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Home Knowledge Hub Superinvestor Tracking: How to Follow Buffett, Soros, and Ackman's Portfolios
Signal Guide
By Meridian Research team Last reviewed 2026-03-01

Superinvestor Tracking: How to Follow Buffett, Soros, and Ackman's Portfolios

Superinvestors — the world's most consistently successful long-term investors — disclose their equity portfolios quarterly via SEC 13F filings. Tracking their positions, understanding their changes, and building a disciplined strategy around the best ideas requires both data access and analytical framework.

45 days
Maximum lag before an institution's quarterly 13F equity positions become public
Source: SEC Form 13F filing rule

TL;DR

Superinvestor tracking uses SEC 13F filings to monitor the equity portfolios of the world's top fund managers — Buffett, Soros, Ackman, Tepper, and others. 13F filings disclose long US equity positions within 45 days of quarter end, letting you see which stocks elite managers accumulated and which they cut. Academic research on high-conviction 'best ideas' and portfolio cloning is mixed and comes largely from working papers, so treat any single backtest figure as illustrative, not a guarantee. Meridian integrates Dataroma superinvestor data with its 13F analysis to surface high-conviction positions across tracked managers.

What Is Superinvestor Tracking?

Superinvestor tracking is the systematic practice of monitoring the disclosed equity portfolios of the world's most successful long-term fund managers — Warren Buffett, George Soros, Bill Ackman, David Tepper, Joel Greenblatt, Seth Klarman, and others — and using their position changes to inform investment decisions. The practice is enabled by the SEC's mandatory 13F filing requirement, which forces any institution managing over $100 million in assets to disclose all long equity positions quarterly.

Academic interest in position cloning is real but the evidence is unsettled and comes largely from working papers. Some backtests report large cloning returns, but they are not peer-reviewed or replicated and should be read as illustrative, not a reliable expectation (see the research table below). A better-established thread is the 'best ideas' literature: in an LSE/SSRN working paper, Cohen, Polk, and Silli found that managers' highest-conviction positions outperformed their other holdings and the benchmark by roughly 1.6 to 2.1 percent per quarter in their sample — suggesting concentration and conviction, not breadth, is where any edge concentrated.

Why Concentration and Conviction Matter

The managers commonly called superinvestors tend to share a style rather than a secret: research depth combined with high conviction. Many institutional funds diversify across 50 to 150 positions, which pulls their results toward the index — and, after fees, a large share of active funds have historically trailed their benchmark. Concentrated managers instead hold roughly 10 to 30 positions with deep research behind each. Concentration cuts both ways: it amplifies a good thesis and an error alike, so it is a source of both potential edge and higher single-name risk, not a guarantee of outperformance.

The Best Ideas Effect

Cohen, Polk, and Silli's working paper reported that, over their sample:

  • Fund managers' highest-conviction positions (top holdings by portfolio weight) outperformed the benchmark by roughly 1.6 to 2.1 percent per quarter
  • The same managers' other holdings showed no comparable statistically significant outperformance
  • The pattern held across the market cycles in their sample window

The implication drawn by the authors: a manager's edge, where it exists, tends to sit in their top ideas rather than the diversified tail. Applied to tracking, that suggests focusing on the top one to five positions rather than mechanically cloning entire portfolios — though this is one in-sample working-paper finding, not a forward guarantee.

The Superinvestor Universe: Who to Track

Tier 1: Long-Term Value Investors

Investor Fund Strategy Primary Strength
Warren Buffett Berkshire Hathaway Value + moat Compounding quality businesses
Seth Klarman Baupost Group Distressed value Safety of principal, special situations
Howard Marks Oaktree Capital Credit + equity cycles Macro cycle awareness
Joel Greenblatt Gotham Funds Statistical value Magic Formula quantitative approach
Mohnish Pabrai Pabrai Investment Funds Buffett replication Concentrated cloning of Buffett ideas

Tier 2: Event-Driven and Activist

Investor Fund Strategy Primary Strength
Bill Ackman Pershing Square Concentrated activist High-conviction, publicly disclosed campaigns
Dan Loeb Third Point Event-driven Tech and international opportunities
David Einhorn Greenlight Capital Long/short value Short selling and special situations
Carl Icahn Icahn Capital Corporate governance Forced change at underperforming companies

Tier 3: Concentrated Growth Investors

Investor Fund Strategy Primary Strength
George Soros Soros Fund Management Macro-informed equity Reflexivity theory, global macro
David Tepper Appaloosa Management Distressed + macro Reading macroeconomic turning points
Li Lu Himalaya Capital Deep value, Asia Value investing in emerging markets
Stanley Druckenmiller Duquesne Family Office Macro concentration Asymmetric macro bets, high conviction

The 45-Day Delay Problem: Managing the Lag

The primary limitation of superinvestor tracking is the 45-day filing delay. By the time a 13F is public, the filing reflects positions as of the quarter-end date — meaning data can be 45 to 135 days old when published and read.

This delay is significant but manageable:

Strategies for Handling the 45-Day Lag

Focus on long-term positions: Superinvestors' best ideas are typically held for 12 to 60+ months. Warren Buffett has held American Express since 1991, Coca-Cola since 1988. A 45-day lag in a position held for a decade is immaterial to the core thesis.

Use real-time signals to confirm: When a 13F position is updated, cross-reference with dark pool data and Form 4 disclosures for the same ticker. If the 13F shows accumulation and dark pool volume anomalies are concurrent, the position change is more likely still active.

Watch for position sizes: Large new positions (over 3 percent of a fund's AUM) generally reflect higher conviction than small starter stakes, and a fund that has committed meaningful capital is often slower to reverse it — so high-weight positions tend to carry a clearer signal than marginal ones. Treat this as a rule of thumb, not a certainty.

Track amendment filings: 13D/A amendments (for activist positions) must be filed within two days of any material change, providing a near-real-time signal of position changes for activist investors. Non-activist investors have no comparable amendment obligation.

Best Practices for Following Superinvestors

Step Action Why
1 Select 10-15 superinvestors with documented long-term track records Diversify across different strategies and market cap ranges
2 Focus on positions representing >3% of fund AUM High conviction signals only
3 Identify stocks held by 3+ superinvestors simultaneously Cross-fund convergence multiplies confidence
4 Verify the thesis has not changed with dark pool and insider data Real-time confirmation that position is still active
5 Set 12-month minimum hold horizon on cloned positions Match the time horizon to the superinvestor's own approach

Superinvestor Performance: What the Research Says

Cloning Research (Read the Caveats)

The academic record on 13F cloning is thin and mostly pre-publication. Treat every figure below as a single study's backtest, not an expected return:

Study What it reported Status / caveat
Verbeek & Wang, Journal of Banking & Finance (2013) Copycat funds free-riding on disclosed holdings roughly matched their targets net of costs (~+0.017% per month on average), improving after the 2004 disclosure rules Peer-reviewed; 'comparable to,' not dramatically better than, the originals
Schroeder, SSRN working paper (2025) Backtest of top-quartile 13F clones (2013–2023, ~150,000 portfolios) reported large annualized risk-adjusted excess returns vs. the S&P 500 Not peer-reviewed or replicated; extraordinary claim — illustrative only
Fleiss et al., SSRN working paper (2021) A deep-reinforcement-learning model on 13F data backtested to ~21% annualized, Sharpe near 1.8 Single ML backtest; overfitting and regime risk
Cohen, Polk & Silli, LSE/SSRN working paper Managers' highest-conviction holdings outperformed their other holdings by ~1.6–2.1% per quarter in-sample Describes where any edge concentrated, not a forward guarantee

Cross-Fund Convergence: A Higher-Confidence Signal

When multiple superinvestors hold the same stock simultaneously, the convergent due diligence from independent, highly capable researchers provides substantially more confidence than any single position. Meridian's platform surfaces these convergence events automatically.

Historical examples of high-conviction cross-fund convergence:

  • Apple (AAPL): Simultaneously held by Berkshire, numerous hedge funds, and growth funds at different thesis stages
  • Visa (V): Held long-term by multiple value-oriented funds; consistent moat thesis agreement
  • Energy transitions 2020-2022: Multiple funds simultaneously accumulated OXY, CVX at energy cycle lows

How to Build a "Follow the Masters" Strategy

Portfolio Construction

Select inputs: Choose 8 to 12 superinvestors across different strategy types — at least two value investors, one activist, one growth-oriented, one macro.

Extract best ideas: From each, identify the top three positions by portfolio weight and any significant new positions (over 1 percent of AUM initiated in the most recent quarter).

Apply cross-fund filter: Prioritize stocks appearing in two or more selected superinvestors' best ideas simultaneously. Typically yields 5 to 15 stocks per quarter.

Allocate equally: Equal-weighting across cloned positions is a common default because it avoids concentrating in whichever names happen to be largest, and it does not require you to second-guess each manager's sizing. It is a simplicity and diversification choice, not a guaranteed performance edge.

Rebalance quarterly: Update the portfolio on each 13F release date (approximately February 14, May 15, August 14, November 14). Do not rebalance more frequently — the strategy is designed for 13F-driven quarterly cycles.

Risk Management

Superinvestor cloning strategies carry specific risks:

  • Exit blind spot: 13F shows entries but not exits until the following quarter's filing. You may not know a superinvestor has exited until 45 to 135 days after exit.
  • Stale thesis: A manager's thesis may have changed but not yet be visible in the filing data.
  • Crowd risk: Highly tracked superinvestors create popular trades that can become crowded. When Ackman's Pershing Square reveals a position publicly, it is often already priced in.

Managing the Exit Problem

The exit blind spot is the most challenging aspect of cloning strategies. Mitigation approaches:

  1. Use 13D/A amendments: For activist positions, amendments signal material changes within 2 days
  2. Monitor dark pool signals: Unusual selling volume in a superinvestor's known position may indicate exit before the 13F confirms
  3. Watch for thesis invalidation events: Major negative news for a position in a superinvestor's portfolio — even without a filing — should trigger a thesis review
  4. Set stop losses: Unlike buy-and-hold superinvestors with long time horizons and no mark-to-market concerns, cloners face market risk. A 15 to 20 percent stop loss preserves capital if the thesis is wrong.

Meridian's Superinvestor Tracking System

Meridian integrates Dataroma's superinvestor data with its proprietary 13F analysis system, covering:

  • Portfolio snapshots for major superinvestors updated on each 13F cycle
  • Position change detection: new, increased, reduced, and exited positions
  • Cross-fund convergence identification (stocks held by 2+ tracked superinvestors)
  • Conviction weighting (position as percentage of fund's disclosed AUM)
  • Real-time cross-reference with dark pool and insider signals for the same tickers

For a detailed introduction to 13F filing mechanics and how to read them, see /knowledge/how-to-read-13f-filings. For Warren Buffett's specific portfolio analysis, see /knowledge/warren-buffett-portfolio-2026.

Frequently Asked Questions

What is a superinvestor?

A superinvestor is an institutional investor widely regarded as having a strong long-term track record relative to market benchmarks over many years. The term was popularized by Warren Buffett's 1984 essay "The Superinvestors of Graham-and-Doddsville." The label is reputational and backward-looking — a past record is not a promise of future results.

How do I track what hedge funds are buying?

SEC 13F filings are the primary mechanism for tracking hedge fund equity positions. Any fund managing over $100 million must disclose all long US equity holdings within 45 days of each quarter end. Free access via SEC EDGAR; aggregated and analyzed via Dataroma, WhaleWisdom, and Meridian.

Does following superinvestors actually work?

The honest answer is that it is unproven and the evidence is mixed. Peer-reviewed work (Verbeek & Wang, 2013) found copycat strategies roughly match — not dramatically beat — the funds they clone, net of costs. A few working-paper backtests report much larger cloning returns, but they are not peer-reviewed or replicated and may not hold going forward. Any outcome depends on which managers you follow, how you handle the 45-day lag, and market conditions. Treat cloning as one input, not a strategy guaranteed to beat the market.

What is the 45-day delay problem in 13F tracking?

The SEC requires 13F filings within 45 days of quarter end, meaning the public sees portfolio data that is 45 to 135 days old. Superinvestors with long-term hold periods (years) are minimally affected by this lag. Day traders or short-term holders' 13Fs are nearly useless. Focus on managers with multi-year holding periods.

Who are the best superinvestors to follow?

For value-oriented cloning: Buffett (Berkshire), Klarman (Baupost), Greenblatt (Gotham). For activist piggybacking: Ackman (Pershing Square), Loeb (Third Point), Starboard. For concentrated growth: Druckenmiller (Duquesne), Tepper (Appaloosa). Diversify across strategy types rather than clustering on similar approaches.

What is cross-fund convergence in superinvestor tracking?

Cross-fund convergence occurs when multiple independent superinvestors hold the same stock simultaneously. This represents convergent independent due diligence — multiple highly capable research teams have reached the same conclusion. Meridian flags these convergence events automatically across all tracked superinvestors.

Educational content, not investment advice. Meridian provides data and signal interpretation for research purposes only. Always do your own due diligence before making investment decisions. See our editorial policy and methodology.

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Academic References

Best Ideas

LSE / SSRN working paper (not peer-reviewed), 2010

→ In-sample, fund managers' highest-conviction (top-weight) positions outperformed the benchmark by roughly 1.6 to 2.1 percent per quarter, while their other holdings showed no comparable outperformance — evidence that any edge concentrated in managers' best ideas. A working paper, not a guarantee of future results.

Better than the Original? The Relative Success of Copycat Funds

Journal of Banking & Finance, 2013

→ Peer-reviewed study finding that copycat funds free-riding on disclosed holdings generated performance roughly comparable to their target funds net of trading costs and expenses (about +0.017% per month on average), with the advantage improving after the 2004 SEC disclosure rules — 'comparable to,' not dramatically better than, the originals.

Outperforming the Market: Portfolio Strategy Cloning from SEC 13F Filings

SSRN working paper (not peer-reviewed), 2025

→ Backtest of top-quartile 13F clones over 2013–2023 (~150,000 portfolios) reported large annualized risk-adjusted excess returns versus the S&P 500. An extraordinary single-study result that has not been peer-reviewed or independently replicated; treat as illustrative only.

Deep Reinforcement Learning & Feature Extraction For Constructing Alpha Generating Equity Portfolios

SSRN working paper (not peer-reviewed), 2021

→ A deep-reinforcement-learning model applied to 13F institutional data backtested to roughly 21% annualized return with a Sharpe ratio near 1.8. A single machine-learning backtest, subject to overfitting and regime risk.