Pith Number

pith:M2FIO6SC

pith:2026:M2FIO6SCGYMCHZTXLVMWXDK5P6

not attested not anchored not stored refs resolved

Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets

Kamil Kashif, Robert \'Slepaczuk

Deep reinforcement learning for portfolio allocation shows competitive performance mainly in the Euro Stoxx 50 but delivers no statistically significant excess returns over buy-and-hold across global equity markets.

arxiv:2605.17307 v1 · 2026-05-17 · q-fin.PM · cs.AI · cs.LG · cs.NE · q-fin.TR

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\usepackage{pith}
\pithnumber{M2FIO6SCGYMCHZTXLVMWXDK5P6}

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1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

RL strategies achieve competitive risk-adjusted performance primarily in the Euro Stoxx 50, where statistically significant abnormal returns are observed, but the central hypothesis is only partially confirmed: no strategy achieves statistically significant excess returns relative to Buy and Hold under HAC-robust inference across all markets.

C2weakest assumption

The sixteen walk-forward out-of-sample folds spanning 2003-2026 provide a sufficiently unbiased test of out-of-sample performance without the RL agent overfitting to the specific market regimes present in the training windows.

C3one line summary

A Soft Actor-Critic reinforcement learning framework for dynamic global equity allocation shows competitive risk-adjusted returns mainly in Euro Stoxx 50 but no consistent statistically significant outperformance versus buy-and-hold across all three markets.

References

48 extracted · 48 resolved · 2 Pith anchors

[1] and Consoli, Sergio and Piras, Luca and Podda, Alessandro Sebastian and Recupero, Diego Reforgiato , title = 2021

[2] Sensors , volume = 2022

[3] Bui, Quynh and. Applying. Physica A: Statistical Mechanics and its Applications , volume =. 2022 , publisher = 2022

[4] The Journal of Finance , volume = 1952

[5] Financial Analysts Journal , volume = 1992

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:03:51.351291Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

668a877a42361823e6775d596b8d5d7fad4f38cccfbdae46467838c4feb78e6a

Aliases

arxiv: 2605.17307 · arxiv_version: 2605.17307v1 · doi: 10.48550/arxiv.2605.17307 · pith_short_12: M2FIO6SCGYMC · pith_short_16: M2FIO6SCGYMCHZTX · pith_short_8: M2FIO6SC

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/M2FIO6SCGYMCHZTXLVMWXDK5P6 \
  | 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: 668a877a42361823e6775d596b8d5d7fad4f38cccfbdae46467838c4feb78e6a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "abd0e83b3f6d9c4281b84fac5b6b371cc777d656f010ff74029f4fff7ad52b4b",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG",
      "cs.NE",
      "q-fin.TR"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "q-fin.PM",
    "submitted_at": "2026-05-17T07:50:37Z",
    "title_canon_sha256": "53eb95b0e12972ed1ded697cd8a8f1036889b02dcee638ffd0588e08686d7010"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17307",
    "kind": "arxiv",
    "version": 1
  }
}