FinanceRoutines.jl

commit 0f1be9a122a74a73dad174c7e1a7cdc56dd4802b
parent f31da659e995ca77a474320d1efc92cf1811bf93
Author: Erik Loualiche <eloualic@umn.edu>
Date:   Sun, 22 Mar 2026 16:56:26 -0500

Fix flaky EventStudy test, restrict CI to tag pushes only [skip ci]

- EventStudy: use seed!(42) and increase event injection from 5% to 20%
  so CAR test reliably passes across platforms
- CI: trigger only on v* tag pushes (saves WRDS query costs)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Diffstat:
M
.github/workflows/CI.yml
 | 
15
+--------------
M
test/UnitTests/EventStudy.jl
 | 
12
++++++++----

2 files changed, 9 insertions(+), 18 deletions(-)
diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
@@ -2,21 +2,8 @@
 name: CI
 on:
   push:
-    branches:
-      - main
     tags:
-      - "*"
-    paths:
-      - "src/**"
-      - "test/**"
-      - "Project.toml"
-      - ".github/workflows/CI.yml"
-  pull_request:
-    paths:
-      - "src/**"
-      - "test/**"
-      - "Project.toml"
-      - ".github/workflows/CI.yml"
+      - "v*"
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
diff --git a/test/UnitTests/EventStudy.jl b/test/UnitTests/EventStudy.jl
@@ -2,6 +2,9 @@
 
     import Dates: Date, Day
     import Statistics: mean
+    import Random: seed!
+
+    seed!(42)  # deterministic random draws
 
     # Build synthetic daily returns panel: 2 firms, 300 trading days
     dates = Date("2010-01-04"):Day(1):Date("2011-04-01")
@@ -20,9 +23,10 @@
     mkt_returns = 0.0005 .+ 0.008 .* randn(n)
     df_ret.mktrf = vcat(mkt_returns, mkt_returns)
 
-    # Inject a positive event: +5% abnormal return on event day for firm 1
+    # Inject a large positive event: +20% abnormal return on event day for firm 1
+    # Must be large enough to dominate cumulative noise over the 21-day event window
     event_idx_firm1 = 270  # well within bounds for estimation window
-    df_ret.ret[event_idx_firm1] += 0.05
+    df_ret.ret[event_idx_firm1] += 0.20
 
     events = DataFrame(
         permno = [1, 2],
@@ -39,8 +43,8 @@
         @test !ismissing(result.car[1])
         @test !ismissing(result.car[2])
         @test result.n_obs[1] == 21  # -10 to +10 inclusive
-        # Firm 1 should have positive CAR (we injected +5%)
-        @test result.car[1] > 0.03
+        # Firm 1 should have positive CAR (we injected +20%)
+        @test result.car[1] > 0.10
     end
 
     # ---- Market model ----