Note on figures cited below. Some statistics in this article come from the V12 universe cohort (227,594 BUY filings, period through 2024). Since 2025-01-01 the live out-of-sample BUY universe (28 markets) shows a 52.96 percent win rate and a +8.59 percent mean 90-day return on 20,688 filings. See methodology for the current numbers and the STRATEGY_PROOF block for the frozen audit reference.
Sector leadership often changes in the tape before it changes in the headlines.
Insiders do not ring a bell at the bottom. They file forms, usually in dribs and drabs, often when everyone else is busy writing obituaries for the sector.
The idea behind sector rotation via insider flow is simple enough to survive contact with data. If corporate officers and directors, taken in aggregate, begin buying their own shares across a sector after a large drawdown, that cluster can reveal improving expected returns before earnings revisions, before sell-side upgrades, and certainly before magazine covers rediscover "value". The difficulty is not the concept. It is measurement. One heroic CEO purchase can be theatre. Fifty open-market buys across a beaten-up industry group are harder to dismiss.
This article looks at the 2020-2026 period through that lens. The emphasis is not on a single stock-picking signal, but on net buying intensity by GICS sector and whether it led subsequent sector ETF performance. We do not have a fresh proprietary data extract for this piece, so specific counts from our filing database are marked as n/a where necessary. That is preferable to the usual market custom of filling gaps with confidence.
Why sector-level insider flow deserves a seat at the table
The academic case for insider trading as an information signal is old, durable, and annoyingly resistant to fashion. Corporate insiders, especially when buying rather than selling, tend to earn abnormal returns on average. The effect is not infinite, not frictionless, and not equally strong everywhere. But it exists. Seyhun's work remains foundational, and later papers refined the point rather than burying it.
What changes when we move from single names to sectors is aggregation. Aggregation solves three problems at once.
First, it reduces idiosyncratic noise. One director may buy to signal confidence after a bad quarter. Another may buy because the board would prefer not to look embarrassed. Across dozens of firms, those motives partly cancel. What remains is closer to a common sector-level expectation.
Second, aggregation aligns better with how allocators actually move money. Most macro and multi-asset investors rotate exposure through sector baskets, futures, or ETFs. They are not trying to infer whether one industrial distributor is cheap. They are trying to decide whether industrials as a whole are moving from "avoid" to "accumulate".
Third, sectors matter because the information set is often shared. Commodity prices, credit conditions, reimbursement changes, regulation, capex cycles, and inventory turns hit groups of firms together. If insiders across the same sector buy within a short window, that is often a statement about the environment, not only their own ticker.
The signal is buying, not selling
This is the part that still catches newcomers. Insider selling is much less informative than insider buying. Executives sell for taxes, diversification, divorce, school fees, and the occasional yacht. They buy for far fewer reasons. Most of those reasons are bullish.
At the sector level, this asymmetry becomes more useful. A wave of selling in technology may tell you many executives are rich. We had suspected as much. A wave of open-market buying in regional banks after a deposit shock is a different animal.
Intensity matters more than raw count
A decent sector signal needs more than a tally of transactions. The useful variants usually scale purchases by market capitalisation, average daily value traded, or recent sector activity. They also net buys against sells and often weight by insider seniority.
That is why I prefer the phrase net buying intensity rather than "insider sentiment". Sentiment is what strategists say on television. Intensity is what you can calculate.
A practical sector measure might look like this:
- open-market insider buy value minus sell value,
- divided by aggregate free-float market cap or ADV,
- computed over a rolling 30- or 60-day window,
- then standardised relative to each sector's own history.
That last step matters. Energy and utilities do not produce filings in the same cadence as software. A raw comparison is a category error wearing a tie.
2020, the first clean test, cyclicals bought before the reopening consensus
The pandemic crash was a gift to signal researchers, if not to humanity. It created a violent, synchronised drawdown across sectors, followed by a staggered recovery. In such periods, insider behaviour is easier to interpret because valuation dislocations are large and public narratives are unstable.
Industrials, consumer discretionary, and financials showed the classic pattern
In March and April 2020, insiders across cyclical sectors stepped in aggressively. This was visible in US Form 4 data and in multiple market commentaries at the time, with a notable rise in insider buying relative to selling as equities bottomed. The broad pattern fit the historical script. Insiders buy hardest when volatility is high, prices are down sharply, and outside investors are still liquidating.
The sector interpretation was straightforward. If management teams in industrials, consumer discretionary, and financials were willing to add exposure into a collapse, they were implicitly underwriting survival first and demand normalisation second. That did not mean an immediate V-shaped recovery in every industry. It did mean expected returns had likely shifted.
The subsequent performance of cyclical sector ETFs through late 2020 and into 2021 broadly validated that reading. Financials and industrials outperformed the market during the reopening and reflation phase. Consumer discretionary was more mixed internally, because the sector contains both durable reopening winners and expensive growth franchises, but the broad cyclical impulse was real.
Energy lagged at first, then became the cleaner insider story
Energy in 2020 was a special case because the sector suffered both pandemic demand destruction and an oil-price shock. The drawdown was so severe that insider buying could initially be read as either informed conviction or old-fashioned denial. Markets are democratic in the sense that they let both theories trade.
By late 2020 and into 2021, however, the insider case improved. Balance sheets had been triaged, capex discipline was rising, and commodity exposure gave the sector obvious operating leverage to reopening and inflation. When insider buying persists after the first bounce, that is often more informative than the first burst. The first burst can be bravery. The second is usually a view.
2021-2022, energy and materials, when insiders liked scarcity before everyone liked inflation
The inflation regime shift produced perhaps the clearest sector-level insider case study of the period. By the time inflation became the only topic at every investment conference, energy had already rerated. The better question is whether insider flow gave an earlier clue.
Energy insiders were buying into hated capital intensity
Energy is exactly the kind of sector where insider flow can matter. It is cyclical, capital-intensive, politically unpopular in some investor circles, and often under-owned after long drawdowns. Those conditions are fertile ground for insiders to exploit mispricing.
By 2021, the setup was improving. Demand was recovering, supply discipline was tighter than in prior cycles, and years of underinvestment were beginning to matter. Public investors were still debating whether the sector was investable or merely a temporary inflation hedge. Insiders, who had a better read on project economics and capital allocation, had less need for debate.
The sector ETF performance that followed in 2021 and especially 2022 was dramatic. Energy was the standout winner among major US sectors, while broader growth-heavy indices struggled under rising rates. You do not need a complicated model to notice that insiders buying a cheap, cash-generative, under-owned sector before a commodity squeeze is useful information. You do need a model to avoid saying this about every commodity bounce that later fizzles.
Materials offered a weaker but related signal
Materials shared some of the macro tailwinds, though with more dependence on China, industrial demand, and specific commodity chains. Insider buying in materials tends to be less clean than in energy because the sector is more heterogeneous. Chemicals, metals, packaging, and construction materials do not move in lockstep.
Still, as an adjacent case study, materials illustrate a broader point. Sector signals are strongest when the sector has a common macro driver and weakens when the GICS bucket becomes a small federation of unrelated businesses. This is one reason healthcare can be tricky, despite often showing bursts of insider buying after biotech drawdowns.
| Market | Regulator | Rule | Deadline | Notes |
|---|---|---|---|---|
| US | SEC | Exchange Act Section 16, Form 4 | T+2 | Rapid disclosure supports short-horizon aggregation of insider purchases. |
| EU | ESMA framework via national regulators | MAR Art 19 | T+3 business days | PDMR transactions are disclosed under a harmonised regime, though local presentation varies. |
| FR | AMF | MAR Art 19 | T+3 business days | French issuer and AMF disclosures can be used for sector-level monitoring, with careful entity mapping. |
2023, regional banks, the sector signal that looked reckless until it looked early
If 2020 was the broad stress test and 2022 was the inflation test, 2023 gave us the cleanest single-sector event study: US regional banks after the banking panic.
Cluster buying after a confidence shock
Following the failures of Silicon Valley Bank and Signature Bank in March 2023, regional bank shares sold off violently. Deposit flight fears, unrealised bond losses, and contagion logic hit the sector in one blow. In the middle of that chaos, a number of bank insiders and directors bought stock in the open market.
This matters because bank insider buying after a confidence shock is unusually informative. Insiders have a better line of sight into deposit stability, funding mix, local franchise health, and supervisory dialogue than outside investors. They are not omniscient, but they are not guessing from Twitter threads either.
The sector did not immediately recover in a straight line. That is another useful lesson. Sector insider flow is a leading indicator, not a magic trick. The market can remain suspicious for months, especially in regulated sectors where solvency and liquidity are hard for outsiders to verify. But subsequent rebounds in several regional-bank baskets showed that the insider cluster was not random noise.
Why this case matters for signal design
Regional banks also reveal the importance of concentration. A sector-level signal can be dominated by a handful of firms if the event is acute. That is not necessarily bad, but it should be measured. If ten banks account for 80 percent of buy value, call it a concentrated cluster, not a broad sector endorsement.
A good research design would therefore track at least four dimensions:
- net buy value,
- number of distinct issuers,
- number of distinct insiders,
- concentration of activity among top names.
Without that, "financials insider buying rose" can mean anything from broad conviction to three directors trying to look calm on a Tuesday.
2024-2026, healthcare and technology, where insider flow became more selective
The later part of the period is less about one giant sector call and more about discrimination within sectors. That is healthy. Real signals become subtler as markets normalise.
Healthcare, fertile ground, but only in the right subsectors
Healthcare often attracts insider buying after biotech drawdowns, reimbursement scares, or device-cycle disappointments. The problem is that healthcare is not one sector in any economically coherent sense. Large pharma, managed care, medtech, tools, and pre-revenue biotech belong in the same GICS bucket but not in the same valuation universe.
That means sector-level net buying intensity can be informative only when the buying is broad enough, or when the article is honest enough to admit it is really an industry-group signal wearing a sector badge.
From 2024 onward, healthcare looked more like a selective hunting ground than a blanket rotation. Insider buying in profitable medtech or cash-rich therapeutics firms after deratings is different from serial insider support in speculative biotech. The former can lead recoveries. The latter can simply lead to more filings.
Technology, the signal weakens when compensation muddies the water
Technology presents the opposite problem. It is a large, liquid sector with substantial insider activity, but much of that activity is contaminated by stock-based compensation, scheduled sales, and founder control structures. Open-market buying by senior executives in large-cap technology can still matter, but it is rarer and often drowned out by mechanical selling.
This makes technology a poor candidate for naive sector-level insider models. If you want a useful signal, you usually need to isolate semiconductors, software, or hardware separately, exclude founders with super-voting control, and focus only on discretionary purchases.
The dry version is that the sector is noisy. The less dry version is that a chief executive receiving another grant of stock options does not count as "bullish conviction", however enthusiastically investor relations phrases it.
Building the indicator properly, what to measure and how to test it
If the goal is to use insider flow as a leading indicator for sector ETF performance, the methodology needs to survive a referee report and a bad month.
A workable definition of net buying intensity
At minimum, define sector net buying intensity as:
[ \text{NBI}{s,t} = \frac{\sum \text{Open-market buy value}{s,t} - \sum \text{Open-market sell value}{s,t}}{\text{Sector market cap or ADV}{s,t}} ]
Then standardise this within each sector over a trailing history to create z-scores. A one-standard-deviation burst in utilities is not the same thing as one in technology.
Better still, build variants:
- Issuer breadth: share of sector constituents with at least one qualifying purchase.
- Insider breadth: number of unique officers/directors buying.
- Role-weighted intensity: heavier weights for CEOs, CFOs, and chairs.
- Cluster score: purchases occurring within a short window after a drawdown.
The cluster score is especially useful. Insider buying after a 25 percent sector drawdown is not equivalent to insider buying after a 5 percent dip.
Match the signal to investable vehicles
For implementation, pair each GICS sector with a liquid sector ETF or futures proxy. In the US context, the SPDR sector ETFs are the obvious baseline. Then test forward returns over 1-, 3-, and 6-month horizons after signal formation.
The usual pattern in the literature is that insider buying has predictive power over intermediate horizons rather than the next afternoon. That makes sense. Information diffuses, narratives catch up, and valuation gaps close gradually.
Use relative returns, not only absolute returns
A sector signal should be judged against alternatives. If energy rises 8 percent after a positive insider signal while the market rises 12 percent, the signal did not help a rotation investor much. Evaluate excess returns versus the broad market and versus sector peers.
This is also where the "leading indicator" claim becomes testable. If insider flow spikes before analyst revisions and before relative performance inflects, you have a plausible lead. If it spikes after the ETF has already rerated, you have discovered a lagging indicator with excellent public relations.
What the 2020-2026 cases suggest, and what they do not
The case studies point in one direction, with caveats attached like legal footnotes.
Where the signal appears strongest
The best sector-level insider signals tend to appear in sectors with four traits:
- recent severe drawdown,
- cyclical or balance-sheet-sensitive fundamentals,
- common macro driver across firms,
- relatively clean open-market purchase data.
That favours energy, financials, industrials, and selected healthcare groups after stress events. It is less favourable for sectors dominated by compensation-driven selling, conglomerate-like composition, or founder control.
Where investors overstate the edge
There are three common exaggerations.
First, that insider flow predicts every turn. It does not. It is most useful near extremes.
Second, that more buying is always better. Not necessarily. Repeated buying into a value trap can reflect genuine conviction and genuine error at the same time. Executives are informed, not infallible.
Third, that sector aggregation eliminates all noise. It reduces noise. It does not abolish it. A sector can show strong buying because a handful of distressed firms are averaging down with board support while the healthier names do nothing.
The missing proprietary numbers
Because no fresh data pull was provided from our 162k filing archive, I cannot tell you that energy net buying intensity hit x standard deviations in y month, or that it led XLE by z basis points over the next quarter. That would be the point where many articles become imaginative. We will remain unfashionably sober and write n/a.
Still, the absence of a live extract does not erase the framework. It simply means the next step is empirical rather than literary.
A practical research agenda for allocators
The sensible way to use this idea is not as a stand-alone trading oracle, but as a ranking input in a sector allocation process.
A simple implementation sketch
Each month:
- collect qualifying insider purchases and sales by issuer,
- map issuers to GICS sectors,
- compute sector net buying intensity and breadth,
- rank sectors by standardised score,
- combine with valuation and price trend filters,
- allocate to the top-ranked sectors, market-neutral or long-only.
The valuation filter matters because insider buying in expensive sectors often has weaker subsequent payoff. The trend filter matters because catching falling knives is a hobby, not a process.
What to test before using real money
At minimum, test:
- 30-, 60-, and 90-day formation windows,
- 1-, 3-, and 6-month holding periods,
- equal-weighted versus role-weighted purchases,
- crisis versus non-crisis subsamples,
- US-only versus cross-market signals under MAR and SEC regimes.
The cross-market angle is underexplored and attractive. European PDMR disclosures under MAR are not as tidy as US Form 4 data, but they are timely enough to support a sector model. France, in particular, offers a useful test bed because AMF-regulated disclosures sit within a harmonised EU framework while still requiring careful issuer mapping.
Illegal insider trading refers generally to buying or selling a security, in breach of a fiduciary duty or other relationship of trust and confidence, on the basis of material, nonpublic information about the security.
That quote is worth including for one reason. We are discussing reported, lawful insider transactions, not clandestine misconduct. The market has a bad habit of hearing "insider trading" and picturing handcuffs. Most useful signals come from the boring legal filings, not from cinema.
The payoff from the 2020-2026 cases is clear enough. Sector-level insider flow is not a replacement for macro, valuation, or earnings revision models. It is a useful complement, especially after sector stress, when public narratives are late and insider incentives become unusually revealing. The concrete next step is straightforward: build a monthly net buying intensity series by GICS sector from the 162k-filing archive, then test whether breadth-adjusted spikes lead 1- to 3-month excess returns in sector ETFs. The open question is the interesting one, namely whether the edge survives once you control for drawdown severity and simple trend reversal effects, or whether insiders are best read as excellent contrarians and only mediocre prophets.
Sources & further reading
- SEC, Forms 3, 4, and 5regulator2026-05-17
- Investor.gov, Insider Trading glossaryregulator2026-05-17
- ESMA, Market Abuse Regulationregulator2026-05-17
- AMF, Market Abuse Regulation overviewregulator2026-05-17
- SPDR Sector ETFs overviewmarket2026-05-17
- H. Nejat Seyhun, Investment Intelligence from Insider Tradingbook1998-01-01
- Josef Lakonishok and Inmoo Lee, Are Insider Trades Informative?paper2001-01-01
- Massimo Massa and Hong Zhang, The Role of Insider Trading in Capital Marketspaper
- NBER Reporter, research summary on insider trading and returnspaper2005-12-22
Editorial note, this article was assembled without live web research (Grok unavailable in this generation pass). Evergreen sources are cited above; numerical claims are pulled from our own database snapshot as of 2026-05-17.
Insiders Trades backtest cohort
Our analysis of insider buying trends reveals significant insights into sector leadership. In the US, we examined 187,150 priced trades within our BUY cohort, yielding a mean 90-day return of -2.28% and a win rate of 40.37%. The annualised Sharpe ratio stands at -0.09, indicating that the performance of these trades has not outpaced risk-adjusted expectations. When we broaden the scope to a global universe, the aggregate data from 227,594 BUY filings across 17 markets shows a mean 90-day return of -1.70% and an aggregate Sharpe ratio of -0.08. These figures suggest a challenging environment for investors relying on insider buying as a predictive signal for sector performance. It is essential to consider that these results are based on a substantial sample size and a 90-day time horizon, which may influence the interpretation of the data.
Last reviewed · 2026-05-18 · By Simon Azoulay · Sources, SEC EDGAR, AMF BDIF, and 28 other regulators.