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This raises the risk of data snooping, since so many indicators are proposed.
We find evidence for the turnover ratio increasing more for illiquid stocks than liquid stocks in response to market events.
Through an analysis of the trailing 2 year correlation between turnover ratio and price impact, we show that this correlation in liquid stocks steadily increases starting from the early 1990s, possibly due to the proliferation of day traders.
I1 give these technical market indicators the benefit of the doubt, but even then I find little evidence that they predict stock market returns.
Many so-called return predictability anomalies disappear over time because investors arbitrage profits away through their trading. The third study investigates what would happen if a completely new technical trading rule – Bollinger Bands – appeared that investors had never used before but which became more popular over time.
Moreover, current studies largely concentrate on price-based technical indicators.
In contrast, the widely used technical market indicators have drawn limited attention.
In a true out-of sample test, the first study finds no evidence that several well-known technical trading strategies predict stock markets over the period from 1987 to 2011.
Further analysis shows that this poor out-of-sample performance most likely is not due to the market becoming more efficient – instantaneously or gradually over time – but is probably a result of bias.
The findings suggests that with proper control over the quality of the data and the use of a larger number of data observations, the random walk model can be a good description of successive price returns in an emerging stock market.
This has been shown to hold irrespective of whether bid, ask, or transaction returns are used.