Paper Citation Record · LEDGER

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.01322.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.01322 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:24:48.424951Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy18
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa2a4326-2399-4dcf-9a27-411068111d84 · outbound

This paper cites an unresolved cited work.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:24:54.846412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 09661c81-7e83-4719-ad02-2bbee61096d3 · outbound

This paper cites 2019 , month = oct, howpublished =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2019 , month = oct, howpublished =

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.614754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79ea5cf5-8977-420a-8c0d-d22204ae5d51 · outbound

This paper cites , title =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory , title =

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.344751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0204c8a2-983b-4e75-b612-3975c36bff57 · outbound

This paper cites Science , volume =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Science , volume =

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.120751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f52b1361-24dc-429b-a605-fb5a54d428c1 · outbound

This paper cites , title =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory , title =

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.943810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f4b3aabd-014c-4a2a-b32a-042de136db12 · outbound

This paper cites 2025 , month =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2025 , month =

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.725705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55cb0ae4-54fa-4fb9-a236-bd450fd7401d · outbound

This paper cites Journal of Political Economy , volume =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Journal of Political Economy , volume =

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.480345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68fc486e-e94d-4314-90fd-33097cfd0690 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:45.744750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be083f8f-6dd5-409b-8c68-6bc4e1fd5c6c · outbound

This paper cites The Review of Financial Studies , volume=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory The Review of Financial Studies , volume=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.061071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3085084e-8c0f-4942-9e15-474fe8285a25 · outbound

This paper cites and Reeb, David M.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory and Reeb, David M

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.829782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a3fdee5d-4e66-4ff9-81ac-7725f34bc7e3 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory BloombergGPT: A Large Language Model for Finance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:46.191207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09d08438-3926-463c-88b0-659850e8084b · outbound

This paper cites 2025 , month = nov, howpublished =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2025 , month = nov, howpublished =

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.608773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 108dd11e-d7fa-4f25-83e3-cfa753352f00 · outbound

This paper cites 2024 , eprint=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2024 , eprint=

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:46.395173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33dd8df8-2783-42e5-9613-66915fe8932a · outbound

This paper cites 2026 , eprint=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2026 , eprint=

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.194773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d9c90bd9-5872-4279-ab3d-d92d3b1d4ae1 · outbound

This paper cites 2023 , eprint=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2023 , eprint=

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:46.867519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a4826d8-b49e-4f09-998a-43644b534542 · outbound

This paper cites 2025 , issn =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2025 , issn =

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T00:24:48.875354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6abf7097-0857-4efe-8961-92e7e8c55a86 · outbound

This paper cites an unresolved cited work.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:47.111801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9326f68c-3c37-4fbc-a264-5735a2bcefd0 · outbound

This paper cites 2023 , eprint=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2023 , eprint=

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:47.275773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5561e27-23d7-4e43-abfc-6a1d74697ce0 · outbound

This paper cites A Semantic Approach to Financial Fundamentals.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory A Semantic Approach to Financial Fundamentals

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.904755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ba4aff5b-9664-4fb1-b5f1-228500d3bad9 · outbound

This paper cites 2024 , eprint=.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2024 , eprint=

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.611257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d942befc-92fc-4ff8-a638-3b5a13a4b461 · outbound

This paper cites 1 and 2 and by the Commission, Regarding Customer and Account Information , howpublished =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 1 and 2 and by the Commission, Regarding Customer and Account Information , howpublished =

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.347343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f1e5850-d1dc-4661-8465-7063fc6b2645 · outbound

This paper cites 2026 , month = jan, note =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory 2026 , month = jan, note =

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.179615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1263f99c-f9ad-4b6e-8083-f4f3a8757375 · outbound

This paper cites an unresolved cited work.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:24:50.879848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 45daff75-ceca-4042-8596-cb30659bc963 · outbound

This paper cites and Verleysen, Michel and Blondel, Vincent D.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory and Verleysen, Michel and Blondel, Vincent D

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:50.620418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3a93201-a917-455a-b925-b9abda3a0ce2 · outbound

This paper cites , title =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory , title =

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:50.308951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f98ddde6-1aee-45c2-a82a-67b7fee99265 · outbound

This paper cites Findings of the Association for Computational Linguistics: EACL 2024 , pages =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Findings of the Association for Computational Linguistics: EACL 2024 , pages =

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:50.046282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac1e6fb8-f323-4011-a6e0-8747ab114bc8 · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:49.721697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d80ebc5a-fde8-495d-8411-e4f0d0df8445 · outbound

This paper cites an unresolved cited work.

Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:24:49.434749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.