Effects of Credit Expansions on Stock Market Booms and Busts: Hansman, Hong, Jiang, Liu & Meng (2025)

What this is. The paper’s core results, the information-revelation model it contributes, and the three empirical strategies (event study, regression discontinuity, panel model) with their equations: enough to know what it found and how, without reading the full 43 pages. To replicate or extend the paper, read the original at doi.org/10.1093/rfs/hhaf008.

TL;DR

Using China’s staggered deregulation of margin lending (three vintages introduced between 2013 and 2014 under a formula-based inclusion rule), the paper establishes causal evidence that an expansion of margin credit substantially raises stock prices, with 60-day cumulative returns roughly 25% higher for newly marginable stocks than for just-below-threshold controls. Crucially, this effect was largely anticipated: stock prices began rising months before formal margin eligibility, consistent with deep-pocketed institutional investors front-running the liberalization. Chinese mutual funds systematically overweighted soon-to-be-marginable stocks as each vintage approached, then sold after eligibility, and earned significant risk-adjusted profits from this strategy. A dynamic information-revelation model rationalizes the gradual exponential run-up with two parameters: the total price impact and the monthly rate of anticipation (roughly 5-7%). The paper also documents that stocks with more margin debt at the 2015 market peak experienced larger crashes, consistent with margin lending amplifying the bust as well. The results extend to equities the causal credit-to-asset-price evidence of Mian and Sufi (2009) and Di Maggio and Kermani (2017) for mortgage markets. Shadow margin data from Bian, Da, He, Lou, K. Shue, and Zhou (2023) is used to measure informal lending in the boom-bust analysis.

Core results

Magnitudes and significance as reported; \* = 10%, \*\* = 5%, \*\*\* = 1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Pre-marginability run-up: DGTW-adjusted returns for soon-to-be-marginable vs. never-marginable stocks are large and significant in months leading to deregulation, but near zero after	Table 2 Panel A, p. 1514	-1 to 0 months: 2.1%***; -3 to 0 months: 7.3%***; -12 to 0 months: 26.9%***; following marginability: statistically zero
R2	RD reduced-form: stocks just above the marginability threshold saw roughly 25% higher 60-day cumulative returns than just-below-threshold stocks	Table 3 Panel B, p. 1522	4.6%*** (5 days), 8.3%** (20 days), 24.6%*** (60 days), all at optimal bandwidth; fixed bandwidth: 4.6%***, 8.2%**, 24.2%***
R3	Fuzzy RD causal effect: 2SLS estimate of becoming marginable on cumulative returns, instrumenting with above-threshold status; first stage is near-perfect (96% jump in marginability)	Table 3 Panel C, p. 1522	4.7%*** (5 days), 8.5%** (20 days), 26.0%*** (60 days) at optimal bandwidth; 25.2%*** at 60 days fixed bandwidth
R4	Mutual fund anticipatory buying: funds systematically increased weights in soon-to-be-marginable stocks as the deregulation approached, interacted with time since last vintage	Table 4, p. 1528	Interaction Top Quintile x Months Since Last Vintage: 0.0034*** (t=3.2, col 1); Top 150 x Months Since Last Vintage: 0.0027*** (t=2.5, col 2); implies roughly 8.1% total overweighting of top 150 stocks across two exchanges
R5	Mutual fund alpha: TBM (to-be-marginable) holdings earned 0.56%/month more than NTBM holdings on a long-short basis; CAPM alpha of long-short = 0.56%/month	Table 5, p. 1530	TBM raw return 1.65%/month (t=2.79); NTBM 1.09%/month; long-short 0.56%** (t=2.10); CAPM alpha 0.56%** (t=2.01)
R6	Dynamic panel model: monthly rate of anticipation theta is 1.05-1.10 (text), implying 5-10% monthly exponential discounting of future credit shock; more than 60% priced in within six months before eligibility	Table 6, p. 1538	Nonlinear NLS: theta = 1.051-1.099, delta_1 = 0.010-0.025***; linear 2SLS: theta = 1.069, delta_1 = 0.035*** (base), 0.044*** (full sample)
R7	Margin debt and the crash: stocks with higher margin debt at the June 2015 market peak experienced significantly larger crash returns in the subsequent 24-week bust	Table 7 Panel B, p. 1541	OLS coefficient on Peak Total Margin: -0.266** (SE=0.116); top quintile of margin stocks dropped 6 percentage points more than others; crash window Jun 12 to Nov 27, 2015

Overall (paper’s conclusion). Major expansions of credit for stock purchases increase equity prices, and this effect is anticipated and priced in gradually by deep-pocketed investors who behave as front-runners. The model and estimates suggest that margin debt contributes meaningfully to stock market fluctuations, including both the boom and the bust.

Theory / model

The paper has no formal asset-pricing equilibrium in the traditional sense; instead it develops a dynamic competitive stock-pricing model with information revelation about a credit supply shock (Section 3, pp. 1531-1538). The model is in the vein of Summers (1986) and DeLong, Shleifer, Summers, and Waldmann (1990), with deep-pocketed unconstrained investors receiving signals about a coming credit supply shock and front-running the price impact. The setup is:

Consider a market for stock with shares outstanding $Q$. The stock pays dividend $\pi \sim N(0, \sigma_\pi^2)$ at terminal date $T$, with the risk-free rate set to zero. A unit mass of unconstrained risk-averse investors have CARA utility $-e^{-\gamma W}$ and are price takers.

At $t = 0$ there is a permanent price-inelastic demand shock of $\Delta$ shares from previously constrained investors receiving margin credit. If the shock were entirely unanticipated, the price would jump at $t=0$ by $m = \Delta \gamma \sigma_\pi^2$. To capture anticipation, unconstrained investors receive signals $m_t$ about the demand shock $m = \sum_{j=-\infty}^0 m_j$ in each period $t \leq 0$. The signals $m_t$ are independent normal with mean zero and variance $\sigma_t^2$. The equilibrium price for any $t$ between $-n$ and $0$ is (p. 1532, equation 7):

$$p_t = p^* + \sum_{j=-n}^{t} m_j - \gamma \left(\sum_{k=t+1}^{0} \sigma_k^2\right) Q \tag{7}$$

The first term captures revealed information about future prices; the second is a risk discount that falls as uncertainty resolves.

Exponential decay information structure. To bring the model to data, the authors impose that for some $\theta > 0$ the variance of each signal is $\sigma_t^2 = \beta(\theta)^t$ (p. 1533). Under this parameterization uncertainty reduces exponentially as $t \to 0$. In a cross-section of treated stocks, the expected price at time $t$ is (p. 1533, equation 8):

$$E[p_t | m > 0] = \bar{p} + \beta(\lambda + \gamma) \sum_{j=-\infty}^{t} \mathbf{1}\{j \leq 0\} \theta^j \tag{8}$$

where $\lambda = \frac{\phi(0)}{\Phi(0)} \frac{1}{\sigma_m}$ and $\bar{p} = p^* - \frac{\gamma \bar{\mu}}{1 - \frac{1}{\theta}}$. The period-to-period expected price increase is:

$$E[p_t - p_{t-1} | m > 0] = \begin{cases} \beta(\lambda + \gamma)\theta^t & \text{if } t \leq 0 \\ 0 & \text{if } t > 0 \end{cases}$$

The parameter $\theta$ captures the exponential rate at which prices rise. Higher $\theta$ means anticipation begins earlier. The net impact of deregulation is recovered from the two parameters as: $\Delta p_{-\infty} = E[p_0 | m > 0] = \frac{\delta_1}{1 - \frac{1}{\theta}}$ where $\delta_1 = \beta(\lambda + \gamma)$.

Identification assumption. The timing of deregulation across vintages is orthogonal to vintage-specific demand shocks for stocks. The deregulation formula used publicly observable real-time data on market cap and volume, providing the basis for the regression discontinuity design.

Method

The paper combines three complementary empirical strategies, each designed to address a different concern about identification.

Event study (Section 2.1). Reduced-form estimation of cumulative DGTW-adjusted returns comparing marginable and never-marginable stocks around announcement/implementation dates (pp. 1513-1517, equations 2-3):

$$Ret_i^k = \beta_0 \text{Marginable}_i^k + \theta_k + \varepsilon_i^k \tag{2}$$

The stacked version plots the full dynamic path (equation 3):

$$Ret_{ikt} = \sum_{\ell} \beta_\ell \times \mathbf{1}\{t - \tau(k) = \ell\} \times Treated_{ik} + \gamma_{ik} + \lambda_{tk} + \varepsilon_{ikt} \tag{3}$$

Regression discontinuity (Section 2.2). Exploiting the formula-based threshold in the inclusion index. Stocks with index above the cutoff $C_E^k$ qualify; the density test shows no bunching (t-stat = 0.96, Figure 3). The RD estimating equation is (p. 1519, equation 4):

$$Y_i^k = \alpha_{0l} + \alpha_{1l}(Index_i^k - C_E^k) + \tau_i^k[\alpha_{0r} + \alpha_{1r}(Index_i^k - C_E^k)] + \theta_k + \lambda_{ind(i)} + \varepsilon_i^k \tag{4}$$

where $\tau_i^k$ is the indicator for being above the threshold. A triangular kernel local linear approach is used. The fuzzy RD instruments marginability with $\tau_i^k$ (2SLS, Panel C of Table 3, p. 1522). Standard errors are clustered at the stock level; CCT robust confidence intervals are applied.

Dynamic panel model (Section 3.3-3.4). Two estimating equations derived from the theoretical model.

Nonlinear approach (equation 9):

$$p_{it}^{treated} - p_{it-1}^{treated} = \underbrace{\delta_1}_{\beta(\lambda+\gamma)} \theta^t + \Delta \varepsilon_{it} \tag{9}$$

Estimated by nonlinear least squares from pre-event price changes for treated stocks.

Linear approach (equation 10):

$$p_{it} = \delta_1 D_{it} + \frac{1}{\theta} p_{it+1} + \alpha_i + \eta_t + e_{it} \tag{10}$$

where $D_{it}$ equals one for treated stocks when $t \geq 0$. Estimated by 2SLS using forward leads $D_{it+2}$ and $D_{it+3}$ as instruments to address the dynamic panel bias (Arellano and Bond (1991), Arellano and Bover (1995), Malani and Reif (2015)).

Mutual fund analysis (Section 2.3). Portfolio-weight panel regression at the semiannual fund-stock level (equation 5):

$$w_{ijt} = \alpha + \beta \, \text{Top ranking}_{it} \times \text{Months since last vintage}_t + \text{Controls}_{it} + \sigma_i + \theta_j + \gamma_t + \epsilon_{ijt} \tag{5}$$

where $w_{ijt}$ is fund $j$‘s dollar-value weight in stock $i$ at time $t$, scaled by total net assets. Standard errors are clustered by fund.

Empirical specifications

Event study (R1, pp. 1513-1517). Sample: all stocks that either become marginable in vintages 1, 2, 3 or are never marginable. Three announcement/implementation events are pooled. Returns are DGTW-adjusted benchmarks from independent quintile sorts on size, book-to-market, and past-12-month returns (125 bins). Cumulative returns measured from 12 months before to 12 months after the announcement/implementation month. Vintage fixed effects $\theta_k$ included. Standard errors clustered at the stock level. Placebo tests use 1,000 iterations randomly drawing three placebo deregulation months from January 1993 to December 2008.

Regression discontinuity (R2, R3, pp. 1518-1523). Running variable is $Index_i^k$, centered at the vintage-exchange threshold $C_E^k$. Window of size one around the threshold. CCT covariate-adjusted MSE-optimal bandwidth (Calonico et al. (2019)) and fixed bandwidth 0.1 both reported. Local linear with triangular kernel. Industry (CSRC 2001) and vintage fixed effects included. Outcome $Y_{ik}$ is raw cumulative return over 5, 20, or 60 trading days after implementation. Fuzzy RD instruments marginability with above-threshold indicator; first stage shows 96% probability jump at threshold (Table 3 Panel A, p. 1522). CCT robust p-values reported. N ranges from 146 to 229 depending on bandwidth and horizon.

Mutual fund panel (R4, R5, pp. 1526-1530). Sample: semiannual fund-stock level observations, December 2011 to June 2014 (columns 1-3) or December 2011 to June 2015 (column 4). N = 324,860 or 455,846. Controls: percentile rank of market cap, book-to-market, turnover, past month and past year return, and their interactions with Months Since Last Vintage. Fund, stock, and time fixed effects. Standard errors clustered by fund. Performance test (Table 5): calendar-time portfolios January 2012 to September 2014, rebalanced semiannually, value-weighted within fund, Newey-West SEs with 11-month lag.

Dynamic panel model (R6, pp. 1535-1538). Monthly data, March 2009 to October 2015. Stock prices normalized to March 2009 price. Base sample includes three main vintages plus never-marginable stocks; full sample adds pilot vintages. Nonlinear NLS estimated from pre-event price changes. Linear 2SLS uses leads $D_{it+2}$ and $D_{it+3}$ as instruments; instruments validated by the Kleibergen-Paap F-statistic (37-41 across specifications). Two-way stock x year and month fixed effects included.

Boom-bust analysis (R7, pp. 1539-1541). Cross-sectional OLS at the stock level. Crash Return = cumulative log return, June 12 to November 27, 2015. Peak Total Margin = stock-level ratio of formal plus shadow margin debt to floating market cap, June 12, 2015. Controls: log floating market cap, book-to-market, turnover, past month and past year return as of end of May 2015. N = 1,645 stocks. Robust standard errors. Quantitative causal interpretation is withheld given the endogeneity of margin debt during the crash.

Datasets used

Dataset	Role in paper	Wiki page
CSMAR (China Stock Market and Accounting Research)	Stock prices, trading data, book-to-market, fund semiannual holdings (complete portfolios), top 10 shareholder ownership data at quarterly frequency	No page yet
Shanghai and Shenzhen exchange margin debt data	Daily stock-level formal margin debt outstanding, collected directly from exchanges	No page yet
Proprietary shadow margin data (single large provider)	Daily stock-level shadow margin balance, approximately 5% of market; used for bust analysis	No page yet
China Securities Finance Co. (CSF) / regulatory formula	Vintage inclusion index inputs (market cap and trading volume); three-step screening rule per exchange	No page yet

Sample: primary analysis March 2009 to May 2015 (daily), with the boom-bust extension through October 2015. Mutual fund analysis: December 2011 to June 2014/2015 (semiannual holdings).

When to read the full paper

Read the original if you are: (a) studying credit-to-equity-price transmission with a quasi-experimental design (the RD, event study, and dynamic panel strategies are all described in full, with Internet Appendix robustness); (b) modeling anticipatory price dynamics in event-study settings more generally (the exponential information-revelation model and its panel estimator have broad applicability); (c) studying institutional front-running around predictable regulatory changes; or (d) analyzing the role of leverage in boom-bust cycles, particularly in markets where short-selling is limited. The locators above point to the exact tables and figures.

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(5), 2025. Published by Oxford University Press. Paywalled; no CC licence. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. Extract-only under fair-use/fair-dealing norms.

Hansman, Christopher, Harrison Hong, Wenxi Jiang, Yu-Jane Liu, and Juan-Juan Meng. “Effects of Credit Expansions on Stock Market Booms and Busts.” The Review of Financial Studies 38, no. 5 (May 2025): 1502-1544. DOI: 10.1093/rfs/hhaf008. (c) 2025 The Author(s). Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved.