Abstract
China’s stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and a significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.
Highlights
The E±cient Markets Hypothesis (EMH) is one of the cornerstones of modernnance.[18,19] There are three forms of market e±ciency, including a weak form, a semistrong form and a strong form
We focus on the Chinese stock market
The Martingale Di®erence Hypothesis (MDH) implies that conditional mean is independent, which is consistent with the EMH
Summary
The E±cient Markets Hypothesis (EMH) is one of the cornerstones of modernnance.[18,19] There are three forms of market e±ciency, including a weak form, a semistrong form and a strong form. The weak-form market e±ciency hypothesis suggests the failure of detecting mispriced assets and the futility of return. This is an Open Access article published by World Scientic Publishing Company. One abnormal phenomenon is related to return predictability in terms of historicalrmspecic information, such as market capitalization or the size e®ect,[6] price-earnings ratio,[7] book-to-market ratio or the value e®ect,[5,48] past prices or the momentum/ contrarian e®ect,[13,28] among others. Other anomalies concern with the abnormal returns associated with some calendar times, called calendar e®ects, including the weekend e®ect,[20] the day of the week e®ect,[21] the January e®ect,[49] the turn of month e®ect,[3] and several others.[4,35,37] These market anomalies cannot be explained by the Capital Asset Pricing Model (CAPM)
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