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Record W2765087350 · doi:10.5430/afr.v6n4p294

Risk-Return Dynamics of Cross-listed Stocks

2017· article· en· W2765087350 on OpenAlexvenueno aff
Ming Jing Yang

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessDiversification (marketing strategy)Stock (firearms)Monetary economicsCapital marketSpillover effectInvestment bankingHome marketVolatility (finance)Private placementFinancial economicsRisk–return spectrumEconomicsFinancePortfolio

Abstract

fetched live from OpenAlex

Since US has been playing a leading role in global economy and technology, any major price changes in the American stock market may affect other stock markets worldwide. The American Depositary Receipts (ADRs), being the substitutes for the foreign securities, provide American investors with appealing investment opportunities to form international portfolios and to achieve the international diversification benefits. These stocks cross-listed on different exchanges not only assist corporations in raising capital abroad, but also provide a better channel for firms to search for price efficiency across the international capital markets. Consequently, the objective of this study is to examine the risk and return dynamics between ADRs and their underlying securities. The empirical results of this study indicate that the mean and volatility spillover effects and information transmission between ADRs and their underlying securities are bi-directional for the Taiwanese securities, but uni-directional (from the underlying securities to their ADRs) for the Chinese securities. Furthermore, while the international center hypothesis and the home bias hypothesis are both supported for the Taiwanese securities cross-listed in US stock markets, this study also provides evidence more in favor of the home bias hypothesis for the Chinese ADRs and their underlying securities.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.366
Teacher spread0.272 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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