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Record W4403133028 · doi:10.3905/jai.2024.1.225

Direct Value Creation and Capture in the Pension Fund Industry: Five Examples

2024· article· en· W4403133028 on OpenAlex
Sébastien Betermier, Eduard van Gelderen, Barbara Zvan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Alternative Investments · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsValue (mathematics)BusinessValue creationPension fundPensionFinanceComputer scienceIndustrial organization

Abstract

fetched live from OpenAlex

How can pension funds create and capture value in financial markets? We study four landmark transactions made by large Canadian pension funds: OTPP’s acquisition of Cadillac Fairview, PSP’s development of Mahi Pono, CDPQ’s development of Réseau Express Métropolitain, and CPP Investment’s acquisition of Antares Capital. The funds create and capture value by achieving scale in strategic markets, reducing fee drag, coordinating stakeholder groups, and developing internal synergies. We identify the primary risks, discuss the risk mitigation strategies implemented by the funds, and then study how UPP, a smaller pension fund, emulates some of these strategies on a smaller scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.291
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it