We propose a novel way to assess information processing in a complex environment of market fragmentation. We take a different angle from the price discovery literature, and investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We show...

A reversal pattern in the time series context from 12 to 24 months after the formation of trend following signals is observed in a universe of 55 liquid futures instruments. We find that instruments with sell signals in the trend following portfolio (i.e. "losers") contribute to this type of...

A two step forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the multi-step ahead forecasts for...

This paper aims at providing a primer on the use of big data in macroeconomic nowcasting and early estimation. We discuss: (i) a typology of big data characteristics relevant for macroeconomic nowcasting and early estimates, (ii) methods for features extraction from unstructured big data to...

In this paper we extend the methodology of our earlier work (Papailias and Thomakos, 2011) on a modified moving average technical trading rule by allowing short sales. We show how short sales change the trading rule which now acts as a dynamic trailing "stop-and-reverse", instead of a dynamic...

This paper proposes a modified version of the widely used price and moving average cross-over trading strategies. The suggested approach (presented in its 'long only' version) is a combination of cross-over 'buy' signals and a dynamic threshold value which acts as a dynamic trailing stop. The...

A modified version of, perhaps, the most widely used technical trading strategy – moving averages – is discussed in this article. The suggested approach combines cross-over "buy" signals and a dynamic threshold value which acts as a trailing stop. The trading behaviour and performance...

This paper analyses the forecasting ability of economic summary indicators in EU economies. We employ the use of Partial Least Squares and Bayesian Shrinkage Regression methods and we predict the growth rates of quarterly GDP and Consumption and monthly Industrial Production. We find evidence...

This paper is concerned with the forecasting performance of variable reduction and variable selection methods using medium and large datasets. The variable reduction methods include Principal Components, Partial Least Squares and Bayesian Shrinkage Regression. The variable selection methods...