Benefits of Emerging Markets Stocks and Foreign Exchange as Alternate Investment Assets and Their Causal Relation
Bibliographic record
Abstract
This dissertation examines the benefit of investing in emerging markets and the use of foreign currency as an investment asset in a diversified portfolio for 9 developed and 6 emerging markets for the period July 2005 to June 2010. This is sub-divided into pre crisis (1 July 2005 to 30 June 2008) and post crisis (1 July 2008 to 30 June 2010) period in order to determine the impact of crisis to the asset’s risk-return trade-off and direction of causal relation between stock price and exchange rates.\nOur empirical results find that stocks from emerging markets outperform stocks from developed markets in a diversified portfolio due to its higher return, lower rate of increase in market volatility during crisis and lower correlation against assets from other countries. As for foreign currencies, we found negative correlation between Yen against US dollar and lower correlation between foreign currencies and US stock implies currency investment offer more risk-reducing potential than foreign stocks. Furthermore, we found evidence that investors prefer holding safe haven currencies like Yen and US dollar right after the outbreak of crisis while lessons learnt from previous financial crisis had made managed floating currencies to be more reliable in this recent crisis.\nWhile analysing the causal relation between stock return and exchange rates changes, we found significant causal relation from exchange rate to stock return in Japan and causal relation from stock return to exchange rate in Canada and Mexico to be consistent throughout the sample period. Our findings concluded that the linkages between the two asset classes cannot be completely explained by single theory as it vary across economies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".