Approaches to the solution to the problem of news-based events forecasting

An overview of the areas of application of approaches and methods of forecasting events based on past events. The substantiation of urgency of a theme is given and possibilities concerning application of results of work are resulted. Requirements for incoming news regarding their quality are defined. It is noted that there are four key criteria for the quality of the media, which are often two-component, namely: the relevance of news, providing the context in which the event, compliance with professional standards and a variety of materials. The key stages of working with data in order to obtain knowledge from them for forecasting events are identified. These include pre-processing of data (reduction to a standardized view that will understand and be able to process the algorithm), their analysis and the forecasting process itself. The spheres of application of associative series and Markov processes for search of causal relations, and time series for definition of the period of occurrence of an event with the set probability are specified

Approaches to the solution to the problem of news-based events forecasting

An overview of the areas of application of approaches and methods of forecasting events based on past events. The substantiation of urgency of a theme is given and possibilities concerning application of results of work are resulted. Requirements for incoming news regarding their quality are defined. It is noted that there are four key criteria for the quality of the media, which are often two-component, namely: the relevance of news, providing the context in which the event, compliance with professional standards and a variety of materials. The key stages of working with data in order to obtain knowledge from them for forecasting events are identified. These include pre-processing of data (reduction to a standardized view that will understand and be able to process the algorithm), their analysis and the forecasting process itself. The spheres of application of associative series and Markov processes for search of causal relations, and time series for definition of the period of occurrence of an event with the set probability are specified. Ref. 7, pic. 2

Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis

Інформаційна система аналізу змісту новин та прогнозування подій на його основі

Пояснювальна записка магістерської дисертації складається з чотирьох розділів, містить 4 таблиці, 4 додатки та 43 джерела – загалом 128 сторінки. Об`єкт дослідження: зміст новин. Мета магістерської дисертації: підвищення релевантності прогнозів виникнення подій на основі аналізу змісту новин. Предмет дослідження – інформаційна система аналізу змісту новин та прогнозування подій. Методи дослідження – методи кластеризації та методи машинного навчання засновані на асоціативних правилах. Наукова новизна отриманих результатів полягає в розробці та модифікації підходів до прогнозування подій на основі новин, зокрема модифікації асоціативних правил щодо об’єднання їх в ланцюги, які дозволили б виявляти причинно-наслідкові зв’язки в тексті новин. Результати досліджень буде опубліковано в міжвідомчому науково-технічному збірнику «Адаптивні Системи Автоматичного Управління» (на стадії друку) [1] та в тезах наукової конференції студентів науково-практичної конференції молодих вчених та студентів «Інженерія програмного забезпечення і передові інформаційні технології» – SoftTech-2021 [2].The explanatory note of the master's dissertation consists of four sections, contains 4 tables, 4 appendices and 43 sources – a total of 128 pages. Object of research: news content. The purpose of the master's dissertation: to increase the relevance of forecasts of events based on the analysis of news content. The subject of research is the information system of news content analysis and event forecasting. Research methods - clustering methods and machine learning methods based on associative rules. The scientific novelty of the obtained results is the development and modification of approaches to forecasting events based on news, in particular the modification of associative rules for combining them into chains, which would reveal the causal links in the news text. The research results will be published in the interdepartmental scientific and technical collection "Adaptive Automatic Control Systems" (in print) [1] and in the abstracts of the scientific conference of students of the scientific-practical conference of young scientists and students "Software Engineering and Advanced Information Technology" - SoftTech- 2021 [2]

Método de reglas de asociación para el análisis de afinidad entre objetos de tipo texto

Maestría en IngenieríaData mining is considered a tool to extract knowledge in large volumes of information. One of the analyzes performed in data mining is the association rules, whose purpose is to look for co-occurrences among the records of a set of data. Its main application is in the analysis of market basket, where criteria for decision making are established based on the buying behavior of customers. Some of the algorithms are A priori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR. These algorithms have been designed to analyze structured databases; At present, various applications require the processing of unstructured data known as text type Objects. The purpose of this research is to generate a method to establish the relationship between the elements that make up an object of text type, for the acquisition of relevant information from the analysis of massive data sources of the same type.La minería de datos es considerada una herramienta para extraer conocimiento en grandes volúmenes de información. Uno de los análisis realizados en minería de datos son las reglas de asociación, cuyo propósito es buscar co-ocurrencias entre los registros de un conjunto de datos. Su principal aplicación se encuentra en el análisis de canasta de mercado, donde se establecen criterios para la toma de decisiones a partir del comportamiento de compra de los clientes. Algunos de los algoritmos son Apriori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR. Estos algoritmos han sido diseñados para analizar bases de datos estructuradas; en la actualidad, diversas aplicaciones requieren el procesamiento de datos no estructurados, como es el caso de los objetos de tipo texto. La investigación planteada tiene como propósito generar un método que permita establecer la relación existente entre los elementos que componen un objeto de tipo texto, para la adquisición de información relevante a partir del análisis de fuentes masivas de datos del mismo tipo

Impact of sectors and political influence on financial distress across Pakistani public listed firms

Identifying financial distress provides information on ways to control and direct firms in achieving their goals. The common approach is to study the relationship between set of explanatory variables and financial distress. However, in order to improve the firms‘ financial structure, there is a need to understand the impact of sectors and different political conditions that affect financial distress. This study investigated the industry effects on financial distress as it is identified that financial distress might differ for firms due to the unique nature of each industry. The study dealt with projected key ideas to evaluate and compare diverse financial distress models to show the robustness of Pakistani listed firms across industries, and study how good financial distress can be predicted. Finally, to alleviate the severe consequences of political instability, the current study underlined the differences in financial distress determinants during different political regimes (Dictatorship and Democratic). The study analysed 153 non-financial firms listed on Karachi Stock Exchange (KSE) during a ten-year period (2004-2013) featuring two political periods; 2004 to 2008 as dictatorship period and 2009 to 2013 as democratic period. Four models were employed, namely logit analysis, decision tree, neural network and paired t-test. A diversity of models was employed to check the strength and prediction correctness of the models and t-test was employed to compare two different political regimes. From the findings, the indirect impact is clearly noticeable due to changes in the signs and magnitude of determinants across sectors. Logit analysis shows better results as compared to the other models as it was based on different industry-level variables and two different political regimes. The mechanism among the variables and financial distress is dependent on different political conditions of the country. The result shows that the impact of different political conditions varies across sectors. In addition, the results of this study are valuable for financial institutions to forecast financial distress and estimate minimum capital requirements to reduce the cost of risk management