Do order imbalances predict Chinese stock returns? New evidence from intraday data

In this paper we examine whether order imbalances can predict the Chinese stock market returns. We use intraday data, a panel data predictive regression model that accounts for persistent and endogenous order imbalances and cross-sectional dependence in returns, and show that order imbalances predict stock returns from 1-minute trading to 90-minute trading. On the basis of this predictability evidence using multiple trading strategies we show that profits persist during the day. These results imply that a source of Chinese market inefficiency is order imbalances.

Initiation of trades on the Chinese stock market

The flow of orders from buyers and sellers, relative to past returns and stock characteristics, was examined in the Chinese stock market. Order imbalance (the gap between buyer-and seller-initiated trades) was found to be negatively related to long term returns. Turn of the calendar year trading provided strong indications of tax-motivated trading as well as support for the flight-to-quality hypothesis, which suggests selling in response to perceived increases in market risk.

Explaining turn of the year order flow imbalance

The paper provides evidence of a turn of the year effect in the order flow imbalance of both retail and institutional investors. In December there is net selling pressure which is reversed in January. We examine high frequency intraday order flow information and find that the changes in order flow imbalance between December and January are related to firm risk factors and characteristics. We find that retail order flow imbalances are associated with a wide range of risk characteristics including beta, illiquidity and unsystematic risk. Imbalances in institutional order flow are associated with only a small number of risk variables. We show that these order flow changes are important because risk premiums are elevated in January. Our results are robust to the effects of decimalization.

A bidding strategy for minimizing the imbalances costs for renewable generators in Spanish power markets

The aim of this paper is to suggest a simple methodology to be used by renewable power generators to bid in Spanish markets in order to minimize the cost of their imbalances. As it is known, the optimal bid depends on the probability distribution function of the energy to produce, of the probability distribution function of the future system imbalance and of its expected cost. We assume simple methods for estimating any of these parameters and, using actual data of 2014, we test the potential economic benefit for a wind generator from using our optimal bid instead of just the expected power generation. We find evidence that Spanish wind generators savings would be from 7% to 26%.

A second order control-volume finite-element least-squares strategy for simulating diffusion in strongly anisotropic media

An unstructured mesh �nite volume discretisation method for simulating di�usion in anisotropic media in two-dimensional space is discussed. This technique is considered as an extension of the fully implicit hybrid control-volume �nite-element method and it retains the local continuity of the ux at the control volume faces. A least squares function recon- struction technique together with a new ux decomposition strategy is used to obtain an accurate ux approximation at the control volume face, ensuring that the overall accuracy of the spatial discretisation maintains second order. This paper highlights that the new technique coincides with the traditional shape function technique when the correction term is neglected and that it signi�cantly increases the accuracy of the previous linear scheme on coarse meshes when applied to media that exhibit very strong to extreme anisotropy ratios. It is concluded that the method can be used on both regular and irregular meshes, and appears independent of the mesh quality.

Speaker identification using higher order spectral phase features and their effectiveness vis-a-vis Mel-Cepstral features

The effectiveness of higher-order spectral (HOS) phase features in speaker recognition is investigated by comparison with Mel Cepstral features on the same speech data. HOS phase features retain phase information from the Fourier spectrum unlikeMel–frequency Cepstral coefficients (MFCC). Gaussian mixture models are constructed from Mel– Cepstral features and HOS features, respectively, for the same data from various speakers in the Switchboard telephone Speech Corpus. Feature clusters, model parameters and classification performance are analyzed. HOS phase features on their own provide a correct identification rate of about 97% on the chosen subset of the corpus. This is the same level of accuracy as provided by MFCCs. Cluster plots and model parameters are compared to show that HOS phase features can provide complementary information to better discriminate between speakers.