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REVIEW 4 major objections 4 minor 44 references

Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Coupling dynamic co-movement graphs with fixed company relations improves next-day stock trend forecasts.

desk verdict A sensible combination of known components with one genuinely new idea—relation-constrained contrastive augmentation—but the evaluation is under-powered and the Zipf edge-budget story needs to be fixed before the claims can be trusted. read the letter →

arxiv 2412.04034 v1 pith:5S3EEUJC submitted 2024-12-05 cs.LG cs.NEq-fin.CP

Yunhua Pei , Jin Zheng , John Cartlidge This is my paper · ORCID
classification cs.LGcs.NEq-fin.CP
keywords ContrastivelearningFinancialmarketforecastingGraphneuralnetworksTemporalStockmovementpredictionDynamicTimeseries
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 tries to establish that stock movement prediction improves when a model learns both kinds of relationships at once: the way stocks co-move over time and the fixed sector/industry links between companies. To that end it proposes DGRCL, a framework that builds a fresh stock graph at each time step, enhances price features with a Fourier-transform based search for dissimilar periods, and uses contrastive learning to keep company relations as constraints while training. On NASDAQ and NYSE data covering 2,763 stocks and about four years, the paper reports average gains over four temporal graph baselines of 2.48% in accuracy, 5.53 in F1 score, and 6.67 in Matthews correlation coefficient. Ablation tests attribute the gain to both new modules, with the embedding enhancement doing the heavier lifting.

What carries the argument

The load-bearing mechanism is the pairing of the Embedding Enhancement (EE) module with the Contrastive Constrained Training (CCT) module. EE supplies the time-varying part: a DTW-derived cost matrix, thresholded by a Zipf-law edge count, plus Fourier-enhanced node features. CCT supplies the static part: sector-industry relations set the probability that an edge survives graph augmentation, and a contrastive loss pushes two augmented views of the same graph to produce aligned node embeddings. The two are fused in a GCN whose layer weights evolve through an RNN, trained with $L = L_{\text{pred}} + \lambda L_{\text{cl}}$.

What would settle it

Recompute Eq. (4) with N=1,026 and N=1,737 over a grid of Zipf exponents and compare against the reported edge counts of 164 and 255; if no exponent reproduces both, the adaptive edge budget is not actually fixed by Zipf's law, and the model's gains should be re-measured with edge budgets swept across a wide range.

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Extended reading notes

Core claim

The central claim is that the next trading day's up/down movement can be predicted more accurately by jointly modelling a dynamic stock graph and static company relations than by either alone. The dynamic graph is constructed data-adaptively: pairwise Dynamic Time Warping distances between stock volume-volatility sequences form a cost matrix at each time step, and an edge budget derived from the expected value of a Zipf distribution decides how many of the cheapest edges to keep. Node features are enhanced by splitting each series into the most dissimilar periods in the Fourier domain. A GCN then produces node embeddings, with layer weights evolved by an LSTM over time, while a contrastive module augments the graph by keeping edges between companies that share at least one sector-industry relation. The paper reports consistent improvements over Graph WaveNet, MTGODE, STGCL, and EvolveGCN on both markets, and ablation shows the full model beats versions with either module removed.

Load-bearing premise

The method depends on an unstated number: the Zipf-style formula that sets how many edges each stock graph gets is never given a concrete exponent, so the 164 and 255 edges used in the experiments are not reproducible from the text, and every graph layer that follows inherits that choice.

Editorial extensions

If this is right

  • Next-day movement classification on large US stock markets can be pushed from roughly 51% to 53–54% accuracy by coupling dynamic and static graph views, a margin that matters when baselines hover near coin-flip performance.
  • Ablations imply that the dynamic temporal component contributes more than the contrastive constraint alone, but their combination is what produces the best results.
  • The framework suggests that hand-built industry labels need not define the graph: data-driven edges plus relation-based constraints can replace expensive knowledge engineering.
  • The reported training cost shows the contrastive module accounts for about 90% of runtime, so the performance gain currently comes with a heavy computational price.
  • Because the same dataset supports several baseline comparisons, the results give a benchmark for later dynamic-graph stock models.

Reading between the lines

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

  • The Zipf-based edge budget in Eq. (4) reads as an expected degree rather than a total edge count, and no exponent is reported; a plausible reading is that the table's 164 and 255 edges are an arbitrary calibration, so the framework's advantage should be re-tested while sweeping the edge budget directly.
  • The same dual-relation recipe—evolving temporal graph plus static constraint graph—could transfer to other settings where a known relation network constrains dynamic forecasts, such as supply-chain or electricity-grid time series.
  • The Fourier-based feature enhancement is a standalone preprocessing idea; a simple test would apply it to non-graph baselines to see whether part of the gain is just input representation rather than graph structure.
  • If the gains survive edge-budget and relation-graph changes, the contrastive constraint is the more durable contribution; if not, the improvement may be a property of the resource graph rather than the learning procedure.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes DGRCL, a temporal graph learning framework for next-day stock movement classification. It constructs dynamic stock graphs by thresholding DTW-based cost matrices with an edge budget attributed to Zipf's law, enhances node features via FFT-based period splitting, extracts node representations through GCN layers whose weights are evolved by an RNN, and adds a contrastive learning module constrained by sector-industry relations. Experiments on NASDAQ and NYSE report consistent accuracy, F1, and MCC improvements over four baselines, together with ablations and a runtime analysis.

Significance. If the reported gains are robust, the combination of data-driven dynamic graph construction and relation-constrained contrastive training would be a useful addition to stock movement prediction research. The paper is commendable for releasing code and data, for including an ablation study that isolates the EE and CCT modules, and for reporting per-epoch training times. However, the central empirical claim currently rests on an under-specified and technically questionable graph-construction rule, on baselines that are not shown to be appropriately tuned or comparable, and on missing statistical significance tests. The contribution is therefore plausible but not yet established.

major comments (4)
  1. [§4.1.1, Eq. (4), Table 1] Equation (4) defines the expectation of a Zipf-distributed random variable on {1,...,N}, which is a mean rank or mean degree, not a target number of edges. The exponent s is never reported. Since any positive-integer Zipf expectation is at least 1, the values 164 edges for N=1026 and 255 edges for N=1737 cannot be produced by interpreting Eq. (4) as an average degree; if Eq. (4) is instead meant as a total edge count, the formula is incorrect. Because every GCN layer in Eq. (11) consumes this adjacency matrix, the headline improvements are contingent on an undocumented heuristic. Please provide the correct derivation, disclose s or replace Eq. (4) with a direct edge-budget rule, and add a sensitivity analysis over edge budgets.
  2. [§5.3, Table 2] The claim that DGRCL "significantly outperforms" state-of-the-art baselines is not supported by statistical tests. The four baselines come from traffic or other non-financial domains, and the paper does not report whether they were re-tuned on the NASDAQ and NYSE datasets; the authors instead attribute their near-50% accuracy to dataset scale. Please report paired significance tests across seeds and describe the hyperparameter search performed for each baseline. Without this, the observed improvements could reflect baseline under-tuning rather than a property of DGRCL.
  3. [§5.2, RQ3] The final values of the two hyperparameters analyzed in the sensitivity study, pe and pτ, are never disclosed. Section 5.2 says they are searched from 0.1 to 0.9 in increments of 0.2, and Fig. 2 shows F1 surfaces, but the operating point used for Tables 2 and 3 is absent. This makes the main results irreproducible and prevents readers from judging whether the reported operating point is a robust interior optimum rather than a selected extreme.
  4. [§4.1.2, Eqs. (5)-(6)] The FFT-based feature optimization is not validated as a component. The optimization over k and periods n1,...,nk is described only schematically, the parameters k0, Δ1, and Δ2 are never specified, and the ablation "w/o EE" removes the entire Embedding Enhancement module, so it cannot isolate the contribution of the Fourier transform. Please provide details of the search procedure and an ablation that keeps the dynamic graph construction fixed while toggling the FFT enhancement.
minor comments (4)
  1. [§4.1.1] The notation E(n) in the sentence following Eq. (4) conflates the expectation operator with an edge-count target; please rename the quantity to avoid confusion.
  2. [§4.1.2, §4.3] There are several typographical errors, including "envolving" in Section 4, "so f tmax" in Section 4.3, and "If(i,j)" in Section 3.1.1; a careful proofreading pass is needed.
  3. [§4.2.1, Eq. (7)] The expression min{..., pτ} uses pτ as an upper bound on the edge-remaining probability, but the description only calls it a cutoff threshold; a sentence clarifying its role would improve readability.
  4. [Table 4] The runtime table reports "w/o EE" and "w/o CCT" variants but does not explain why removing EE leaves runtime almost unchanged while removing CCT reduces it by an order of magnitude; a brief clarification of what each variant includes would help.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the dynamic graph and contrastive constraints are built without the prediction labels, and the reported movements are evaluated on held-out data; the Zipf edge-budget issue is under-specification, not circularity.

full rationale

The central derivation chain is not circular: the dynamic edges are computed from DTW cost matrices with an edge budget from Eq. (4); the EE module enhances features using FFT and maximum-entropy period search; the CCT module applies unsupervised contrastive learning constrained by sector-industry relations. The next-trading-day movement labels enter only through the final prediction loss Lpred on the test split, and the CCT hyperparameters pe and ptau are selected on validation. The self-citations to You et al. (2024a,b) are used merely to justify dataset choice and evaluation metrics and are not load-bearing. The main weakness is Eq. (4): as written it is the expectation of a Zipf-distributed random variable, i.e., a mean rank or mean degree, not an edge-count budget; the exponent s is never specified, so Table 1's 164 and 255 edges are an under-specified graph-construction heuristic. This is an external-validity and reproducibility concern, not circularity, because the edge budget does not encode the labels or the reported accuracy/F1/MCC. No step reduces a prediction to a fitted input by construction.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim is an empirical performance claim, so it rests less on mathematical axioms and more on modeling choices. The most consequential choices are the Zipf edge budget, the FFT period separation, and the use of sector-industry relations as contrastive constraints. These are implemented as domain assumptions rather than derived from data or theory.

free parameters (7)
  • Zipf exponent s
    Controls the expected edge budget in Eq. (4); never specified, yet it determines graph sparsity at every time step.
  • Temperature tau = 0.4
    Adopted from Zhu et al. 2021; not tuned in this paper.
  • Edge removal probability pe
    Grid searched from 0.1 to 0.9 in increments of 0.2; final value not reported.
  • Cutoff threshold ptau
    Grid searched from 0.1 to 0.9; final value not reported.
  • Loss trade-off lambda = 0.1
    Chosen empirically to bring prediction and contrastive losses to the same order of magnitude.
  • DTW lookback window delta = 20
    Set to approximately one month of trading days; no sensitivity analysis for this choice.
  • FFT period parameters k0, Delta1, Delta2
    Constraints in Eqs. (5) and (6) are never specified, yet they control the FFT-based feature enhancement.
assumptions (5)
  • ad hoc to paper Stock graphs follow a scale-free or power-law degree distribution, so the Zipf expectation is the appropriate edge budget.
    The scale-free claim is cited to Sawhney et al. 2021b and Barabasi and Albert 1999, but the leap from a power-law degree distribution to a total edge count is the paper's own and the exponent s is never stated.
  • domain assumption Splitting stock series into the most dissimilar FFT-domain periods and using rising, falling, and fluctuating representations improves prediction under distribution shift.
    This follows AdaRNN (Du et al. 2021), but no evidence is provided for financial series, and the claim that rising and falling markets have similar FFT sequences is unsupported.
  • domain assumption Sector and industry relations are a valid constraint set, and every dynamic edge has at least one relation type as required by Eq. (8).
    Relations come from Feng et al. 2019; the paper assumes these static relations remain informative for future stock movement.
  • domain assumption Contrastive augmentation with relation-degree-based edge keeping probabilities preserves semantically important graph structure.
    This is an adaptation of GCA (Zhu et al. 2021), but no validation is provided for financial graphs.
  • domain assumption GCN weight evolution via an RNN as in Eq. (13) transfers learned convolutional weights across time steps.
    This is the EvolveGCN assumption, imported without additional justification in the financial setting.

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Pith. "Pith review of Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations." pith.science (2026). https://pith.science/paper/5S3EEUJC

@misc{pith2026241204034,
  author       = {Pith},
  title        = {Pith review of: Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5S3EEUJC}},
  note         = {Machine review of arXiv:2412.04034}
}
read the original abstract

Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on dynamically capturing the temporal evolution of stock data, while the CCT module enforces static constraints based on stock relations, refined within contrastive learning. This dual-relation approach allows for a more comprehensive understanding of stock market dynamics. Our experiments on two major U.S. stock market datasets, NASDAQ and NYSE, demonstrate that DGRCL significantly outperforms state-of-the-art TGL baselines. Ablation studies indicate the importance of both modules. Overall, DGRCL not only enhances prediction ability but also provides a robust framework for integrating temporal and relational data in dynamic graphs. Code and data are available for public access.

Figures

Figures reproduced from arXiv: 2412.04034 by the authors.

Figure 1
Figure 1. Schematic illustration of DGRCL framework. Top-left: In the process of embedding enhancement, dynamic stock [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Sensitivity study of the overall probability of removing edges [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

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Works this paper leans on

44 extracted references · 37 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

  2. [2]

    and Albert, R

    Barab \'a si, A.-L. and Albert, R. (1999). Emergence of scaling in random networks. Science , 286(5439):509--512

    1999

  3. [3]

    Bloomfield, P. (2004). Fourier analysis of time series: An introduction . John Wiley & Sons

    work page 2004

  4. [4]

    and Ge, L

    Chen, S. and Ge, L. (2019). Exploring the attention mechanism in LSTM -based H ong K ong stock price movement prediction. Quantitative Finance , 19(9):1507--1515

    work page 2019

  5. [5]

    T., Cooley, J

    Cochran, W. T., Cooley, J. W., Favin, D. L., Helms, H. D., Kaenel, R. A., Lang, W. W., Maling, G. C., Nelson, D. E., Rader, C. M., and Welch, P. D. (1967). What is the fast F ourier transform? Proceedings of the IEEE , 55(10):1664--1674

    work page 1967

  6. [6]

    Z., and Chen, H

    Deng, S., Zhang, N., Zhang, W., Chen, J., Pan, J. Z., and Chen, H. (2019). Knowledge-driven stock trend prediction and explanation via temporal convolutional network. In Companion Proceedings of The 2019 World Wide Web Conference , pages 678--685

    work page 2019

  7. [7]

    Du, Y., Wang, J., Feng, W., Pan, S., Qin, T., Xu, R., and Wang, C. (2021). Ada RNN : Adaptive learning and forecasting of time series. In Proceedings of the 30th ACM international conference on information & knowledge management , pages 402--411

    work page 2021

  8. [8]

    Feng, F., He, X., Wang, X., Luo, C., Liu, Y., and Chua, T.-S. (2019). Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) , 37(2):1--30

    work page 2019

Show all 44 references
  1. [9]

    Gao, J., Ying, X., Xu, C., Wang, J., Zhang, S., and Li, Z. (2021). Graph-based stock recommendation by time-aware relational attention network. ACM Transactions on Knowledge Discovery from Data (TKDD) , 16(1):1--21

    2021

  2. [10]

    and Khasahmadi, A

    Hassani, K. and Khasahmadi, A. H. (2020). Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning , pages 4116--4126

    2020

  3. [11]

    Hsu, Y.-L., Tsai, Y.-C., and Li, C.-T. (2021). Fin GAT : Financial graph attention networks for recommending top-k profitable stocks. IEEE Transactions on Knowledge and Data Engineering , 35(1):469--481

    2021

  4. [12]

    T., Nguyen, M

    Huynh, T. T., Nguyen, M. H., Nguyen, T. T., Nguyen, P. L., Weidlich, M., Nguyen, Q. V. H., and Aberer, K. (2023). Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction. In Proceedings of the Sixteenth ACM International Conference on W...

    2023

  5. [13]

    Jaynes, E. T. (1982). On the rationale of maximum-entropy methods. Proceedings of the IEEE , 70(9):939--952

    1982

  6. [14]

    K., and Omitaomu, O

    Jeong, Y.-S., Jeong, M. K., and Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classification. Pattern Recognition , 44(9):2231--2240

    2011

  7. [15]

    X., Kim, J.-C., and Wood, R

    Jiang, C. X., Kim, J.-C., and Wood, R. A. (2011). A comparison of volatility and bid--ask spread for NASDAQ and NYSE after decimalization. Applied Economics , 43(10):1227--1239

    2011

  8. [16]

    Jin, M., Zheng, Y., Li, Y.-F., Chen, S., Yang, B., and Pan, S. (2022). Multivariate time series forecasting with dynamic graph neural ODE s. IEEE Transactions on Knowledge and Data Engineering , 35(9):9168--9180

    2022

  9. [17]

    H., Jeong, M., Lee, S., Kim, J., and Kang, J

    Kim, R., So, C. H., Jeong, M., Lee, S., Kim, J., and Kang, J. (2019). HATS : A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999

    2019 arXiv

  10. [18]

    Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J. (2021). Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations

    2021

  11. [19]

    Kipf, T. N. and Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations

    2017

  12. [20]

    Li, C., Song, D., and Tao, D. (2019). Multi-task recurrent neural networks and higher-order M arkov random fields for stock price movement prediction: Multi-task RNN and higher-order MRF s for stock price classification. In Proceedings of the 25th ACM SIGKDD International Conf...

    2019

  13. [21]

    Lian, Y.-M., Jhong, Y.-J., Wang, P.-H., and Chen, W.-M. (2022). An empirical examination of VIX market fluctuations. Advances in Management and Applied Economics , 12(4):1--6

    2022

  14. [22]

    Liu, C., Yan, J., Guo, F., and Guo, M. (2022a). Forecasting the market with machine learning algorithms: an application of NMC-BERT-LSTM-DQN-X algorithm in quantitative trading. ACM Transactions on Knowledge Discovery from Data (TKDD) , 16(4):1--22

    2022

  15. [23]

    Liu, X., Liang, Y., Huang, C., Zheng, Y., Hooi, B., and Zimmermann, R. (2022b). When do contrastive learning signals help spatio-temporal graph forecasting? In Proceedings of the 30th international conference on advances in geographic information systems , pages 1--12

    2022

  16. [24]

    Liu, Y., Jin, M., Pan, S., Zhou, C., Zheng, Y., Xia, F., and Philip, S. Y. (2022c). Graph self-supervised learning: A survey. IEEE transactions on knowledge and data engineering , 35(6):5879--5900

    2022

  17. [25]

    Matsunaga, D., Suzumura, T., and Takahashi, T. (2019). Exploring graph neural networks for stock market predictions with rolling window analysis. In Proceedings of the NeurIPS Workshop on Machine Learning for Finance (NeurIPS 2019) . Published in NeurIPS 2019 Workshop on Machi...

    2019

  18. [26]

    Newman, M. (2018). Networks . Oxford University Press

    2018

  19. [27]

    Newman, M. E. (2005). Power laws, P areto distributions and Z ipf's law. Contemporary physics , 46(5):323--351

    2005

  20. [28]

    Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., and Leiserson, C. (2020). Evolve GCN : Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 34, pag...

    2020

  21. [29]

    Sawhney, R., Agarwal, S., Wadhwa, A., Derr, T., and Shah, R. R. (2021a). Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 497--504

    2021

  22. [30]

    Sawhney, R., Agarwal, S., Wadhwa, A., and Shah, R. (2020). Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 8415--8426

    2020

  23. [31]

    Sawhney, R., Agarwal, S., Wadhwa, A., and Shah, R. (2021b). Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading. In Proceedings of the Web Conference 2021 , pages 11--22

    2021

  24. [32]

    L., Li \`o , P., Bengio, Y., and Hjelm, R

    Velickovic, P., Fedus, W., Hamilton, W. L., Li \`o , P., Bengio, Y., and Hjelm, R. D. (2019). Deep graph infomax. ICLR (Poster) , 2(3):4

    2019

  25. [33]

    Wang, H., Wang, T., Li, S., Zheng, J., Guan, S., and Chen, W. (2022). Adaptive long-short pattern transformer for stock investment selection. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence , pages 3970--3977

    2022

  26. [34]

    Wang, J., Sun, T., Liu, B., Cao, Y., and Zhu, H. (2019). CLVSA : A convolutional LSTM based variational sequence-to-sequence model with attention for predicting trends of financial markets. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intell...

    2019

  27. [35]

    Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI) , pages 1907--1913

    2019

  28. [36]

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

    2021 arXiv

  29. [37]

    Yin, T., Liu, C., Ding, F., Feng, Z., Yuan, B., and Zhang, N. (2022). Graph-based stock correlation and prediction for high-frequency trading systems. Pattern Recognition , 122:108209

    2022

  30. [38]

    You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020). Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems , 33:5812--5823

    2020

  31. [39]

    You, Z., Shi, Z., Bo, H., Cartlidge, J., Zhang, L., and Ge, Y. (2024a). DGDNN : Decoupled graph diffusion neural network for stock movement prediction. In Proceedings of the 49th IEEE International Conference on Acoustic, Speech and Signal Processing ( ICASSP ) , pages 6545--6549

    2024

  32. [40]

    You, Z., Zhang, P., Zheng, J., and Cartlidge, J. (2024b). Multi-relational graph diffusion neural network with parallel retention for stock trends classification. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 6545...

    2024

  33. [41]

    Zhang, H., Lin, S., Liu, W., Zhou, P., Tang, J., Liang, X., and Xing, E. P. (2023). Iterative graph self-distillation. IEEE Transactions on Knowledge and Data Engineering , 36:1161--1169

    2023

  34. [42]

    Zhao, Y., Du, H., Liu, Y., Wei, S., Chen, X., Zhuang, F., Li, Q., and Kou, G. (2022). Stock movement prediction based on bi-typed hybrid-relational market knowledge graph via dual attention networks. IEEE Transactions on Knowledge and Data Engineering

    2022

  35. [43]

    Zhou, F., Zhou, H.-m., Yang, Z., and Yang, L. (2019). Emd2fnn: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Systems with Applications , 115:136--151

    2019

  36. [44]

    Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. (2021). Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021 , pages 2069--2080

    2021

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