موضوع مهم قیمت و یا اقتصاد به لحاظ اقتصادی مرتبط: مورد پیش بینی بازار بورس چینی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|17783||2014||15 صفحه PDF||سفارش دهید||9663 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of International Money and Finance, Volume 41, March 2014, Pages 95–109
We explore whether economic links via trade affect aggregate Chinese stock market returns. We find that market return indices from countries that China net imports from can forecast the Chinese aggregate market return at the weekly time horizon. The stock returns of countries that China net exports to have no consistently significant OOS predictability. The economic intuition for our results follows from the fact that China has positioned itself as a low-cost provider competing on price. As a low-cost provider China has a more difficult time passing cost increases through to export customers because of sticky prices. However, import costs, e.g., raw materials, are subject to both consumption and speculative demand and thus vary. We can conclude that costs will drive short term economic gains for the overall Chinese economy. One interpretation of our results is that supply shocks are absorbed within 2 weeks.
Can aggregate Chinese stock returns be forecast? To date, there is mixed US evidence on out-of-sample (OOS) predictability using fundamentals and macro variables, the two work horses of the predictability literature. However, outside the US there is mounting evidence that markets are predictable by alternative variables.1 This paper adds to the international predictability literature by exploring whether economic links between countries are useful for identifying predictor variables for the Chinese market. Chan et al. (2007) provide a recent survey of financial research on China.2 They discuss several cross-sectional predictability studies but virtually nothing on aggregate market OOS predictability.3 Two recent studies explicitly consider OOS forecasts of the Chinese market. Rapach et al. (2013) study the forecast power of Chinese fundamentals and Goh et al. (2013) study the forecast power of both Chinese and US fundamentals. These studies provide some initial evidence that the Chinese equity market index has a predictable component. In contrast to these studies we i) examine other countries' returns as predictors, ii) examine weekly frequency data and iii) focus on China's major trading partners. According to The Economist (2011) China could surpass the US as the number one economy by 2020. Thus, China is growing in importance in terms of world trade.4 Thus, understanding whether China is substantially different from other large economies5 and whether the returns of trade partner economies are useful in forecasting the aggregate Chinese stock market return index is of vital importance. The purpose of this paper is to fill this gap and investigate whether trade relations impact out-of-sample (OOS) predictability of Chinese stock market returns.6 There are two potential theories that might motivate superior OOS predictability to one subset of countries over another. China has positioned itself as an export economy.7 Being an export oriented manufacturing economy has important implications, foremost is that China will compete on price. China exports a lot of manufactured goods, whose prices are sticky since contracts are previously agreed; therefore, export prices cannot respond quickly to economic fluctuations. Hence net export countries returns will not impact Chinese returns in the short-run. On the other hand, China is a major importer of raw materials, whose prices are determined daily on the global market; therefore raw material costs are flexible in the short term. Hence, shocks to Chinese firm's costs will affect its profits (since export prices are sticky) in the short-run and thus affect equity market returns. Hence net import countries returns will impact Chinese returns in the short-run. This sticky-price theory suggests that countries for which China is a net exporter should not forecast the performance of the aggregate Chinese market. However, China's cost are linked to those countries for which China is a net importer. That is, raw materials, such as commodities, are subject to speculative demand in addition to demand for production, which will induce short-term variation in China's import prices. Thus, the sticky-price theory also suggests that these net import countries should have short term OOS predictability for China. An alternative to the sticky-price theory is the economically-linked countries theory. This is a macro extension of the results in Cohen and Frazzini (2008) that consumer firms have predictive power for supplier firms. In our context, this would imply that China's net export countries (China's customers) should forecast the aggregate Chinese market. For net exporters, the economically-linked theory makes predictions that are diametrically opposite to those made under the sticky-price theory. We conduct tests in order to differentiate between these two competing theories.8 Overall, we find clear evidence of OOS predictability of the Chinese equity market return at short horizons for countries for which China is a net importer. The significance is both statistically and economically significant. Not only can forecast accuracy be improved by a statistically significant margin, but investor welfare can be enhanced via a portfolio allocation strategy. Given that ETFs on China's A shares are now available, market timing strategies could be implemented for China by all investors, not just Chinese nationals. We also find that the sticky-price theory is better at predicting the dynamic relationship between China and its net import/export trade partners. Finally, although it has been extensively documented that combination of forecasts outperform single model predictions, we document a novel finding in that combining across just a subset of predictors performs better than combining across all predictors. An insight from our results is that applying economic theory can help enhance forecast gains from combination forecast methods.
نتیجه گیری انگلیسی
There is an ongoing debate on whether aggregate market returns can be forecast. Most papers consider fundamental or macro predictors. The results have been mixed. Although China has attracted a lot of attention concerning cross-sectional predictability, to date, China's stock market return, a major emerging market in world trade, has few OOS predictability studies. Both prior OOS studies focus on standard fundamental and macro variables. Evidence on predictable relations between trade partners based on economic theory is lacking. We try to fill this gap. Motivated by three results: (1) the US has strong OOS predictability for other industrialized nations, (2) China is a strongly commodity oriented economy and thus will face sticky revenues and fluctuating costs, and (3) economically-linked customer returns may provide valuable information for supplier nations, we propose two hypotheses. First, the US stock market return is useful in forecasting the Chinese stock market return. Second, we are able to discern between the sticky-price and economically-linked theories as they make opposite predictions concerning OOS predictability by countries that China are net exporters. First, we find weak support for US OOS predictability of China. Thus, the results of Rapach et al. (2013) receive limited support in the case of China and thus may not hold for other emerging markets. Second, we find consistent support for the importance of the direction of trade links to China's economy. Net import countries, e.g., Australia, Brazil, and Malaysia, show strong OOS predictability for the aggregate Chinese equity market return. When forming combination forecasts, excluding net export countries improves the forecast gains both statistically and economically for all combination methods. Our empirical results are supportive of the sticky price theory. That is, due to producing manufactured exports where inflexible contracts determine prices, China's short term export revenue is sticky. However, price movements in raw materials, e.g., due to commodity speculation, must be absorbed in order to maintain output. Finally, our results provide evidence that noise is one of the major reasons combination methods achieve improvements over individual predictor models. That is, since export countries do not forecast in single predictor forecasts, including these in combination forecasts introduces noise. When these “noise” countries are removed, the significance of the combination forecast outperformance increases sharply. This is a novel result as research typically demonstrates that combinations over many predictor models usually outperform. Our results suggest some interesting avenues for future research. The OOS predictability we document is short horizon in nature. Does short term OOS predictability exist in other emerging markets? Or is this due to institutional details of the Chinese market, e.g., Chinese A shares are mostly owned locally and the majority are held by the Chinese government. Will more open emerging markets exhibit different behavior? Thus, replicating our study for other markets, e.g., smaller emerging markets, more open markets, or markets with varying reliance on trade, would be interesting extensions. The existence of ETFs for China ‘A’ shares mean some benefits can be realized for foreign investors even though they cannot trade the underlying.