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## Step Down and Step Up Statistical Procedures for Stock Selection with Sharp Ratio

Stock selection by Sharp ratio is considered in the framework of multiple statistical hypotheses testing theory. The main attention is paid to comparison of Holm stepdown and Hochberg step up procedures for different loss functions. Comparison is made on the basis of condittional risk as a function of selection threshold. This approach allows to discover that properties of procedures depend not only on relationship between test statistics, but also depend on dispersion of Sharp ratios. Difference in error rate between two procedures is increasing when the concentration of Sharp ratios is increasing. When Sharp ratios do not have a concentration points there is no significant difference in quality of both procedures.

A class of distribution free multiple decision statistical procedures is proposed for threshold graph identification in correlation networks. The decision procedures are based on simultaneous application of sign statistics. It is proved that single step, step down Holm and step up Hochberg statistical procedures for threshold graph identification are distribution free in sign similarity network in the class of elliptically contoured distributions. Moreover it is shown that these procedures can be adapted for distribution free threshold graph identification in Pearson correlation network.

Financial market can be modeled as network represented by a complete weighted graph. Differet characteristics of this graph (minimum spanning tree, market graph ad others) give an impotant information on the network. In the pesent paper it is studied how the choice of measure of similarity between stocks influences the statistical errors in the calculation of network characteristics. It is shown tat sign correlation is a robust measure of similarity in contrast with Person correlation widely used in market network analysis. This gives a possibility to get more precise information on stock market from observations.

The paper deal with uncertainty in market network analysis. The main problem addressed is to investigate statistical uncertainty of Kruskal algorithm for the minimum spanning tree in market network. Uncertainty of Kruskal algorithm is measured by the probability of q incorrectly included edges. Numerical experiments are conducted with the returns of a set of 100 financial instruments traded in the US stock market over a period of 250 days in 2014. Obtained results help to estimate the reliability of minimum spanning tree in market network analysis.

Random matrix theory (RMT) is applied to investigate the cross-correlation matrix of a financial time series in four different stock markets: Russian, American, German, and Chinese. The deviations of distribution of eigenvalues of market correlation matrix from RMT global regime are investigated. Specific properties of each market are observed and discussed.

Market graph is known to be a useful tool for market network analysis. Cliques and independent sets of the market graph give an information about con- centrated dependent sets of stocks and distributed independent sets of stocks on the market. In the present paper the connections between market graph and classical Markowitz portfolio theory are studied. In particular, efﬁcient frontiers of cliques and independent sets of the market graph are compared with the efﬁcient frontier of the market. The main result is: efﬁcient frontier of the market can be well ap- proximated by the efﬁcient frontier of the maximum independent set of the market graph constructed on the sets of stocks with the highest Sharp ratio. This allows to reduce the number of stocks for portfolio optimization without the loss of quality of obtained portfolios. In addition it is shown that cliques of the market graphs are not suitable for portfolio optimization.

The article deals with the investment skill of the Russian mutual funds’ managers. Studies of the American market performed in the second half of XX century produced mixed results, however those completed after 2000 uniformly confirm that investment skill exists only for periods of less than five years, if it exists in principle. The outperformance of the market happens more frequently in emerging markets. The authors find that most managers do not possess investment skill. 90% of the funds do not outperform the market consistently. Three of the four applied methodologies (Jensen’s Alfa, Sharpe ratio, Treynor ratio) revealed that in 2004–2009 there existed more successful funds during the booming market of 2004–2007, while during the downturn (2008–2009) the number of successful funds diminished significantly. The information ratio confirmed the opposite picture, i.e. more funds outperformed the benchmarking portfolio during the downturn. There are only about 5% of funds in the sample (3–4 out of 74) whose managers are able to outplay the market in the long run. The study revealed that the management company of a successful fund either is affiliated with a large state companie, a large banks, or has persons affiliated with stock exchanges among its shareholders. The affiliation with the stock exchanges is the key success factor in Russia. Meanwhile, the study showed that there is no correlation of a fund’s success with affiliation with Russian oligarchs. Participation of foreign professional management companies in a shareholder capital of Russian management companies does not guarantee success as well.

A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.

This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.

A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.