Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
0Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5136816393
ORCID evidence
No valid ORCID is stored.
Observed aliases (4)
- Oskar Åström (openalex, provider refresh)
- Oskar Åström (openalex, source metadata)
- Åström, Oskar (openalex, provider refresh)
- Åström, Oskar (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Firm Revenue: 1 paper
- Other: 1 paper
- Research Productivity: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Tree-based machine‑learning models pick undervalued Nordic stocks successfully: XGBoost (with Random Forest close behind) delivered the best classification performance, and a backtested value portfolio beat the MSCI Nordic by roughly 8 percentage points annually. However, the result rests on historical backtests that do not report key robustness checks (transaction costs, survivorship protection or out‑of‑sample validation).openalex | Åström, Oskar provider id |
2026-01-01 | 0 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.