International Diversification with Small-Cap Stocks: Mean-Variance Spanning Tests
Bibliographic record
Abstract
INTRODUCTION International portfolio investments are fairly attractive to investors from the perspective of risk diversification. Since Grubel (1968), there has been a body of literature, in the study of the ex-post performance of an efficient portfolio, focusing on benefits arising from an internationally diversified portfolio (Levy and Sarnat, 1970; Lessard, 1973, 1976; Solnik, 1974; Solnik and Noetzlin, 1982). A plausible explanation for the sources of benefits from international diversification is that each country's stock market is not perfectly integrated with other countries' markets. However, recent research results (Longin and Solnik, 1995; De Jong and De Roon, 2005; Goetzmann et al., 2005; Carrieri et al., 2007; Pukthuanthong and Roll, 2009) reveal that global stock markets are more correlated than ever as international capital markets become more integrated. In this vein, Eun et al. (2008) insist that benefits from diversified international investments have eroded because most of these investments go to large-cap funds which are usually more integrated than small-cap funds, and thus investors can benefit from investing in foreign small-cap funds. This study reexamines Eun et al.'s argument with more recent data. From the perspective of the U.S. investors who invest in both small- and large-cap funds in major foreign economies, we study whether the U.S. investors can effectively utilize benefits from international diversification even in the more globalized markets. (Of course, risks from foreign exchange rate changes also play an important role in international fund investments. However, for the convenience of analysis, this article assumes that foreign exchange risks are completely hedged. Therefore, a caution is required to interpret the results of this analysis.) The rest of the article is organized as follows. Section II presents the dataset and econometric methodology, Section III discusses the estimation results and Section IV summarizes the main findings. DATA AND METHODOLOGY Data for this study are from Datastream's MSCI monthly stock indices from 10 major countries for the period from June 1994 to April 2009. As in Eun et al., we also consider 10 developed countries that have relatively open markets: Australia, Canada, France, Germany, Hong Kong, Italy, Japan, the Netherlands, the U.K. and the U.S. Monthly returns for small-, midand large-cap funds are computed using each country's MSCI index which classifies each stock's market capitalization into small-cap, mid-cap and large-cap. According to each stock's market capitalization, the Investable Market Index divides stocks into large-, mid-, and smallcap, while the Standard Index classifies stocks into large- and mid-cap. The large-cap index accounts for 70% of the total market capitalization, followed by 15% of the mid-cap and 14% of the small-cap index. Cap-based MSCI indices for France are not available and thus returns for this country are calculated based on the market value of stocks included in each capitalization size. Therefore, a cautious approach is required to interpret the data for the country. We first analyze correlations of the U.S. with 9 major developed economies among capbased funds, using monthly data. We also examine, by restricting the sample only to recent period, the impact of the global financial crisis on the correlations. We then test whether the U.S. investors benefit more from portfolio diversification with small-cap international funds than with large-cap funds or index funds. If investments in small-cap funds do not provide additional benefits from diversified international investments compared to large-cap funds, investments in small-cap funds may be unnecessary. Following Huberman and Kandel (1987)'s spanning tests, we test, as in Eun et al., if small-cap funds can be spanned by MSCI country indices. We regress each country's small-cap fund returns on major countries' benchmark asset returns to check whether any small-cap fund returns exceed the benchmark. …
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How this classification was reachedexpand
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".