Dissecting financial markets: Sectors and states

By analyzing a large data set of daily returns with data clustering technique, we identify economic sectors as clusters of assets with a similar economic dynamics. The sector size distribution follows Zipf's law. Secondly, we find that patterns of daily market-wide economic activity cluster into classes that can be identified with market states. The distribution of frequencies of market states shows scale-free properties and the memory of the market state process extends to long times ($\sim 50$ days). Assets in the same sector behave similarly across states. We characterize market efficiency by analyzing market's predictability and find that indeed the market is close to being efficient. We find evidence of the existence of a dynamic pattern after market's crashes.Comment: 6 pages 4 figures. Additional information available at http://www.sissa.it/dataclustering/fin

SAFETY AND HEALTH SITE INSPECTIONS FOR ON-FIELD RISK ANALYSIS AND TRAINING

The field of construction is always affected by a large number of accidents at work that have many different causes and responsible. Therefore, it is of utmost importance to focus on all these issues, in order to reduce all risk factors that can undermine individuals’ safety on building sites. The objective of the research is then the development of a method for quick on site analysis of all critical issues that can create accidents and identification of the related causes in order to directly provide a correct and focused training identified as the best method to act on the causes to reduce accidents. The research was carried on during construction of the Universal Exhibition of Milan – Expo 2015 – that counted almost 70 contemporary construction sites. To reach the goals further research steps has been followed and in particular: (i) inspections on building sites through all the Expo area; (ii) analysis of the main identified problems; (iii) development of a methodology to quickly identify the cause of problems; (iv) validation of the method through back office analysis of site documents; (v) correct on-site training according to found problem. During the whole construction site, the improvements in criticalities solving have been visible thanks to the focused training. The developed method, carried on in a high-risk environment, is applicable in any other building sites and environment as independent from the boundary conditions of the place

Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode

We investigate the emergence of a structure in the correlation matrix of assets' returns as the time-horizon over which returns are computed increases from the minutes to the daily scale. We analyze data from different stock markets (New York, Paris, London, Milano) and with different methods. Result crucially depends on whether the data is restricted to the ``internal'' dynamics of the market, where the ``center of mass'' motion (the market mode) is removed or not. If the market mode is not removed, we find that the structure emerges, as the time-horizon increases, from splitting a single large cluster. In NYSE we find that when the market mode is removed, the structure of correlation at the daily scale is already well defined at the 5 minutes time-horizon, and this structure accounts for 80 % of the classification of stocks in economic sectors. Similar results, though less sharp, are found for the other markets. We also find that the structure of correlations in the overnight returns is markedly different from that of intraday activity.Comment: 12 pages, 17 figure

Cost functions for pairwise data clustering

Cost functions for non-hierarchical pairwise clustering are introduced, in the probabilistic autoencoder framework, by the request of maximal average similarity between the input and the output of the autoencoder. The partition provided by these cost functions identifies clusters with dense connected regions in data space; differences and similarities with respect to a well known cost function for pairwise clustering are outlined.Comment: 5 pages, 4 figure

Data clustering and noise undressing for correlation matrices

We discuss a new approach to data clustering. We find that maximum likelihood leads naturally to an Hamiltonian of Potts variables which depends on the correlation matrix and whose low temperature behavior describes the correlation structure of the data. For random, uncorrelated data sets no correlation structure emerges. On the other hand for data sets with a built-in cluster structure, the method is able to detect and recover efficiently that structure. Finally we apply the method to financial time series, where the low temperature behavior reveals a non trivial clustering.Comment: 8 pages, 5 figures, completely rewritten and enlarged version of cond-mat/0003241. Submitted to Phys. Rev.

Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Amino-acid sensing and degrading pathways in immune regulation

Abstract Indoleamine 2,3-dioxygenases (IDOs) − belonging in the heme dioxygenase family and degrading tryptophan − are responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD + ). As such, they are expressed by a variety of invertebrate and vertebrate species. In mammals, IDO1 has remarkably evolved to expand its functions, so to become a prominent homeostatic regulator, capable of modulating infection and immunity in multiple ways, including local tryptophan deprivation, production of biologically active tryptophan catabolites, and non-enzymatic cell-signaling activity. Much like IDO1, arginase 1 (Arg1) is an immunoregulatory enzyme that catalyzes the degradation of arginine. Here, we discuss the possible role of amino-acid degradation as related to the evolution of the immune systems and how the functions of those enzymes are linked by an entwined pathway selected by phylogenesis to meet the newly arising needs imposed by an evolving environment

Serum neutrophil gelatinase-B associated lipocalin (NGAL) levels in Down’s syndrome patients

Neutrophil gelatinase-associated lipocalin (NGAL) is a group of proteins with different functions