Information-theoretic approach to lead-lag effect on financial markets

Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated with such. Under Efficient Market Hypothesis such relationships are not possible as all information is embedded in the prices. In this paper we analyse lead-lag relationships of financial instruments and extend known methodology by using mutual information instead of Pearson's correlation coefficient, which not only is a more general measure, sensitive to non-linear dependencies, but also can lead to a simpler procedure of statistical validation of links between financial instruments. We analyse lagged relationships using NYSE 100 data not only on intraday level but also for daily stock returns, which has usually been ignored.Comment: 9 pages, 19 figure

Frequency Effects on Predictability of Stock Returns

We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price changes affects the predictability of those. We also relations between price changes predictability and the deviation of the price formation processes from iid as well as the stock's sector. We also briefly comment on the complicated relationship between predictability of price changes and the profitability of algorithmic trading.Comment: 8 pages, 16 figures, submitted for possible publication to Computational Intelligence for Financial Engineering and Economics 2014 conferenc

Maximum Entropy Production Principle for Stock Returns

In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.Comment: 14 pages, 5 figure

ANALYSIS OF THE TIME EVOLUTION OF NON-LINEAR FINANCIAL NETWORKS

We treat financial markets as complex networks. It is commonplace to create a filtered graph (usually a Minimally Spanning Tree) based on an empirical correlation matrix. In our previous studies we have extended this standard methodology by exchanging Pearson’s correlation coefficient with information—theoretic measures of mutual information and mutual information rate, which allow for the inclusion of non-linear relationships. In this study we investigate the time evolution of financial networks, by applying a running window approach. Since information—theoretic measures are slow to converge, we base our analysis on the Hirschfeld-Gebelein-Rényi Maximum Correlation Coefficient, estimated by the Randomized Dependence Coefficient (RDC). It is defined in terms of canonical correlation analysis of random non-linear copula projections. On this basis we create Minimally Spanning Trees for each window moving along the studied time series, and analyse the time evolution of various network characteristics, and their market significance. We apply this procedure to a dataset describing logarithmic stock returns from Warsaw Stock Exchange for the years between 2006 and 2013, and comment on the findings, their applicability and significance

Capillary coating as an important factor in optimization of the off-line and on-line MEKC assays of the highly hydrophobic enzyme chlorophyllase

The choice between bare and coated capillaries is a key decision in the development and use of any methods based on capillary electrophoresis. In this work several permanently and dynamically coated capillaries were successfully implemented in a previously developed micellar electrokinetic chromatography (MEKC) assay of the plant membrane enzyme chlorophyllase. The results obtained demonstrate the rationale behind the use of capillary coating, which is crucial for successful optimization of both the off-line mode and the on-line/electrophoretically mediated microanalysis assay mode. The application of an amine permanently coated capillary (eCAP) is a simple way to significantly increase the repeatability of migration times and peak areas, and to ensure a strong electroosmotic flow that considerably decreases the overall analysis time. A dynamic coating (CEofix) allows one to apply an on-line incubation to control the reaction progress inside the capillary, and to increase the signal-to-noise ratio and peak efficiency. The dynamic coating is possible with use of both the normally applied uncoated silica capillary and the precoated amine capillary, which ensures more repeatable migration times. The strong points of the uncoated silica capillary are its attractive price and wide range of pH that can be applied. The characteristics presented may simplify the choice of capillary modification, especially in the case of hydrophobic analytes, MEKC-based separations, and other enzymatic assays. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00216-016-0097-5) contains supplementary material, which is available to authorized users

Photochemical study of a new bimolecular photoinitiating system for vat photopolymerization 3D printing techniques under visible light

In this work, we presented a new bimolecular photoinitiating system based on 2-amino-4,6-diphenylpyridine-3-carbonitrile derivatives as visible photosensitizers of diphenyliodonium salt. Real-time FTIR and photo-DSC photopolymerization experiments with a cycloaliphatic epoxide and vinyl monomers showed surprisingly good reactivity of the bimolecular photoinitiating systems under UV-A, as well as under visible light sources. Steady-state photolysis, fluorescence experiments, theoretical calculations of molecular orbitals, and electrochemical analysis demonstrated photo-redox behavior as well as the ability to form initiating species via photo-reduction or photo-oxidation pathways, respectively. Therefore, the 2-amino-4,6-diphenylpyridine-3-carbonitrile derivatives were also investigated as a type II free-radical photoinitiator with amine. It was confirmed that the 2-amino-4,6-diphenylpyridine-3-carbonitrile derivatives, in combination with different types of additives, e.g., amine as a co-initiator or the presence of onium salt, can act as bimolecular photoinitiating systems for cationic, free-radical, and thiol-ene photopolymerization processes by hydrogen abstraction and/or electron transfer reactions stimulated by either near-UV or visible light irradiation. Finally, the 2-amino-4,6-diphenylpyridine-3-carbonitrile derivatives were selected for 3D printing rapid prototyping experiments. Test objects were successfully printed using purely cationic photosensitive resin, created on a 3D printer with a visible LED light source