3.1 Sample and Data Sources

Our sample covers January 1, 2016 to December 31, 2024. We start from all firms listed on the Korea Exchange (KOSPI and KOSDAQ). We exclude financial firms and regulated utilities. We keep firms with daily price data and annual accounting data from FnGuide. The resulting universe contains 1,636 firms.

We define the two key cross-sectional variables explicitly. Export is the firm’s export-to-sales ratio at fiscal year-end t-1, computed from FnGuide segment-disclosure data, and is used as a continuous variable in the main regressions. For descriptive splits and figures we additionally use a binary indicator HighExport, which equals one for firms whose Export ratio is above the cross-sectional median in year t-1 (median ≈ 0.379; mean ≈ 0.396). FC is a binary financial-constraint indicator constructed following Hadlock and Pierce (2010): we compute the firm-year HP/SA index as -0.737×Size + 0.043×Size2 - 0.040×Age (with Size = log (total assets) winsorized at log(4.5 billion KRW) and Age in years since listing, capped at 37), and assign FC = 1 to firms in the top tercile of the index in year t-1 and FC = 0 otherwise. By construction, FC is more common among smaller and younger firms, which accounts for the strong negative correlation between FC and Size in Table 3 ($\rho \approx -0.79$).

We assemble three datasets. First, we obtain daily stock prices and firm fundamentals from FnGuide, which allows us to compute returns and lagged controls consistently across KOSPI and KOSDAQ firms. Second, we hand-collect KEXIM policy announcements from the official KEXIM website and verify major releases using postings by the Ministry of Economy and Finance (MOEF), the Financial Services Commission (FSC), and mainstream financial newswires. For every candidate release, we search MOEF and FSC press-room postings and at least two major financial newswires within a [-1,+1] calendar-day window. Releases posted after the KOSPI close or on non-trading days are shifted to the next trading day, with external timestamps used as a tie-breaker. Administrative notices and candidates that cannot be corroborated by at least one external source are dropped. This procedure removes 38 candidate items and yields 521 event dates. Third, we quantify announcement tone using KOSELF and a finance-domain dictionary approach following standard financial text-analysis practice (Loughran & McDonald, 2011). The daily panel is used to estimate abnormal returns, while the main regressions use a stacked firm-announcement sample. Table 1 summarizes the manually coded, mutually exclusive announcement categories.

3.2 Abnormal Returns and Event Windows

We measure price reactions using abnormal returns. Let (e) denote the KEXIM announcement date, aligned to the relevant trading day. For each firm (i), we estimate a market model over a pre-event estimation window:

Abnormal return is the fitted residual:

and cumulative abnormal return over an event window [a,b] is:

We focus on three horizons. The main announcement window is $\mathit{CAR}_{i,e}[-1,1]$. This window allows limited leakage and short-run adjustment. To address timing uncertainty from after-close releases, we also use $\mathit{CAR}_{i,e}[0,1]$. As a placebo pre-trend check, we compute $\mathit{CAR}_{i,e}[-3,-1]$. In the absence of information leakage, this pre-event window should be close to zero on average.

3.3 Empirical Specification

The main tests use a stacked firm–announcement design. For each announcement date e, we construct an event-window outcome Yi,ew for each firm i, where

We estimate:

$\mathit{TONE}_e$ measures announcement tone, $\mathit{TYPE}_e$ captures announcement categories, and $X_{i,e-1}$ includes lagged firm controls. We include firm fixed effects $\eta_i$ and year-month fixed effects $\theta_{ym(e)}$. Because announcements are common shocks across firms, we cluster standard errors by firm and event date (Petersen, 2009).

3.4 Descriptive Statistics

Table 2 reports descriptive statistics. The sample contains 1,636 KRX firms and 521 KEXIM event dates. PROGRAM and SECTOR are the most frequent announcement categories. TONE has a mean close to zero and a standard deviation of 0.21, providing substantial cross-event variation. The mean Export ratio is 0.40, with an interquartile range of 0.25 to 0.54, and 40% of firm-years are classified as financially constrained under the SA-index tercile rule. Short-window abnormal returns are centered near zero, and the pre-event placebo window also has a mean close to zero.

3.5 Correlation Analysis

Table 3 reports pairwise correlations among the key variables. Correlations between short-window abnormal returns and text tone are small in magnitude. FC is strongly negatively correlated with Size, reflecting the construction of the financial-constraint indicator.

3.6 Hypothesis Tests

1. Synopsis

This teaching case examines stock-market responses to policy communication by an Export Credit Agency (ECA). Using announcements by the Export-Import Bank of Korea (KEXIM), it focuses on how information about prospective credit, guarantees, project finance, and sectoral support is incorporated into equity prices.

Because ECAs allocate policy-backed finance to identifiable firms and industries, their announcements may alter expectations about financing conditions before funds are disbursed. The case uses this setting to discuss event-study design, heterogeneous exposure, and the interpretation of policy-linked market reactions.

2. Learning Objectives

The teaching case has five learning objectives:

Explain how export credit agencies differ from central banks in the scope and allocation of policy support.

Assess why an announcement may affect asset prices before any lending or guarantee is executed.

Interpret short-window abnormal returns and distinguish statistical evidence from mechanism claims.

Compare market responses across firms with different export exposure and financing constraints.

Evaluate whether observed price reactions are consistent with information effects, policy support, or both.

3. Target Audience & Courses

4. Case Questions

• 1. Through which information channels could a KEXIM announcement affect equity prices before financing is disbursed?

• 2. Which announcement characteristics are associated with larger short-window returns in the case evidence?

• 3. How should the stronger estimates for export-oriented and financially constrained firms be interpreted?

• 4. What does the stress-period evidence add to the baseline results, and what does it not establish?

• 5. Which additional data or research designs would be needed to distinguish information, certification, and credit-supply mechanisms?

5. Case Analyses & Key Concepts