Влияние тональности писем CEO на финансовые показатели компании
The paper is devoted to the analysis of CEO letters as an instrument for influencing the expectations of shareholders and potential investors. The aim of the research is to analyze empirically the influence of semantic characteristics of CEO letters on financial indicators of the company. The authors suggested that CEO letter’s tonality, its length and readability have a great impact on the company’s financial indicators, their prediction and mid-year stock value. To check the hypotheses stated, a sample group of 102 Russian companies was analyzed with the use of “bag of words” method (specialized dictionaries were applied). For this purpose, neural network models were also developed. The results obtained confirmed the influence of CEO letter’s semantic characteristics on the stock value of company.
Online petitions are usually regarded as one of the most popular channels to involve citizens in the political process. In our paper we have analyzed texts and voting data (pro and against) from 9705 e-petitions submitted from 2013 until 2017 at Russian Public Initiative project. Analysis of dynamics showed stabilization of interest to this resource (emergence of a new authors, growth of “strong” petitions etc.). Studying success factors of electronic petitions at the Russian public initiative project we found out that the topic and lexical information are significant factors, as well as the level of petitions.
The article discusses development of the segmented characters classifier of the Russian alphabet a nd of the Arabic numerals on the basis of block neural network structures including the plurality of blocks for each individual character recognition and for the synthesis block decision. Keywords: pattern recognition, neural network, training of neural n etworks, base of hand - written characters, recognition of hand - written characters
Intelligent Systems Conference (IntelliSys) 2018 is the fourth research conference in the series. This conference is a part of SAI conferences being held since 2013. The conference series has featured keynote talks, special sessions, poster presentation, tutorials, workshops, and contributed papers each year. The conference focus on areas of intelligent systems and artificial intelligence (AI) and how it applies to the real world. IntelliSys is one of the best respected Artificial Intelligence (AI) Conference.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
In work the developed model of adaptive management by the vertically integrated companies based on the system approach supporting the mechanism of an operational management in a uniform cycle of strategic planning, within the limits of faster time is presented. Thus for a finding of optimum values of operating parameters special algorithms of a class of genetic algorithms are used, neural networks the example of the developed system of adaptive management for the vertically-integrated oil company is etc. presented.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
This article presents the analysis of blogs — a new text type in the Internet. The texts are discussed from the perspective of text linguistics. Nowadays, Internet texts gain increasing popularity with the users; however, traditional methods of text linguistics are not capable of revealing the many-sided nature and uniqueness of the given types of texts. Therefore, introduction of new criteria of the analysis and estimation are essential for the fundamental analysis of the new phenomena in text linguistics.
A Casebook aims at enhancing language and communicative competences of master students of law through teaching legal textology in English at research workshops as the primary training form. A major aim consists in integrating linguistics, specifically text linguistics, and law. A new teaching methodology employed draws largely on comparative and text linguistics, comparative law, as well as intercultural communication. The selected case-studies address the less elaborated law fields: indirect discrimination at workplace, I-space regulation and IT-fraud as part of cybercrime against the on-going IT advancement. These topics as vaguely defined legal areas with few statutory remedies and insufficient enforcement background are viewed in couple with sociocultural, economic and philosophical factors. A Casebook is designed for LLM students but may draw interest of much wider range of MA students in humanities, as well as their tutors.
The paper theorizes on the general architectonics of idealized cognitive models (ICMs) and their involvement in metonymy and metaphor. The article posits that an ICM's structure should reflect the architecture of the neural network/s engaged in processing of a given concept. The ICM nodes, or cogs, construct a complex, hierarchically organized neural connections, with the superior nodes being highly selective, invariant and prototypical. Signals travelling from one cog to another within one ICM are essentially metonymical, while a cog shared by two or more ICMs marks a metaphoric shift.