The SEAF (Sector ETF Asset Flows) Model

SEAF is an acronym for Sector ETF Asset Flows.  The SEAF Model was created to quantitatively identify long / overweight opportunities in US market sectors.  SEAF does this by “following the money” as it moves around the 11 Select Sector SPDR ETFs, which together comprise the S&P 500.  Following the movement of money identifies these opportunities sooner and more accurately because, with the SEAF Model, we can see the money moving before the trends this money movement creates become apparent.

SEAF Model Graphic

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How We Derive The SEAF Model Signals

In the SEAF Model Graphic, the top two sectors in each category according to percent change in inflows are highlighted in green and the top two sectors in each category according to percent change in outflows are highlighted in red.   The premise of the model is to invest in the sectors that the money is going to and to avoid the sectors that the money is leaving.

Using & Interpreting The SEAF Graphic & Accompanying Chart

The Ranking column in the SEAF Model Graphic above sorts the entire table of 11 sector ETFs according to the sum of rankings in the Trading, Tactical, and Strategic categories, from largest inflows to largest outflows.  The SEAF Model Scores chart below displays this ranking according to Favored (score of 3-15, green), Neutral (score of 16-24, yellow), and Avoid (score of 25-33, red) sectors.  

SEAF Model Scores Chart

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Performance Data

The next two tables below display performance data for the SEAF Model from January 2020 through December 2023.

The first table below displays the quarter-by-quarter relative performance of the SEAF Model vs. the S&P 500, showing that SEAF has outperformed the S&P 500 in 11 of the past 16 quarters (69%) tested through December 2023.  

The bottom part of the table shows that SEAF has:

  • outperformed the S&P 500 every year since 2020,
  • outperformed the S&P 500 (SPY) by 20.8% during the 2021 bull market,
  • outperformed SPY by 27.9% during the 2022 bear market,
  • and has outperformed SPY by 50.4% throughout the entire period beginning Q1 2020.












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The next table below compares quantitative performance vs. the S&P 500 from Q1 2020 through Q4 2023, showing that the SEAF Model outperformed the S&P 500 in all nine categories shown. 

SEAF Model Quantitative Metrics Q1 2020 – Q4 2023

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SEAF Model performance Highlights:

The charts below plot the daily performance of the SEAF Model vs. the S&P 500, and the quarter-by-quarter performance of the SEAF Model vs. the S&P 500, in terms of percentage return since January 2020.

SEAF Model Daily Relative Performance vs. S&P 500: Q1 2020 – Q4 2023

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SEAF Model Quarterly Relative Performance vs. S&P 500: Q1 2020 – Q4 2023

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, does not involve actual client portfolios, does not consider cash flows and 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 various technical trading strategies and techniques. Technical trading models are mathematically driven based on 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.