Academic Signal #9

1. To beat the market, don’t try to outsmart it (!)

TLDR

1. Compounding beats cleverness when you keep costs low and stay invested.

2. Do the simple things: automate, rebalance occasionally, and ignore timing.

In modern markets the winning strategy is NOT trying to predict where prices are going. It’s accepting uncertainty, buying broad exposure, and letting time and compounding do the work, while minimizing fees.

This is the triumph of the “unintelligent” investor (over the professional investor): someone who admits they don’t know, owns simple index exposure, contributes regularly, and resists the urge to optimize.

What does “faith” mean here? It’s the conviction that enterprise, technology, and functioning institutions will keep creating value. If you own a low-cost index fund and hold it, you benefit from that collective engine without having to predict winners.

Why the herd often wins

When many investors believe in broad market progress and keep buying, that steady demand supports returns over time. Trying to predict the market’s direction and trade every twist usually means higher costs and behavior errors.

Here’s some evidence:

• BRK.B compounded at 12.5% (10-year CAGR as of August 5, 2025), IVV at 11.5%, and QQQ at 17.4%, underscoring how straightforward, rules-based exposure can match or beat marquee stock pickers.

• In 2008, Buffett bet that the S&P would outperform hedge funds over the following decade. The S&P 500 returned 126% over 2008-2017 while a basket of hedge funds averaged 36% (with fees doing much of the damage).

• The 2024 SPIVA scorecard (S&P Indices vs Actives) found 65% of active large-cap U.S. funds lagged the S&P 500 for the year, and over 15 years a majority underperformed in every equity category.

But not all “faiths” are created equal

The paper argues that each asset class rests on a different kind of belief and set of institutional supports. For most investors, the index fund version has the best odds because it harnesses the broadest engine with the fewest failure points.

1. Index funds: diversified enterprise keeps compounding

2. Bitcoin: a fixed-supply digital asset will hold value

3. Gold: traditional faith rooted in history and scarcity

4. Real estate: a mix of faith and some entrepreneurship

Private equity: active value creation behind closed doors

How to use this

If your job is to compound client capital:

• Keep it simple: make low-fee US index funds the default core and build around them.

• Treat country selection as risk control: Lean toward markets with strong investor protections, keep EM small or active.

• Pre-commit behavior: Automate contributions, set add-on bands for drawdowns, avoid prediction games.

• Keep fees low: Every 1% you save in friction raises the terminal value of the “faith.”

None of this is sexy. But it’s where most of the long-run edge lives.

2. VC is just a levered Nasdaq bet! Here’s how to replicate VC returns… without the fees and illiquidity

TLDR

• A diversified VC portfolio behaves like a levered Nasdaq-100 position. Replication explains ~90% of the variation in VC IRRs.

• Since 2000, every VC vintage underperforms that levered Nasdaq benchmark. Pre-2000 matched or slightly beat it.

• Practical takeaway: you can benchmark or replicate VC variance and beat average performance using staged betas with Nasdaq-100 futures at low cost.

VCs fund startups in rounds. After each round, they choose either to keep going or to stop. Think of each round as buying a ticket that lets you decide later whether to buy the next ticket. That kind of “option on an option” is called a compound option. (Here’s a paper on the valuation of compound options)

The authors built a model around that idea. To keep the model simple and testable, they feed it only basic historical facts about outcomes, not returns: how often startups end in IPO, acquisition, or failure, and how long that usually takes.

Then, they calibrate the model to reproduce the real-world observations that ~8% of startups end up in IPO and ~65% end up as a write-off.

The outcome is that the model is able to match two big patterns we see in the real world: 

• the waves in IPO activity over time, and 

• the performance differences across VC vintage years.

From there they suggest a public benchmark anyone can implement:

The core results

1. Replication works: A vintage-level strategy that applies stage-specific betas to a Nasdaq-100 portfolio explains 90% of the variation in VC vintage returns.
   

2. VC underperformance since 2000: Against the levered Nasdaq benchmark, pre-2000 VC vintages match or slightly outperform; every vintage since 2000 underperforms. VC vintages 2000–2016 average 13–14% IRR, while the model and levered NDX strategy generate roughly 23–25% for those same vintages.
   

3. Implied market betas by VC investment stage: a beta of 2.2 in early stage (the first 2 years), 1.6 in late stage (the next 2 years), and 1.4 at mezzanine (the following 2 years, to year 6), giving an all-stage beta near 1.7 vs Nasdaq-100.

How to use this

Here’s a step by step example to illustrate how you could replicate VC risk with better returns and without the illiquidity, using levered Nasdaq-100 exposure.

Let’s assume that you want to replicate a $10M “early-stage” sleeve today and let it de-lever over time. For easy math, assume the Nasdaq-100 (NDX) is at 20,000.

We’ll use NQ contracts (E-mini Nasdaq-100 futures) as our instrument of choice for leverage. Each NQ contract is $20 x the Nasdaq-100 Index. For example, if NDX=20,000, one NQ contract gives us $400,000 notional exposure.

1. Initiate exposure: buy the front or next quarter NQ to the desired notional.

   1. Years 0 to 2 (use 2.2x as the early-stage beta)

   2. Target exposure = 2.2 x $10M = $22M

   3. Buy = $22,000,000 / $400,000 = 55 NQ contracts

   4. Indicative initial margin for 55 NQ is in the order of ~$1.8M (but varies).

   5. Keep the remainder ($10M - $1.8M margin) in T-bills as collateral.
      

2. Roll quarterly: contracts list Mar/Jun/Sep/Dec. Roll a few days before first notice to avoid liquidity cliffs.
   

3. Step-down cadence: at month 24 and 48, cut exposure to 1.6x and 1.4x of then-current sleeve NAV (the NAV of your replication proxy, which is the $10M starting point + futures P&L). At month 72, move to 1.0x. This mirrors the paper’s stage betas and horizons.

   1. For years 2 to 4 (late stage beta = 1.6x)

      1. If the sleeve NAV is ~$10M, exposure = $16M

      2. You need NQ ≈ 40 contracts, so sell 15 NQ contracts to de-lever

   2. For years 4 to 6 (mezz beta = 1.4x), at $10M NAV, NQ ≈ 35 so sell 5 NQ contracts.

   3. After year 6 (mature @ 1.0x beta with NDX), hold unlevered NDX exposure (e.g., ~25 NQ if sleeve is still ~$10M and NDX=20,000) or switch to a cash equity index fund tracking NDX.

But what about taxes?

The paper’s comparisons are pre-tax on both sides, but if you replicate VC risk with Nasdaq-100 futures your gains are taxed each year under IRC Section 1256’s “60/40” rule (60% long-term, 40% short-term, regardless of holding period).

Here’s an example of how annual taxes impact the “replication proxy”:

A good back-of-the-envelope is: after-tax return ≈ pre-tax return × (1 − tax rate). With 60/40 and top federal brackets, 1 − 0.306 = 0.694.

Assuming federal taxes only and the average annual returns since 2000 from the paper:

A) Pre-tax “replication proxy” returned 18% (IRR of the levered Nasdaq-100 replication strategy over 2004Q1–2024Q), taxed annually at 30.6%

• The after-tax annual return ≈ 18% × 0.694 = 12.49%

• 10y wealth multiple ≈ 3.25x

B) VC index returned 12% pre-tax (IRR of the Cambridge Associates VC Index over the same 2004Q1–2024Q2 period), tax deferred to the end at 23.8% LTCG+NIIT

• 10y after-tax annual return ≈ 10.05%

• 10y wealth multiple ≈ 2.60x

Takeaway: even after annual taxation, the proxy still wins by a lot if you actually earn 18% pre-tax and VC earns 12% pre-tax.

So are VC funds good for diversification?

Not so much, if your portfolio already has meaningful public equity exposure, especially to US tech growth.

Why:

• The paper’s core finding is that diversified VC behaves like levered Nasdaq. Stage betas are ~2.2 early, ~1.6 later, ~1.4 by year 6, averaging ~1.7 to NDX. That means VC is largely high-beta tech growth risk.
  

• IPO cycles and VC performance co-move with multi-year NDX returns. When NDX booms, exits and marks are good; when NDX sags, exits and marks fade.
  

• The appearance of diversification in many multi-asset portfolios often comes from VC’s lagged, appraised NAVs, not from low true economic correlation.