The Correction Protection Model (CPM) is a completely data-driven, dynamic stocks/bonds allocation model. CPM utilizes six quantitative inputs to determine the daily internal health of the US stock market. It objectively determines if investors should be adding or subtracting equity risk from portfolios. When the stock market is internally healthy according to CPM, the model is fully invested in the S&P 500 (SPY). When the stock market is internally weak/vulnerable according to CPM, the model is fully invested in short term US Treasuries (BIL).
Designed to protect investor assets during market declines, eliminate large drawdowns, and reduce volatility in portfolios by moving assets out of the market during adverse conditions.
CPM was designed to be a wealth preservation tool, to “play the game with less risk”.
CPM is completely data-driven. No opinions. No forecasting. Just data.
CPM is binary: either Risk On (in the market) or Risk Off (out of the market)
CPM averages 5 round turns per year
There are no short positions or derivatives (options, futures, etc.) trading
CPM annualized total return is slightly better than the S&P 500
with significantly lower risk than the S&P 500 (according to standard deviation, beta, maximum drawdown)
CPM has better risk-adjusted returns than the S&P 500 (according to Sharpe Ratio, Sortino Ratio)
CPM can become an alpha-generating model by using the same signals with leverage.
The tables and chart below display quantitative performance data and hypothetical returns since 2017 when applying the CPM signals to the SPDR S&P 500 ETF Trust (SPY, which tracks the S&P 500).
CPM was initially developed for older or more risk-averse investors who wanted or needed to participate in the stock market but were uncomfortable with the risk. The tables and chart below are based on using the SPDR S&P 500 ETF Trust (SPY) as the trading vehicle.
This table displays the year-by-year performance of CPM versus the S&P 500 (SPX) from Q4 2017 through 4th Quarter 2023. It has underperformed the S&P 500 by 1.3% during this 6-year period or by an average of 0.2% per year.
The next table below compares CPM to the S&P 500 in 9 quantitative categories, showing that CPM has outperformed in 8 of them including total return, maximum drawdown, standard deviation, and beta.
The line chart below plots the daily performance of CPM using SPY (green) versus the S&P 500 (blue) from Q4 2017 through Q4 2023.
The column chart below displays the year-by-year performance of CPM using SPY (green) versus the S&P 500 (blue) from 2018 through 2023.
To help investors better understand how CPM reduces risk, the following tables and charts display quantitative performance data and hypothetical returns, also from Q4 2017 through 4th Quarter 2023, when applying the CPM signals to the ProShares Ultra S&P500 (SSO), a double leveraged ETF that also tracks the S&P 500.
They show that, by incrementally adding risk back into the CPM ModeI, its character and performance can shift from defensive to offensive. This strategy may be of interest to those who are willing to accept a little more risk than standard CPM (using SPY) for the potential of producing better-than-average returns.
The table below displays the year-by-year performance of CPM using SSO versus the S&P 500 (SPX) through 3rd Quarter 2023. It has outperformed the S&P 500 by 60.6% during this 7-year period or by an average of 8.7% per year while outperforming the index in 5 of the 7 individual years.
The next table below compares CPM using SSO to the S&P 500 in 9 quantitative categories, showing that CPM outperforms in 5 of them including total return, Sharpe Ratio, and up capture.
The line chart below plots the daily performance of CPM using SSO (green) versus the S&P 500 (blue) from 2017 through 2023.
The column chart below displays the year-by-year performance of CPM using SSO (green) versus the S&P 500 (blue) from 2018 through Q3 2023.
Information about the various quantitative metrics referred to above can be found on Investopedia.com.
Editor’s Note: We are not advocating or promoting the use of leveraged financial products to trade financial assets, but rather using them as expressed here to more thoroughly explain how the CPM Model works. The examples show that the significant amount of risk that CPM removes from trading the S&P 500 can be incrementally added back into the model by utilizing different types of ETFs. The risk of loss trading in financial assets can be substantial and different types of securities, including ETFs and leveraged products, involve varying degrees of risk. Therefore, investors should carefully consider whether such trading is suitable for them in light of their own financial condition.
Disclosure/Disclaimer: The information on this website is provided solely for informational purposes and is not intended to be an offer to sell securities or a solicitation of an offer to buy securities. The strategies employed in managing this and other model portfolios may involve algorithmic techniques such as trend analysis, relative strength, moving averages, various momentum, and related strategies. There is no assurance that these strategies and techniques will yield positive outcomes or prevent losses. Past performance as indicated from historical back-testing is hypothetical in nature and does not involve actual client portfolios, does not consider cash flows or market events, and is not predictive of future performance. The model is managed by contemporaneously recording hypothetical trades. Such trades are not live trades and are not influenced by emotional or subjective reactions to extraneous market, economic, political and related factors. The performance for such model(s) is derived from utilizing a variety of technical trading strategies and techniques. Technical trading models are mathematically driven based upon historical data and trends of domestic and foreign market trading activity, including various industry and sector trading statistics within such markets. Technical trading models utilize mathematical algorithms to attempt to identify when markets are likely to increase or decrease and identify appropriate entry and exit points. The primary risk of technical trading models is that historical trends and past performance cannot predict future trends and there is no assurance that the mathematical algorithms employed are designed properly, new data is accurately incorporated, or the software can accurately predict future market, industry, and sector performance. Asbury Research LLC does not and cannot provide any assurance that an investment in the model portfolios will yield profitable outcomes. The risk of loss trading in financial assets can be substantial, and different types of investment vehicles, including ETFs, involve varying degrees of risk. Therefore, you should carefully consider whether such trading is suitable for you in light of your financial condition. An investor’s personal goals, risk tolerance, income needs, portfolio size, asset allocation and securities preferences, income tax, and estate planning strategy should be reviewed and taken into consideration before committing to a specific investment program. Please consult with your financial advisor to discuss the appropriateness of any strategy prior to investing. All investments involve risk. Principal is subject to loss, and actual returns may be negative. Returns are not guaranteed in any way and may vary widely from year to year.
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