The Two Layers
of Alpha
How sector tilts and stock picks each contribute, and what time does to both, over 25 years.
A 25-year empirical study of the Advising Alpha model portfolios, applying the Brinson-Hood-Beebower attribution framework to decompose alpha into sector allocation and stock selection layers.
The question, the framework, the finding.
If you could only know one thing about your portfolio for the next twenty-five years, would it be the sectors you owned, or the specific stocks you owned within those sectors?
Most retail investing media treats stock selection as the whole game. Most academic research treats sector allocation as the dominant source of variance in portfolio outcomes. Both views are too simple, and the truth in the middle is the most interesting question in active management.
This paper does not resolve that question for the universe of all active managers. That work has been done elsewhere, most famously by Brinson, Hood, and Beebower in their 1986 study of ninety-one large pension funds. What this paper does is demonstrate what the two layers look like in practice when applied with discipline over twenty-five years.
Specifically, we take each historical rebalance of the four Advising Alpha model portfolios, build a parallel Sector Mirrorportfolio that holds the same sector weights via SPDR sector ETFs, and decompose the alpha versus the S&P 500 into two layers:
- Sector allocation alpha. What you would have captured by replicating the sector tilts alone.
- Stock selection alpha. What the actual holdings added on top of the Sector Mirror.
These findings are specific to portfolios built with our methodology, where both the sector posture and the within-sector stock selection are deliberate. A manager who picks losing stocks within their sector exposure would show a negative selection layer; a manager whose sector tilts mirror the index exactly would show no sector layer. Our finding is that when both layers are pursued with discipline, both contribute meaningful alpha, and time amplifies the combined result dramatically.
This decomposition informs the structure of Advising Alpha. Sector allocation guidance is published free, because the empirical evidence is too useful to gate. Pro membership unlocks the stock-picking layer that compounds the same dollars into multiples of the Sector Mirror’s outcome.
The data is in the pages that follow. Read, verify, decide.
Why we wrote it. Where we’re coming from.
Advising Alpha is a research-led publisher run by serious investors with decades of combined experience in equity markets. Our methodology emerges from a long-running discipline of testing what actually works on Wall Street and being honest about what does not. We are not contrarians for the sake of being contrarian; we are skeptical of conventional wisdom that has not been verified against data, and willing to follow the data wherever it leads.
A representative example of how we work
The investment industry frequently cites a statistic about the cost of “missing the ten best days in the market,” showing that an investor who is out of the market for the ten best trading days of a given period materially underperforms a buy-and-hold investor. The implicit lesson is that you should never sit out, because you might miss those days. The statistic is true. The framing is incomplete.
The question that statistic does not address is what happens if you also miss the ten worst days. Run that test and the result reverses dramatically: an investor who misses both the ten best AND the ten worst days outperforms buy-and-hold, with materially less volatility and shallower drawdowns along the way. We ran this analysis nearly fifteen years ago, and others have run it since with the same conclusion.
What we found in the data, and what makes the conventional framing misleading, is that the ten best and ten worst days are not randomly distributed. They tend to cluster within the same ninety-day windows of high volatility. When markets are anxious and clarity is low, you get wild swings in both directions, and which side of those swings you happen to catch depends substantially on luck.
Findings like that are the kind of work this paper is part of. We start from a question, run the data honestly, and accept whatever conclusion the math produces. Either way, the discipline is the same.
Two acknowledgments before we proceed
On scope. Four portfolios is a small sample by any statistical standard. Brinson, Hood, and Beebower studied ninety-one pension funds and the academic literature has refined their findings for decades. We are not claiming to extend that universal academic case. What we are claiming is that twenty-five years of our specific methodology, applied through quarterly rebalances across multiple market regimes (the dot-com aftermath, the financial crisis, the post-2009 recovery, the 2020 pandemic, the rate-hike cycle, the AI rally), shows the two-layer pattern with consistency and at meaningful magnitudes.
On bias. The authors of this paper have spent decades developing and refining the methodology being studied. We have inherent perspective on what kinds of selection criteria work and what kinds do not. The findings here are consistent with our priors. We have done our best to present the data honestly, with the methodology and source files available for verification, and we encourage skeptical reading. The Brinson-Hood-Beebower framework we apply is widely accepted; the data we run through it is ours, and the interpretation is ours.
This is research, offered freely as part of an ongoing body of work. It is not investment advice. We hope it is useful.
The question.
Imagine two investors at the start of 2001. Each puts $10,000 to work. Neither touches the money for twenty-five years.
Investor A puts the money in an S&P 500 index fund. By April 2026, the account is worth roughly $54,000. The math of broad-market compounding at about 7% annually over a quarter-century.
Investor B picks a strategy with sector tilts. The same broad universe of stocks, but allocated differently from the index. Maybe overweight technology in the early 2000s, underweight financials before 2008, overweight energy in the 2014-2016 slump. The actual specific stocks held inside each sector are the index versions. By April 2026, the account is worth roughly $89,000.
Investor C does what Investor B does, but with one additional discipline. Inside each sector, instead of holding the index version of that sector, Investor C holds a curated portfolio of specific names selected with some methodology. By April 2026, the account is worth roughly $264,000.
The same $10,000 starting stake. The same twenty-five-year horizon. Three very different outcomes.
Most of the popular investing literature gives one of two answers, both of which are too simple. The first comes from passive-index advocacy: stock picking does not work, sector allocation does not work, just buy the index. This view has academic support in the average mutual fund’s failure to beat its benchmark, but it ignores the dispersion among active managers and treats every stock-picking strategy as equally indistinguishable from random.
The second comes from active-management marketing: stock picking is the alpha, sectors are noise, find the next NVIDIA before it’s a household name. This view has surface appeal because the wins are dramatic, but it dramatically overweights the role of selection and underweights the structural decisions about what kinds of businesses to own at all.
The honest answer requires data. Specifically, twenty-five years of paired historical portfolios, where one tracks actual selection and the other tracks the same sector weights without selection, both compared against the same broad-market benchmark. That paper is the paper you are reading.
The framework.
We are not the first to ask this question. In 1986, three pension fund researchers named Gary Brinson, Randolph Hood, and Gilbert Beebower published a paper in the Financial Analysts Journal titled “Determinants of Portfolio Performance.”They studied ninety-one large pension funds across a ten-year window and decomposed each fund’s return into three sources:
- Asset allocation. The mix of stocks, bonds, and cash.
- Stock selection within each asset class. Picking specific securities.
- Market timing. Tactically moving between asset classes.
Their finding became one of the most-cited results in modern portfolio theory: asset allocation explained roughly 93.6% of the variance in pension fund returns. Stock selection and market timing combined explained the rest.
The result has been argued and refined for nearly forty years. The exact magnitudes differ depending on what you measure (variance versus return, decade-specific results, the kind of fund being studied). But the directional finding has held up across multiple replications: the structural decision about what kinds of investments to own does most of the heavy lifting in long-term portfolio outcomes. The specific securities chosen within those structures contribute, but the contribution is smaller than retail investors typically assume.
Adapting the framework to all-equity portfolios
Brinson-Hood-Beebower was about pension funds with multiple asset classes (stocks, bonds, cash). For an all-equity portfolio like Advising Alpha’s, the asset-allocation layer collapses. There is only equity. So the framework simplifies to two layers:
- Sector allocation: how much alpha comes from being overweight Technology, Healthcare, Energy, or other GICS sectors versus the broad market?
- Stock selection within sectors: how much additional alpha comes from picking specific companies within those sectors versus owning the sector index?
The two layers add up to total alpha versus the S&P 500. If a portfolio compounds twenty-five percentage points ahead of the S&P 500 over a decade, that twenty-five could come entirely from sector tilts (the equity equivalent of asset allocation), entirely from stock selection within sectors, or any mix of the two. The decomposition tells us which.
Applying the framework requires a counterfactual. For any historical actual portfolio, you need to construct a parallel what-if portfolio that holds the same sector weights through the same windows but with the sector index instead of the actual selections. Then you compare returns through every period. That counterfactual portfolio is what the rest of this paper calls the Sector Mirror.
The methodology.
For each Advising Alpha portfolio (Core 20, Market Masters, Tepper Tactical, BioTech 10), we walked the entire historical backtest record from inception through April 2026. At each rebalance date, we recorded the portfolio’s actual holdings and their weights.
For each holding, we classified the company by GICS Level-1 sector. The eleven GICS sectors used throughout this paper are: Information Technology, Financials, Health Care, Consumer Discretionary, Communication Services, Industrials, Consumer Staples, Energy, Utilities, Real Estate, and Materials.
We rolled the holdings up into sector weights at each rebalance date. Then for that rebalance window (running from the current rebalance to the next), we built a parallel Sector Mirror Portfolio that held the same sector weights, but instead of the actual stocks, held the corresponding SPDR sector ETF for each sector:
| Sector | SPDR ETF |
|---|---|
| Information Technology | XLK |
| Financials | XLF |
| Health Care | XLV |
| Consumer Discretionary | XLY |
| Communication Services | XLC (post-2018; XLK before) |
| Industrials | XLI |
| Consumer Staples | XLP |
| Energy | XLE |
| Utilities | XLU |
| Real Estate | XLRE (post-2015; XLF before) |
| Materials | XLB |
For each rebalance window, three returns were computed: the Actual portfolio return (weighted total return of held tickers), the Sector Mirror return (same sector weights, executed in sector ETFs), and the S&P 500 return. These were chained through every window across the full twenty-five-year history to produce cumulative growth lines. The decomposition is straightforward arithmetic:
Sector allocation alpha = Sector Mirror return minus S&P 500 return
Stock selection alpha = Actual return minus Sector Mirror return
A few caveats apply, listed honestly. Pre-2018 Communication Services holdings are mapped to XLK; the GICS classification did not separate Communication Services until the 2018 reorganization. Pre-2015 Real Estate holdings are mapped to XLF; XLRE launched in October 2015. Some historical tickers are no longer publicly traded; the price-data provider returns whatever history is available. Returns use dividend-and-split-adjusted closing prices, which differs slightly from the published WhaleWisdom backtest in absolute level but the decomposition ratios are stable.
What the actual portfolios are doing.
The Sector Mirror, by construction, holds whatever the sector ETF holds (the index-weighted average company in each sector). The actual Advising Alpha portfolios do not. They hold specific names selected by a deliberate methodology.
The Advising Alpha selection methodology starts from institutional behavior. We track the holdings, additions, and reductions reported by professional investors with verifiable long-term records, primarily through the SEC’s quarterly 13F filings. Stocks consistently being accumulated by managers with strong track records get screened more rigorously for fundamentals. Stocks with no institutional support get ignored regardless of how interesting they look on a chart. Within sectors, we are systematically biased toward names with structural buying pressure from informed capital.
This matters for interpreting the decomposition results. The selection alpha we measure (Actual minus Sector Mirror) is not the alpha from any stock-picking within sectors. It is the alpha from this specific methodology of selecting stocks with documented institutional support. A different selection methodology, or a manager picking randomly within sectors, would produce a different selection alpha, possibly negative.
A note on the rebalance cadence
The Advising Alpha portfolios rebalance four times per year, in 5-day windows on the 20th to 25th of February, May, August, and November. The cadence has a structural reason and a methodological reason.
The structural reason is the 13F filing cycle. Institutional managers with more than $100 million under management must report their long-only equity holdings to the SEC within 45 days of each calendar quarter end. Our rebalance windows land roughly ten days after each 13F deadline, which is the soonest we can act on the most recent quarter of institutional flow data while leaving operational slack for data ingestion, validation, and trade-list publication.
The methodological reason is the earnings cycle. We prefer to enter and hold positions through at least one full earnings reporting cycle, ideally several. Many of the stocks we select have a thesis built on an expected earnings catalyst (margin expansion, new product cycle, capital structure shift, end-market recovery), and the share-price reflection of that catalyst takes time to develop. Trading mid-quarter, between the bulk of one quarter’s earnings releases and the start of the next, lets us hold positions through the catalyst windows where the thesis actually plays out.
The combined effect is a quarterly cadence anchored to two separate cycles (institutional disclosure and corporate reporting) running in parallel. Both cycles are external to us; the rebalance schedule is the disciplined response to both.
Results: Core 20
Core 20 is Advising Alpha's flagship diversified portfolio. The published thesis selects twenty quality compounders across multiple sectors and rebalances quarterly. The strategy emphasizes companies with durable moats, high returns on capital, and disciplined capital allocation.
Backtest window: May 2001 → February 2026 (24.75 years).
Cumulative returns
| Cumulative | Approximate CAGR | |
|---|---|---|
| Core 20 actual | +2,334% | 13.9% |
| Sector Mirror | +790% | 9.2% |
| S&P 500 | +436% | 7.0% |
Decomposition of total alpha vs S&P 500
| Sector allocation alpha | +355pp |
| Stock selection alpha | +1,544pp |
| Total alpha | +1,899pp |
| Sector share of total alpha | 18.7% |
Reading the result
Core 20 tells the most balanced story of the four portfolios. Both layers worked. The sector tilt mattered — being overweight Technology, quality Consumer Staples, and select Financials throughout the backtest period generated 355 percentage points of compounding above the S&P 500. The stock selection then magnified those tilts by another 1,544 percentage points, with names like Apple, Microsoft, Moody's, American Express, and others compounding well ahead of their sector ETFs over multiple cycles.
The pattern is consistent with how the strategy was designed to work. Core 20's screening discipline both selects sectors with durable competitive structures and selects the leading compounders within those sectors. The decomposition shows the discipline doing its job at both levels.
Results: Market Masters
Market Masters is the institutional-flow-following diversified portfolio. The published thesis identifies sectors and themes where institutional capital is rotating in, and selects names within those rotations.
Backtest window: August 2007 → April 2026 (18.7 years).
Cumulative returns
| Cumulative | Approximate CAGR | |
|---|---|---|
| Market Masters actual | +1,769% | 16.9% |
| Sector Mirror | +618% | 11.1% |
| S&P 500 | +388% | 8.8% |
Decomposition of total alpha vs S&P 500
| Sector allocation alpha | +230pp |
| Stock selection alpha | +1,151pp |
| Total alpha | +1,381pp |
| Sector share of total alpha | 16.6% |
Reading the result
Market Masters is structurally similar to Core 20 in its decomposition shape. The sector layer delivered roughly 230 percentage points of compounding above the S&P 500, and stock selection delivered another 1,151 percentage points on top.
The thematic concentration in homebuilders, industrial distributors, energy services, and select consumer cyclicals over the backtest period was on average a winning sector posture. Some specific themes paid off dramatically (the post-2009 housing recovery, the 2017-2019 industrial cycle); others were timing-sensitive. The aggregate sector layer cleared the broad market by a comfortable margin even before any specific stock picks were made. The stock selection layer is where the strategy's identification work compounded — picking the right homebuilder within the housing trade, the right industrial distributor within the industrial cycle, the right energy services name within the capex recovery.
Results: Tepper Tactical
Tepper Tactical is the most concentrated of the diversified portfolios. The published thesis identifies high-conviction tactical positions across sectors, with willingness to hold notable positions in single names and themes. Three of the current twenty-five positions are sector or country ETFs (KraneShares CSI China Internet, iShares MSCI South Korea, SPDR S&P Biotech), used as expressive vehicles for thematic conviction.
Backtest window: May 2001 → February 2026 (24.75 years).
Cumulative returns
| Cumulative | Approximate CAGR | |
|---|---|---|
| Tepper Tactical actual | +9,897% | 20.5% |
| Sector Mirror | +1,007% | 10.1% |
| S&P 500 | +436% | 7.0% |
Decomposition of total alpha vs S&P 500
| Sector allocation alpha | +571pp |
| Stock selection alpha | +8,890pp |
| Total alpha | +9,462pp |
| Sector share of total alpha | 6.0% |
Reading the result
Tepper Tactical is overwhelmingly a stock-selection story. The sector layer matters (a Sector Mirror that compounds at 10.1% annually for twenty-five years is a successful product on its own), but the actual portfolio's stock-selection layer is several multiples larger.
The reason is the strategy's structural willingness to take large concentrated positions in specific names and themes during their setup periods. Owning Apple, NVIDIA, Microsoft, Meta, Amazon, and Taiwan Semiconductor in the periods where each ran from undervalued to dominant, plus the China internet ETFs during the 2010-2014 emerging-market cycle, plus specific cyclical positions across multiple regimes, generated compounding that no broad sector ETF can replicate. A sector ETF gives you the average company in a sector. Tepper Tactical was structurally biased toward holding the leading names within sectors, and the leaders dramatically outperformed the averages.
Results: BioTech 10
BioTech 10 is Advising Alpha's specialty sector portfolio. Unlike the diversified portfolios, all ten holdings sit within Health Care and specifically within Biotechnology. The strategy selects clinical-stage biotech names with catalysts and platform potential, against a 50% stop-loss discipline. Because BioTech 10 is structurally a single-sector portfolio, the Sector Mirror reduces to the SPDR S&P Biotech ETF (XBI) at 100% of the portfolio weight throughout.
Backtest window: February 2002 → February 2026 (24 years).
Cumulative returns
| Cumulative | Approximate CAGR | |
|---|---|---|
| BioTech 10 actual | +5,272% | 18.1% |
| Sector Mirror | +760% | 9.4% |
| S&P 500 | +523% | 7.9% |
Decomposition of total alpha vs S&P 500
| Sector allocation alpha | +236pp |
| Stock selection alpha | +4,512pp |
| Total alpha | +4,749pp |
| Sector share of total alpha | 5.0% |
Reading the result
BioTech 10 is the cleanest stock-selection story in the study. Even just owning the biotech sector via XBI from 2002 through 2026 would have outperformed the S&P 500 by 236 percentage points. Biotech as a sector has been a long-run winner, and any disciplined exposure would have captured that.
The actual BioTech 10 strategy, however, compounded its starting capital another factor of roughly seven times beyond what XBI alone delivered. Selecting specific clinical-stage names against catalyst calendars, sizing for asymmetric outcomes, and applying the 50% stop-loss discipline to control downside generated the dominant share of the portfolio's outperformance.
Cross-portfolio comparison.
The four portfolios decompose differently. The summary table:
| Portfolio | Window | Sector α | Selection α | Sector share |
|---|---|---|---|---|
| Core 20 | 2001 → 2026 | +355pp | +1,544pp | 18.7% |
| Market Masters | 2007 → 2026 | +230pp | +1,151pp | 16.6% |
| Tepper Tactical | 2001 → 2026 | +571pp | +8,890pp | 6.0% |
| BioTech 10 | 2002 → 2026 | +236pp | +4,512pp | 5.0% |
Two patterns emerge.
First, every portfolio’s Sector Mirror beat the S&P 500. The smallest sector alpha (Market Masters at +230pp) translates to roughly 250 basis points of CAGR outperformance over almost nineteen years. Even the most modest sector tilt in the study would have outperformed roughly four out of five professional active managers as a standalone strategy. Sector allocation, applied consistently, compounds.
Second, stock selection within sectors adds the dominant share of the alpha across all four portfolios. The selection layer ranges from 1,151 to 8,890 percentage points, always larger than the sector layer. In every portfolio, the act of choosing specific companies within sectors compounded several multiples ahead of just owning the sector ETF.
The variation across the four portfolios is also interesting. Core 20 and Market Masters both show balanced decompositions with sector tilts contributing 16-19% of total alpha. Tepper Tactical and BioTech 10 are closer to pure stock selection, with sector tilts contributing only 5-6%. The pattern reflects strategy design: the balanced diversified portfolios use sector tilts as a meaningful tool, while the more concentrated portfolios derive most of their alpha from individual name selection within their concentrated sector exposures.
A reader picking which Advising Alpha portfolio fits their preferences can use this decomposition. A reader who values the structural sector-tilt discipline as much as the name-by-name selection might gravitate toward Core 20 or Market Masters. A reader looking for the maximum stock-selection premium might gravitate toward Tepper Tactical or BioTech 10.
The time argument.
The most important context for everything in this paper is time.
A portfolio that compounds at 9.2% per year (Core 20’s Sector Mirror CAGR) and a portfolio that compounds at 13.9% per year (Core 20’s actual CAGR) sound similar at the year-one level. The difference is roughly $475 on a $10,000 stake after twelve months. An investor reading those numbers might reasonably conclude that the difference is small enough not to bother with.
Compound the same two rates over twenty-five years and the conclusion changes entirely.
$10,000 invested across four time horizons
| Strategy | CAGR | 1 year | 5 years | 10 years | 25 years |
|---|---|---|---|---|---|
| S&P 500 | 7.0% | $10,700 | $14,026 | $19,672 | $54,274 |
| Sector Mirror (Core 20 tilts) | 9.2% | $10,920 | $15,524 | $24,099 | $89,007 |
| Core 20 (actual, with selection) | 13.9% | $11,390 | $19,180 | $36,789 | $264,031 |
| Tepper Tactical (actual) | 20.5% | $12,050 | $25,406 | $64,553 | $1,043,500 |
At year one, the differences between rows are modest. By year five, the gaps are visible. By year ten, the gaps are wide enough to change a financial plan. By year twenty-five, the gaps are decisive.
This is the central asymmetry of long-term compounding. The right time horizon for evaluating either strategy is at least ten years, ideally twenty. Anything shorter is noise.
The implication for retail investors is structural. The single most powerful advantage available to a retail investor that no institution can replicate is time horizon. A pension fund has obligations to beneficiaries every year. A mutual fund has redemption pressure every quarter. A retail investor who is genuinely investing for retirement can hold for decades without forced selling. The retail investor who refuses to give up that horizon advantage captures all the compounding the framework promises. The retail investor who churns positions every twelve to twenty-four months captures none of it, no matter which framework they use.
The Advising Alpha methodology is built for the long-horizon retail investor. Trade alerts are calm because urgency is the wrong frame for a strategy whose advantage is durable, structural, and compounding. The Pro tier exists because stock-selection alpha is real, but the value of that alpha is realized only over years, not weeks.
What this means for investors.
Three concrete takeaways:
1. Sector allocation is real, durable, and compoundable.
The data is consistent across all four portfolios studied: every Sector Mirror cleared the S&P 500 by a meaningful margin over its respective backtest window. Investors who use sector ETFs to express thoughtful sector tilts can outperform passive index investing without picking individual stocks. This is the core proposition of Sector Alpha, our free model portfolio published quarterly to all signed-in Advising Alpha members. Sector Alpha follows the same sector tilts as Core 20, executed through the SPDR sector ETFs documented in this paper.
2. Stock selection within sectors adds substantially more alpha on top.
This finding is also consistent across all four portfolios. In every case, the selection premium dominates the sector premium by a factor of two to ten. Investors who want the full Advising Alpha methodology, including the actual stock selections within sector tilts, can subscribe to Pro membership and follow the published holdings of Core 20, Market Masters, Tepper Tactical, and BioTech 10 directly.
3. Time is the multiplier on both layers.
The decomposition findings are mathematically true at every horizon, but the dollar magnitudes scale dramatically with time. A meaningful difference at one year is a transformative difference at twenty-five. This is the most important framing for any investor evaluating their own portfolio strategy. The right time horizon for evaluating an active equity strategy is the holding period over which the strategy is expected to be held. Short-horizon evaluation is mostly noise; long-horizon evaluation is where the framework’s value emerges.
We built Advising Alpha for serious investors who think about their portfolio in years and decades, not weeks and quarters. The data in this paper is part of why.
About Sector Alpha.
Sector Alpha is published free to all signed-in Advising Alpha members. It implements the sector tilts identified by Core 20’s methodology, executed through SPDR sector ETFs at the same target weights. The model rebalances on the same quarterly schedule as Core 20 (the 20th to 25th of February, May, August, and November), with a published trade list each quarter.
Sector Alpha is not investment advice. It is a published model portfolio, intended for educational and research purposes. Investors who choose to follow it execute trades in their own brokerage accounts at their own discretion, after conducting their own investment research, and with their own consideration of taxes, account constraints, time horizon, risk tolerance, and other factors specific to their situation.
Sector Alpha exists to give serious investors a way to access the sector-allocation layer of the Advising Alpha methodology directly, without an active equity-selection commitment. Members who want the full methodology including individual stock selection can subscribe to Pro for access to the four model portfolios in their published form.
Pro tier: Adds Core 20, Market Masters, Tepper Tactical, BioTech 10 (every holding, every weight, every trade), the Pro Weekly Brief (Saturday performance recap), the Stock Normality Indicator on every position, member commentary, and rebalance trade alerts the day each portfolio rebalances.
See pricing and full feature comparison at advisingalpha.com/pricing.
About Advising Alpha.
Advising Alpha publishes original model portfolios built by studying what’s actually working on Wall Street. Our methodology examines institutional flows, fundamental quality, and sector dynamics to construct portfolios that anyone can run. We publish quarterly trade lists; members execute the trades in their own brokerage accounts after conducting their own investment research.
A note on our regulatory status
Advising Alpha operates as a financial publisher under the publisher exemption of the Investment Advisers Act of 1940, Section 202(a)(11)(D). The Supreme Court clarified the scope of this exemption in Lowe v. SEC, 472 U.S. 181 (1985), establishing that bona fide publishers offering impersonal investment commentary of general and regular circulation are not investment advisers and are not required to register as such under the Act. Our publications meet that standard: the model portfolios and research we publish are impersonal, distributed to all subscribers in the same form, and produced on a regular schedule.
Operating under this exemption means several things must be stated plainly:
- We are not registered investment advisers, and nothing we publish is, or should be construed as, personalized investment advice.
- Nothing in this paper or any other Advising Alpha publication is a recommendation to any individual reader to buy, sell, or hold any specific security.
- We do not know your financial situation, your tax situation, your other holdings, your time horizon, your risk tolerance, your goals, or any of the other facts that would be required to advise you personally.
- Specific positioning decisions in your own account are yours to make in consultation with a registered investment adviser, accountant, or other professional with knowledge of your specific situation.
We treat the publisher framework as a discipline, not a loophole. Our model portfolios are research artifacts that any reader can study, learn from, and choose to follow at their own discretion. They are not, and never have been, an instruction set telling any individual reader what to do with their own money.
More at advisingalpha.com.
Disclaimers and references.
Disclaimers
The performance results presented in this paper are based on historical backtested data. Backtested performance is hypothetical and does not represent the results of actual trading. Backtested results have inherent limitations, including the benefit of hindsight in selecting the methodology and the holdings used. Backtested performance does not reflect transaction costs, brokerage commissions, taxes, or other expenses an investor would have incurred had they actually traded the strategy. No representation is made that any account is likely to achieve profits or losses similar to those shown.
Past performance is not indicative of future results. Future returns from any of the strategies discussed may differ materially from the historical results presented here, including the possibility of losses.
Sector Alpha and the Advising Alpha Pro model portfolios are educational and informational; they are not personalized investment advice. Investors should consult with their own financial, tax, and legal advisers before acting on any strategy discussed.
References to specific securities or sector ETFs are for illustration of the methodology and do not constitute recommendations to buy or sell those securities.
References
- Brinson, Gary P., Hood, L. Randolph, and Beebower, Gilbert L. “Determinants of Portfolio Performance.” Financial Analysts Journal, July/August 1986, pp. 39-44.
- Brinson, Gary P., Singer, Brian D., and Beebower, Gilbert L. “Determinants of Portfolio Performance II: An Update.” Financial Analysts Journal, May/June 1991, pp. 40-48.
- S&P Dow Jones Indices, S&P 500 Sector Indices Methodology, available at spglobal.com/spdji.
- State Street Global Advisors, SPDR Sector ETF fact sheets, available at ssga.com/spdrs.
- Advising Alpha, “Sector Mirror Backtest Methodology and Findings,” May 2026 (source code and full output files available on request).