Pith Number

pith:K2QW6COO

pith:2026:K2QW6COO2VK4UW6RXTETEV3MPS

not attested not anchored not stored refs resolved

Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction

Jae Wook Song, Namhyoung Kim

PRISM-VQ combines vector-quantized discrete latent factors with financial priors and a mixture-of-experts to improve dynamic cross-sectional stock return predictions.

arxiv:2605.13407 v1 · 2026-05-13 · cs.LG · cs.CE · q-fin.ST

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\pithnumber{K2QW6COO2VK4UW6RXTETEV3MPS}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experiments on CSI 300 and S&P 500 show consistent improvements in cross-sectional return prediction and portfolio performance over strong baselines while preserving interpretability.

C2weakest assumption

That vector quantization reliably suppresses noise while preserving predictive cross-sectional structure and that the discrete codes provide effective routing signals for the mixture-of-experts without introducing regime misclassification.

C3one line summary

PRISM-VQ integrates vector-quantized latent factors with financial priors and a structure-conditioned mixture-of-experts to deliver improved cross-sectional stock return predictions and portfolio performance on CSI 300 and S&P 500.

References

37 extracted · 37 resolved · 3 Pith anchors

[1] Hypergraph neural networks to predict stock movements by exploring higher-order relationships 2025

[2] Matcc: A novel approach for ro- bust stock price prediction incorporating market trends and cross-time correlations 2024

[3] Xgboost: A scalable tree boosting system 2016

[4] A simple framework for contrastive learning of visual representations 2020

[5] Automatic de- biased temporal-relational modeling for stock investment recommendation 2024

Receipt and verification

First computed	2026-05-18T02:44:47.496754Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

56a16f09ced555ca5bd1bcc932576c7caecbcbae6c488d1ecb8733b209064b83

Aliases

arxiv: 2605.13407 · arxiv_version: 2605.13407v1 · doi: 10.48550/arxiv.2605.13407 · pith_short_12: K2QW6COO2VK4 · pith_short_16: K2QW6COO2VK4UW6R · pith_short_8: K2QW6COO

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/K2QW6COO2VK4UW6RXTETEV3MPS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 56a16f09ced555ca5bd1bcc932576c7caecbcbae6c488d1ecb8733b209064b83

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a89973fc992f256639caf4be2a6d2f5a5e0ba9184d9c1483d9a425f63b410ae7",
    "cross_cats_sorted": [
      "cs.CE",
      "q-fin.ST"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T12:02:53Z",
    "title_canon_sha256": "894bfb691198821fdae3d7f06f5ce87e286e98fc01ef1597f5b6d342848f8d3d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13407",
    "kind": "arxiv",
    "version": 1
  }
}