Pith Number

pith:NGLYHHK5

pith:2026:NGLYHHK5AO4FL4FIDKXWUSUMUN

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Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

Karan Sehgal, Khawar Naveed Bhatti

Gradient-boosting models achieve higher sensitivity to rare financial distress cases than statistical baselines under severe class imbalance.

arxiv:2605.14067 v1 · 2026-05-13 · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experimental evaluation demonstrates that gradient-boosting approaches achieved improved minority-class sensitivity relative to baseline statistical classifiers under severe imbalance conditions.

C2weakest assumption

That the chosen financial datasets and SMOTE-generated samples are representative of real-world distress distributions and that performance gains are not artifacts of the synthetic oversampling process.

C3one line summary

Gradient boosting models with SMOTE oversampling show better minority-class sensitivity than statistical baselines for financial distress prediction under severe imbalance.

References

8 extracted · 8 resolved · 0 Pith anchors

[1] The Journal of Finance , volume=

[2] Journal of Accounting Research , volume=

[3] Journal of Accounting Research , volume=

[4] Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages=

[5] Advances in Neural Information Processing Systems , volume=

Receipt and verification

First computed	2026-05-17T23:39:12.461787Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6997839d5d03b855f0a81aaf6a4a8ca3757ffcdc7f43f5912069542afb9136c6

Aliases

arxiv: 2605.14067 · arxiv_version: 2605.14067v1 · doi: 10.48550/arxiv.2605.14067 · pith_short_12: NGLYHHK5AO4F · pith_short_16: NGLYHHK5AO4FL4FI · pith_short_8: NGLYHHK5

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NGLYHHK5AO4FL4FIDKXWUSUMUN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6997839d5d03b855f0a81aaf6a4a8ca3757ffcdc7f43f5912069542afb9136c6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a5ab52b2384d0aade4fea23cbfcaebb547f7ae2d29a350c7a63010c35ee76c8d",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T19:44:25Z",
    "title_canon_sha256": "a69307c4c4ebda532840f86b5adcc828d52c9e1c00190c278f483b834d88798e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14067",
    "kind": "arxiv",
    "version": 1
  }
}