This study investigates intraday herding on the Euronext, the world's first cross-border consolidated exchange. Intraday herding is significant in the Euronext as a group and presents us with size, industry and country effects. Importantly, the trading dynamics of the group's member markets significantly affect each other and can, in the case of the Netherlands, promote herding formation. Intraday herding is found to be significant before, during and after the 2007-09 financial crisis period, with its presence appearing the least strong during the crisis. Overall, we demonstrate for the first time in the literature that cross-border exchanges harbour versatile herding dynamics at intraday level, a finding which reflects recent advances in financial technology and the ongoing financial integration in Europe. JEL classification: G02; G15

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints -eprint.ncl.ac.uk

In this study a Krill Herd-Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity Exchange Traded Funds (ETFs) on a daily basis over the period 2012-2014. The inputs of the KH-vSVR models are selected through the Model Confidence Set (MCS) from a large pool of linear predictors. The KH-vSVR's statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on Heterogeneous Autoregressive (HAR) volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.

This paper examines the commonality in liquidity for individual equity options trading at NYSE LIFFE. We use high-frequency data to construct a novel index of liquidity commonality and we find that it can explain a substantial proportion of the liquidity variation of individual options.The explanatory power of the common liquidity factor is more pronounced during periods of higher implied volatility at the market level. The common factor's impact on individual options' liquidity is found to depend on the options' idiosyncratic characteristics, while there is limited evidence of systematic liquidity spillover effects among the NYSE LIFFE exchanges.JEL Classifications: G12; G19

No abstract

In this paper, we present two Neural Network based techniques, an adaptive evolutionary Multilayer Perceptron (aDEMLP) and an adaptive evolutionary Wavelet Neural Network

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints-eprint.ncl.ac.uk Bernales A, Cañón C, Verousis T. Bid-Ask Spread and Liquidity Searching Behaviour of Informed Investors in Option Markets.

a b s t r a c tThis is the first paper to systematically investigate price clustering in new equity assets using a high frequency transactions dataset. We test the hypotheses that past price information and market maker activities are related to price clustering. We report that price clustering in IPOs is substantially greater than the clustering observed for non-IPO assets, which supports the hypothesis that the decision of going public is followed by haziness about the true price. Underpricing is a significant determinant of price clustering for orderbook trades, which supports the notion that underpriced IPOs partially reflect price uncertainties. Tick size specifications can be restrictive for individual investors, while giving execution priority to market makers. The characteristics of price clustering for off-book trades differ substantially to price clustering in the order-book.