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The Effect of Investment Horizons on Risk, Return and End-of-Period Wealth for Major Asset Classes in Canada

2009· article· en· W2048326664 on OpenAlexaffvenueabout
Lakshman Alles, George Athanassakos

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsDiversification (marketing strategy)Bootstrapping (finance)EconomicsInvestment (military)Financial economicsWelfare economicsHumanitiesEconomyPolitical scienceBusinessArt

Abstract

fetched live from OpenAlex

Abstract The objective of this paper is to investigate whether the current practice among financial planners of recommending stocks at an early age and progressively moving into cash or bonds as retirement approaches would be appropriate. We computed returns, risks and end-of-period wealth distributions of various Canadian asset classes at increasing horizons between 1957 and 2003, based on the bootstrapping technique. Results show that investment outcomes at short horizons can be quite different from outcomes at longer horizons. Evidence is provided in favour of time diversification, while the current market practice of life cycle investing is not fully supported as stocks continue to exhibit more favourable risk-return payoffs than other asset classes, even at shorter time intervals. Résumé Cet article se propose d'étudier le bien-fondé de la pratique actuelle qui consiste à recommander des actions aux investisseurs dans leur jeunesse et l'argent liquide ou les obligations lorsqu'ils approchent l'âge de la retraite. Grâce à la technique de bootstrapping, nous calculons les retours sur investissement, les risques et la distribution de richesse en fin de période pour plusieurs types d'actifs canadiens à horizons divers entre 1957 et 2003. Les résultats présentent des différences importantes entre les investissements à court terme et les investissements à long terme. Les données disponibles soutiennent l'idée de la diversification temporelle et réfutent partiellement la pratique actuelle du cycle de vie d'investissement. De fait, les actions comportent toujours un profil risques-bénéfices plus favorable que les autres types d'actifs, même pour des intervalles de temps réduits.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.417
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.037
GPT teacher head0.288
Teacher spread0.251 · 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 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

Citations10
Published2009
Admission routes3
Has abstractyes

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