Mining textual contents of financial report

The message, stylistic focus, language and readability of financial reports are good indicators of the perspectives and developments of any company. These indicators can guide companies' decision makers to more efficient actions in the dynamic business environment. In this paper, we have studied the language and contents of quarterly financial reports using automated linguistic and text mining methods. We aim at comparing the results from linguistic analysis of quarterly reports by means of collocational networks and the results obtained from text mining analysis of quarterly report by means of the prototype matching. We perform the study on the quarterly reports from three leading companies in the telecommunications sector. Our results are somewhat controversial: some of the reports from the companies have as their closest matches the reports with similar collocational networks and some do not have.El mensaje, el enfoque estilístico, el idioma y facilidad para leer de los reportes financieros son buenos indicadores de las perspectivas y desarrollos de cualquier compañía. Estos indicadores pueden guiar la toma de decisiones de las compañías y dirigirlas hacia decisiones eficientes en el entorno dinámico de los negocios. En este artículo, hemos estudiado el lenguaje y contenidos de reportes financieros usando métodos lingüísticos automatizados. Nuestro objetivo es comparar los resultados del análisis lingüístico en función de las redes online de colocación y los resultados en función de los prototipos que encaje. Realizamos el estudio en los informes de tres empresas líderes en el sector de la telecomunicación. Nuestros resultados son de alguna forma controvertidos: algunos de los informes de las compañías tienen mayor coincidencia los informes con redes de colocación más similares, mientras que otros no

Mining textual contents of financial report

The message, stylistic focus, language and readability of financial reports are good indicators of the perspectives and developments of any company. These indicators can guide companies' decision makers to more efficient actions in the dynamic business environment. In this paper, we have studied the language and contents of quarterly financial reports using automated linguistic and text mining methods. We aim at comparing the results from linguistic analysis of quarterly reports by means of collocational networks and the results obtained from text mining analysis of quarterly report by means of the prototype matching. We perform the study on the quarterly reports from three leading companies in the telecommunications sector. Our results are somewhat controversial: some of the reports from the companies have as their closest matches the reports with similar collocational networks and some do not have

Abstract

There is a vast amount of financial information on companies ’ financial performance available to investors today. While automatic analysis of financial figures is common, it has been difficult to automatically extract meaning from the textual part of financial reports. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data mining methods for analyzing quantitative and qualitative data from financial reports, in order to see if the textual part of the report contains some indication about future financial performance. The quantitative analysis has been performed using selforganizing maps, and the qualitative analysis using prototype-matching text clustering. The analysis is performed on the quarterly reports of three leading companies in the telecommunications sector