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. In the baseline and most robustness specifications, stronger legal environments are associated with 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. The pattern is inconsistent with a simple deterrence prediction that stricter enforcement lowers the returns of observed trades; the authors interpret it as selective deterrence, in which observed returns rise because low-return opportunities are filtered away. Firm-level litigation exposure (lnRISK), constructed following Kim and Skinner (2012), is also positively associated with BHAR, suggesting insiders incorporate both provincial and firm-specific legal risk into their trading decisions. The tests find no evidence that M&A rumors, financial literacy, or political connections explain BHAR (Sections 5.2-5.4). The legal-environment associations generally persist when corporate-governance controls are included, though some estimates are not significant (Section 5.5). Kacperczyk and Pagnotta (2024) show a related legal-risk channel for illegal insider trading in the US; this paper extends that logic to an emerging-market setting with substantial 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
R7	The third provincial proxy, judicial resources (LAW^Resources), also predicts BHAR positively in the baseline specifications	Table 4, cols. 3 and 6, p. 7	coeff 0.009*** (SE 0.003) without firm controls; 0.008** (SE 0.003) with controls; N = 491
R8	Stronger provincial legal indices predict greater firm-level ex ante litigation risk, a first link in the proposed channel	Table 5, cols. 1-3, p. 8	lnRISK coefficients: LAW^Institution 0.005** (SE 0.002), LAW^Environment 0.005* (SE 0.002), LAW^Resources 0.007* (SE 0.004); text reports a 1-SD increase predicts 1.99%-2.66% higher investigation likelihood
R9	Firm litigation risk and each provincial legal-environment proxy retain positive associations in the mediation specifications	Table 6, cols. 1-3, p. 8	lnRISK coefficients 0.181*** (SE 0.049), 0.180*** (SE 0.049), 0.172*** (SE 0.041); LAW^Institution 0.013*** (SE 0.002), LAW^Environment 0.014*** (SE 0.002), LAW^Resources 0.008** (SE 0.003)
R10	A second Heckman correction based on the top-profitability selection equation leaves two of the three legal-environment effects significant	Table 7, Panel B, p. 10	LAW^Institution 0.011*** (SE 0.003), LAW^Environment 0.012*** (SE 0.003), LAW^Resources 0.004 (SE 0.004, nonsignificant)
R11	The legal-environment association holds when BHAR is recalculated using the highest stock prices during the trading period	Table 8, cols. 1-3, p. 10	BHAR_high coefficients: LAW^Institution 0.013*** (SE 0.003), LAW^Environment 0.015*** (SE 0.003), LAW^Resources 0.010*** (SE 0.003)
R12	Replacing continuous legal indices with above-national-average dummies yields positive coefficients for all three proxies	Table 9, cols. 1-3, p. 11	D_LAW^Institution 0.078*** (SE 0.022), D_LAW^Environment 0.107*** (SE 0.022), D_LAW^Resources 0.064*** (SE 0.024)
R13	Legal-environment slopes are larger in the Beijing-Tianjin-Hebei subsample near the CSRC than in other provinces	Table 10, cols. 1-6, p. 12	Near vs. far: LAW^Institution 0.483*** vs. 0.011***; LAW^Environment 0.483*** vs. 0.012***; LAW^Resources 0.047*** vs. 0.004 (nonsignificant); near-sample N = 26 or 31
R14	The provincial legal-environment association differs across bear and bull markets, with a resource-index exception	Table 11, cols. 1-6, p. 13	Bear vs. bull: LAW^Institution 0.0252*** vs. 0.00609 (ns); LAW^Environment 0.0245*** vs. 0.00698*; LAW^Resources 0.00607 (ns) vs. 0.0160***
R15	Median-split univariate differences also support higher returns for Environment and, less consistently, Resources	Table 3, Panels B-C, p. 6	LAW^Environment mean difference 0.088*** and median difference 0.033***; LAW^Resources mean difference 0.032 (ns) and median difference 0.021*
R16	The tested M&A-information and financial-literacy alternatives do not explain BHAR in the appendix regressions	Appendix Table A3, Panels A-B, p. 16	DRINFO coefficients 0.022, 0.045, 0.046, 0.037 (all ns); DFINANCE coefficients -0.039, -0.032, -0.030, -0.046 (all ns)
R17	Political-connection indicators do not predict BHAR in the alternative-channel specifications	Appendix Table A4, cols. 1-6, p. 16	PC dummy coefficients 0.020, -0.016, 0.033; PC-level coefficients 0.001, -0.008, 0.010; all nonsignificant
R18	Adding governance controls leaves legal-environment coefficients significant in most specifications	Table 12, pp. 13-14	11 of 12 legal-environment coefficients are significant, ranging from 0.007* to 0.087***; LAW^Resources with G-index is 0.005 (ns)

Overall (paper’s conclusion). Provincial legal quality plays a central 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. The findings remain largely consistent across selection-correction procedures, alternative return measures, and dummy-variable legal proxies; subgroup tests show exceptions for some legal proxies. Political connections, M&A rumors, and financial literacy of the insider do not explain the premium. The estimated legal-environment associations are larger in the small subsample closer to the CSRC in Beijing (Table 10), which the authors interpret as consistent with tighter central oversight raising the required risk premium.

Theory / model

The paper has no formal structural model. Its testable hypotheses come from Becker’s (1968) rational-crime trade-off, and contrast deterrence with risk compensation (pp. 2-3). H1a predicts that provincial legal risk lowers illegal-trading profitability because expected penalties rise. H1b predicts a positive relationship among trades that remain: higher enforcement deters low-return opportunities, so the observed sample is selected toward high-information, high-return cases. H2 predicts that firm-level ex ante litigation risk is positively associated with BHAR after provincial legal risk is controlled.

The decision rule implied by the text is that an insider trades when expected gains exceed expected enforcement costs. This is a verbal theoretical implication, not a numbered equation in the paper:

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

The paper’s empirical outcomes are conditional on adjudicated cases, so a positive association with returns does not imply greater overall illegal activity. The authors interpret it as a composition effect among the observed trades (pp. 2-3, 7).

Method

The design is observational cross-sectional regression of case-level illegal-trading returns, with provincial legal-index variation and firm-level controls. The main OLS specifications include industry and year fixed effects, cluster standard errors at firm and year, and use a two-month lag for fundamentals. Table 4 has 478 cases for LAW^Institution and LAW^Environment and 491 for LAW^Resources. Models with firm characteristics have the same sample counts (pp. 5-7).

The mediation equations test whether provincial legal indices predict firm litigation risk and whether that risk adds explanatory power for BHAR (eqs. 5-6, p. 8). The source prints β_1 for both terms in equation (5). Tables 5 and 6 include industry and year fixed effects and firm/year clustered standard errors. Their samples are N = 455, 455, and 464 across the three legal proxies (p. 8):

$$\text{Med}_{i,j,t} = \beta_0 + \beta_1 \text{Law}_{j,t} + \beta_1 \text{Firm Characteristics}_{i,j,t} + \text{Ind}_{i,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{5}$$ $$\text{BHAR}_{i,j,t} = \gamma_0 + \gamma_1 \text{Med}_{i,j,t} + \gamma_2 \text{Law}_{j,t} + \gamma_3 \text{Firm Characteristics}_{i,j,t} + \text{Ind}_{i,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{6}$$

For selection, the paper first states a population outcome equation and its observed-case version, then models selection into observation (eqs. 7-9, pp. 8-9):

$$y = \alpha + \beta_1 x + \beta X + \varepsilon \tag{7}$$ $$S y = S \alpha + \beta_1 S x + \beta S X + S \varepsilon \tag{8}$$ $$S = F(X', \varepsilon') + \eta \tag{9}$$

The inverse Mills ratio is defined piecewise in equation (10), using the standard normal density (\phi) and cumulative distribution (\Phi) (p. 9):

$$\text{IMR} = \mathbb{E}(\varepsilon \mid D) = f(x) = \begin{cases} \frac{\phi(F(X',\varepsilon'))}{\Phi(F(X',\varepsilon'))}, & S=1 \\ -\frac{\phi(F(X',\varepsilon'))}{1-\Phi(F(X',\varepsilon'))}, & S=0 \end{cases} \tag{10}$$

The two first-stage selection equations are described as probit models (eqs. 11-12, p. 9). Equation (11) predicts observed litigation/detection cases from firm characteristics; equation (12) predicts whether illegal income ranks in the top 30% using size, book-to-market, momentum, and turnover. The paper does not report first-stage sample counts, fixed effects, or standard-error adjustments for these probits:

$$\Pr(S_{\text{litigation}}=1) = a + b_1 \text{Firm Characteristics} + \eta \tag{11}$$ $$\Pr(S_{\text{profit}}=1) = a + b_1 \ln ME + b_2 \ln(BE/ME) + b_3 MOM + b_4 TURNOVER \tag{12}$$

Their IMRs enter the second-stage BHAR model in equation (13). The Heckman regressions use industry and year fixed effects, cluster standard errors by firm and year, and have N = 478 for LAW^Institution and LAW^Environment and N = 491 for LAW^Resources (Table 7, pp. 9-10). The paper states that its exclusion restriction assumes the first-stage variables have no direct effect on BHAR; it tests low correlations with ROA but this is not a causal design.

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

Empirical specifications

The dependent variable is reconstructed from legal records. Equation (1) defines the raw return as illegal income divided by trading volume times the closing price on purchase day. Equation (2) defines the market return over the same holding period (p. 4):

$$\text{retRaw}_i = \frac{\text{Amount of illegal income}_i}{\text{Trading volume}_i \times \text{Closing price}_{i,\text{PurchaseDay}}} \tag{1}$$ $$\text{retBenchmark}_i = \frac{\text{Closing price}_{i,\text{SellDay}} - \text{Closing price}_{i,\text{PurchaseDay}}}{\text{Closing price}_{i,\text{PurchaseDay}}} \tag{2}$$

BHAR is the difference between the raw insider return and the benchmark return. For trades spanning multiple dates the authors use average daily closing prices. The two baseline estimating specifications are equations (3) and (4) (p. 5):

$$\text{BHAR}_{i,j,t} = \alpha_0 + \alpha_1 \text{LAW}_{i,j,t} + \text{Ind}_{i,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{3}$$ $$\text{BHAR}_{i,j,t} = \alpha_0 + \alpha_1 \text{LAW}_{i,j,t} + \alpha_2 \text{Firm Characteristics}_{i,j,t} + \text{Ind}_{i,t} + \text{Year}_t + \varepsilon_{i,j,t} \tag{4}$$

LAW is entered in separate specifications as LAW^Institution, LAW^Environment, or LAW^Resources for the firm’s province and year. Equation (3) has industry and year fixed effects; equation (4) adds lagged firm controls: institutional and state ownership, size, book-to-market, momentum, turnover, leverage, ROE, cash/assets, and firm age. Continuous independent variables are winsorized at the 1st and 99th percentiles. The estimation sample consists of adjudicated common-stock purchase cases from 2006-2018; firm fundamentals are lagged two months. Standard errors are clustered by firm and year. No additional estimating equation is numbered for the robustness and heterogeneity tables; those specifications re-estimate equation (4), replacing the outcome, legal-index measure, or sample split as described at the relevant result locators.

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 2026-10-04 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.