Pith. sign in

REVIEW 5 major objections 6 minor 24 references

FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Adding news-dissemination data to LLM prompts lifts stock-movement accuracy from 55% to 63%.

desk verdict A clearly written data-prep recipe for LLM stock prediction whose headline 8% gain is not yet supported because the paper never discloses the train/test split. read the letter →

arxiv 2412.10823 v2 pith:4KLRNJLI submitted 2024-12-14 cs.CL cs.LGq-fin.CPq-fin.TR

Yixuan Liang , Yuncong Liu , Neng Wang , Hongyang Yang , Boyu Zhang , Christina Dan Wang This is my paper · ORCID
classification cs.CLcs.LGq-fin.CPq-fin.TR
keywords financialsentimentanalysisstockmovementpredictionlargelanguagemodelsinstructiontuningnewsclusteringBERTopicdissemination-awarecontext-enrichedprompts
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that stock-movement prediction from financial news improves when the prompt given to an instruction-tuned language model includes not just article text but also how the news spread and what the price data looked like day by day. The proposed pipeline first enriches stock-price context with daily closing prices and returns, then clusters weekly news into topics and keeps one representative article per cluster together with the cluster's size and temporal span. These enriched prompts are used to fine-tune a Llama3-8B model, and the resulting system predicts weekly up/down moves with 63.0% binary accuracy versus 55.0% for the baseline on 380 observations spanning 20 companies. The authors see the 8-percentage-point gain as evidence that dissemination breadth and explicit temporal instructions help LLMs read financial news.

What carries the argument

The load-bearing object is the HG-NC prompt format, a data-processing and prompt-engineering pipeline. In the HG step, weekly aggregate price trends are replaced by a table of daily closing prices and daily returns, giving the model an explicit timeline. In the NC step, BERTopic clusters the week's news for a company; for clusters whose average pairwise similarity exceeds 0.6 the pipeline selects the article closest to the centroid and records cluster size and temporal span, while low-cohesion clusters are capped at two articles. The prompt then instructs the model to read the centroid article as the topic's most complete statement, to treat cluster size as a proxy for market impact, and to separate short-term from long-term news effects. This instruction-tuning dataset, generated with GPT-4o outputs and used to fine-tune Llama3-8B, is what carries the reported accuracy gain.

What would settle it

Replace each centroid article with a randomly chosen article from the same cluster and re-measure binary accuracy on the same 380 observations; if accuracy does not drop, the centroid assumption is not the carrier. Likewise, shuffle the cluster-size and temporal-span metadata while keeping the article text fixed; if the 63% result persists, the dissemination signal is not doing the work.

Watch

Extended reading notes

Core claim

The paper's central claim is a data-format effect: representing a week's news as a small set of topic clusters, each reduced to its centroid article plus two dissemination numbers (how many articles formed the cluster and over what time span), and pairing this with daily rather than weekly price data, moves an instruction-tuned LLM's weekly directional accuracy from 55.0% to 63.0%. The paper attributes the jump to two mechanisms: daily price granularity lets the model align news events with same-day market reactions, and cluster metadata quantifies a topic's reach. Reasoning output also shifts, with the words 'long-term' and 'short-term' appearing far more often, and ROUGE scores for the prediction-analysis section rise, which the paper takes as evidence that the model is weighing temporal and dissemination factors rather than merely fitting text.

Load-bearing premise

The results rest on the assumption that a cluster's centroid article is informationally sufficient and that cluster size tracks market impact, with no independent check of either.

Editorial extensions

If this is right

  • If the 8-point gain is real, news dissemination metadata is a usable signal for LLM-based financial forecasting, not just the sentiment polarity of individual articles.
  • Daily price granularity supports a distinction between short- and long-term news effects, and the model's increased use of those terms suggests the instruction is being followed.
  • Because the method is data-format driven and model-independent, the same clustering-and-granularity treatment can be layered onto other financial LLMs and prompt-based predictors.
  • On Boeing, weeks with a high ratio of articles in high-cohesion clusters tended to be weeks where HG-NC beat HG, suggesting the clustering quality itself predicts when the method will help.
  • ROUGE improvements imply the enriched prompts produce more comprehensive reasoning, not just better binary guesses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reader can test whether the centroid assumption is the active ingredient by replacing each centroid article with a random article from the same cluster and re-measuring accuracy; if the gain survives, dissemination metadata alone, not article choice, is doing the work.
  • The reported comparison treats all 380 observations as a single pool. A natural next step, not in the paper, is to report per-company and per-week accuracy with confidence intervals, because a few clustered weeks or companies could drive the difference.
  • The clustering-ratio correlation in the Boeing case suggests a broader testable law: other companies should show a positive relationship between high-cohesion news share and the HG-NC advantage.
  • Because the prompts are the only thing changed between conditions, the design isolates a format effect; one can extend it to non-text LLM data by encoding dissemination as structured features rather than prose.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes FinGPT, a data-centric pipeline for sentiment-based weekly stock movement prediction. The method increases stock price granularity from weekly to daily, clusters weekly news with BERTopic, enriches LLM prompts with cluster metadata (cluster size and temporal span) and temporal instructions, and constructs an instruction-tuning dataset with GPT-4o-generated analyses. Llama3-8B is fine-tuned on this dataset, and the authors report binary accuracy and ROUGE scores, claiming an 8% accuracy improvement over a FinRobot-based baseline (63.0% vs. 55.0% on 380 observations across 20 companies). A Boeing case study is used as additional qualitative evidence.

Significance. If the empirical claim were validated, the idea of making LLM prompts dissemination-aware through news clustering would be a useful and broadly applicable contribution to sentiment-based financial prediction. The paper's strengths include a clearly described data-processing pipeline, explicit prompt templates in the appendix, and a training-parameter table that aids reproducibility. The central result, however, is currently not established: the evaluation lacks a described train/test split, uncertainty quantification, external baselines, and validation of the two assumptions on which the HG-NC improvement rests. These gaps are load-bearing because the headline 8% gain is the paper's main empirical contribution.

major comments (5)
  1. [§4.4 and §5 (Table 1)] The manuscript never specifies how the 380 evaluation observations are separated from the instruction-tuning data. Because the GPT-4o targets are generated "based on known future movements," any temporal or company overlap between training and test instances would let the fine-tuned Llama3-8B memorize answers and would make the 63.0% versus 55.0% gap a leakage artifact. Please report the training period, test period, company overlap, and the exact construction of the 380-instance test set, and verify that all test prompts are disjoint from the instruction-tuning prompts.
  2. [§5, Table 1] No uncertainty quantification is provided for the headline accuracies. With 380 binary observations, the 8-percentage-point difference needs a paired significance test (e.g., McNemar's test) and per-company/per-week breakdowns; the word "significant" in the table caption is not justified by the evidence presented.
  3. [§1 and §5] The abstract and conclusion claim improvement over "existing methods," but the only comparator in Table 1 is the authors' own FinRobot data format used as the baseline. No existing published model or standard stock-movement benchmark is evaluated. Either add external baselines (e.g., FinBERT, LLMFactor, or a news-only LLM baseline) or restrict the claim to "improves the FinRobot prompt format."
  4. [§4.2] The two stated assumptions—that the centroid article is informationally sufficient and that cluster size indicates market impact—are load-bearing for the HG-NC result but are not validated. I recommend an ablation that compares (a) centroid article only, (b) centroid plus cluster metadata, and (c) a random or full-cluster sample, along with a direct correlation analysis between cluster size and subsequent weekly return.
  5. [§5, Case Study] The Boeing case study is presented as supporting evidence, but it is a post hoc analysis of a single company, the "strong correlation" in Figure 2 is not quantified, and the case definitions are internally inconsistent (the figure caption says "Case 1: HG-NC correct vs. NC incorrect," where NC should presumably be HG). This section should be either removed or reframed as an illustrative example with proper statistics.
minor comments (6)
  1. [Table 1] The table header is corrupted ("Method A vg Acc Long term Short term"); the column names need to be cleaned.
  2. [Table 4 and §4.2] Table 4 uses "News Ration" instead of "News Ratio," and the text alternates between "high-coherence" and "high-cohesion"; please standardize terminology.
  3. [Appendix A and §4.4] The model name appears as "LLAMA-3-8B" in Appendix A and "Llama3-8B" elsewhere; use one consistent spelling.
  4. [References] The reference list contains malformed citation keys such as "elahi2024combining" and "F ingpt"; the bibliography needs cleanup.
  5. [Reproducibility] For a data-centric paper, a data/code availability statement and release of the instruction-tuning dataset with explicit splits would materially improve reproducibility.
  6. [§5, ROUGE Scores] The ROUGE evaluation compares generated reasoning to GPT-4o-generated references, not to human ground truth; the text should say "reference" rather than "ground-truth sentiment-based analysis."

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the reported accuracy gains are empirical comparisons between prompt/data formats; the missing train/test split is an evaluation risk, not a definitional circularity.

full rationale

The paper's central claim is an empirical accuracy comparison (63.0% baseline vs 55.0% for HG-NC in Table 1), not a derivation that reduces to its inputs. No equation defines the predicted movement labels in terms of the proposed cluster metadata; the fine-tuning procedure is standard supervised instruction tuning of Llama3-8B, and the reported accuracies are measured on 380 observations. The two stated assumptions about centroid articles and cluster size are explicit assumptions of the method, not smuggled conclusions, so they do not constitute a self-definitional circle. Self-citations to FinGPT and FinRobot appear, and the baseline uses the authors' own FinRobot data format, but this self-comparison is a benchmarking choice rather than a citation that supplies the claimed result, so it is not load-bearing circularity. The most legitimate concern is the absence of a disclosed train/test split combined with GPT-4o targets generated 'based on known future movements': if the 380 evaluation observations overlap with instruction-tuning instances, the comparison would be leakage rather than prediction. However, that is an evaluation-validity risk requiring evidence of overlap, not a demonstrated equivalence between prediction and fitting. Therefore no circular step is established by the manuscript text.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. It relies on hand-chosen data-processing thresholds and two unvalidated domain assumptions about centroid articles and cluster size as proxies for dissemination and market impact. These are the main free parameters and assumptions that the central claim depends on.

free parameters (8)
  • high_cohesion_similarity_threshold = 0.6
    Threshold for average pairwise similarity to classify a cluster as high-cohesion; hand-chosen in Cluster Quality Assessment.
  • high_cohesion_cluster_supplement_threshold = 6
    If fewer than six high-cohesion clusters are found, low-cohesion clusters are added; hand-chosen in Topic Selection Strategy.
  • low_cohesion_topic_size_cap = 2
    For low-cohesion clusters, the topic size is limited to 2; hand-chosen.
  • low_cohesion_supplement_cap = 4
    At most 4 low-cohesion clusters are used when supplementing; hand-chosen.
  • max_token_length = 8000
    Maximum input text length for the LLM, set to fit weekly news volume; hand-chosen in Appendix A.
  • learning_rate = 1e-5
    AdamW learning rate for Llama3-8B fine-tuning, Appendix A.
  • batch_size = 32
    Fine-tuning batch size, Appendix A.
  • training_epochs = 5
    Instruction tuning epochs, Appendix A.
assumptions (5)
  • domain assumption The centroid article of each cluster encapsulates the most comprehensive information for LLM processing.
    Stated in the Introduction as key assumption (i); the entire data compression step relies on it.
  • domain assumption Cluster size indicates the topic's market impact, with larger clusters signifying more significant events.
    Stated in the Introduction as key assumption (ii); this is the basis for using reporting frequency and temporal span as dissemination signals.
  • ad hoc to paper Average pairwise cosine similarity above 0.6 is a valid measure of cluster cohesion and information quality.
    Introduced in Cluster Quality Assessment; the threshold is hand-chosen and there is no validation that it corresponds to market information content.
  • domain assumption GPT-4o-generated analysis based on known future movements is a valid supervision target for instruction tuning.
    Instruction-tuning targets are created by prompting GPT-4o with ground-truth future price movements; this assumes the generated analysis is a good teacher signal.
  • domain assumption Weekly prediction using prior-week news, prices, and quarterly fundamentals has predictive signal for the twelve-class movement label.
    The problem setting treats stock movement prediction as learnable from these inputs; no baseline above chance is demonstrated beyond the 55% baseline.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs." pith.science (2026). https://pith.science/paper/4KLRNJLI

@misc{pith2026241210823,
  author       = {Pith},
  title        = {Pith review of: FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4KLRNJLI}},
  note         = {Machine review of arXiv:2412.10823}
}
read the original abstract

Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its dissemination, which hampers accurate prediction of short-term stock movements. Additionally, current methods often lack sufficient contextual data and explicit instructions in their prompts, limiting LLMs' ability to interpret news. In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. This data is used to construct an instruction tuning dataset to fine-tune an LLM for predicting short-term stock price movements. Our experimental results show that our approach improves prediction accuracy by 8\% compared to existing methods.

Figures

Figures reproduced from arXiv: 2412.10823 by the authors.

Figure 1
Figure 1. Overall framework. In Data Processing part, we in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The ratio of news articles in high-coherence clus [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. HG Prompt Template. This template includes [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: Baseline Prompt Template. This template contains [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 6
Figure 6. Figure 6: BA prediction labels during test period. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: The prediction outcome of HG-NC method: Boe [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 13 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Araci, D. 2019. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. arXiv preprint arXiv:1908.10063

    arXiv 2019

  4. [4]

    Atkins, A.; Niranjan, M.; and Gerding, E. 2018. Financial news predicts stock market volatility better than close price. The Journal of Finance and Data Science, 4(2): 120--137

    work page 2018

  5. [5]

    W.; and Chong, M

    Chan, S. W.; and Chong, M. W. 2017. Sentiment analysis in financial texts. Decision Support Systems, 94: 53--64

    work page 2017

  6. [6]

    Day, M.-Y.; and Lee, C.-C. 2016. Deep learning for financial sentiment analysis on finance news providers. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 1127--1134. IEEE

    work page 2016

  7. [7]

    Elahi, A.; and Taghvaei, F. 2024. elahi2024combiningCombining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models. arXiv preprint arXiv:2411.01368

    work page Pith review arXiv 2024

  8. [8]

    Grootendorst, M. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794

    arXiv 2022

Show all 24 references
  1. [9]

    L.; Clark, K.; Leskovec, J.; and Jurafsky, D

    Hamilton, W. L.; Clark, K.; Leskovec, J.; and Jurafsky, D. 2016. Inducing domain-specific sentiment lexicons from unlabeled corpora. In Proceedings of the conference on empirical methods in natural language processing. conference on empirical methods in natural language proces...

    2016

  2. [10]

    Han, X.; Wang, N.; Che, S.; Yang, H.; Zhang, K.; and Xu, S. X. 2024. Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research. In ICAIF 2024: Proceedings of the 5th ACM International Conference on AI in Finance, 538--546

    2024

  3. [11]

    Lin, C.-Y. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, 74--81

    2004

  4. [12]

    T.; and Trajanov, D

    Mishev, K.; Gjorgjevikj, A.; Vodenska, I.; Chitkushev, L. T.; and Trajanov, D. 2020. Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE access, 8: 131662--131682

    2020

  5. [13]

    Rizinski, M.; Peshov, H.; Mishev, K.; Jovanovik, M.; and Trajanov, D. 2024. Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex). IEEE Access

    2024

  6. [14]

    Sohangir, S.; Wang, D.; Pomeranets, A.; and Khoshgoftaar, T. M. 2018. Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 5(1): 1--25

    2018

  7. [15]

    Tai, Y.-J.; and Kao, H.-Y. 2013. Automatic domain-specific sentiment lexicon generation with label propagation. In Proceedings of international conference on information integration and web-based applications & services, 53--62

    2013

  8. [16]

    Wang, M.; Izumi, K.; and Sakaji, H. 2024. LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction. arXiv preprint arXiv:2406.10811

    2024 arXiv

  9. [17]

    Wang, N.; Yang, H.; and Wang, C. D. 2023. Fingpt: Instruction tuning benchmark for open-source large language models in financial datasets. arXiv preprint arXiv:2310.04793

    2023 arXiv

  10. [18]

    Wu, S.; Irsoy, O.; Lu, S.; Dabravolski, V.; Dredze, M.; Gehrmann, S.; Kambadur, P.; Rosenberg, D.; and Mann, G. 2023. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564

    2023 arXiv

  11. [19]

    Xu, Y.; and Cohen, S. B. 2018. Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1970--1979

    2018

  12. [20]

    Yang, H.; Liu, X.-Y.; and Wang, C. D. 2023. Fingpt: Open-source financial large language models. arXiv preprint arXiv:2306.06031

    2023

  13. [21]

    Yang, H.; Zhang, B.; Wang, N.; Guo, C.; Zhang, X.; Lin, L.; Wang, J.; Zhou, T.; Guan, M.; Zhang, R.; et al. 2024. FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models. arXiv preprint arXiv:2405.14767

    2024 arXiv

  14. [22]

    Zhang, B.; Yang, H.; Zhou, T.; Ali Babar, M.; and Liu, X.-Y. 2023 a . Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the fourth ACM international conference on AI in finance, 349--356

    2023

  15. [23]

    J.; and Bing, L

    Zhang, W.; Deng, Y.; Liu, B.; Pan, S. J.; and Bing, L. 2023 b . Sentiment analysis in the era of large language models: A reality check. arXiv preprint arXiv:2305.15005

    2023 arXiv

  16. [24]

    Zhou, T.; Wang, P.; Wu, Y.; and Yang, H. 2024. FinRobot: AI Agent for Equity Research and Valuation with Large Language Models. In ICAIF 2024: The 1st Workshop on Large Language Models and Generative AI for Finance

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.