New financial theory suggests that stock return predictability stems from a counter cyclical variation in expected return. Such findings provide a case for active management. This study investigates how investors might capitalize on predictability through short-term country allocation in emerging equity markets. This type of active management is a sub category of Global Tactical Asset Allocation. The appeal of emerging market equity investment is analyzed from a general and a country allocation perspective. It is found, that there is a high scope for diversification benefits and for profit through active management due to low levels of correlation. As emerging markets develop co-movement increases but alignment with developed markets is curbed by the fact that the latter also move more in tandem over time. As such, benefits can be expected to persist for a while. Successful country allocation relies on good return forecasts. The predictive ability of 8 conditioning variables is studied. It appears that output scaled by prices, dividendprice ratios, and price-earnings ratios are the best predictors of return. Inflation and short term interest rates exhibit some predictive ability and there is weak evidence for mean reversion. However, no predictive ability is found using three and six months momentum. Multivariate prediction models are created and used for country allocation. It is problematic to base the construction of such models on the overall evidence of predictability because there is not sufficient commonality in the factors that drive returns. Country allocation based on general prediction models does not generate a higher return than a market capitalization weighted benchmark. It is concluded, that the best prediction models include different conditioning information for every country. Allocation based on such country specific models offers a higher return than the general approach. However, a market capitalization weighted portfolio is still the better investment.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||102|