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Record W2924803672 · doi:10.1111/1540-6229.12459

Household portfolio choice before and after a house purchase

2023· article· en· W2924803672 on OpenAlexaff
Ran Sun Lyng, Jie Zhou

Bibliographic record

VenueReal Estate Economics · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Winnipeg
FundersDanmarks Frie Forskningsfond
KeywordsPortfolioEconomicsAsset (computer security)Margin (machine learning)Diversification (marketing strategy)Equity (law)Market liquidityDebtHome equityMonetary economicsPanel dataFinanceFinancial economicsBusinessEconometrics

Abstract

fetched live from OpenAlex

Abstract Using a unique administrative panel data from Denmark, this article documents the dynamic evolution of households' financial wealth, the equity market participation rate (extensive margin), and the conditional risky asset share of financial wealth (intensive margin) over a 7‐year period around a house purchase. We find that households' equity market participation rate falls during the year of house purchase. Conditional on participation, the risky asset share of financial wealth follows a V‐shape around the house purchase. It decreases and reaches the lowest point 1 year before a house purchase, but jumps up immediately after. This finding suggests that of the three channels identified in the literature that are related to the risky asset demand after a house purchase, the debt retirement channel and the diversification effect dominate the liquidity concern.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.216
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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