What Drives Investors' Portfolio Choices: Choukhmane & de Silva (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 44 pages. To replicate or extend it, use the original.

TL;DR

Using quasi-experimental variation in 401(k) default asset allocations (money-market fund vs. target-date fund) across 4 million employees at hundreds of thousands of plans (Dec 2006-Dec 2017), the paper separates investors’ underlying risk preferences from participation frictions. Absent frictions, 94% of retirement investors prefer stock market participation and the average preferred equity share is 76%, declining with age: patterns broadly consistent with standard life-cycle portfolio choice models. These preferences differ markedly from observed allocations, where participation and equity shares are lower and hump-shaped. A life-cycle model estimated via SMM recovers moderate risk aversion ($\gamma = 2.54$), EIS = 0.25, and a $156 portfolio adjustment cost. Low stock market participation in retirement accounts is driven by one-time frictions, not nonstandard preferences.

Core results

Magnitudes and significance are as reported. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Lower bound on fraction preferring stock market participation is 42%; upper bound is 95%	§II.C.1, Fig. 4 (p. 22), Table IAII	At tenure 2 years: bounds 78%–95% (money-market-to-TDF sample); lower bound rises as more investors reveal preferences over tenure
R2	Point estimate: 94% of investors prefer stock market participation in their retirement accounts (under Assumption 6)	§II.C.3, Fig. IA9	At tenure = 3: average preferred participation = 94%; average preferred stock share = 76%; stable over life cycle
R3	Preferred equity share is high (>60% at all ages) and declining with age (opposite of the observed hump-shaped profile)	§II.C.4, Fig. 6 (p. 26), right panel	Preferred share ~80% at age 25, declining to ~60% at age 60; observed share hump-shaped and strictly below preferences at all ages
R4	Observed participation and equity shares diverge from preferences; TDF-auto-enrolled investors’ choices most closely approximate friction-free preferences	Fig. 7 (p. 27)	SCF 2007-16 stock share: 27%; not-auto-enrolled 401(k): 40%; auto-enrolled TDF: 80%; friction-free preference estimate: 76%
R5	Baseline structural estimate: relative risk aversion $\gamma = 2.54$ (EZW model, SMM on 38 moments)	Table III col. (1), p. 41	$\gamma = 2.54$ (SE 0.09); discount factor $\beta = 0.94$ (SE 0.001); EIS $\sigma^{-1} = 0.253$ (SE 0.018)
R6	Portfolio adjustment cost = $156; contribution adjustment cost = $488	Table III col. (1), p. 41	$k_\theta$ = $156 (SE $6.01); $k_s$ = $488 (SE $16.60); contribution cost larger, consistent with frictions in DC plan enrollment as additional driver of nonparticipation
R7	Without frictions, risk-aversion estimates are implausibly heterogeneous: $\gamma = 18.94$ using money-market-default data alone vs. $\gamma = 2.25$ using TDF-default data alone	Table III cols. (3) and (4), p. 41	Same population (employees hired within 12 months of the same policy change), frictionless model produces $\gamma$ 18.94 vs. 2.25 depending on which half of the data is used; baseline model reconciles both with $\gamma = 2.54$
R8	Treatment group (TDF default) maintains ~95% stock market participation and ~80% equity share throughout tenure; control group (money market default) starts near 0% and converges over years	Fig. 2 (p. 16), Table IAII	Treatment-control gap: 19-25 pp in participation rate, 20-23 pp in stock share of retirement wealth; convergence is gradual, inconsistent with pure time-dependent (Calvo) frictions

Overall (paper’s conclusion). Participation frictions, not nonstandard risk preferences such as loss aversion or ambiguity aversion, are the primary driver of limited stock market participation in retirement accounts. Investors’ true preferences align with standard life-cycle models once one-time adjustment costs are accounted for.

Datasets used

Dataset	Role in paper	Wiki page
401(k) administrative records (large U.S. record-keeper, anonymized), Dec 2006–Dec 2017, ~4 million employees, >600k plans	Main data: portfolio allocations, participation, contribution rates, plan defaults	No page yet (proprietary; no public access)
CRSP Value-Weighted Index (1925–2006)	Equity premium and return volatility calibration (6.4% premium, 20% vol)	WRDS / CRSP (licensed)
Survey of Income and Program Participation (SIPP)	Labor income process estimation, employment transition probabilities	SIPP
Survey of Consumer Finances (SCF), 2007 and 2016 waves	External validation of financial wealth, stock market participation benchmarks	No page yet

Sample: ~4 million employees, more than 600,000 401(k) plans, ages 25-64, 2006-2017. Quasi-experiment #1 (money-market-to-TDF): 1,086 control + 1,321 treatment investors at 6 firms. Quasi-experiment #2 (opt-in-to-TDF): 40,337 control + 52,400 treatment investors at 191 firms.

Theory / model

The paper combines two frameworks: a nonparametric revealed-preference approach and a structural life-cycle model. The structural side builds on the survey of life-cycle portfolio choice models in Gomes (2020) as the benchmark framework.

Revealed-preference setup (Section II, pp. 19-24). Individual $i$ has unobserved preferred participation $Y^*_{it} \in \{0,1\}$ and preferred equity share $\theta^*_{it} \in [0,1]$ at tenure $t$. Observed allocations $Y_{it}$, $\theta_{it}$ may differ because of inertia or frictions. The 401(k) plan carries a default $D_i \in \{0,1\}$ ($D=1$ means TDF default; $D=0$ means money market or opt-in). Consistency indicators are (p. 19):

$$C^Y_{it} = 1 \text{ if } Y_{it}(0) = Y_{it}(1), \quad 0 \text{ otherwise.}$$ $$C^\theta_{it} = 1 \text{ if } \theta_{it}(0) = \theta_{it}(1), \quad 0 \text{ otherwise.}$$

Four identifying assumptions (pp. 20-22):

$$\text{Assumption 1 (Frame Separability):} \quad (Y^*_{it}, \theta^*_{it}) \text{ independent of } D_i.$$ $$\text{Assumption 2 (Frame Exogeneity):} \quad D_i \text{ independent of } (Y_{it}(0), Y_{it}(1), \theta_{it}(0), \theta_{it}(1)).$$ $$\text{Assumption 3 (Frame Monotonicity):} \quad Y_{it}(1) \geq Y_{it}(0), \quad \theta_{it}(1) \geq \theta_{it}(0).$$ $$\text{Assumption 4 (Consistency):} \quad C^Y_{it} = 1 \Rightarrow Y_{it} = Y^*_{it}; \quad C^\theta_{it} = 1 \Rightarrow \theta_{it} = \theta^*_{it}.$$

Proposition 1 (p. 21) shows the average preference for participation is partially identified under Assumptions 1-4:

$$\mathbb{E}_\tau(Y^*_{it}) \in [\mathbb{E}_\tau(Y_{it} \mid D_i=0),\ \mathbb{E}_\tau(Y_{it} \mid D_i=1)]. \tag{1}$$

For the average equity share (continuous variable), a fifth assumption is needed. Assumption 5 (p. 22): an investor who deviates from the default chooses her preferred share (consistent with fixed-cost models). Proposition 2 (p. 22):

$$\mathbb{E}_\tau(\theta^*_{it}) \geq \mathbb{E}_\tau(\theta_{it} \mid D_i=0).$$

For point identification, Assumption 6 (p. 23): preferences of consistent (active) and inconsistent (passive) investors are uncorrelated at any tenure:

$$\text{cov}_\tau(Y^*_{it},\, C^Y_{it}) = \text{cov}_\tau(\theta^*_{it},\, C^\theta_{it}) = 0.$$

Under Assumption 6, Proposition 3 (p. 23) gives:

$$\mathbb{E}_\tau(Y^*_{it}) = \mathbb{E}_\tau(Y^*_{it} \mid C^Y_{it} = 1) - \frac{1}{\mathbb{E}_\tau(C^Y_{it})} \cdot \text{cov}_\tau(Y^*_{it},\, C^Y_{it}) \tag{2}$$ $$\mathbb{E}_\tau(\theta^*_{it}) = \mathbb{E}_\tau(\theta^*_{it} \mid C^\theta_{it} = 1) - \frac{1}{\mathbb{E}_\tau(C^\theta_{it})} \cdot \text{cov}_\tau(\theta^*_{it},\, C^\theta_{it}) \tag{3}$$

where the first term is the preferences of consistent investors (identified as their observed active choices, eq. 4) and the second is a selection bias term. Under Assumption 6 the bias is zero and preferences equal the active investors’ average choices.

Life-cycle model (Section III, pp. 29-36). Investors have Epstein-Zin-Weil recursive preferences. The estimated preferences are broadly consistent with the CRRA life-cycle model of Merton (1969) and its prediction of an equity share that declines with age. The value function for a retired investor (state vector $X_t$) satisfies (p. 35):

$$V_t = \max_{d^{dc}_t,\, s^l_t,\, \Xi_t} \left\{ (1-\beta) n_t \left[ \frac{c_t - k_\theta \mathbf{1}\{\Xi_t \neq \Xi_{d,t}\}}{n_t} \right]^{1-\sigma} + \beta \left[ m_t \mathbb{E}_t V^{1-\gamma}_{t+1} \right]^{(1-\sigma)/(1-\gamma)} \right\}^{1/(1-\sigma)}$$

subject to: (10), (11), (12), (14), (15), (17), and budget constraint

• $\gamma$ = relative risk aversion
• $\sigma^{-1}$ = EIS
• $\beta$ = discount factor
• $n_t$ = equivalence scale
• $k_\theta$ = portfolio adjustment cost (utility units)
• $m_t$ = survival probability

For the working life (employment states $E$ or $JJ$), an additional contribution adjustment cost $k_s$ is incurred when $s^{dc}_t \neq s_{d,t}$ (p. 36):

$$V_t = \max_{s^{dc}_t,\, s^l_t,\, \Xi_t} \left\{ (1-\beta) n_t \left[ \frac{c_t - k_\theta \mathbf{1}\{\Xi_t \neq \Xi_{d,t}\} - k_s \mathbf{1}\{s^{dc}_t \neq s_{d,t}\}}{n_t} \right]^{1-\sigma} + \beta \left[ m_t \mathbb{E}_t V^{1-\gamma}_{t+1} \right]^{(1-\sigma)/(1-\gamma)} \right\}^{1/(1-\sigma)}$$

subject to: (7), (8), (10), (11), (12), (14), (15), (17), and

$$s^{dc}_t \cdot w_t + s^l_t = w_t - c_t - \text{tax}_i(y^{\text{tax}}_t).$$

Three financial assets: risk-free bond (gross return $R_f$), risky stock (log return process, p. 31):

$$\ln R^S_t = \ln R_f + \mu_s + \epsilon_t, \quad \epsilon_t \sim N(0,\, \sigma^2_s). \tag{10}$$

Liquid savings account (p. 31):

$$L_{t+1} = (L_t + s^l_t)[1 + r(1 - \tau_c)], \quad L_0 = 0. \tag{11}$$

Labor income follows an AR(1) process with a deterministic cubic-in-age component (pp. 30-31):

$$\ln w_t = \delta_0 + \delta_1 a_t + \delta_2 a^2_t + \delta_3 a^3_t + \eta_t, \tag{7}$$ $$\eta_t = \rho \eta_{t-1} + \xi^E_t, \quad \xi^E_0 \sim N(0,\, \sigma^2_{\xi_0}), \quad \xi^E_t \sim N(0,\, \sigma^2_\xi) \text{ for } t>0.$$

Default options follow: at a new job, defaults are the employer’s settings $\theta^j_{e}$ (portfolio) and $s^{dc}_{e}$ (contribution rate); in subsequent periods the default is the prior period’s choice (eqs. 14-16, pp. 33-34).

Method

The estimation has two stages. The first stage sets demographics, income-process parameters, asset returns, and tax/benefit rules outside the model using auxiliary data and calibration (pp. 36-38). The second stage estimates five preference parameters by SMM (Simulated Method of Moments): $\beta$, $\gamma$, $\sigma^{-1}$, $k_\theta$, $k_s$ (pp. 36, 39-40).

The life-cycle model is solved by standard numerical discrete-time dynamic programming. The state vector has 10 dimensions (age, labor productivity, employment status, employer identity, tenure, average lifetime earnings, DC retirement savings, liquid savings, default portfolio shares, default contribution rate). Controls are: consumption, portfolio shares for new and accumulated DC assets, DC contribution rate, DC withdrawal, and liquid savings.

The SMM objective minimizes the weighted squared distance between model-simulated and empirical moments (pp. 39-40):

$$\min_{\theta} (\hat{m} - m(\theta))' W (\hat{m} - m(\theta))$$

• $\hat{m}$ are 38 empirical moments
• $m(\theta)$ are their model counterparts simulated on 7,500 investors (approximately five times the estimation sample size)
• $W$ is the inverse covariance matrix of the empirical moments (i.e., the optimal SMM weight matrix), estimated via Erickson-Whited (2002) to avoid bootstrapping-weight-matrix bias

The method builds on Epstein-Zin-Weil preferences (separating $\gamma$ from $\sigma^{-1}$, following Epstein and Zin (1989) and Weil (1990)) and on revealed-preference bounds (Goldin and Reck (2020) extended to continuous shares here).

Key identifying variation for each parameter:

• $k_\theta$ and $k_s$: identified by bunching at default options at various tenure levels; the degree of bunching pins down adjustment cost size.
• $\gamma$: identified primarily by asset allocation decisions of consistent (active) investors who deviate from the default.
• $\sigma^{-1}$: identified by bunching at the employer match threshold (6% of salary cap), following Best et al. (2020) and Choukhmane (2025).
• $\beta$: identified by the overall level of retirement contributions.

Empirical specifications

The paper does not use OLS regressions with fixed effects as the primary estimating procedure. Instead, two reduced-form exercises and one SMM structural estimation generate the results.

Quasi-experimental comparison (R8, R1, pp. 13-17). For each sample (money-market-to-TDF and opt-in-to-TDF), the estimating object is the difference in observed portfolio outcomes between investors hired within 12 months before versus after the 401(k) default asset allocation change at the same set of firms:

$$\text{Outcome}_{it} = f(\text{tenure}_t,\, D_i), \quad D_i = \mathbf{1}[\text{hired after default change at firm } e]$$

No regression equation is reported; the result is the raw time-path of stock market participation $Y_t$ and equity share $\theta_t$ by years of tenure for treatment vs. control (Figure 2, p. 16). Standard errors are clustered by investor (quasi-experiment #1) or by firm (quasi-experiment #2). Sample: money-market-to-TDF: 1,086 control + 1,321 treatment at 6 firms; opt-in-to-TDF: 40,337 control + 52,400 treatment at 191 firms.

Nonparametric preference bounds and point estimates (R1-R4, pp. 21-27). The estimating objects are the tenure-specific conditional means from Propositions 1-3 applied to the quasi-experimental data (p. 25):

$$\mathbb{E}_\tau(Y^*_{it}) = \mathbb{E}_\tau(Y_{it} \mid Y_{it} \neq D_i,\, \text{age}=A) \tag{5}$$ $$\mathbb{E}_\tau(\theta^*_{it}) = \mathbb{E}_\tau(\theta_{it} \mid \theta_{it} \neq \theta^d_i(D_i),\, \text{age}=A) \tag{6}$$

Standard errors are clustered by investor (quasi-experiment #1) and by firm (quasi-experiment #2). Life-cycle preference profiles by age are displayed in Figures 5 and 6 (pp. 25-26). The point estimates (Assumption 6) are displayed in Figure IA9: average preferred participation = 94%, average preferred equity share = 76%, at tenure = 3.

SMM estimation (R5-R7, pp. 39-41). Targeting 38 moments: 14 stock market participation rates by tenure from the money-market-to-TDF quasi-experiment; 16 average stock shares by age for each default group at end of first tenure year; and 8 contribution rate distribution moments from the opt-in-to-TDF quasi-experiment. Table III (p. 41) reports the five preference parameters with standard errors:

$$\beta = 0.940\ (\text{SE}\ 0.001), \quad \gamma = 2.54\ (\text{SE}\ 0.09), \quad \sigma^{-1} = 0.253\ (\text{SE}\ 0.018),$$ $$k_\theta = \$156\ (\text{SE}\ \$6.01), \quad k_s = \$488\ (\text{SE}\ \$16.60).$$

Robustness: column (2) imposes CRRA ($\sigma = \gamma$); column (3) zeros adjustment costs and uses only control-group moments (yields $\gamma = 18.94$); column (4) zeros adjustment costs and uses only TDF-default moments (yields $\gamma = 2.25$). The stark contrast between columns (3) and (4) is the paper’s key identification claim for the role of frictions (R7).

When to read the full paper

Use the original if you are: replicating the nonparametric bounds or the SMM estimation; extending the life-cycle model to brokerage accounts or international settings; reviewing the Internet Appendix robustness (peer effects, passive rebalancing, survivorship, cohort/year effects, income conditioning); or auditing a specific coefficient from Table III. The locators above point to the exact table or figure.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1). This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The paper is CC BY 4.0 and mirroring is permitted; no PDF mirror has been set up in this batch.

Attribution (CC BY 4.0). Choukhmane, Taha, and Tim de Silva. “What Drives Investors’ Portfolio Choices? Separating Risk Preferences from Frictions.” The Journal of Finance 81, no. 1 (February 2026): 5–48. DOI: 10.1111/jofi.70013. © 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.