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Record W4284894801 · doi:10.1002/ijfe.2668

Asset allocation, limited participation and <scp>flight‐to‐quality</scp> under ambiguity of correlation

2022· article· en· W4284894801 on OpenAlexaff
Helen Hui Huang, Yanjie Wang, Shunming Zhang

Bibliographic record

VenueInternational Journal of Finance & Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsDiversification (marketing strategy)EconomicsAmbiguityMicroeconomicsQuality (philosophy)Asset (computer security)Risk aversion (psychology)Asset allocationEconometricsFinancial economicsExpected utility hypothesisBusinessComputer scienceMarketingPhysics

Abstract

fetched live from OpenAlex

Abstract This paper investigates asset allocation decisions made by three types of traders depending upon incomplete information in market equilibrium. Limited participation phenomenon is observed in the equilibrium. Moreover, we show that traders with more information might not hold more risky assets than others who have less information. Less‐informed traders trade‐off between a diversification effect induced by risk‐averse attitude and a “flight‐to‐quality” effect by their aversion towards correlation ambiguity. In equilibrium, the magnitudes between equilibrium positions of different traders are affected by the true correlation coefficient, the upper‐bound correlation conceived by naive traders and the quality of risky assets simultaneously. In some scenarios, we observe that naive and uninformed traders “escape” from low‐quality assets to high‐quality ones.

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.

How this classification was reachedexpand

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 score0.745
Threshold uncertainty score0.616

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 head0.046
GPT teacher head0.273
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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