Target Date Benchmarks – ChatGPT

Target-date funds are over 50% of 401(k) assets.  Litigation around target date funds I think shows a lack of knowledge around investment performance.    Target date funds were created to hide fees and mask performance concerns, and are impossible to benchmark.  In fact, many were created to manipulate benchmarks.   I feel many plaintiff attorneys are not getting the full potential out of their cases by not looking under the hood of Target Date funds to find out what is really going on. 

In my testimony for the June 2009 joint SEC-DOL hearing on target date funds, I contended that it’s huge growth would further concentrate management into an oligopoly and higher fees. I also criticized the target date structure as a step backward for transparency of fees, performance, and risk. I thought it would push plans toward a one-provider, closed platform that would lead to conflicts of interest and higher fees.

Target Date Fund Asset Allocation Drives Risk and Performance

Asset allocation is the main driver of performance and risk.  Because of the lack of transparency and investment policy guidelines, performance gaming of the benchmark in target date funds in invited.  Target date fund specialist Ron Surz warns against target date funds holding high equity allocations for those close to retirement

Joe Nagengast of Target Date Analytics in the June 2009 joint SEC-DOL hearing on target date funds. “The way to win the short-term performance horse race for target date funds (and the resulting market share) has been, in many time periods, through higher equity allocations.”

I asked ChatGPT to elaborate on this issue and specifically benchmarking to the S&P target date fund index

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From ChatGPT.  Asset allocation explains ~90% of return variability, then comparing TDFs with vastly different glide paths or allocations is not apples-to-apples.


⚖️ Why It’s a Problem

401(k) Target Date Funds often differ dramatically in:

  • Equity vs. bond allocations (e.g., 90/10 vs. 60/40 at the same “target date”)
  • Glide path design (to retirement vs. through retirement)
  • Exposure to alternatives, real assets, or high-yield bonds
  • Risk levels (volatility and drawdown potential)

So, simply comparing returns (e.g., over 1, 3, or 5 years) may reward higher-risk funds during bull markets, misleading fiduciaries and participants.

🧠 Bottom Line

You cannot responsibly compare TDFs on return alone when their asset allocations diverge. Fiduciaries must benchmark based on:

  • Risk-adjusted performance
  • Glide path risk levels
  • Appropriateness for the participant demographic
  • Alignment with ERISA’s fiduciary standards

ChatGPT said:

benchmarking Target Date Funds (TDFs) to something like the S&P Target Date Fund Index can be misleading and easily gamed, particularly because:

  1. Benchmarks like the S&P Target Date Index only reflect the average allocation (e.g., average equity/glidepath of funds in that vintage),
  2. There is no requirement that the TDF match the risk profile of the benchmark, and
  3. A higher equity allocation will often outperform the benchmark in bull markets, misleading fiduciaries into thinking the fund is “better managed” rather than just riskier.

🔎 How This Benchmarking Can Be Gamed

TDF managers can “game” the benchmark by:

  • Allocating more to equities (especially international or small-cap)
  • Taking more duration risk in bonds
  • Adding alternative assets like private equity or real estate (often with smoothed NAVs)
  • Using leverage (in CITs especially)

These tactics may result in higher performance in a rising market compared to the benchmark, even though the fund is taking on significantly more risk.


📊 Examples of Equity Overweighting vs. Benchmark

Let’s compare some common 2040 TDFs with the S&P Target Date 2040 Index, which in 2024 had approximately:

  • Equity: ~74%
  • Fixed Income: ~24%
  • Cash/Other: ~2%
Target Date FundEquity AllocationOverweight vs S&P IndexComments
Fidelity Freedom 2040 (Investor)~84%+10%Higher US equity tilt, more aggressive glidepath
T. Rowe Price Retirement 2040~89%+15%Very equity-heavy approach; often leads in returns in bull markets
American Funds 2040~78%+4%Slight overweight, includes active mgmt in underlying funds
Vanguard Target Retirement 2040~72%-2%Closest to S&P Index; more conservative relative to peers
BlackRock LifePath Index 2040~74%0%Benchmarked tightly to average; often used as a baseline
State Street Target Retirement 2040 (CIT)~78%+4%Slightly more risk in equity; often embeds alternatives in CITs

Sources: Fund prospectuses, Morningstar, 2024 allocation snapshots


⚠️ Fiduciary Implications under ERISA

  • Plan fiduciaries may be misled into thinking a fund is “better performing” when it simply takes more risk.
  • Impartial Conduct Standards under ERISA require fiduciaries to consider both performance and risk—a higher return via higher risk is not inherently better.
  • Courts may ask: Did the fiduciary understand that this TDF’s outperformance came from taking more risk than the benchmark?
  • This is especially risky when plan sponsors do not document why they selected or retained a more aggressive fund.

🧠

2. How Much Does It Matter?

  • The 1986 Brinson, Hood & Beebower (BHB) study found that asset allocation explained 93.6% of the variation in quarterly returns of pension fund portfolios.
  • Later updates and critiques (e.g., by Ibbotson & Kaplan in 2000) clarified that while asset allocation doesn’t explain everything, it still dominates over market timing or security selection in terms of predicting return variability.
    • Strategic asset allocation (long-term mix) accounts for about 90% of return variability.
    • Tactical allocation, timing, and security selection account for much less.

3. Impact on Performance vs. Variability

  • Asset allocation drives risk-adjusted returns, not just variability.
  • Picking the right mix of stocks, bonds, and alternatives greatly affects total returns, especially over longer horizons.
  • During market crises or recoveries, asset classes behave very differently—diversification reduces drawdowns.

Key Takeaway

  • TDFs should not be benchmarked only on return versus the S&P TDF Index—risk-adjusted performance and glidepath assumptions matter. A 2040 fund with 90% equity will likely beat the benchmark in good years, but may collapse harder in downturns—especially harming near-retirement investors if the glidepath is too steep.

There are no valid benchmarks for Target Date Funds.  Comparing funds with different asset allocations does not work either.  Target date funds are specifically designed to avoid accountability and thus need the most scrutiny. You have to look under the hood at the individual investment allocations and benchmark those.   Attorneys are defying investment principles and basic math in insisting on using TDF benchmarks in litigation.

Hiding Private Equity in 401(k) Target Date Funds risks the entire TDF as a Prohibited Transaction- ChatGPT

Hiding Private Equity in 401(k) Target Date Funds risks the entire TDF as a Prohibited Transaction- ChatGPT

Because Private Equity is clearly an ERISA prohibited transaction,  https://commonsense401kproject.com/2025/07/02/private-equity-is-a-prohibited-transaction-via-chat-gpt/  it can create the false illusion that it adds value to a portfolio like a Target Date fund by overstating performance and understating risks.   As I testified to the ERISA advisory committee last year, SEC-registered mutual funds will not allow this ruse, but that weak state-regulated Collective Investment Trusts (CIT’s) would. https://commonsense401kproject.com/2025/06/29/erisa-advisory-council-testimony-released/ 

Target Date funds that allocate to Private Equity, can become Prohibited Transactions.

I asked ChatGPT how Private Equity cheats on performance and risk.  

⚠️ Core Concern:

Private Equity (PE) self-reports inflated returns and artificially smooths volatility, which distorts inputs used in mean-variance optimization (MVO), risk-parity models, and target-date glidepaths.


🔍 How the Distortion Happens:

1. Self-Reported, Marked-to-Model Valuations

  • Unlike publicly traded equities, PE funds are not marked to market.
  • General Partners (GPs) have wide latitude in determining “fair value” using Level 3 inputs under GAAP/IFRS.
  • There’s no external price discovery like in public markets—creating upward bias in reported IRRs and TVPIs.

Example: GPs may hold a portfolio company at $2.3B valuation for 12 months—until a sale occurs at $1.8B, but all the prior return models assumed $2.3B.


2. Return Smoothing Reduces Apparent Risk

  • PE firms smooth returns by reporting only quarterly, and avoiding mark-downs in downturns unless forced (e.g., in LP secondaries).
  • This lowers measured standard deviation, making PE appear less risky than it truly is.

Ludovic Phalippou, in multiple SSRN papers, shows that real economic volatility of PE is closer to or greater than public equities, but accounting practices mask this.


3. Low Correlation Is Largely an Illusion

  • PE is often shown to have low correlation with the S&P 500 or public equity indices.
  • This is not because the underlying assets are fundamentally different—but because quarterly smoothed valuations fail to reflect the real-time economic correlation.

Empirical studies (e.g., Ang, Chen, Goetzmann 2013) show that lagged public-equity returns can explain much of PE fund performance.


📉 Implications for Asset Allocation Models:

1. Misleading Inputs into MVO and Risk Budgeting

  • MVO and Black-Litterman-type optimizations overweight PE due to inflated Sharpe ratios (high returns / low volatility).
  • Allocators wrongly assume PE offers high return with diversifying low risk.

2. Target-Date Funds and Pension Glides Paths Are Over-Allocating

  • Plan sponsors may use PE-heavy CITs or TDFs based on deceptively low risk and attractive correlation stats.
  • This is especially problematic in state-regulated CITs, where oversight of inputs is weak.

3. Stress Testing Fails to Capture Liquidity & Downside Risk

  • Smoothing hides tail risk and liquidity gaps during downturns (e.g., GFC, COVID).
  • PE dry powder and capital calls may spike just when public markets are down.

🔎 Supporting Academic and Regulatory Sources

  • Ludovic Phalippou, “An Inconvenient Fact: Private Equity Returns & the Billionaire Factory”
    → Calls out manipulated IRRs, opaque fees, and correlation illusions.
  • Andrew Ang (Columbia/BlackRock)
    → PE appears low beta only due to infrequent marking; in reality, it is leveraged equity.
  • U.S. SEC under Gensler has warned of valuation opacity and risk-model distortions in private funds.
  • CFA Institute (2021 paper):
    “Asset Allocation to Alternative Investments” (CFA Institute, 2025):
    Highlights that illiquid assets often have stale and artificially smoothed returns, recommends testing for serial correlation, and states clearly that analysts “need to unsmooth the returns to get a more accurate representation of the risk and return characteristics of the asset class.” blogs.cfainstitute.org+13cfainstitute.org+13
  • 2019 SSRN paper “Unsmoothing Returns of Illiquid Assets” by Couts, Gonçalves, and Rossi, which critiques existing methods and proposes a novel three-step unsmoothing technique—highly relevant for private equity modeling. papers.ssrn.com+2
  •  


🧨 Bottom Line:

Private equity appears safer and more diversifying than it really is because it controls its own marks.
This undermines the integrity of asset allocation models and can lead to over-allocation, particularly in pensions, endowments, and TDFs.

This misrepresentation may expose fiduciaries to legal risks under ERISA’s Impartial Conduct Standards or trust law’s duty of prudence—especially if they’ve failed to adjust for these distortions.


These distortions also apply to private debt, hedge funds, real estate, and other ERISA prohibited transactions like Crypto https://commonsense401kproject.com/2025/07/03/crypto-is-erisa-prohibited-transaction-chatgpt-do-not-use-in-401k/

And Annuities  https://commonsense401kproject.com/2025/06/13/annuities-are-prohibited-transactions-via-chat-gpt/

Target Date funds that use any inputs that are prohibited transactions are at a high risk of making the entire TDF a Prohibited Transaction.