Paper Citation Record · LEDGER

QuantBench: Benchmarking AI Methods for Quantitative Investment

As of 17 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2504.18600.

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

pith.paper-citation-record.v1
2504.18600 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:32:52.452900Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:18:08.949900Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:18:09.332617Z

Reference resolution

68 of 68 outbound references displayed

verified exact6
verified fuzzy19
unresolved43
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 511c8a6c-8c4a-4a41-9fe2-5dae4a4e0d88 · outbound

This paper cites ImageNet : A large-scale hierarchical image database.

QuantBench: Benchmarking AI Methods for Quantitative Investment ImageNet : A large-scale hierarchical image database

Reference 1

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Observation 6cff0050-7fc9-4036-9846-831b7d5a1424 · outbound

This paper cites an unresolved cited work.

QuantBench: Benchmarking AI Methods for Quantitative Investment Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:32:53.931539Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 07f1f01a-f6dc-48fc-a6bd-cfae8adbd46b · outbound

This paper cites Introduction to Alpha Design.

QuantBench: Benchmarking AI Methods for Quantitative Investment Introduction to Alpha Design

Reference 3

Resolution: verified exact; doi, observed 2026-08-16T10:32:52.594310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad339687-f0da-4f64-ba2b-60c37b961810 · outbound

This paper cites AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment.

QuantBench: Benchmarking AI Methods for Quantitative Investment AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.213159Z

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Observation fd3ef3ec-73eb-4ddb-b489-edb451892792 · outbound

This paper cites AlphaEvolve : A Learning Framework to Discover Novel Alphas in Quantitative Investment.

QuantBench: Benchmarking AI Methods for Quantitative Investment AlphaEvolve : A Learning Framework to Discover Novel Alphas in Quantitative Investment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.217233Z

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Observation 0aa31962-496e-4817-923c-fe22f8c2ec27 · outbound

This paper cites Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.220894Z

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Observation 78f29695-9015-497c-b563-26b773940e71 · outbound

This paper cites Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models.

QuantBench: Benchmarking AI Methods for Quantitative Investment Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.224983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a89a2a1-7eee-4c5a-a551-85bbef975879 · outbound

This paper cites Temporal Relational Ranking for Stock Prediction.

QuantBench: Benchmarking AI Methods for Quantitative Investment Temporal Relational Ranking for Stock Prediction

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-16T10:32:53.574782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 951a5489-6d9b-4358-8114-b3e4e8de6f13 · outbound

This paper cites Cattaneo, Richard K.

QuantBench: Benchmarking AI Methods for Quantitative Investment Cattaneo, Richard K

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 712b7184-df23-4216-927e-cfcefda114a9 · outbound

This paper cites Portfolio Selection.

QuantBench: Benchmarking AI Methods for Quantitative Investment Portfolio Selection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.236076Z

Source-reported events for the cited work

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Observation 70200a52-59d4-44c6-9aa6-f617271e0419 · outbound

This paper cites Markowitz.

QuantBench: Benchmarking AI Methods for Quantitative Investment Markowitz

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.921636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9b286c0-a025-4f10-9360-44a079b76db3 · outbound

This paper cites Optimal control of execution costs.

QuantBench: Benchmarking AI Methods for Quantitative Investment Optimal control of execution costs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.912404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29eb454a-e8fd-49ff-85cf-ee1a1ac2a222 · outbound

This paper cites Optimal execution of portfolio transactions.

QuantBench: Benchmarking AI Methods for Quantitative Investment Optimal execution of portfolio transactions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.902743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 01143c76-4fe5-4533-8cff-9df16371b7ce · outbound

This paper cites an unresolved cited work.

QuantBench: Benchmarking AI Methods for Quantitative Investment Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.250289Z

Source-reported events for the cited work

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Observation b64be2ff-4c05-4ad2-b047-a2caf677507a · outbound

This paper cites Universal Trading for Order Execution with Oracle Policy Distillation.

QuantBench: Benchmarking AI Methods for Quantitative Investment Universal Trading for Order Execution with Oracle Policy Distillation

Reference 15

Resolution: verified exact; doi, observed 2026-08-16T10:32:52.575655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d98ca245-261c-4629-9749-c2e5d24cd9a9 · outbound

This paper cites Learning Multi - Agent Intention - Aware Communication for Optimal Multi - Order Execution in Finance.

QuantBench: Benchmarking AI Methods for Quantitative Investment Learning Multi - Agent Intention - Aware Communication for Optimal Multi - Order Execution in Finance

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.258549Z

Source-reported events for the cited work

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Observation dc53b240-51bb-403b-a3fb-f285215aee01 · outbound

This paper cites Qlib: An AI-oriented Quantitative Investment Platform.

QuantBench: Benchmarking AI Methods for Quantitative Investment Qlib: An AI-oriented Quantitative Investment Platform

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.262079Z

Source-reported events for the cited work

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Observation 191c88c2-6c28-4ae2-8ef8-b3ca9b63b743 · outbound

This paper cites 101 Formulaic Alphas.

QuantBench: Benchmarking AI Methods for Quantitative Investment 101 Formulaic Alphas

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.266021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fea687b-73ad-4cde-8dc7-e5abe841d511 · outbound

This paper cites Multi-factor Stock Selection via Short -term Volume -price Patterns.

QuantBench: Benchmarking AI Methods for Quantitative Investment Multi-factor Stock Selection via Short -term Volume -price Patterns

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.893260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 579848cc-afe9-46a7-8a91-377446dee78e · outbound

This paper cites XGBoost : A Scalable Tree Boosting System.

QuantBench: Benchmarking AI Methods for Quantitative Investment XGBoost : A Scalable Tree Boosting System

Reference 20

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Observation 462b4f7a-1d5e-4a1d-83cd-13aaa543a3a8 · outbound

This paper cites LightGBM : A Highly Efficient Gradient Boosting Decision Tree.

QuantBench: Benchmarking AI Methods for Quantitative Investment LightGBM : A Highly Efficient Gradient Boosting Decision Tree

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.883502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16732d7d-4517-45c0-a865-a42b9e4045da · outbound

This paper cites CatBoost : unbiased boosting with categorical features.

QuantBench: Benchmarking AI Methods for Quantitative Investment CatBoost : unbiased boosting with categorical features

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.873267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b10eab17-5071-4689-b1be-7976843115a3 · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data? June 2022.

QuantBench: Benchmarking AI Methods for Quantitative Investment Why do tree-based models still outperform deep learning on typical tabular data? June 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.863415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 27df80b7-90df-4853-bd68-b548bd8c84f9 · outbound

This paper cites Long short-term memory.

QuantBench: Benchmarking AI Methods for Quantitative Investment Long short-term memory

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.853450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 33fffe2d-1193-4439-bcd3-7acc06cb8e1b · outbound

This paper cites Stock Price Prediction via Discovering Multi - Frequency Trading Patterns.

QuantBench: Benchmarking AI Methods for Quantitative Investment Stock Price Prediction via Discovering Multi - Frequency Trading Patterns

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.293314Z

Source-reported events for the cited work

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Observation d309abc9-085d-4e2f-b2cc-ea9c1e23ae4a · outbound

This paper cites Cottrell.

QuantBench: Benchmarking AI Methods for Quantitative Investment Cottrell

Reference 26

Resolution: verified exact; doi, observed 2026-08-16T10:32:52.564520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c909636-3a6a-4fcf-b7c1-1617d4c22868 · outbound

This paper cites Exploring the Scale - Free Nature of Stock Markets : Hyperbolic Graph Learning for Algorithmic Trading.

QuantBench: Benchmarking AI Methods for Quantitative Investment Exploring the Scale - Free Nature of Stock Markets : Hyperbolic Graph Learning for Algorithmic Trading

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.301887Z

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Observation 43a8bac5-88b6-4fff-afa7-2c1911102413 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

QuantBench: Benchmarking AI Methods for Quantitative Investment An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.305490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974178f5-6108-4571-a00c-db671ef4042b · outbound

This paper cites MLP-Mixer: An all-MLP Architecture for Vision.

QuantBench: Benchmarking AI Methods for Quantitative Investment MLP-Mixer: An all-MLP Architecture for Vision

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.309122Z

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Unavailable: canonical work link unavailable.

Observation 5ae40c8c-f41d-43b9-aef9-22ff3826154a · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time - Series Forecasting.

QuantBench: Benchmarking AI Methods for Quantitative Investment Informer: Beyond Efficient Transformer for Long Sequence Time - Series Forecasting

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.313330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4c57ac0-3320-4a88-8845-32036eae0375 · outbound

This paper cites Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting.

QuantBench: Benchmarking AI Methods for Quantitative Investment Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.317325Z

Source-reported events for the cited work

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Observation ff63a1b8-29c1-4623-9741-ed7f74cc2f5d · outbound

This paper cites FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting.

QuantBench: Benchmarking AI Methods for Quantitative Investment FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.320995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 699848d7-38e2-4356-9629-22817d148424 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

QuantBench: Benchmarking AI Methods for Quantitative Investment A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.324948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e431f616-2f95-460e-8eb7-13a5ca6b736f · outbound

This paper cites Graph Attention Networks.

QuantBench: Benchmarking AI Methods for Quantitative Investment Graph Attention Networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.328987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d79ea717-fff2-43c5-93e4-59bb6402f5e9 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

QuantBench: Benchmarking AI Methods for Quantitative Investment Semi-Supervised Classification with Graph Convolutional Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.334221Z

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Observation 9eae11a7-f3a3-4c09-81c2-83970ceeeeed · outbound

This paper cites Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling.

QuantBench: Benchmarking AI Methods for Quantitative Investment Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.338000Z

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Observation fc4fd1e1-7d0a-4fa5-8f7e-a16386aff33b · outbound

This paper cites Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction.

QuantBench: Benchmarking AI Methods for Quantitative Investment Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.341656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd9a52d-68fd-4157-b4a3-241046a02403 · outbound

This paper cites Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting.

QuantBench: Benchmarking AI Methods for Quantitative Investment Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.345901Z

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Unavailable: canonical work link unavailable.

Observation 3460f5ee-f3a9-47f9-9d6d-018c710bb1a8 · outbound

This paper cites Stock Selection via Spatiotemporal Hypergraph Attention Network : A Learning to Rank Approach.

QuantBench: Benchmarking AI Methods for Quantitative Investment Stock Selection via Spatiotemporal Hypergraph Attention Network : A Learning to Rank Approach

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.349457Z

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Unavailable: canonical work link unavailable.

Observation 83704d63-0f48-4357-a349-1c21f1b631f7 · outbound

This paper cites Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies.

QuantBench: Benchmarking AI Methods for Quantitative Investment Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.353198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e314534-e4c0-4ecd-b9f0-6ad399861ac3 · outbound

This paper cites E2EAI : End -to- End Deep Learning Framework for Active Investing.

QuantBench: Benchmarking AI Methods for Quantitative Investment E2EAI : End -to- End Deep Learning Framework for Active Investing

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.356967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34acd664-e8a5-4c94-9bc1-efbceb539be9 · outbound

This paper cites Advances in Financial Machine Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment Advances in Financial Machine Learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.843428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2c3c646e-5ff7-40d0-a19a-0f0c251fefb6 · outbound

This paper cites Financial Machine Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment Financial Machine Learning

Reference 43

Resolution: verified exact; doi, observed 2026-08-16T10:32:52.523700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc020959-fbf0-4187-b071-293d1b15274d · outbound

This paper cites Knowledge graph-based event embedding framework for financial quantitative investments.

QuantBench: Benchmarking AI Methods for Quantitative Investment Knowledge graph-based event embedding framework for financial quantitative investments

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.833483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation db3a9f4f-8886-41e6-895b-606db6e7a42c · outbound

This paper cites A Survey of Forex and Stock Price Prediction Using Deep Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment A Survey of Forex and Stock Price Prediction Using Deep Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.371159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b881af-58d0-4154-a39b-e81ad7167793 · outbound

This paper cites Stock price prediction using artificial intelligence: A survey.

QuantBench: Benchmarking AI Methods for Quantitative Investment Stock price prediction using artificial intelligence: A survey

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.822987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c8a1854-a546-4012-a099-dd84a2cf7ad3 · outbound

This paper cites Adarnn: Adaptive learning and forecasting of time series.

QuantBench: Benchmarking AI Methods for Quantitative Investment Adarnn: Adaptive learning and forecasting of time series

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.812616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8c1b07e-6352-4198-b3d2-2dd87a3f8f95 · outbound

This paper cites Hierarchical Adaptive Temporal - Relational Modeling for Stock Trend Prediction.

QuantBench: Benchmarking AI Methods for Quantitative Investment Hierarchical Adaptive Temporal - Relational Modeling for Stock Trend Prediction

Reference 48

Resolution: verified exact; doi, observed 2026-08-16T10:32:52.506314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1b21ad63-3d44-49be-b08f-7d9034cbe29a · outbound

This paper cites Knowledge-driven stock trend prediction and explanation via temporal convolutional network.

QuantBench: Benchmarking AI Methods for Quantitative Investment Knowledge-driven stock trend prediction and explanation via temporal convolutional network

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.802545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b09ab1e3-7640-4ebd-b53c-e3a8d1c58b52 · outbound

This paper cites Hierarchical multi-scale Gaussian transformer for stock movement prediction.

QuantBench: Benchmarking AI Methods for Quantitative Investment Hierarchical multi-scale Gaussian transformer for stock movement prediction

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.791326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5cfe5090-4198-4e25-9dda-a21bc5e3dea1 · outbound

This paper cites Incorporating corporation relationship via graph convolutional neural networks for stock price prediction.

QuantBench: Benchmarking AI Methods for Quantitative Investment Incorporating corporation relationship via graph convolutional neural networks for stock price prediction

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.780049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 94c9ffbd-3c41-4b09-9306-78800982a2dd · outbound

This paper cites A Review on Graph Neural Network Methods in Financial Applications.

QuantBench: Benchmarking AI Methods for Quantitative Investment A Review on Graph Neural Network Methods in Financial Applications

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.396166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02796a20-ffbd-47dd-969f-773ce9b49b1f · outbound

This paper cites HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information.

QuantBench: Benchmarking AI Methods for Quantitative Investment HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.399848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bd2aa4b-43d8-4ec5-8693-4011fc3d2655 · outbound

This paper cites A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.

QuantBench: Benchmarking AI Methods for Quantitative Investment A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.403805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7cdbca-bf92-496a-afe4-ca89c73ebb3d · outbound

This paper cites Cost- Sensitive Portfolio Selection via Deep Reinforcement Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment Cost- Sensitive Portfolio Selection via Deep Reinforcement Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.407387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5363e054-162a-471e-8fc2-bea4bbb0a767 · outbound

This paper cites DeepTrader : A Deep Reinforcement Learning Approach for Risk - Return Balanced Portfolio Management with Market Conditions Embedding.

QuantBench: Benchmarking AI Methods for Quantitative Investment DeepTrader : A Deep Reinforcement Learning Approach for Risk - Return Balanced Portfolio Management with Market Conditions Embedding

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.768773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f09ea743-17a4-48e3-88ee-ddc1f9f393c7 · outbound

This paper cites AlphaStock : A Buying - Winners -and- Selling - Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks.

QuantBench: Benchmarking AI Methods for Quantitative Investment AlphaStock : A Buying - Winners -and- Selling - Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.414391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8897ee5-a981-4491-8427-b2cf0baf203a · outbound

This paper cites MetaTrader : An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization.

QuantBench: Benchmarking AI Methods for Quantitative Investment MetaTrader : An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.418168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b4c9c39-bc6f-4bf3-b793-5250a3c55ed9 · outbound

This paper cites Adaptive Quantitative Trading : An Imitative Deep Reinforcement Learning Approach.

QuantBench: Benchmarking AI Methods for Quantitative Investment Adaptive Quantitative Trading : An Imitative Deep Reinforcement Learning Approach

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.421644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f698c6-4227-4b43-bf2e-61acf9ea7d54 · outbound

This paper cites Probabilistic Framework for Modeling Event Shocks to Financial Time Series.

QuantBench: Benchmarking AI Methods for Quantitative Investment Probabilistic Framework for Modeling Event Shocks to Financial Time Series

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.425086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb8de11b-7af2-4e16-85b2-6d65c13f3189 · outbound

This paper cites DDG - DA : Data Distribution Generation for Predictable Concept Drift Adaptation.

QuantBench: Benchmarking AI Methods for Quantitative Investment DDG - DA : Data Distribution Generation for Predictable Concept Drift Adaptation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.758295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b4dd33ab-9903-4c08-bb82-a66468a18d83 · outbound

This paper cites Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts.

QuantBench: Benchmarking AI Methods for Quantitative Investment Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.432267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2963d71-993c-4c43-8fd0-7f62646b661e · outbound

This paper cites DoubleAdapt : A Meta -learning Approach to Incremental Learning for Stock Trend Forecasting.

QuantBench: Benchmarking AI Methods for Quantitative Investment DoubleAdapt : A Meta -learning Approach to Incremental Learning for Stock Trend Forecasting

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.435766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94a31f03-5b08-46e7-9714-63e2f6f9c904 · outbound

This paper cites FinRL - Meta : Market Environments and Benchmarks for Data - Driven Financial Reinforcement Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment FinRL - Meta : Market Environments and Benchmarks for Data - Driven Financial Reinforcement Learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.746823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40a85b95-663c-4c4b-a8a3-61514e8cf9e5 · outbound

This paper cites TradeMaster : A Holistic Quantitative Trading Platform Empowered by Reinforcement Learning.

QuantBench: Benchmarking AI Methods for Quantitative Investment TradeMaster : A Holistic Quantitative Trading Platform Empowered by Reinforcement Learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:32:53.735189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 43fa51c2-b7f8-475d-8ba2-72a13e7eb929 · outbound

This paper cites @esa (Ref.

QuantBench: Benchmarking AI Methods for Quantitative Investment @esa (Ref

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.445596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f34a33f2-e5f0-4e8c-9040-863ccc9a6819 · outbound

This paper cites an unresolved cited work.

QuantBench: Benchmarking AI Methods for Quantitative Investment Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.449633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af0f64ae-8f93-4296-af9f-e0361d624207 · outbound

This paper cites Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies.

QuantBench: Benchmarking AI Methods for Quantitative Investment Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T10:32:52.452900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0cc96188-aa37-4b9f-a641-f67f668c787e · inbound

AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining cites this paper.

AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining QuantBench: Benchmarking AI Methods for Quantitative Investment

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:18:09.337943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.