Strategic Timing of IPOs and Disclosure: A Dynamic Model of Multiple Firms

We study a dynamic timing game between multiple firms, who decide when to go public in the presence of possible information externalities. A firm's IPO pricing is a function of its privately observed idiosyncratic type and the level of investor sentiment, which follows a stochastic, mean-reverting process. Firms may wish to delay their IPOs in order to observe the market reception of the offerings of their peers. We characterize the unique symmetric threshold equilibrium, whereby pioneer firms with high idiosyncratic types endogenously emerge. The results provide novel implications regarding variation in IPO timing, sequential clustering, IPO droughts, the composition of new issues over time, and how IPO volume fluctuates over time. These include, among others, that in more populated industries, a lower proportion of firms emerge as industry pioneers, but follower IPO volume is intensified. Additionally, heightened uncertainty over investor sentiment exacerbates delay and leads to lower IPO volume.

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram<br> (EEG) data.

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram<br> (EEG) data.

Changes in Russia's sectoral and regional industrial structures

Subject. This article considers the industry structure of the economy as a system susceptible to rapid quantitative and qualitative changes. Objectives. The article aims to comprehensively study shifts and changes in the sectoral and regional structures of the Russian industry. Methods. For the study, we used the methods of theoretical differentiation and insight of the object, sequential clustering, analytical population, grouping of data on an adjacent basis, and conceptual algorithmization. Results. The article highlights structural changes in the Russian industry, significant disparities in the types of economic activity under study in terms of regions, positive shifts, growth zones, and prospective regions for investment. Positive trends need to be promoted. Conclusions. The Russian industrial policy traditionally gravitates towards a vertical model and the preferential use of budgetary and quasi-budget financing tools. The consequence of this situation is the excessive fragmentation of the policy. Taking into account the variety of cases of the industrial policy implementation in Russia, the success achieved can be considered relatively local.

An Efficient Sequential Clustering Based Classification Model for Diabetes Diagnosis and Prediction

At last decade, the development of diverse models and the excessive data creation leads to an enormous production of dataset and source. The healthcare field offers rich in information and it needs to be analyzed to identify the patterns present in the data. The commonly available massive amount of healthcare data characterizes a rich data field. The way of extracting the medical design is difficult because of the characteristics of healthcare data like massive, real, and complicated details. Various machine learning (ML) algorithms has developed to predict the existence of the diabetes disease. Due to the massive quantity of diabetes disease dataset, clustering techniques can be applied to group the data before classifying it. A new automated clustering based classification model is applied for the identification of diabetes. To cluster the healthcare data, sequential clustering (SC) model is applied. Then, logistic regression (LR) model is applied for the effective categorization of the clustered data. The experimentations have been directed by the benchmark dataset. The simulation outcomes demonstrate that the efficiency of the SC-LR method beats the prevailing methods to predict the diabetes diseases.