Paper Citation Record · LEDGER

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.08382.

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

pith.paper-citation-record.v1
2411.08382 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:43:52.978442Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

30 of 30 outbound references displayed

verified exact11
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 100e6fc4-4500-411e-9616-b12796149eae · outbound

This paper cites Cross-impact of order flow imbalance in equity markets.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Cross-impact of order flow imbalance in equity markets

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.849202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f699cbe8-fe83-4169-94dc-9666192846bd · outbound

This paper cites López de Prado, and Maureen O’Hara.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading López de Prado, and Maureen O’Hara

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.853735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6224fa8-166b-41ec-8a64-45b699d3d556 · outbound

This paper cites Kolm, Jeremy Turiel, and Nicholas Westray.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Kolm, Jeremy Turiel, and Nicholas Westray

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.858072Z

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Unavailable: canonical work link unavailable.

Observation 4c1f4e1a-0878-492d-a43f-39550a8ca737 · outbound

This paper cites A vector-autoregression analysis of credit and liquidity factor dynamics in US LIBOR and Euribor swap markets.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading A vector-autoregression analysis of credit and liquidity factor dynamics in US LIBOR and Euribor swap markets

Reference 4

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.181207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e72f1fa2-067b-4aa7-90e1-4c3508fcc3b9 · outbound

This paper cites Estimation of slowly decreasing Hawkes kernels: application to high-frequency order book dynamics.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Estimation of slowly decreasing Hawkes kernels: application to high-frequency order book dynamics

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.867262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e91b927d-b67f-43c6-8400-938f1ba67baf · outbound

This paper cites NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

Reference 6

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.166800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 88c4b88b-8351-4ff2-bce2-1c725df8b219 · outbound

This paper cites Mahmoudi, and Javier E.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Mahmoudi, and Javier E

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.877496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31b68986-8423-4e89-9030-1036cf4cbec8 · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 8

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.152480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95738f4d-9dd5-486b-bb8f-685ff52863a4 · outbound

This paper cites Trade size, order imbalance, and the volatility–volume relation.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Trade size, order imbalance, and the volatility–volume relation

Reference 9

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.138526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1376faea-a132-4748-a6b2-ed39790751ce · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.890494Z

Source-reported events for the cited work

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Observation ef9165c3-2400-4a88-8e1b-7c4e6773c04b · outbound

This paper cites Vector Autoregressions.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Vector Autoregressions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.894861Z

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Observation 90aad307-13c2-4995-b834-30d5e3eddf70 · outbound

This paper cites Toda and Peter C.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Toda and Peter C

Reference 12

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.104113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1dd0d385-eed8-46a0-8d06-8b4f7e1080d7 · outbound

This paper cites Forecasting High Frequency Order Flow Imbalance.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Forecasting High Frequency Order Flow Imbalance

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.903786Z

Source-reported events for the cited work

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Observation 55dfabd9-a14b-44be-a99e-9168cbeea50f · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 14

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.089272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 38541aea-fe98-4225-a8e9-5ccde4e1d344 · outbound

This paper cites Neural networks in business: a survey of applications (1992–1998).

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Neural networks in business: a survey of applications (1992–1998)

Reference 15

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.073063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7de16340-50ca-4750-b3ed-124a3b427f57 · outbound

This paper cites Enhancing trading strategies with order book signals.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Enhancing trading strategies with order book signals

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.917481Z

Source-reported events for the cited work

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Observation 5322cd19-e4b3-4d68-a247-4884c3783d17 · outbound

This paper cites Hawkes model for price and trades high-frequency dynamics.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Hawkes model for price and trades high-frequency dynamics

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T21:43:52.921552Z

Source-reported events for the cited work

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Observation 6acb9dc2-5b56-43f9-ab79-74ef6fa594da · outbound

This paper cites Capturing the Order Imbalance with Hidden Markov Model: A Case of SET50 and KOSPI50.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Capturing the Order Imbalance with Hidden Markov Model: A Case of SET50 and KOSPI50

Reference 18

Resolution: malformed identifier; doi_truncated, observed 2026-08-12T21:43:53.058087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 993992f9-c43c-438d-af8e-db3d24a15e2b · outbound

This paper cites Optimal Execution with Dynamic Order Flow Imbalance.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Optimal Execution with Dynamic Order Flow Imbalance

Reference 19

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.042805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8bd48eb3-2615-4db4-8798-9ce0f0aad9b2 · outbound

This paper cites Order Imbalance, Liquidity, and Market Efficiency: Evidence from the Chinese Stock Market.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Order Imbalance, Liquidity, and Market Efficiency: Evidence from the Chinese Stock Market

Reference 20

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.027980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56a601d5-cd09-4afa-9075-306133234141 · outbound

This paper cites Fung and Philip L.H.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Fung and Philip L.H

Reference 21

Resolution: verified exact; doi, observed 2026-08-12T21:43:53.012823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c4a8744-d53d-42eb-a98c-c5f96ca28b14 · outbound

This paper cites Effect of order flow imbalance on market impact across market states.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Effect of order flow imbalance on market impact across market states

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T21:43:53.354731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a64cbf4-34ec-40d2-b20d-f4168260108b · outbound

This paper cites Dynamic relations between order imbalance, volatility and return of top gainers.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Dynamic relations between order imbalance, volatility and return of top gainers

Reference 23

Resolution: verified exact; raw_fallback, observed 2026-08-12T21:43:53.278972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b4c1046c-d8b3-4cfa-9648-b80e31475333 · outbound

This paper cites • For lag order p, complexity per variable is O(n · p2).

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading • For lag order p, complexity per variable is O(n · p2)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:43:53.807562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81f9f7bc-63dc-4467-946b-2952e58f4cc2 · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:43:53.792929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6b9d9fa-739f-458a-817c-8ef073c02785 · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:43:53.777673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c65c43b-9f6c-423d-a6a3-18f8a645a878 · outbound

This paper cites • With M epochs, the total complexity for FNN training becomes: O(M · n · d · h).

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading • With M epochs, the total complexity for FNN training becomes: O(M · n · d · h)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:43:53.763418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfc883d7-1076-493c-b88d-e2e20f3a0b17 · outbound

This paper cites an unresolved cited work.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:43:53.748668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5544ed12-f29c-4ec4-b4e5-fdcf473051f1 · outbound

This paper cites The graph indicates how the model’s loss decreased over the epochs, along with validation loss to monitor overfitting or underfitting behavior.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading The graph indicates how the model’s loss decreased over the epochs, along with validation loss to monitor overfitting or underfitting behavior

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:43:53.734029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68d4d847-6bef-4b41-8104-55663dd1e44c · outbound

This paper cites The steady decline in loss, along with minimal divergence between training and validation loss, suggests effective learning and good generalization on unseen data.

Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading The steady decline in loss, along with minimal divergence between training and validation loss, suggests effective learning and good generalization on unseen data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:43:53.718929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.