Paper Citation Record · LEDGER

AI Trading: Evaluating Large Language Models for Technical Market Analysis

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.15414.

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

pith.paper-citation-record.v1
2607.15414 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:29:52.003268Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 02a6fede-280e-4008-a9be-c9e4e03621a0 · outbound

This paper cites Analyzing and modelling 1+1d markets,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Analyzing and modelling 1+1d markets,

Reference 1

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Observation 82b75ee2-7854-4792-b8fd-b4dfe7709d2a · outbound

This paper cites Price formation and optimal trading in intraday electricity markets,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Price formation and optimal trading in intraday electricity markets,

Reference 2

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Observation 84da0ed9-1e05-4664-8aac-4eb464a9cc6c · outbound

This paper cites Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,

Reference 3

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Observation e48f3c05-4ae4-4e35-a88f-9223939a18cc · outbound

This paper cites Text-based sentiment analysis in finance: Synthesising the existing literature and exploring future directions,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Text-based sentiment analysis in finance: Synthesising the existing literature and exploring future directions,

Reference 4

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Attention is all you need,

Reference 5

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Observation a7a527b8-fef1-4c64-9ab6-ea15aa975287 · outbound

This paper cites Bulkowski, Encyclopedia of Candlestick Charts.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Bulkowski, Encyclopedia of Candlestick Charts

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T23:29:49.269837Z

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Observation 905d094d-aa53-40f0-b900-81911b4a9e83 · outbound

This paper cites Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies

Reference 7

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Observation 7afdcf31-8e39-4232-9248-2f173589d99a · outbound

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Long short-term memory,

Reference 8

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Observation 1ca379da-360b-4e36-8b47-85c68ed2ef35 · outbound

This paper cites Integrating Large Language Models in Financial Investments and Market Analysis: A Survey.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Integrating Large Language Models in Financial Investments and Market Analysis: A Survey

Reference 9

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This paper cites Giving content to investor sentiment: The role of media in the stock market,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Giving content to investor sentiment: The role of media in the stock market,

Reference 10

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AI Trading: Evaluating Large Language Models for Technical Market Analysis When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks,

Reference 11

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Observation d8b4e37d-a8c3-43a0-93c2-5f6f41fe9328 · outbound

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

AI Trading: Evaluating Large Language Models for Technical Market Analysis FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 12

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Observation bfff0d9c-0bc0-44e4-99f1-986d6f6bded1 · outbound

This paper cites GPT-4 Technical Report.

AI Trading: Evaluating Large Language Models for Technical Market Analysis GPT-4 Technical Report

Reference 13

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Claude 3 Model Card,

Reference 14

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This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 15

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This paper cites Llama 3: Meta's most advanced publicly available frontier language model,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Llama 3: Meta's most advanced publicly available frontier language model,

Reference 16

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AI Trading: Evaluating Large Language Models for Technical Market Analysis FinGPT: Open-source financial large language models,

Reference 17

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Unresolved cited work

Reference 18

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This paper cites Nison, Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East, 2nd ed.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Nison, Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East, 2nd ed

Reference 19

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Observation 90412b35-b6d9-430e-bc06-e11e215dc84b · outbound

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

AI Trading: Evaluating Large Language Models for Technical Market Analysis BloombergGPT: A Large Language Model for Finance

Reference 20

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Observation 2554c10c-2f95-462c-8f73-0ac59d55fd36 · outbound

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AI Trading: Evaluating Large Language Models for Technical Market Analysis FinQA: A dataset of numerical reasoning over financial data,

Reference 21

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AI Trading: Evaluating Large Language Models for Technical Market Analysis TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance,

Reference 22

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AI Trading: Evaluating Large Language Models for Technical Market Analysis FinanceBench: A New Benchmark for Financial Question Answering

Reference 23

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AI Trading: Evaluating Large Language Models for Technical Market Analysis FinBen: A holistic financial bench‐ mark for large language models,

Reference 24

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This paper cites Can ChatGPT forecast stock price movements? Return predictability and large language models,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Can ChatGPT forecast stock price movements? Return predictability and large language models,

Reference 25

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Observation 0d08ba44-5a38-4052-8aaf-1d041d6763be · outbound

This paper cites Learning to generate explainable stock predictions using self-reflec‐ tive large language models,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Learning to generate explainable stock predictions using self-reflec‐ tive large language models,

Reference 26

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Observation fdf62176-6edf-42ed-872f-5b928de1a3a2 · outbound

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Finance Agent Benchmark: Benchmarking LLMs on Real-world Financial Research Tasks

Reference 27

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

Reference 28

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Observation bbbbd4c4-9c69-4dc4-8334-7b785867115e · outbound

This paper cites FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design.

AI Trading: Evaluating Large Language Models for Technical Market Analysis FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design

Reference 29

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Efficient capital markets: A review of theory and empirical work,

Reference 30

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Active retrieval augmented generation,

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AI Trading: Evaluating Large Language Models for Technical Market Analysis A backtesting protocol in the era of machine learning,

Reference 32

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AI Trading: Evaluating Large Language Models for Technical Market Analysis and the cross-section of expected returns,

Reference 33

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Survey of hallucina‐ tion in natural language generation,

Reference 34

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Lost in the middle: How language models use long contexts,

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Self-refine: Iterative refinement with self-feedback,

Reference 36

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Market efficiency, long-term returns, and behavioral finance,

Reference 37

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This paper cites LLM-based agents for trading: A comparative study,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis LLM-based agents for trading: A comparative study,

Reference 38

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AI Trading: Evaluating Large Language Models for Technical Market Analysis AI-Powered Trading and Market Intelligence Platform,

Reference 39

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AI Trading: Evaluating Large Language Models for Technical Market Analysis Risk Alert: Observations from Examinations of Investment Advisers Using Automated Investment Tools,

Reference 40

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This paper cites Regulation (EU) 2016/679 (MiFID II),.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Regulation (EU) 2016/679 (MiFID II),

Reference 41

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AI Trading: Evaluating Large Language Models for Technical Market Analysis The limits of arbitrage,

Reference 42

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This paper cites What happened to the quants in August 2007? Evidence from factors and transac‐ tions data,.

AI Trading: Evaluating Large Language Models for Technical Market Analysis What happened to the quants in August 2007? Evidence from factors and transac‐ tions data,

Reference 43

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AI Trading: Evaluating Large Language Models for Technical Market Analysis MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning

Reference 44

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AI Trading: Evaluating Large Language Models for Technical Market Analysis FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models

Reference 45

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Observation 333c61b8-23ce-4a4c-839b-10da09586400 · outbound

This paper cites FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance.

AI Trading: Evaluating Large Language Models for Technical Market Analysis FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

Reference 46

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This paper cites Available: https://ai.meta.com/.

AI Trading: Evaluating Large Language Models for Technical Market Analysis Available: https://ai.meta.com/

Reference 2024

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Pith citing papers

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