Time-series analysis for detecting structure changes and suspicious accounting activities in public software companies

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Abstract

This paper offers a novel methodology using several new ratios and comparison approaches to investigate public software companies' financial activities and condition. The methodology focuses on time-series data mining, monitoring and analyzing. The dataset is based on 100 U.S. software companies with least ten-year SEC verified income statement, balance sheets and cash flow statement. The contribution of this paper is creating and applying several new financial ratios combined with traditional approach to detect companies' financial structure changes and accounts manipulation. For cash flow statement operating section, our proposed major account to operating net cash inflow and outflow ratios provide a better visualization of the cash sources and usage, which help analysts to observe major cash flow structure changes and make predication. For investing section, our proposed investing cash flow growth contribution ratio is used to identify irregular investment behavior. Combining with the traditional financial ratio tests, we believe that our approach significantly facilitates early detection on suspicious financial activities and the evaluation of its financial status. © 2013 The Authors. Published by Elsevier B.V.

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APA

Zhang, Z., & Trafalis, T. B. (2013). Time-series analysis for detecting structure changes and suspicious accounting activities in public software companies. In Procedia Computer Science (Vol. 20, pp. 466–471). Elsevier B.V. https://doi.org/10.1016/j.procs.2013.09.304

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