The core philosophy of The Connors Group is that all investment decisions should be objective and based on quantified information. The Machine Advisor contains over 28,000 active investing strategies developed by Connors Research over the past 9 years. These investing models are quantified and statistically validated using historical, simulated trades. Many of these models are being used today by some of the most successful money managers and hedge funds.
The limitations of passive investing and conventional asset class diversification were clearly illuminated during the 2008 financial crises. The objective of active investing is to out-perform the benchmarks with significantly lower risk. This objective can be achieved using strategies that build on a few of the concepts developed in the rapidly growing field of Behavioral Finance.
The Machine Advisor also provides an alternative to the outdated, conventional asset allocation methodologies. Many asset classes have become increasingly correlated since 2008. This severely limits the usefulness of the asset allocation approach and the conventional implementation of Modern Portfolio Theory (MPT).
The models within The Machine Advisor are designed to have much lower correlations to the market. Strategy-level diversification thus tends to provide much better portfolio risk mitigation capabilities than traditional asset-level diversification.
The Machine Advisor has been developed to allow financial professionals and their clients to leverage the theories of some of the greatest minds in finance in a practical, easy-to-use, and completely objective manner.
The models within The Machine Advisor quantify, for individual investment decisions, the concepts developed by Nobel Prize winner Daniel Kahneman (author of Thinking, Fast and Slow a New York Times Top 10 Book for 2011) and others.
The work of Kahneman and Amos Tversky in the 1970′s explained some of the short comings of the Efficient Market Hypothesis (EMH) and lead to the creation of the field of study now called Behavioral Finance. In short, Behavioral Finance explains the fundamental human behavior that drives investors to overreact to news or events and to act irrationally at times.
Investors consistently tend to buy stocks, bonds, and other investments after their prices have increased substantially because they “want to get in on the action” and don’t like seeing other investors profit while they are on the sidelines. Likewise, our very human nature causes us to systematically sell assets that are undervalued and have essentially had their prices “marked down.”
This irrational “herd” behavior was particularly evident in the two major bubbles in the 2000′s. During the dot-com bubble investors bought high flying internet stocks at 100 or more times their forward earnings. The housing bubble and subsequent sub-prime credit crises revealed the irrationality of both individual and professional investors.
While the theories of behavioral finance have only recently started to find their way into the mainstream, these concepts have been used by the world’s best traders and investors for as long as markets have existed.
Warren Buffet is famously quoted as saying “Be fearful when others are greedy and be greedy when others are fearful.” Sir John Templeton advised investors to “invest at the point of maximum pessimism.” And Lord Rothschild is said to have stated that “the time to buy is when there’s blood on the streets.”
The great investors have always known how to profit from temporary market inefficiencies caused by the human behaviors described by Behavioral Finance.
The models in The Machine Advisor take this a step farther and quantify these human behaviors that play out every day in price action in the financial markets. There are three basic types of models in The Machine Advisor that quantify three different aspects of this fundamental human behavior:
Each of the over 28,000 models in The Machine Advisor quantifies one of these behaviors. The methodology provides precise quantified entry and exit signals to buy and sell equities and ETFs such that active investors can potentially profit from these basic human behaviors instead of being hurt by them.
Modern Portfolio Theory has come under significant scrutiny over the past decade as many of its assumptions have been proven to be, at best, only partially true. Despite these questionable assumptions MPT can still provide a useful framework for investors.
By modifying a few of the assumptions in MPT and implementing it within the context of Behavioral Finance we can provide a framework for successful investing in a world where humans do not always behave rationally.
The most fundamental tenet of MPT remains its most useful ingredient – investors have the greatest opportunity for success if they build portfolios of diversified components. The cliche, “Don’t put all of your eggs in one basket,” is as true today as when the phrase was first uttered.
The concept is simple and intuitive as the following graphic shows.
Portfolios will exhibit reduced volatility when non-correlated investments are combined. Investments that move in tandem together are considered highly correlated. Investments are considered non-correlated if they do not move together and their movements are instead random. In a perfect world investments would have negative correlation and move in opposite directions while each investment still provided a positive return.
The problem with the conventional implementation of MPT is that the assets typically utilized have become more and more correlated over time. The advantages of strategic and tactical asset allocation have been largely negated by the increased correlations and reduced returns of the standard assets used.
The “lost decade” for equities has been well documented. The returns for the broad market and most of the typical equities sectors have been flat since 2000. More importantly, the correlations of the asset classes typically used for classical portfolio allocation have increased substantially as shown on the following table.
Most equity sectors are now correlated 85-95% or more with the S&P 500, significantly diminishing their value for diversification purposes. Even non-equity asset classes such as commodities, bonds, and REITs have seen their correlations increase.
Likewise, the following graphic demonstrates that correlations of country ETFs have increased substantially over the past decade and continue to trend upward.
So if conventional asset allocation is not the solution, what is?
The Machine Advisor offers a new way to provide the advantages of portfolio diversification. The benefits of diversifying investments promised by MPT can still be achieved by utilizing strategy (i.e. model) diversification instead of, or in addition to, asset class diversification.
The Machine Advisor allows financial professionals to build portfolios from diversified strategies with lower and more stable correlations to the broad market. The table below compares the correlations of typical asset classes used for MPT portfolio development compared to the active investing strategies available with The Machine Advisor.
Many active investing strategies have half the correlation of the typical equity asset classes. Of these classes used in conventional asset allocation, only bonds and gold provide decent non-correlated diversification. The Machine Advisor allows for bonds and gold to be added to active investing strategies to provide a complete solution for MPT 2.0.
The Machine Advisor provides financial professionals with over 28,000 active investing strategies (i.e. quantified models) that can be blended into well diversified portfolios. There are 7 categories of strategies:
Each strategy group leverages certain principles from the field of Behavioral Finance. As Kahman, Tversky, and others in the field have shown, basic human behaviors repeatedly manifest themselves in price movements of the financial markets.
These strategies attempt to identify equities that are in the early stages of a trend. These strategies benefit from the “herd” mentality.
The strategies trade low volatility stocks that tend to be large cap, “story” stocks. A recent example of one of these extended trends has been Apple (AAPL).
These strategies rely on concepts from academic studies which show persistency in prices, especially in low volatility stocks. The three main components of the strategies are: 1) momentum going into a stock; 2) low historical volatility; and 3) multiple trailing stops to identify trend change.
These trends tend to last many months and the average hold time for these positions is 6 to 9 months (though some trades can last over a year and some positions may be exited well before the 6 month period).
These strategies trade, on average, between 20 and 50 times per year.
When the general market is in a broad down trend these strategies tend to move to cash as there are few, if any, trending stocks.
These strategies are similar to the Equity Trend Following strategies except that they trade low volatility ETFs rather than low volatility stocks. Some models trade a general universe of unleveraged ETFs and other models trade only the Country ETFs. Inverse ETFs are not included in these models.
The ETF Trend Following strategies typically hold ETF positions for periods of 2 to 5 months. The strategies tend to trade between 10 and 100 times per year.
Whereas the previous trend following strategies attempt to identify behavior motivated by investor greed that is likely to result in a protracted upward trend, these strategies attempt to identify the opposite – investor fear that is likely to continue for a period of time.
These strategies also look at low volatility ETFs. Only unleveraged ETFs are considered. No inverse ETFs are considered.
These strategies hold a maximum of either 5 or 10 positions. They typically hold the positions for 1 to 3 months (down trends are typically not as persistent as upward trends).
These strategies may only trade a few times a year in a bull market environment, but they may trade 40 or more times in a year like 2011 or 2008.
The Selective Value strategies look to identify intermediate term opportunities in S&P 500 stocks. These strategies will generally hold positions between 1 and 3 months.
These strategies attempt to identify stocks which are under-valued due to investor over-reactions to news or market conditions. These positions have historically provided high probability opportunities over the 1 to 3 month timeframe.
These strategies will move into cash if market conditions indicate – based on the quantified model – that the market could become more over-sold in the short term.
These strategies typically trade between 10 and 150 times a year.
Weekly Rotational strategies are quantified models that identify equities that have a high statistical probability of being under-valued (i.e. over-sold) in the short term. These are long only strategies. The models trade either the S&P 500 stocks or a dynamic universe of low volatility stocks (large cap or mid cap stocks).
These strategies trade on a defined frequency of once a week. Different models trade on different days of the week. Models will hold a maximum of 10 to 20 positions at any one time. Each of the positions held is rotated the following week.
If the general market conditions signal a high statistical probability of the broad market becoming more over-sold in the short term, the models may move partially or completely into cash.
These strategies will generally trade between 200 and 500 times per year on average (round trip trades). Different models will have different activity levels and corresponding levels of exposure to the market.
The Semi-Monthly Rotation strategies are similar to the Weekly Rotation strategies with respect to the behavior that they target and the model construction. The primary difference is that the Semi-Monthly Rotation strategies will rotate the stock holdings twice per month rather than weekly.
The Semi-Monthly strategies are only available with S&P 500 stocks. These strategies generally trade between 100 and 200 times per year.
The Machine Advisor has been developed to allow financial professionals and their clients to leverage the theories developed by Nobel laureate Daniel Kahman, Amos Tversky, and others in the field of Behavioral Finance.
The Machine Advisor also takes the concepts behind Modern Portfolio Theory to a new level. Financial advisors can move beyond conventional asset allocation that relies on a buy and hold strategy using highly correlated vehicles.
The Machine Advisors active investing strategies based on the principles of Behavioral Finance have much lower correlation to the general market and provide much better diversification for investors.
The Machine Advisor provides your practice with a quantified and systematic approach to active investing, portfolio management, and risk management.
This completely objective methodology can create a lifetime value for your business and be your solution to succession planning.
The Machine Advisor also provides you with a unique means to interactively demonstrate simulated performance of customized active investment portfolios. This can become a powerful sales tool to assist you in growing your AUM.
With The Machine Advisor you can potentially increase the profitability of your business by complimenting lower fee passive investing with higher fee active investing – while your clients benefit from a superior investment methodology.
The Connors Group, Inc. (The Connors Group) is a leading innovator in the development and distribution of financial market investment information. The Connors Group has created one of the largest databases in the world of short term market behavior. The company’s investment research on U.S. equities and ETFs is used by high net worth individuals, financial advisors, and institutions including by some of the larger hedge funds and fund of funds.
The Connors Group was founded in 1999 by a group of professional traders led by Larry Connors, along with industry leaders — including Kevin Haggerty, former head of trading for Fidelity Capital Markets.
Connors Research, LLC, the research affiliate of The Connors Group, owns a proprietary, core database of over 11 million equity trades – a unique, hand-groomed repository of data on short-term market behavior. Connors Research continually adds to and improves this data, and uses it to create model-driven, quantitatively validated trading methodologies.
To talk to a sales representative or register for an online webinar overview of The Machine Advisor call 1-888-484-8220 ext. 1. For more information, please email us at firstname.lastname@example.org.
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