This article presents a comprehensive examination of technical trading rules in the Chinese yuan-based foreign exchange market. The investigation employs daily data spanning seven years for 14 developed and 10 emerging market currencies. The analysis encompasses a vast universe of 41,660 trading rules, representing a significant expansion over the previous studies. The stepwise tests, which was employed to address the data-snooping bias, discover excess profitability in at least half of the developed and emerging currencies, implying the heterogeneous market efficiency across currencies. Our results are robust to sub-sample analysis and different parameter values of the stepwise tests.

The Chinese stock market is known for high synchronicity and the market portfolio represents a prominent risk factor to investors in the Chinese stock market. We conjecture that as a result, stocks with high exposure to market risk in China earn higher returns. Indeed, we find that CAPM beta is positively related to daily and monthly stock returns in the Chinese stock market. To substantiate our argument, we further show that the betareturn relation is stronger during periods when market risk is high. Moreover, we find that market risk is priced only during the day but not overnight in the Chinese stock market. We explore the effect of several unique trading rules in China and show evidence that the “T+1” trading rule is likely the cause.

We propose a new joint model of intraday returns and durations to study the dynamics of several Chinese stocks. We include IBM from the U.S. market for comparison purposes. Flexible innovation distributions are used for durations and returns, and the total variance of returns is decomposed into different volatility components associated with different transaction horizons. Our new model strongly dominates existing specifications in the literature. The conditional hazard functions are non-monotonic and there is strong evidence for different volatility components. Although diurnal patterns, volatility components, and market microstructure implications are similar across the markets, there are interesting differences. Durations for lightly traded Chinese stocks tend to carry more information than heavily traded stocks. Chinese investors usually have longer investment horizons, which may be explained by the specific trading rules in China.

China’s Stock Market is the most important emerging market awaiting for investigation by both academics and industrials. We study the pro…tability of long position in winner-based threshold momentum strategies after accounting for the trans-action cost. We …nd substantial pro…ts (double to octuple the money every year) in daily threshold trading strategies when trading cost is not accounted. However, at very low level of trading cost, say 0.2%, all pro…ts disappear. We employ a model that rebalance the portfolio carefully to save the transaction cost, but the trading rules still fail to profit at a reasonable level of trading cost. Thus, the momentum pro…ts may not compete with the trading cost.

Abstract: China is the most important emerging market awaiting for investigation by both academics and industrials. We study the profitability of long position in winner-based threshold momentum strategies after accounting for the transaction cost. We find substantial profits (double to octuple the money every year) in daily threshold trading strategies when trading cost is not accounted. However, at very low level of trading cost, say 0.2%, all profits disappear. We employ a model that rebalance the portfolio carefully to save the transaction cost, but the trading rules still fail to profit at a reasonable level of trading cost. Thus, the momentum profits may not compete with the trading cost.

Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper tests the simplest and most popular trading rules―moving average―in the Chinese stock market. Overall, our results are similar to the ones of Brock et al. (1992) and Lo et al. (2000), providing strong support for the technical strategies. In fact, technical indicators do provide incremental information, and buy signals consistently generate higher returns than sell signals. We also find that the asymmetric phenomenon between the buy and sell signals, and we attributed it to the investors’ behaviors.