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Record W3015667543 · doi:10.3390/jrfm13040072

The Impact of Tax Preferences on the Investment Attractiveness of Bonds for Retail Investors: The Case of Russia

2020· article· en· W3015667543 on OpenAlexvenueno aff
Natalia Boldyreva, Liudmila Reshetnikova, Елена Александровна Тарханова, Zhanna Pisarenko, Svetlana A. Kalayda

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsBondAttractivenessInvestment (military)Yield (engineering)BusinessIncentiveGovernment (linguistics)Monetary economicsEconomicsFinanceMarket economy

Abstract

fetched live from OpenAlex

The impact of tax incentives on the investment attractiveness of bonds for retail investors is assessed in the article. The paper presents a comparative empirical analysis of investment attractiveness of Russian bonds and bank deposits for domestic retail investors. We identify investment preferences of retail investors in Russia, analyze investment characteristics of deposits in Russian banks and a variety of bonds available for retail investors. Given the tax benefits of the recently introduced Individual Investment Account, we show that the real yield of investment in government bonds is over eight times higher than the yield of bank deposits. Despite higher risks of investing in bonds, we conclude that government bonds taking into account the tax benefits of the Individual Investment Account could be a realistic alternative to bank deposits for Russian retail investors.

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)0.003
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.049
GPT teacher head0.243
Teacher spread0.194 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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