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Record W3204814494 · doi:10.3386/w27969

Millionaires Speak: What Drives Their Personal Investment Decisions?

2020· report· en· W3204814494 on OpenAlexaff
Svetlana Bender, James J. Choi, Danielle Dyson, Adriana Robertson

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInvestment (military)BusinessPersonal accountInternet privacyLawComputer sciencePolitical scienceArt

Abstract

fetched live from OpenAlex

We survey 2,484 U.S. individuals with at least $1 million of investable assets about how well leading academic theories describe their financial beliefs and personal investment decisions. The wealthy's beliefs about financial markets and the economy are surprisingly similar to those of the average U.S. household, but the wealthy are less driven by discomfort with the market, financial constraints, and labor income considerations. Portfolio equity share is most affected by professional advice, time until retirement, personal experiences, rare disaster risk, and health risk. Concentrated equity holding is most often motivated by belief that the stock has superior riskadjusted returns. Beliefs about how expected returns vary with stock characteristics frequently differ from historical relationships, and more risk is not always associated with higher expected returns. Active equity fund investment is most motivated by professional advice and the expectation of higher average returns. Forty-two percent of respondents agree with the first assumption, 33% with the second, and 19% with both.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.326
GPT teacher head0.445
Teacher spread0.119 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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