There is a vast amount of financial information on companies' financial performance available to investors in electronic form today. While automatic analysis of financial figures is common, it has been difficult to extract meaning from the textual parts of financial reports automatically. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data and text mining methods for analysing quantitative and qualitative data from financial reports, in order to see if the textual part of the report contains some indications about future financial performance. The quantitative analysis has been performed using self-organizing 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. Copyright

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