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Record W4390451671 · doi:10.61190/fsr.v31i2/3.3530

Investment literacy, overconfidence and cryptocurrency investment

2023· article· en· W4390451671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFinancial Services Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsYork University
FundersTIAA Institute
KeywordsCryptocurrencyOverconfidence effectFinancial literacyInvestment (military)Behavioral economicsEconomicsBusinessMonetary economicsFinancePsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Cryptocurrency has been increasingly popular with investors. Using the 2018 National Financial Capability Study Investor survey, we examined the association between investment literacy and cryptocurrency investment—about 13% of investors invested in cryptocurrency directly or indirectly. Results from regression analyses show that objective investment literacy was negatively while sub- jective literacy was positively associated with holding cryptocurrency. Overconfident investors were more likely to invest in cryptocurrency, and results were robust across three overconfidence meas- ures. This study has implications for investment advice, financial education, and research.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.016
GPT teacher head0.258
Teacher spread0.241 · 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