Bibliographic record
Abstract
Standard finance theory argues that changes in exchange rate carry transaction and economic exposures on a firm’s expected future cash flows, which in turn affect the firm value. An extension of the theory further suggests that the foreign exchange effect may also be asymmetric. Although numerous empirical studies have attempted to detect the sensitivity of stock returns to exchange rate changes, conclusive evidence is far and between. The overall mixed findings in the literature could in part due to two specification problems, namely omission of relevant factors proposed by several theories and the presence of conditional heteroscedasticity in share price and exchange rate changes. Most studies on currency risk focus on major industrialized countries, such as Canada, France, Germany, Japan, the United Kingdom and the United States (US). A few studies across Asia Pacific have limited their investigation on firms or industries in a single country and examine only the exposure to the US dollar. Recent global financial crisis of 2007-2009 and fears of a sovereign debt crisis in some European countries have resulted in more volatile stock markets and exchange rate movements. This study aims to re-evaluate whether more volatile exchange rate movement has asymmetrically affected returns on the Australian stock market. Given that China, Japan and the European Union have overtaken the United States as Australia’s top three trading partners in recent years, these currencies would have significant impact on its stock market. Using weekly data, this study examines exposure to the Chinese yuan, the Japanse yen, the European euro, the US dollar and the trade-weighted index on the market and sector returns in Australia from January 1990 to June 2011. The generalised autoregressive conditional heteroscedasticity model is used to overcome the problem of conditional heteroskedasticity in price changes. The empirical results suggest significant exchange rate effects on Australian market returns for all five exchange rate series with asymmetric exposure to the Chinese yuan. At the sector level, domestic market return is found to have more significant influence than exchange rate risk on all sector returns. Of all 10 sectors, the four sectors that do not have any exchange rate exposure are Consumer Goods, Technology, Telecommunication and Utilities. The six sectors that have significant asymmetric exposure to at least one currency are Basic Materials, Consumer Services, Financials, Health Care, Industrials, and Oil & Gas. Of the six sectors, Basic Materials would benefit from the appreciation of the Australian dollar while the other five sectors would benefit from the depreciation of domestic currency. The results also suggest the Chinese yuan exposure on the sector returns has increased in recent years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".