Introduction
Sector rotation strategies seek to achieve relative outperformance by investing in sectors poised to surpass the broader market. At Asbury Research, we utilize our own proprietary algorithm for tracking investor asset flows — what we call “following the money” — to identify sectors with favorable quantitative conditions for upcoming relative outperformance.
Market Conditions Dictate Relative Outperformance Opportunities
However, the potential for successful relative outperformance is ultimately dictated by the market. Specifically, 1) how many sectors are outperforming the S&P 500, 2) how long they have been outperforming for, and 3) by how much directly determines the potential for successful sector rotation. And these conditions change over time, just as the market itself changes from uptrend to downtrend and from trending to non-trending environments.
When not many sectors are outperforming the S&P 500, and what outperformance trends there are do not last very long and are small in magnitude (typically due to market indecision), opportunities for relative outperformance are limited. We are in one of those environments now.
As of May 30th, 2024, nine of the eleven sectors are up for the year. Yet, a closer examination reveals that only three sectors — Communication Services, Technology, and Utilities — are actually outperforming the S&P 500. This outperformance has been tricky to capture, as highlighted in our “Beyond The SEAF Model” videos. Asset flows this year have been relatively weak compared to the average and have experienced quick and wild swings between offensive and defensive sectors. These fluctuations appear to be driven by a number of things including uncertainty about upcoming Federal Reserve Policy (when is the Fed going to lower interest rates?), an over-extended market (the S&P 500 is up 31% since October 2023), and uncertainty about the upcoming presidential election.
The key attribute of The SEAF Model is its ability to track where the money is flowing and how fast, which triggers and fuels relative performance trends. However, when the sector landscape is churning as it is now, the opportunities to capture what little outperformance is there become extremely limited. The graphic below shows that, through May 30th, only three sectors were outperforming the broad market S&P 500 and two of those were only outperforming by about 1%.
Conclusion
Market conditions change over time, from bullish to bearish trends and from trending to non-trending environments. And data-driven trading strategies are typically, if not invariably, more successful in some environments than others. In our view, the key to success in utilizing backtested, data-driven trading strategies like the SEAF Model is the consistent following of the model’s signals and the strict execution of them in the market, over time, to ensure that you are “in the game” when market conditions are the most favorable for generating profits.
Source: Select Sector SPDRs https://www.sectorspdrs.com/sectortracker
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.