Illegal Insider Trading Profitability and the Legal Environment: Batten, Liu & Sha (2026)

What this is. The paper’s core results, the hypotheses it tests (risk-compensation vs. deterrence), the regression specifications, and the datasets used: enough to know what it found and how, without reading all 17 pages. To replicate or extend it, read the full source at the original.

TL;DR

This paper asks whether legal risk is priced in illegal insider trading in China. Using 521 adjudicated insider-trading cases hand-collected from court judgments and China Securities Regulatory Commission (CSRC) sanction documents (2006-2018), the authors measure each insider’s buy-and-hold abnormal return (BHAR) and regress it on three proxies for provincial legal environment quality, combined with firm-level ex ante litigation risk. Across all specifications, stronger legal environments are associated with significantly higher per-trade profitability, consistent with a risk-compensation mechanism: stricter enforcement screens out low-return trades, leaving only those with sufficiently high expected gains to justify elevated detection risk. This counterintuitive pattern rules out the simple deterrence story (stricter enforcement reduces profits) in favor of a selective-deterrence story in which observed returns rise because low-return opportunities are filtered away. Firm-level litigation exposure (lnRISK), constructed following Kim and Skinner (2012), further raises BHAR, suggesting insiders incorporate both provincial and firm-specific legal risk into their trading decisions. The findings also rule out M&A rumors, financial literacy, political connections, and corporate governance quality as alternative channels (Sections 5.2-5.5), and survive selection-correction and a range of robustness tests. Kacperczyk and Pagnotta (2024) show a related legal-risk channel for legal insider trading in the US; this paper extends that logic to illegal trading in an emerging-market setting with rich within-country legal variation. The evidence aligns with the rational-crime model of Becker (1968): insiders behave as rational agents weighing expected gains against expected penalties. It also extends work by Ahern (2020) on the determinants of illegal insider trading profitability and by Sha et al. (2020) on the puzzle of low average returns in China’s insider-trading cases.

Core results

Magnitudes and significance are as reported; \*\*\*/\*\*/\* = 1%/5%/10%. Locators point to the source PDF.

#	Result	Locator	Magnitude
R1	Provincial market development index (LAW^Institution) positively and significantly predicts BHAR from illegal insider trading; industry and year fixed effects included	Table 4, col 1, p. 7	coeff 0.011*** (SE 0.002); adj. R² = 0.155; N = 478
R2	Provincial legal environment index (LAW^Environment) positively and significantly predicts BHAR, confirming the pattern across a second legal quality proxy	Table 4, col 2, p. 7	coeff 0.013*** (SE 0.002); adj. R² = 0.162; N = 478
R3	Economic magnitude: 1-SD improvement in legal environment quality predicts 2.77-5.78 percentage-point increase in insider-trading abnormal returns (all three proxies)	Table 4, p. 7	2.77 pp (LAW^Institution) to 5.78 pp (LAW^Environment) per 1-SD increase
R4	Ex ante litigation risk (lnRISK) positively predicts BHAR, incremental to provincial legal environment: both firm-level and provincial risk are priced in illegal insider trades	Table 6, col 1, p. 8	lnRISK coeff 0.181*** (SE 0.049); a 1-pp increase in lnRISK → 18.1 bp higher BHAR
R5	Univariate test: high-legal-environment provinces yield significantly higher insider-trading BHAR than low-legal-environment provinces	Table 3, Panel A, p. 6	mean BHAR difference 0.085*** (high vs. low LAW^Institution); median difference 0.044***
R6	Heckman selection correction confirms the legal environment effect persists after accounting for potential selection bias from undetected cases	Table 7, Panel A, col 1, p. 10	LAW^Institution coeff 0.011*** (SE 0.003) in Heckman model; same sign and significance as OLS baseline

Overall (paper’s conclusion). Provincial legal quality plays a decisive role in shaping insider-trading outcomes. Insiders weigh expected gains against enforcement risk and trade only when the anticipated return exceeds the expected penalty, consistent with the rational-crime framework of Becker (1968) and extending the law and finance literature of La Porta et al. (1998) to the enforcement of securities law. Stricter legal environments produce higher conditional profitability because only high-return trades survive deterrence. Firm-level litigation exposure reinforces this relationship. Results are robust to selection-correction procedures, alternative return measures, dummy-variable legal proxies, and geographic heterogeneity tests. Political connections, M&A rumors, and financial literacy of the insider do not explain the premium. Insiders who are closer to the CSRC in Beijing face the strongest legal environment effects (Table 10), consistent with tighter central oversight raising the required risk premium.

Theory / model

The paper has no formal structural model; the theoretical frame is organized around two competing hypotheses derived from the rational-crime model of Becker (1968) and extended by the law-and-finance literature of La Porta et al. (1998).

Deterrence hypothesis (H1a): Stronger legal environments raise expected penalties, reducing the profitability of illegal insider trades by making even high-information trades unattractive.

Risk-compensation hypothesis (H1b): Stronger legal environments deter low-return trades altogether. The trades that still occur are a selective right-tail subset with unusually high expected gains. The conditional average observed return rises even as the unconditional volume falls. The selection effect dominates the deterrence effect.

Firm-level litigation hypothesis (H2): Firm-level ex ante litigation risk (probability of regulatory sanction, conditional on trading at a particular company) increases BHAR because insiders at high-litigation-risk firms require higher compensation for elevated firm-specific enforcement exposure. This is incremental to the provincial-level effect.

The hypotheses predict opposite signs on the legal environment coefficient in the BHAR regression: H1a predicts a negative coefficient (deterrence reduces profits), H1b predicts a positive coefficient (risk compensation raises the conditional mean). The data strongly support H1b and H2.

The intuition for H1b follows from the rational-crime trade-off. Define the insider’s decision as: trade if and only if expected gain exceeds expected penalty:

$\mathbb{E}[\text{gain}] > p(\text{detection}) \times \text{penalty}$

In a stronger legal environment, $p(\text{detection})$ rises, so the threshold gain required to justify trading also rises. The observed (adjudicated) trades are draws from the right tail of the gain distribution. As the threshold rises with legal quality, the observed conditional mean rises even if the full distribution of potential gains is unchanged. The paper’s data capture only adjudicated cases, so the composition of observed returns shifts upward in high-enforcement provinces.

Method

The paper applies panel-regression as the primary estimator and probit-regression as a first-stage tool for constructing the ex ante litigation-risk mediator following the methodology of Kim and Skinner (2012).

Baseline OLS with fixed effects (Models 3-4, pp. 5-6, Table 4). BHAR is regressed on the provincial legal environment proxy, optionally with firm characteristics, and with industry and year fixed effects. Standard errors are clustered at both the firm and year levels. Three legal environment proxies are used in separate columns: LAW^Institution (the “market intermediaries and legal environment” sub-index of the Wang et al. (2017) Marketization Index), LAW^Environment (the overall legal environment sub-index), and LAW^Resources (the provincial judicial resources index of Gao et al. (2016)).

Mediation analysis (Models 5-6, pp. 7-8, Table 6). To assess whether ex ante litigation risk is the channel through which the legal environment affects BHAR, a two-equation mediation system is estimated. First, litigation risk (lnRISK) is regressed on the legal environment proxies and firm characteristics (eq. 5, p. 8):

\text{Med} = \beta_0 + \beta_1 \text{Law} + \beta_2 \text{Firm Characteristics} + \text{Ind} + \text{Year} + \varepsilon \tag{5}

Second, BHAR is regressed on both the mediator and the legal environment proxies (eq. 6, p. 8):

\text{BHAR} = \gamma_0 + \gamma_1 \text{Med} + \gamma_2 \text{Law} + \gamma_3 \text{Firm Characteristics} + \text{Ind} + \text{Year} + \varepsilon \tag{6}

If $\gamma_1$ is significant and $\gamma_2$ remains significant, the litigation risk channel is a partial (not complete) mediator of the legal environment effect. Results in Table 6 confirm both coefficients are significant at the 5% level across all three legal environment proxies.

Heckman selection correction (Models 7-13, pp. 8-10, Table 7). Since only detected insider-trading cases are observable, the observed BHAR may be a biased estimate of the full population BHAR. The paper addresses this via Heckman’s two-step procedure. Two probit first-stage models identify the probability of appearing in the sample: (i) detection likelihood based on company characteristics (eq. 11), and (ii) top-30% profitability rank within the sample (eq. 12):

\text{Prob}(S_{\text{litigation}} = 1) = a + b_1 \text{Firm Characteristics} + \eta \tag{11}

\text{Prob}(S_{\text{profit}} = 1) = a + b_1 \ln\text{ME} + b_2 \ln\text{BE/ME} + b_3 \text{MOM} + b_4 \text{TURNOVER} \tag{12}

The Inverse Mills Ratio (IMR) from each first stage is included as an additional control in the BHAR regression (eq. 13):

\text{BHAR}_{i,j,t} = \alpha_0 + \alpha_1 \text{LAW}_{j,t} + \alpha_2 \text{Firm Characteristics}_{i,j,t} + \rho\sigma\, \text{IMR}_{i,j,t} + \text{Ind}_{j,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{13}

The legal environment coefficient remains positive and significant at the 1% level after the IMR correction in both panels of Table 7, ruling out selection bias as the driver of the main result.

Empirical specifications

Dependent variable construction (eqs. 1-2, p. 4, Table 1). For each adjudicated insider-trading case $i$, the raw holding-period return is computed from the legal documents:

\text{ret}_{\text{Raw}_i} = \frac{\text{Amount of illegal income}_i}{\text{Trading volume}_i \times \text{Closing price}_{i,\text{PurchaseDay}}} \tag{1}

The market-adjusted benchmark return for the same holding period is:

\text{ret}_{\text{benchmark}_i} = \frac{\text{Closing price}_{i,\text{SellDay}} - \text{Closing price}_{i,\text{PurchaseDay}}}{\text{Closing price}_{i,\text{PurchaseDay}}} \tag{2}

BHAR equals the difference between the raw insider trading return and the benchmark return. For cases where trading occurs over multiple days, the average daily closing price during the trading dates is used. The amount of illegal income, trading volumes, and dates are read directly from the court judgment or CSRC sanction document for each case.

Baseline regression specifications (eqs. 3-4, p. 5, Table 4). The two baseline models are:

\text{BHAR}_{i,j,t} = \alpha_0 + \alpha_1 \text{LAW}_{j,t} + \text{Ind}_{j,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{3}

\text{BHAR}_{i,j,t} = \alpha_0 + \alpha_1 \text{LAW}_{j,t} + \alpha_2 \text{Firm Characteristics}_{j,t} + \text{Ind}_{j,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{4}

where $i$ indexes the insider-trading case, $j$ the company, and $t$ the year. $\text{LAW}_{j,t}$ is one of the three provincial legal environment proxies for the province where company $j$ is registered. Firm characteristics (lagged two months to align with public availability) include: size (lnME), book-to-market ratio (lnBE/ME), momentum (MOM), turnover ratio, leverage (DEBT/ASSET), return on equity (ROE), cash/assets ratio, firm age, institutional ownership (FUND), and state-ownership dummy (DSOE). Industry and year fixed effects are included in all specifications; standard errors are clustered at both the firm and year levels.

Table 4 columns 1-3 report model (3) for the three legal environment proxies; columns 4-6 report model (4). All six coefficients on the legal environment measures are positive and significant (1% for LAW^Institution and LAW^Environment; 1% and 5% for LAW^Resources). The adjusted R² ranges from 0.139 to 0.207.

Robustness. Alternative dependent variable: BHAR_High, computed using the highest stock price during the holding period (Table 8). Alternative legal proxies: binary dummies based on the national median of each index (Table 9). Selection correction: Heckman two-step with two alternative first stages (Table 7, Panels A and B). Geographic heterogeneity: subsamples of cases near the CSRC (Beijing-Tianjin-Hebei region) vs. far provinces (Table 10). Market conditions: bear vs. bull market subsamples (Table 11). Corporate governance: ESG score, managerial ownership, CEO duality, and G-index added to model (4) (Table 12); legal environment coefficients remain positive and significant.

Datasets used

Dataset	Role in paper	Wiki page
Hand-collected court judgments and CSRC sanction documents (PKU-LAW, Lawyee databases)	Primary dataset: 521 insider-trading cases with trading dates, volumes, execution prices, illicit gains, and case characteristics; 312 unique companies, 2006-2018	No page yet
China Stock Market and Accounting Research (CSMAR)	Firm-level control variables: market capitalization, book-to-market ratio, past returns, turnover, leverage, return on equity, cash/assets, institutional ownership, state ownership, firm age	CSMAR (licensed)
Wang, Fan and Yu (2017) Marketization Index of China’s Provinces (NERI)	Two provincial legal environment proxies: LAW^Institution (market intermediaries and legal sub-index) and LAW^Environment (overall provincial legal environment sub-index); updated biannually	No page yet
Gao et al. (2016) provincial judicial resources index	LAW^Resources proxy: provincial count of lawyers and legal service offices; measures availability of non-public judicial resources	No page yet
Bloomberg (appendix only)	M&A event verification for alternative-channel tests in Section 5.2 (dummy DRINFO)	No page yet

Sample: 521 insider-trading cases involving 312 companies, 2006-2018. Regression sample N = 478 for most specifications (limited by legal environment data coverage); N = 491 for specifications using LAW^Resources. All continuous independent variables winsorized at the 1st and 99th percentiles. A two-month lag is applied between firm fundamentals and the insider-trading date.

When to read the full paper

Read the full original if you are: studying the determinants of illegal insider trading profitability in emerging markets; modeling risk-return trade-offs in illicit market activity; extending the rational-crime or law-and-finance framework to securities law enforcement; working on empirical cross-regional legal variation using the Chinese provincial institutional setting; or building on the hand-collected dataset of 521 Chinese insider-trading cases (data available upon request per the paper’s data-availability statement). Table 10 is particularly useful for understanding how geographic proximity to the central regulator moderates the legal environment effect.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 185 (2026) 107609. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Batten, Jonathan A., Lanlan Liu, and Yezhou Sha. “Illegal insider trading profitability and the legal environment.” Journal of Banking and Finance 185 (2026): 107609. DOI: 10.1016/j.jbankfin.2025.107609. © 2025 The Author(s). Published by Elsevier B.V. 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.