Improving financial data quality using ontologies

a r t i c l e i n f o The performance of financial decision-making directly concerns both businesses and individuals. Data quality is a key factor for decision performance. As the availability of online financial data increases, it also heightens the problem of data quality. In this paper, a taxonomy is created for data quality problems. More importantly, an ontology-based framework is proposed to improve the quality of online financial data. An empirical evaluation of the framework with the financial data of real-world firms provides preliminary evidence for the effectiveness of the framework. The framework is expected to support decision-making in finance and in other domains where data is spread across multiple sources with overlap but complementary in content. Today's widespread financial problems and the economic downturn highlight the importance of financial decision-making to individuals , businesses, and organizations. Intelligence gathering is the first stage of decision making [57], and data quality is a key factor for decision performance [29]. It is reported [63] that 20% of asset managers , investment bankers and hedge fund professionals spend between 25% and 50% of their time in validating data, which prevents them from focusing on tasks that contribute to the bottom line. According to a recent study of the costs and other consequences of dirty or inconsistent data in the secondary mortgage market in the U.S. [22], inaccurate data results in slow and expensive loan processing , weak underwriting, incorrect portfolio management, and other costs to lenders and mortgage investors. Given that financial data including financial statements, market data, and business news are being used increasingly by investors in stock market predictions [16,53], data quality has become an important and widespread issue in financial decision-making. The problems with financial data come in a variety of forms. The main problems of financial data include ambiguity, inconsistency, missing values, inaccuracy, misrepresentation, incompletion, and so on [40]. For instance, missing values are not uncommon in Standard & Poor's Compustat North America dataset. Such problems can directly impact the performance of financial decision-making. Under this backdrop, this study aims to answer the following research question: How should one address the quality problems of financial data so as to improve the performance of financial decision-making? Both qualitative and quantitative approaches have been proposed to address various types of data quality problems [42]. For example, missing values can be replaced with global means or the most …