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Record W2752610870 · doi:10.1111/fima.12328

Are the risk attitudes of professional investors affected by personal catastrophic experiences?

2020· article· en· W2752610870 on OpenAlex

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.

Bibliographic record

VenueFinancial Management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsYork University
Fundersnot available
KeywordsPortfolioVolatility (finance)Agency (philosophy)Actuarial scienceBusinessNatural disasterBasis pointFinanceFinancial economicsEconomicsBondGeography

Abstract

fetched live from OpenAlex

Abstract We adopt a novel empirical approach to show that the risk attitudes of professional investors are affected by their catastrophic experiences—even for catastrophes without any meaningful economic impact on these investors or their portfolio firms. We study the portfolio risk of U.S.‐based mutual funds that invest outside the United States before and after fund managers personally experience severe natural disasters. Using a difference‐in‐differences approach, we compare managers in disaster versus nondisaster counties matched on prior disaster probability and fund characteristics. We find that monthly fund return volatility decreases by roughly 60 basis points in year +1 and the effect disappears by year +3. Systematic risk drives the results. Additional analyses do not support wealth effects (using disasters with no property damage) or managerial agency, skill, and catering explanations.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.213
Teacher spread0.186 · 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