Volatility-based filtering is proposed to pre-process historical daily return data of stock indexes before applying to price-based technical analysis trading rules. Any “nearly flat” days which have daily gains or losses less than a threshold about 20% of a daily volatility measure, is...

Purpose This paper examines whether there are differences in the nature of the price discovery process across established versus emerging stock markets using a twenty-country sample. Design/methodology/approach The authors analyse security returns for traces of predictability or non-randomness...

The paper re-examines whether investors can predict oil and gas stock prices for abnormal returns using autocorrelation-based trading and filter rules and moving average based strategies. Short and long lengths moving averages were employed and their performances measured against the returns...

We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance...

In a true out of sample test we find no evidence that several well-known technical trading strategies predict stock markets over the period of 1987 to 2011. Our test is free of the sample selection bias, data mining, hindsight bias, or any of the other usual biases that may affect results in our...

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency...