AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation
Yajing Yang, Yunshan Ma, Kelvin J. L. Koa, Min-Yen Kan
cs.AI
Quick verdict
Expert-report pattern library lifts LLM insight rates 1.7–3.7×; convincing ablations, but LLM-judged metrics and possible benchmark overlap need scrutiny.
Summary
The paper argues that financial report generation should compose reports from data-derived insights rather than fixed section templates. It introduces AnalysisBank, an offline pipeline that distills expert reports (550 daily market-analysis reports from DataTales and 550 equity-research reports from Seeking Alpha) into a library of Analyses, each a triple of an entity-free data signal, an executable analytical move, and the verbatim expert span it was distilled from. A four-pass pipeline (induce, generalize, deduplicate, quality-filter) yields 1,422 and 3,889 Analyses respectively, covering 47-52 signal types across 13 move types with a heavy tail. At inference, input signals retrieve Analyses by embedding similarity; each is applied independently and findings are composed into a report. Across DataTales and Earnings2Insights with four backbones (Qwen3-8B to GPT-5.1), insight rate rises 1.7-3.7x over the strongest baselines, and ablations attribute the gain to signal abstraction and per-move execution. Human studies support the trend, though claim-level agreement on the novel category is weak (kappa 0.25). Key limitations: the headline metric is LLM-judged, main results lack significance tests, novel-claim contradiction reaches 15.3% on Qwen3-8B, reports cost roughly 30 LLM calls each, disjointness between the distillation corpus and test references is unstated, and BoT reverses the insight-rate gain on SciGen.
Review complete
Assessment at a glance
5
risk flags identified
● high 1
● medium 2
● low 2
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- Financial report generation should operate at the analytical level, composing signal-specific insights rather than filling structural section templates that leave reasoning to LLM defaults.
- Expert reports can be automatically distilled into a reusable library of (data_signal, analytical_move, reference_text) Analyses covering a heavy-tailed distribution of 47-52 signal types across 13 move types.
- Retrieving and executing these Analyses at inference raises novel insight rate 1.7-3.7x over prompting and structural-level baselines (BoT, AWM) on DataTales and Earnings2Insights across four LLM backbones.
- The gains stem from the library mechanism — signal abstraction for retrieval and per-move execution — not from prompting effects, as shown by the Table 3 ablations and the 80.4% claim-tracing result.
- The representation transfers to scientific writing at the pipeline level (best overall quality on SciGen), though BoT slightly wins on insight rate there.
Strengths
Three-field representation is design-hypothesis tested, not asserted
Section 6.1 ablations justify each field: the abstracted data_signal index retrieves 20x more entries than reference_text at cosine 0.85, with 90% of retrieved entries from a different sector (vs 44% for reference text); single-field drops insight rate 3.5 points, reference-text retrieval 4.5 points, skipping per-Analysis execution 4.6 points (Table 3).
Unusually complete reproducibility documentation
Appendices A-C publish all prompts (Pass A-D, Stages 1-4), hyperparameters (Tables 5-7: temperatures, cosine 0.88 clustering, per-type signal caps, k=5 retrieval, 1.2x sector boost) and exact call counts (30 LLM calls/report on E2I, 11 on DataTales).
Honest reporting of unfavorable results
Mechanistic evidence beyond aggregate scores
Corpus characterization supports the core motivation
Weaknesses
Headline metric is LLM-judged with weak claim-level agreement on the decisive category
No significance testing or variance on main results
Compute asymmetry confound
Unstated train/test disjointness between distillation corpus and benchmarks
Structural baselines appear absent for the strongest backbone
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Scope & Claims
The paper targets analytical report generation from financial data, arguing that prior systems (plan-then-write, multi-agent, retrieve-organize-write; Section 2) control report structure but leave analytical substance to LLM defaults. AnalysisBank's thesis is that expert reports already embody reusable signal-to-move pairings that can be distilled offline and executed at inference. Three central claims: (i) a structural-vs-analytical level distinction that existing methods do not cross; (ii) a three-field Analysis representation (data_signal, analytical_move, reference_text) that separates retrieval key from generation instruction; (iii) 1.7-3.7x insight-rate gains over prompting and structural-pattern baselines (BoT, AWM) on DataTales (460 instances) and Earnings2Insights (132 instances) across four backbones, plus partial transfer to SciGen.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is AnalysisBank in one sentence?
It is a method that automatically distills expert financial reports into a reusable library of analytical patterns — each pairing a data signal, an executable analytical move, and the original expert passage — which an LLM retrieves and executes at inference time to write more insightful, data-grounded reports.
How much does AnalysisBank improve over existing methods?
On the DataTales and Earnings2Insights benchmarks with four LLM backbones (Qwen3-8B, Qwen3.5-9B, DeepSeek-V4-Flash, GPT-5.1), it raises the share of novel, data-grounded insights by 1.7-3.7x over the strongest baselines, with the smallest gain on GPT-5.1 for earnings transcripts where direct prompting is already strong.
Is the code and data available?
Yes — code and the distilled DataTales library are released at github.com/yajingyang/AnalysisBank. The Earnings library is not redistributable because its source reports come from Seeking Alpha, and full reproduction requires specific commercial LLM APIs.
Does the approach generalize beyond finance?
Partially. On the SciGen scientific-writing benchmark the pipeline produced the best-rated reports overall, but Buffer-of-Thoughts slightly beat it on the insight-rate metric (17.9% vs 16.7%), suggesting the long-tail advantage is finance-specific where analytical moves are more varied.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- The provided extraction ends mid-sentence at 'content the input does not' in Appendix F.2, so a small amount of appendix text after that point may be missing from this review's basis.
- In the extracted Table 2, GPT-5.1 has no BoT/AWM rows; this may be a table-layout extraction artifact rather than an omission in the paper, so the corresponding weakness is framed as a question.
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