribers are aware I do not utilize the Provident Fund “C” account as my chosen investment vehicle in which to invest the bulk of my investment capital. I have pointed out on numerous prior occasions on the website how the limited number of funds available within the account does not allow for sufficient asset diversification. In addition, the “locked-in” nature of the funds with slow switching between funds does not allow for tactical account management as market conditions rapidly change.
As a result, probably the single most common question I receive from investors on a consistent and ongoing basis is “what do you do with your own money”. In the following installment I will detail the ECAM investment strategy.
In order to describe the strategy I utilize it will be necessary for readers to suffer through some investment jargon. I will attempt to keep it as “light” as possible but within the confines of adhering to sufficient disclosure to allow those interested in the strategy to understand the mechanics involved to allow for peer review.
I previously discussed the investment strategy I had utilized for the past number of years (along with a white paper I produced on the subject). The ECAM Managed Asset Allocation Model employed the use of a broad sweep of varied asset classes (U.S. Equities, Non-U.S. Equities, Emerging Market Equities, Bonds, Real Estate and Commodities) chosen according to fixed percentage allocations within the portfolio. These asset classes were accessed through the purchase of Exchange Traded Funds (ETFs) traded through any brokerage firm in the U.S. and held as long as their monthly closing price remained above their respective 10 month simple moving averages. When prices closed below those averages, their portfolio percentage allocation was moved into cash.
As can be seen on the below equity curve and performance statistics, utilizing this strategy resulted in a low volatility portfolio that showed a respectable long term rate of return over a complete equity market cycle (outperforming the S&P 500 Index). It does very well when compared to a given equity benchmark in times of market turbulence (limiting losses during bear market declines) but tends to struggle to “catch up” and keep pace with a given equity benchmark such as the S&P 500 Index in the good times (as has been the case since the current bull market began in 2009).
This “performance lag” is common for most hedged strategies that protect downside risk.
Given this relative under-performance during bull market advances, I was interested in developing an alternative investment strategy that would provide a similar level of safety to protect the downside during bear market declines but also provided significantly increased returns to keep up with (or exceed) equity benchmarks in bull market advances.
My search to build a “better mousetrap” started with defining the specific investment parameters I was looking for:
-First, I was looking to develop a strategy that would consistently provide relative out-performance to my chosen benchmark (the S&P 500 Index) in both bull market and bear market conditions combined with the requirement to have a volatility level and maximum draw down below that of the chosen equity benchmark during ALL defined time periods.
-Second, I was not satisfied with mere “relative” out-performance of the benchmark as having the benchmark decline “X” % and having the portfolio “outperform” on a relative basis (but still lose REAL money in a given year) was not satisfactory. The strategy would also have had to deliver back tested yearly “absolute” positive returns (not only outperforming the benchmark but also requiring positive absolute returns) for the past 10 years (which would include 2 bull markets and the brutal 2007-2008 bear market decline period; the second worst stock market decline in history after the 1930’s Great Depression).
-Third, given my insistence upon relative safety, it became obvious the chosen investment vehicle would have to be U.S. based Exchange Traded Funds (ETFs) given only these investment vehicles allow broad exposure to a variety of companies within targeted asset class (thereby lessening company-specific exposure to reduce risk).
It was only through the satisfaction of all the above parameters that I felt this type of strategy would allow it to be utilized by those looking for long term consistent portfolio returns superior to those found in traditional retirement accounts, mutual funds and hedge fund offerings.
This tall task I knew would require the identification and implementation of one of many forms of absolute return strategies predominantly utilized by the hedge fund industry. Through many years of personal experience, extensive research and back testing it became clear to me the optimal strategy I would employ would be based upon momentum.
It may seem counter intuitive to most but the dream every money manager and investor has of “buying low” and “selling high” just does not work on an ongoing and consistent basis. While it is possible (on rare occasions) to buy oversold out-of-favor asset classes near their bottoms and see them subsequently rise significantly higher, most times those that employ this sort of strategy (whether employing a “long only” or “long-short” hedged strategy) more often than not get caught trying to “catch a falling knife”. Many fingers have been lost over the years (along with billions of dollars lost) by those trying to be “heroes” and “bottom pickers” attempting to time this strategy.
Quite the opposite; what became clear to me in both my ongoing research and personal experience over the years was a “buy-high” and “sell-higher” strategy consistently delivered the superior rates of return I was seeking.
What I was dissatisfied with was this type of strategy came with the possibility of extreme downside risk due to “momentum crashes” associated with sudden and extreme short term market draw downs occurring in a given narrowly defined asset class over a relatively short period of several weeks/months (as occurred during 1929-1932 crash and the 2007-2008 crash) so I knew a degree of safety had to be built into the strategy for it to be suitable as a long term investment program.
It was at this point I began to develop a “risk adjusted” momentum strategy utilizing volatility. It was clear to me in my research and personal investment experience that many times the market begins to “telegraph” possible turbulent, unpredictable, or inconsistent market conditions through increased volatility in advance of a subsequent serious decline in a chosen asset class. Given this behavior, the use of a “volatility skew” inputted into a strictly defined momentum strategy resulted in a much less volatile portfolio with greater returns, very strong Sharpe Ratio increases and significantly reduced draw downs over all time periods. At this point I knew I was on to something.
Late in 2010 I began researching and developing the various parameters associated with my chosen volatility adjusted scaled momentum strategy.
I began by defining my “investment universe” which would be composed of the ETFs I would use within my strategy. My choice of ETFs was to be based upon the following parameters:
1) The chosen ETFs must be highly liquid to allow ease of entry/exit at any given moment during the trading day. Utilizing this liquidity also minimizes the bid/ask spread to allow for minimal slippage associated with the trade.
2) The chosen ETFs must define a broad part of its universe without too much exposure to a single entity (for example, a broad based Emerging Market ETF is suitable; an ETF that targets a specific emerging market country such as Brazil, Russia, India, China, etc would not be suitable).
3) The chosen ETFs would provide varied correlations relative to the S&P 500 Index from strongly correlated to strongly inverse-correlated. The intent was to have an entire “universe” with an overall combined positive correlation relative to the S&P 500 (to allow the portfolio to match its performance during equity advances) but an overall correlation less than 0.40 (which was determined via back testing as optimal to allow rotation into alternate defensive non-correlated asset classes during equity declines).
The result of this extensive research produced a universe of 23 ETFs which broadly defines the entire investment universe. They include the following:
1) U.S. Equity
2) International Equity
3) Emerging Markets Equity
4) Quasi-Equity
5) Commodities
6) Government Bonds
7) Corporate Bonds
8) Currencies
9) Volatility Derivatives
Having defined my investment universe, I then had to define both my momentum investment look-back periods and percentage allocation to the investment categories described above. Through hundreds of hours of back testing (literally; this was a very laborious task), I determined the optimal “look-back periods”, added an associated “volatility skew” and applied these parameters to my investment universe. I then compared this optimal setting to my ETF universe to define the number of ETFs to be invested in during each investment period. Once this was accomplished I was required to determine the specific period upon which this risk-adjusted momentum strategy would need to be reviewed and adjusted to optimize absolute rates of return.
Having gone through all the above, all I really care about is results. The results have been impressive. Utilizing the facilities of a third party data provider for which I do not have any professional or personal interests or association (ETFreplay.com); their software database produced the following independent back tested results for the ECAM Risk Adjusted Momentum Strategy for the period 01 January, 2003 to 15 May 2013 inclusive:
-Year to date (to 15 May 2013) +6.8%
-1 year Compound Annual Growth Rate (2012) +34.2%
-3 year Compound Annual Growth Rate (2010-2012) +37.0%
-5 year Compound Annual Growth Rate (2008-2012) +32.4%
-10 year Compound Annual Growth Rate (2003-2012) +28.1%
When selecting a back test period, I deemed this “look-back” period from 01 Jan 2003-to-present as particularly relevant as this period included 2 strong bull market advances (2003-2007) and (2009-Present) along with the worst bear market decline since the Great Depression (2007-2009). As such, the past 10 years have provided an investment climate as challenging as any in history and, in my opinion, any investment strategy considered must be bench-marked against this period to truly validate the validity and robustness of the chosen investment strategies.
As can be seen on the following chart for the period 01 Jan 2003-31 Dec 2012, the strategy has produced a very impressive rising and steady equity curve (green line vs. blue line for SPY; the ETF that matches the performance of the S&P 500 index) with significant out-performance (a total return of +1084% the past 10 years):
ECAM Risk Adjusted Momentum Strategy vs. S&P 500 Index (SPY):
-Total Return +1084% vs. +96%
-Volatility 16.3% vs. 20.7%
-CAGR* +28.1% vs. +7.0%
-Sharpe Ratio** 1.43 vs. 0.30
-Maximum Draw Down -15.8% vs. -55.2%
-Years with Positive Absolute Returns 10 (all years to end 2012)
-Years with Negative Absolute Returns 0 (none)
-Periods Outperforming Benchmark 9 (all years except 2009 which returned +17.3% vs. +26.4%)
*CAGR is the Cumulative Annual Growth Rate of the portfolio. It is a common measure used to compare the cumulative percentage performance return of Hedge/Mutual Funds over various periods of time
**Sharpe Ratio is a common measure used to compare the performance of Hedge/Mutual Funds to help investors determine how much return they're getting in exchange for the level of risk they're taking on (risk vs. return). Created by Nobel laureate William Sharpe, a Stanford University finance professor, the ratio is intended to be a measure of what an investment returned for each "unit" of risk it carried relative to a risk-free investment, typically defined as three-month Treasury bills, over that same period. The higher the ratio, the more an investor is compensated for the risk he takes on.
The results speak for themselves. I am very comfortable and satisfied with the consistent rates of return delivered over multiple market cycles (2 bull market advances and 1 severe bear market decline). A +1084% cumulative rate of return over 10 years is stellar absolute return performance given the +96% overall return of the S&P 500 Index over the same time frame.
Even more so, I am very satisfied by the low relative volatility within the portfolio (16.3% vs. 20.7% for the S&P 500 Index), strong Sharpe Ratio and reduced maximum draw downs. In other words, this is the type of portfolio that delivers consistent superior returns (in the top 5% of all equity/hedge strategies currently available anywhere) and still allows you to sleep at night.
The most impressive feature of the strategy is how it performs during transitions into poor equity market conditions. It attempts to stay with the equity uptrend while times are good (which is very difficult for a hedged strategy to do) but when things begin to decline it rotates fairly rapidly into defensive asset classes. As such, it is able to deliver positive absolute rates of return irrespective of equity market conditions.
Having developed such a strategy, I was interested to see how my risk adjusted momentum strategy would measure up against the other offerings in the hedge fund industry. The nearest benchmark is the HFRI Hedge Fund Index. From their website:
“The HFRI Monthly Indices (HFRI) are equally weighted performance indexes, utilized by numerous hedge fund managers as a benchmark for their own hedge funds. The HFRI are broken down into 4 main strategies, each with multiple sub strategies. All single-manager HFRI Index constituents are included in the HFRI Fund Weighted Composite, which accounts for over 2200 funds listed on the internal HFR Database”.
Below is the return of the HFRI Weighted Hedge Fund Index for the period 2003-2011 (in this case bench-marked against the MSCI World Index which has a 98% correlation with the S&P 500 Index but gives you an idea of the average hedge fund return over the period).
As can be seen on the graph, during the period 2003-2011 the average total cumulative hedge fund returns approached +95% (for comparison my strategy during the same period returned +784%). It is also interesting to note during the 2008 decline the average hedge fund LOST approximately 20% (in a year where my hedge strategy returned +35.2%).
In all cases the strategy I employ significantly outperforms literally anything currently offered in the hedge fund community.
Conclusion:
I went “live” with this strategy in early 2011 (February 01, 2011) and have achieved similarly superior results to previous back testing. I have enhanced the strategy from a purely “mechanical strategy” (which is what the algorithm is based upon) to include daily analysis of price action with defined stops via technical analysis. The result has been even better than the back test results indicate (year to date my return is currently + 17.0% vs. +6.8% as shown on the chart). It has done so with significantly reduced volatility and appears to be on track to deliver another consistent superior positive annual rate of return for 2013.
I have been highly involved in equity investments for well over 30 years. In that time I have gained extensive experience in just about every form of risk asset investment and technique. Having said this, I am comfortable to declare the above described strategy is the single best long term consistent, low volatility, high return strategy I have encountered. My confidence in this strategy is reflected in the fact 100% of my own investment capital and those of my investors is now invested in this strategy.
I invite any investment adviser (of which a number are subscribers to this website) to match or compare the validity of the data presented or propose ANY alternative investment strategy that delivers comparable statistical rates of return tempered with the low volatility offered by this strategy.
Should you have any questions please feel free to contact me.