Pith. sign in

REVIEW 4 major objections 5 minor 43 references

GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A factor-only stock predictor extracts inter-stock correlations and beats a multi-source model on CSI300.

desk verdict Plausible architecture, unreproducible headline: the 0.134-vs-0.131 IC edge over HIST is inside the noise and the paper contradicts itself on the training loss. read the letter →

arxiv 2411.18997 v1 pith:WIIVGKT7 submitted 2024-11-28 q-fin.CP cs.AI

Yonggai Zhuang , Haoran Chen , Kequan Wang , Teng Fei This is my paper · ORCID
classification q-fin.CPcs.AI
keywords stocktrendpredictioninter-stockcorrelationAlpha360factorsgraphneuralnetworkGRUCSI300informationcoefficientfactor-onlymodels
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

The paper claims that a stock prediction model fed only the standard Alpha360 factor set can learn inter-stock relationships directly from those factors, without any extra data such as industry labels, financial reports, or sentiment. The proposed architecture, GRU-PFG, first processes each stock's factor sequence with a GRU, then builds explicit stock-to-stock correlation matrices from the learned features and uses them as graph-style weights. On the CSI300 dataset it reports an information coefficient of 0.134, above the 0.131 of the multi-source HIST model, while using fewer inputs. If the result holds, it would mean that inter-stock correlation information is already latent in price-and-volume-derived factors, and that standardization-friendly factor-only models need not sacrifice accuracy to multi-source models.

What carries the argument

The load-bearing mechanism is a two-stage correlation construction on GRU feature outputs. The GRU treats the 360-dimensional Alpha360 vector as 60 time steps of 6 factors and compresses it to a 64-dimensional representation; softmax over rows and columns then produces two views, one capturing relationships within a stock's own features and one capturing relationships across stocks. Pearson correlation over these views yields an aggregation matrix R1, and a residual matrix computed by subtracting weighted row and column views gives a second aggregation matrix R2; the final stock feature is a learnable weighted sum of original, primary, and secondary graph-weighted features. The graph operation is what lets factor-only inputs carry inter-stock information.

What would settle it

Retrain GRU-PFG and HIST on training data that stops in 2014 and test separately on market regimes with known breaks such as the 2015 crash, the 2017-2018 trade tensions, and the 2020 COVID sell-off; if GRU-PFG's IC advantage over HIST is confined to one regime and reverses in another, the claim of stable learned inter-stock correlations is not supported. A second check is to shuffle stock identities in the test set: if the graph component still yields high IC when the correspondence between features and stock labels is broken, the correlation module is not extracting genuine inter-stock information.

Watch

Extended reading notes

Core claim

The central discovery the paper argues for is that inter-stock correlations needed for prediction can be extracted from Alpha360 factors alone, rather than supplied from external sources. GRU-PFG projects the factor-derived GRU hidden states into a graph by computing row-wise and column-wise softmax normalizations and then applying Pearson correlation to form aggregation matrices; these matrices weight the features of every stock by their relation to every other stock. Combining the primary and a secondary residual relationship extraction, the model reaches IC 0.134 and rank IC 0.128 on CSI300, outperforming HIST (0.131 and 0.126) and all factor-only baselines. The authors read this as evidence that the factor set contains shared inter-stock structure, and that a graph-based projection can recover it.

Load-bearing premise

The paper's edge depends on the assumption that the correlation matrix built from softmax-normalized GRU states during 2007-2014 training captures stock relationships that remain predictive on the 2017-2020 test period; if that correlation structure does not generalize across market regimes, the reported advantage over baselines would not persist.

Editorial extensions

If this is right

  • On CSI300, GRU-PFG reports IC 0.134, beating multi-source HIST's 0.131 while using only Alpha360 factors.
  • It outperforms all factor-only baselines tested, including GRU (0.113), ALSTM (0.115), and Transformer (0.106).
  • Its advantage over the factor-only baselines grows from CSI100 to CSI300, suggesting that the graph component benefits from a larger pool of stock relationships.
  • Ablation shows performance improves as the primary and secondary relationship extraction blocks are added, and Pearson correlation outperforms cosine similarity for this aggregation.
  • Because the model needs no industry labels, reports, or sentiment data, it is easier to apply to new stocks and new markets.

Reading between the lines

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

  • Editorial inference beyond the paper: If inter-stock correlation is recoverable from Alpha360 alone, then a similar graph-projection layer could be attached to other sequence encoders such as LSTM or Transformer, and it may yield comparable improvement without their external data.
  • Editorial inference beyond the paper: The result suggests that part of the advantage of multi-source models may be redundant with information already encoded in cross-sectional factor patterns; a direct test would be to feed HIST's extra inputs to GRU-PFG and see whether the gain over factor-only inputs is smaller than the gain HIST obtains.
  • Editorial inference beyond the paper: The correlation matrices R1 and R2, being learned from training-period data, could be inspected over time; if their entries track known industry or sector transitions, the model would provide an interpretable, data-driven alternative to fixed industry classifications.
  • Editorial inference beyond the paper: Since GRU-PFG benefits from more stocks (CSI300 over CSI100), its performance might improve further on larger universes such as CSI500 or the full A-share market, provided the factor computation remains standard.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes GRU-PFG, a stock prediction model that uses only Alpha360 factors as input. The model first applies a GRU to obtain per-stock feature representations, then constructs two levels of inter-stock relationship graphs by applying softmax along rows and columns and computing Pearson correlations, and finally combines the resulting features through learned weights. The model is evaluated on CSI100 and CSI300 over a 2007-2020 split and compared with factor-only baselines (MLP, LSTM, GRU, SFM, GATs, ALSTM, Transformer) and multi-source baselines (HIST, ALSTM+TRA). The headline claim is that on CSI300, GRU-PFG reaches an IC of 0.134, exceeding HIST's 0.131 despite using fewer input sources. The paper also includes an ablation study and a comparison between Pearson correlation and cosine similarity for building the graph.

Significance. If the empirical claims are reproducible, the result would be interesting: it would show that a factor-only model can extract inter-stock correlations from Alpha360 data and match or exceed a multi-source model like HIST on CSI300. The paper reports standard deviations, uses standard qlib baselines, and includes an ablation study, which are positive features. However, the central empirical claim is currently unverifiable because of an unresolved contradiction in the training objective, an ambiguous label definition, and the absence of code, hyperparameters, and seeds. The reported 0.003 IC advantage over HIST is also within the reported standard deviations, so the headline claim needs a statistical test rather than a point comparison. The contribution is incremental but potentially useful if the protocol is clarified and the code is released.

major comments (4)
  1. [§4, Eq. (8) and §5.1] The training objective is specified inconsistently: Eq. (8) defines the loss as MSE, while §5.1 states "During training, IC is used as the loss function." These two objectives are not equivalent, and the reported IC values depend on which one was actually optimized. Please state the true objective, provide the training loss formula actually used, and release code or at least all hyperparameters and seeds so the 0.134 result can be reproduced.
  2. [§3.1, Eq. (2)] The label definition is ambiguous relative to the qlib baselines. Eq. (2) defines the daily return as (P_close - P_open)/P_open, an open-to-close intraday return, whereas the standard qlib daily label used by the cited baselines is a close-to-close return. If the labels fed to GRU-PFG differ from those used for HIST and the other baselines, Table 3 is not a like-for-like comparison. Specify the exact label used for all models and confirm that it matches the qlib convention.
  3. [Table 3] The headline CSI300 IC advantage of GRU-PFG (0.134) over HIST (0.131) is 0.003, which is smaller than the reported standard deviations (3.2e-3 for GRU-PFG and 2.2e-3 for HIST). Please report the number of runs, the seeds, and a paired significance test (e.g., a paired t-test over test dates or a Diebold-Mariano test) before claiming outperformance.
  4. [§4, Eq. (4) and Eq. (6)] The correlation matrix construction is not fully specified: Eq. (4) refers to Fx and Fy without indicating whether the Pearson correlation is computed across the 64 hidden dimensions of each stock or across stocks, and the dimensions of R1 and R2 are not stated even though they multiply X and Xhid in Eq. (6). Please define all dimensions and the exact normalization steps so the graph aggregation is reproducible.
minor comments (5)
  1. [Abstract and Table 3] The abstract's statement that GRU-PFG achieves "results better than the second category models" is contradicted by Table 3 on CSI100, where HIST's IC is 0.120 versus GRU-PFG's 0.116; please qualify the claim to the CSI300 dataset.
  2. [Throughout] Please correct typographical errors: "T able 1/2/3", "Fundmentals", "Stock F eature", "F last" in Eq. (7), and "the others models relies" in the abstract.
  3. [§5.3] The comparison between Pearson correlation and cosine similarity is described only qualitatively; please add the corresponding IC, Rank IC, and Precision@N values so the claimed superiority of Pearson correlation can be assessed.
  4. [References] Reference [23] is incomplete (missing authors and title), and several references lack page numbers or DOIs; please complete the bibliography.
  5. [Figures 3 and 4] Figures 3 and 4 are difficult to read at the current resolution; please provide larger panels, clearer legends, and axis labels.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central claim is an external empirical benchmark comparison, and the loss-definition inconsistency is a reproducibility concern, not a circular reduction.

full rationale

The paper's central claim is an empirical performance comparison: GRU-PFG, using only Alpha360 factors, reports IC 0.134 on CSI300 versus HIST's 0.131. The model's correlation matrices R1 and R2 are computed from GRU hidden states as described in Section 4, and they are learned functions of the input factors rather than being defined in terms of the evaluation labels or the final reported metric. No fitted parameter is renamed as a prediction, and no derivation reduces to its own inputs by construction. The comparison is made against external baselines implemented on the qlib platform, and the load-bearing related work (HIST, TRA, qlib) is prior work by other authors, not self-citation. The only notable flaw is an internal inconsistency in the loss definition: Eq. 8 defines the loss as MSE, while Section 5.1 states that IC is used as the loss function during training. This is a reproducibility and fairness concern about which objective was actually optimized, but it is not circularity: test IC is still evaluated on held-out data and is not identical to the training objective by construction. The 'correlation stability' assumption identified by the reader is a modeling generalization assumption, not a circular step. Therefore the paper has no significant circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the predictive power of Alpha360 factors, the stability of learned correlation matrices, and the fairness of baseline comparisons. The free parameters are standard in a neural network; the domain assumptions are not tested directly. No new entities are introduced.

free parameters (3)
  • hidden dimension (64) = 64
    Chosen by the authors; no search or justification is given in the paper.
  • Pearson vs cosine similarity for graph weights = Pearson
    Chosen after comparing on the validation set; cosine values are not reported.
  • Model weights (GRU, Wa, Wb, Wc, Wd, We, Wf, Wl, bl) = learned
    Trained via MSE loss on historical data; standard supervised model parameters.
assumptions (3)
  • domain assumption Alpha360 factors contain sufficient information to predict future returns
    Section 3.1 states that Alpha360 captures variables affecting returns, but no proof or external validation is provided.
  • domain assumption The Pearson correlation of GRU hidden states captures stable inter-stock relationships
    Eq. 4 computes correlations but no evidence is given that these correlations are stable over time or that they generalize.
  • domain assumption The qlib implementation of baselines matches the original published setups
    Section 5.1 claims all baselines are from qlib; no configuration details are listed, so fairness of comparison is assumed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network." pith.science (2026). https://pith.science/paper/WIIVGKT7

@misc{pith2026241118997,
  author       = {Pith},
  title        = {Pith review of: GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WIIVGKT7}},
  note         = {Machine review of arXiv:2411.18997}
}
read the original abstract

The complexity of stocks and industries presents challenges for stock prediction. Currently, stock prediction models can be divided into two categories. One category, represented by GRU and ALSTM, relies solely on stock factors for prediction, with limited effectiveness. The other category, represented by HIST and TRA, incorporates not only stock factors but also industry information, industry financial reports, public sentiment, and other inputs for prediction. The second category of models can capture correlations between stocks by introducing additional information, but the extra data is difficult to standardize and generalize. Considering the current state and limitations of these two types of models, this paper proposes the GRU-PFG (Project Factors into Graph) model. This model only takes stock factors as input and extracts inter-stock correlations using graph neural networks. It achieves prediction results that not only outperform the others models relies solely on stock factors, but also achieve comparable performance to the second category models. The experimental results show that on the CSI300 dataset, the IC of GRU-PFG is 0.134, outperforming HIST's 0.131 and significantly surpassing GRU and Transformer, achieving results better than the second category models. Moreover as a model that relies solely on stock factors, it has greater potential for generalization.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 28 canonical work pages

  1. [1]

    Journal of Pension Economics and Finance 19(1), 1–20 (2020) https://doi.org/10.1017/S1474747218000227

    Hastings, J., Mitchell, O.S.: How financial literacy and impatience shape retire- ment wealth and investment behaviors. Journal of Pension Economics and Finance 19(1), 1–20 (2020) https://doi.org/10.1017/S1474747218000227

    work page doi:10.1017/s1474747218000227 2020

  2. [2]

    Journal of Business Ethics 150(1), 15–40 (2018) https://doi.org/10.1007/ s10551-016-3122-x

    Faller, C.M., Knyphausen-Aufseß, D.: Does equity ownership matter for corporate social responsibility? a literature review of theories and recent empirical find- ings. Journal of Business Ethics 150(1), 15–40 (2018) https://doi.org/10.1007/ s10551-016-3122-x

    work page 2018

  3. [3]

    The North American Journal of Economics and Finance 37, 374–392 (2016) https://doi.org/10.1016/j.najef.2016.05.007 13

    Bampinas, G., Panagiotidis, T.: Hedging inflation with individual us stocks: A long-run portfolio analysis. The North American Journal of Economics and Finance 37, 374–392 (2016) https://doi.org/10.1016/j.najef.2016.05.007 13

    work page doi:10.1016/j.najef.2016.05.007 2016

  4. [4]

    Inter- national Journal of Disaster Recovery and Business Continuity 11(3), 977–990 (2020)

    Riaz, S., Ahmed, R., Parkash, R., Ahmad, M.J.: Determinants of stock market investors’ behavior in covid-19: A study on the pakistan stock exchange. Inter- national Journal of Disaster Recovery and Business Continuity 11(3), 977–990 (2020)

    work page 2020

  5. [5]

    Sustainabil- ity Accounting, Management and Policy Journal 12(2), 241–266 (2021) https: //doi.org/10.1108/SAMPJ-03-2019-0114

    Lakshan, A.M.I., Low, M., Villiers, C.: Management of risks associated with the disclosure of future-oriented information in integrated reports. Sustainabil- ity Accounting, Management and Policy Journal 12(2), 241–266 (2021) https: //doi.org/10.1108/SAMPJ-03-2019-0114

    work page doi:10.1108/sampj-03-2019-0114 2021

  6. [6]

    Expert Systems with Applications 184, 115537 (2021) https://doi.org/ 10.1016/j.eswa.2021.115537

    Jiang, W.: Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications 184, 115537 (2021) https://doi.org/ 10.1016/j.eswa.2021.115537

    arXiv 2021

  7. [7]

    Expert systems with applications 42(1), 259–268 (2015)

    Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications 42(1), 259–268 (2015)

    work page 2015

  8. [8]

    Expert Systems with Applications 208, 118128 (2022) https://doi.org/10.1016/j.eswa.2022.118128

    Wang, C., Chen, Y., Zhang, S., Zhang, Q.: Stock market index prediction using deep transformer model. Expert Systems with Applications 208, 118128 (2022) https://doi.org/10.1016/j.eswa.2022.118128

    arXiv 2022

Show all 43 references
  1. [9]

    Applied Soft Computing 64, 596–613 (2018) https://doi.org/10.1016/j.asoc

    Almeida, B., Ferreira Neves, R., Horta, N.: Combining support vector machine with genetic algorithms to optimize investments in forex markets with high lever- age. Applied Soft Computing 64, 596–613 (2018) https://doi.org/10.1016/j.asoc. 2017.12.047

    2018 doi

  2. [10]

    Expert Systems with Applications 207, 117986 (2022) https://doi

    Gupta, U., Bhattacharjee, V., Bishnu, P.S.: Stocknet—gru based stock index prediction. Expert Systems with Applications 207, 117986 (2022) https://doi. org/10.1016/j.eswa.2022.117986

    2022

  3. [11]

    ArXiv abs/2402.06633 (2024)

    Qian, H., Zhou, H., Zhao, Q., Chen, H., Yao, H., Wang, J., Liu, Z., Yu, F., Zhang, Z., Zhou, J.: Mdgnn: Multi-relational dynamic graph neural network for com- prehensive and dynamic stock investment prediction. ArXiv abs/2402.06633 (2024)

    2024 arXiv

  4. [12]

    ArXiv abs/2110.13716 (2021)

    Xu, W., Liu, W., Wang, L., Xia, Y., Bian, J., Yin, J., Liu, T.-Y.: Hist: A graph- based framework for stock trend forecasting via mining concept-oriented shared information. ArXiv abs/2110.13716 (2021)

    2021 arXiv

  5. [13]

    Information Processing & Management 50(2), 426–441 (2014) https://doi.org/10.1016/j.ipm.2013.12.002

    Fortuny, E., De Smedt, T., Martens, D., Daelemans, W.: Evaluating and under- standing text-based stock price prediction models. Information Processing & Management 50(2), 426–441 (2014) https://doi.org/10.1016/j.ipm.2013.12.002

    2014 doi

  6. [14]

    Accounting and Business Research 33(3), 171–185 (2003) https://doi.org/10.1080/00014788.2003.9729645 14

    Clatworthy, M., Jones, M.J.: Financial reporting of good news and bad news: evidence from accounting narratives. Accounting and Business Research 33(3), 171–185 (2003) https://doi.org/10.1080/00014788.2003.9729645 14

    2003

  7. [15]

    Decision Support Systems 50(4), 651–661 (2011) https://doi.org/10.1016/j.dss.2010.08.017

    Chai, S., Kim, M., Rao, H.R.: Firms’ information security investment decisions: Stock market evidence of investors’ behavior. Decision Support Systems 50(4), 651–661 (2011) https://doi.org/10.1016/j.dss.2010.08.017

    2011 doi

  8. [16]

    Expert Systems with Applications 204, 117478 (2022) https://doi.org/10.1016/ j.eswa.2022.117478

    Almeida, R.L.d., Neves, R.F.: Stock market prediction and portfolio composi- tion using a hybrid approach combined with self-adaptive evolutionary algorithm. Expert Systems with Applications 204, 117478 (2022) https://doi.org/10.1016/ j.eswa.2022.117478

    2022

  9. [17]

    In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp

    Mahdi Pakdaman, N., Taremian, H., Homa Baradaran, H.: Stock market value prediction using neural networks. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 132–

    2010

  10. [18]

    In: 2017 International Joint Con- ference on Neural Networks (IJCNN), pp

    Nelson, D.M.Q., Pereira, A.C.M., Oliveira, R.A.d.: Stock market’s price move- ment prediction with lstm neural networks. In: 2017 International Joint Con- ference on Neural Networks (IJCNN), pp. 1419–1426. https://doi.org/10.1109/ IJCNN.2017.7966019

    2017

  11. [19]

    In: 2021 International Conference on Computer Infor- mation Science and Artificial Intelligence (CISAI), pp

    Wang, X., Zhu, S., Ma, X., Zhang, K., Fan, X.: Forecasting model of shanghai stock index based on alstm. In: 2021 International Conference on Computer Infor- mation Science and Artificial Intelligence (CISAI), pp. 130–134. https://doi.org/ 10.1109/CISAI54367.2021.00032

    2021

  12. [20]

    In: 2020 25th International Con- ference on Pattern Recognition (ICPR), pp

    Ye, J., Zhao, J., Ye, K., Xu, C.: Multi-graph convolutional network for relationship-driven stock movement prediction. In: 2020 25th International Con- ference on Pattern Recognition (ICPR), pp. 6702–6709. https://doi.org/10.1109/ ICPR48806.2021.9412695

    2020

  13. [21]

    Information & Management 57(8), 103387 (2020) https://doi.org/10.1016/j.im.2020.103387

    Liu, R., Mai, F., Shan, Z., Wu, Y.: Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach. Information & Management 57(8), 103387 (2020) https://doi.org/10.1016/j.im.2020.103387

    2020

  14. [22]

    Expert Systems with Applications 178, 115019 (2021) https://doi.org/10.1016/j.eswa.2021.115019

    Jing, N., Wu, Z., Wang, H.: A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Systems with Applications 178, 115019 (2021) https://doi.org/10.1016/j.eswa.2021.115019

    2021

  15. [23]

    https://doi.org/10.1145/3097983.3098117

    (2017). https://doi.org/10.1145/3097983.3098117 . https://doi.org/10.1145/ 3097983.3098117

    2017

  16. [24]

    Journal of Interaction Science 5, 75–89 (2016)

    Kakushadze, Z., Tulchinsky, I.: Performance versus turnover: a story by 4000 alphas. Journal of Interaction Science 5, 75–89 (2016)

    2016

  17. [25]

    Risk Management & Analysis in Financial Institutions eJournal (2015)

    Kakushadze, Z.: 101 formulaic alphas. Risk Management & Analysis in Financial Institutions eJournal (2015)

    2015

  18. [26]

    ArXiv abs/2009.11189 (2020)

    Yang, X., Liu, W., Zhou, D., Bian, J., Liu, T.-Y.: Qlib: An ai-oriented quantitative 15 investment platform. ArXiv abs/2009.11189 (2020)

    2020 arXiv

  19. [27]

    IEEE Transactions on Knowledge and Data Engineering 35(1), 469–481 (2023) https://doi.org/10.1109/TKDE.2021

    Hsu, Y.L., Tsai, Y.C., Li, C.T.: Fingat: Financial graph attention networks for recommending top- kk profitable stocks. IEEE Transactions on Knowledge and Data Engineering 35(1), 469–481 (2023) https://doi.org/10.1109/TKDE.2021. 3079496

    2023 doi

  20. [28]

    Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021)

    Lin, H., Zhou, D., Liu, W., Bian, J.: Learning multiple stock trading patterns with temporal routing adaptor and optimal transport. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021)

    2021

  21. [29]

    In: 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), pp

    Deepalakshmi, R., Revathy, J.S., Revathy, S.P., Kousika, N.: Design and implementation of a comprehensive intelligent e-learning systems integrated web app solution for dyslexic students. In: 2023 International Conference on Research Methodologies in Knowledge Management, Arti...

    2023

  22. [30]

    Expert Systems with Applications 243, 122956 (2024) https://doi.org/10.1016/j.eswa.2023.122956

    Du, Y., Xie, L., Liao, S., Chen, S., Wu, Y., Xu, H.: Dtsmla: A dynamic task scheduling multi-level attention model for stock ranking. Expert Systems with Applications 243, 122956 (2024) https://doi.org/10.1016/j.eswa.2023.122956

    2024

  23. [31]

    In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp

    Dey, R., Salem, F.M.: Gate-variants of gated recurrent unit (gru) neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1597–1600. https://doi.org/10.1109/MWSCAS.2017.8053243

    2017

  24. [32]

    Neural Computation 31(7), 1235–1270 (2019) https://doi.org/10.1162/neco a 01199

    Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: Lstm cells and network architectures. Neural Computation 31(7), 1235–1270 (2019) https://doi.org/10.1162/neco a 01199

    2019 doi

  25. [33]

    Neurocomputing 399, 342–351 (2020) https: //doi.org/10.1016/j.neucom.2020.02.065

    Wang, Q., Hao, Y.: Alstm: An attention-based long short-term memory frame- work for knowledge base reasoning. Neurocomputing 399, 342–351 (2020) https: //doi.org/10.1016/j.neucom.2020.02.065

    2020 doi

  26. [34]

    arXiv (2017)

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. arXiv (2017)

    2017

  27. [35]

    Information Systems 69, 81–92 (2017) https://doi.org/10.1016/j.is.2016.10.001

    Li, B., Chan, K.C.C., Ou, C., Ruifeng, S.: Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Information Systems 69, 81–92 (2017) https://doi.org/10.1016/j.is.2016.10.001

    2017 doi

  28. [36]

    Big Data Cogn

    Zhang, W., Lu, B.: Stock trend prediction with machine learning: Incorporat- ing inter-stock correlation information through laplacian matrix. Big Data Cogn. Comput. 8, 56 (2024)

    2024

  29. [37]

    In: 2016 IEEE/ACIS 15th 16 International Conference on Computer and Information Science (ICIS), pp

    Akita, R., Yoshihara, A., Matsubara, T., Uehara, K.: Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th 16 International Conference on Computer and Information Science (ICIS), pp. 1–6. https://doi.org/10.1109/ICIS.2016.7550882

    2016

  30. [38]

    Finanse, Rynki Finansowe, Ubezpieczenia (79), 13–26 (2016)

    Arkan, T.: The importance of financial ratios in predicting stock price trends: A case study in emerging markets. Finanse, Rynki Finansowe, Ubezpieczenia (79), 13–26 (2016)

    2016

  31. [39]

    In: 2017 International Conference on Service Systems and Service Management, pp

    Jiahong, L., Hui, B., Junjie, W.: Sentiment-aware stock market prediction: A deep learning method. In: 2017 International Conference on Service Systems and Service Management, pp. 1–6. https://doi.org/10.1109/ICSSSM.2017.7996306

    2017

  32. [40]

    Narayan, P.K., Narayan, S., Thuraisamy, K.S.: Can institutions and macroe- conomic factors predict stock returns in emerging markets? Emerging Markets Review 19, 77–95 (2014) https://doi.org/10.1016/j.ememar.2014.04.005

    2014 doi

  33. [41]

    Journal of Economic Dynamics and Control 69, 375–408 (2016) https://doi.org/ 10.1016/j.jedc.2016.06.001

    Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., Stiglitz, J.E.: Agent based-stock flow consistent macroeconomics: Towards a benchmark model. Journal of Economic Dynamics and Control 69, 375–408 (2016) https://doi.org/ 10.1016/j.jedc.2016.06.001

    2016 doi

  34. [42]

    In: 2016 4th International Conference on Cyber and IT Service Management, pp

    Lahitani, A.R., Permanasari, A.E., Setiawan, N.A.: Cosine similarity to deter- mine similarity measure: Study case in online essay assessment. In: 2016 4th International Conference on Cyber and IT Service Management, pp. 1–6. IEEE 17

    2016

  35. [136]

    https://doi.org/10.1109/CISIM.2010.5643675

    2010

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.