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Evaluation of Foreign Exchange Rate Exposure for Publicly Listed Firms of Chinese A Shares

2010· article· en· W1924448135 on OpenAlexvenueno aff
Fei-xue Huang

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsRenminbiExchange rateEconomicsHumanitiesExposition (narrative)Foreign exchangeDepreciation (economics)Welfare economicsPhilosophyMonetary economicsMicroeconomicsArt

Abstract

fetched live from OpenAlex

This study's objective was to evaluation the issue of exchange rate exposure about publicly listed firms of Chinese A Shares. A new model of Measure and Calculate with lagged exchange rate variable is proposed. The new model can estimate the dynamic relation between exchange rate movements and firm value, hence overcome the deficiency of Jorion's model. The compare of the two models' empirical results indicates that the new model is more useful and effective in estimating the foreign exchange rate exposure. The following conclusions:⑴ 43.59% of the sample firms are highly exposed to lagged foreign exchange risks and 85.29% of them benefit from the depreciation of RMB; ⑵ not to find the significant relation between firm size and exposure; ⑶ to consider the more degree of foreign exchange rate exposure for manufacturing firms. Key words: foreign exchange rate; risk exposure; risk evaluation model; Chinese A Shares; public listed firmsResume: L'objectif de cet article est d'evaluer les problemes du risque de change des societes chinoises cotees aux actions A. Un nouveau modele de mesure et de calcul avec une variable retardee de l'exposition au risque de change est propose. Ce modele peut evaluer les effets dynamiques des fluctuations de taux de change sur les rendements boursiers et compenser les insuffisances du modele de Jorion qui n'arrive pas a detecter les defauts de l'exposition au risque de change des societes cotees en Chine. En comparant le nouveau modele avec le modele de Jorion, nous constatons que le nouveau modele peut expliquer mieux le probleme de l'exposition au risque de change des societes cotees en Chine: ⑴ 43,59% des entreprises selectionnees sont exposees aux risquex de change retardes de premier ordre et 85,29% d'entre elles beneficient d'une depreciatin de RM; ⑵ Il n'y a pas de correlations significatives entre la taille de l'entreprise et l'exposition au risque de change; ⑶Les societes chinoises de manifacturation assument une exposition importante exposition au risque de change.Mots-cles: devise etrangere; exposition au risque; modele d'evaluation des risque; actions A, societes cotees

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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)0.005
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.276
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

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".

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Citations0
Published2010
Admission routes1
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