Comparing the Predictability of Stock Markets: A Comparative Study of Chinese and US Markets

In the quest for financial success, many investors turn to technical analysis to help them make informed decisions about the stock market. By understanding the predictability of price series in different markets, investors can better utilize technical trading strategies. In their study, "Scaling and Predictability in Stock Markets: A Comparative Study," researchers H. Zhang, J. Wei, and J. Huang analyzed the strength of predictability in the Chinese and US stock markets.

The researchers utilized the profit landscape method and one of the most basic sold-and-bought trading strategies to establish the profit landscape and calculate parameters to characterize the strength of predictability. They found that Chinese individual stocks are harder to predict than US ones, and individual stocks are harder to predict than indexes in both Chinese and US stock markets.

These findings are significant for investors because they provide insight into the dynamics of asset pricing. By better understanding the predictability of different markets, investors can make more informed decisions about their investments. Additionally, the study's findings highlight the importance of considering the predictability of indexes versus individual stocks in investment strategies.

The research also has implications for understanding the physical mechanisms of different kinds of markets in terms of scaling, which goes beyond the efficient markets hypothesis. It is well known that the US stock market has a long history and is the most developed and mature stock market in the world. On the other hand, the Chinese stock market is much more emerging and only precedes a quarter of a century. According to the Efficient Markets Hypothesis, the more developed and efficient a market is, the weaker its predictability by technical analysis. Therefore, this comparative study on the markets of these two countries is of potential value not only for conducting technical analysis but also for understanding the physical mechanisms of different kinds of markets in terms of scaling.

Overall, this study provides valuable insight into the predictability of different markets and the importance of considering the predictability of indexes versus individual stocks in investment strategies. The research has implications for both investors and academics interested in understanding the dynamics of asset pricing and the physical mechanisms of different kinds of markets.

The article discusses the predictability of stock markets using basic trading strategies and the profit landscape method. The study compares the predictability of indexes and individual stocks in the Chinese and US stock markets, finding that Chinese individual stocks are harder to predict than US ones, and individual stocks are harder to predict than indexes in both markets. The article also explains the meaning of the scaling factor and the significance of predictability for designing trading strategies. Finally, the comparative study is seen as valuable not only for conducting technical analysis but also for understanding the physical mechanisms of different kinds of markets in terms of scaling, which goes beyond the efficient markets hypothesis.