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Record W2905297563 · doi:10.3386/w25344

Monetary Policy and Reaching for Income

2018· report· en· W2905297563 on OpenAlexaff
Kent Daniel, Lorenzo Garlappi, Kairong Xiao

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsMonetary policyMonetary economics

Abstract

fetched live from OpenAlex

We study the impact of monetary policy on investors' portfolio choices and asset prices.Using data on individual portfolio holdings and on mutual fund flows, we find that a low-interest-rate monetary policy increases investors' demand for high-dividend stocks and drives up their prices.The increase in demand is more pronounced among investors who fund consumption using dividend income.To explain these empirical findings, we develop an asset pricing model in which investors have quasi-hyperbolic time preferences and use dividend income as a commitment device to curb their tendency to over-consume.When accommodative monetary policy lowers interest rates, it reduces the income stream from bonds and induces investors who want to keep a desired level of consumption to ``reach for income'' by tilting their portfolio toward high-dividend stocks.Our finding suggests that low-interest-rate monetary policy may influence the risk premium of income-generating assets, lead to under-diversification of investors' portfolios, and cause redistributive effects across firms that differ in their dividend policy.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.351
GPT teacher head0.465
Teacher spread0.114 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations15
Published2018
Admission routes1
Has abstractyes

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