This Blog

This blog describes -- and gives examples of -- a way to convert the data found in publicly available financial statements, including 10-K and 10-Q reports filed with the SEC, into numerical measures -- or gauges -- of Cash Management, Growth, Profitability, and Value.

We begin with an overview of the basics of financial data reporting. We then briefly describe the Income Statement, Balance Sheet, and Statement of Cash Flows, which are the three principal financial tables publicly traded corporations release quarterly. Our descriptions identify the kinds of performance or value metrics that can be gleaned from each statement. PepsiCo's 10-Q report for the quarter ending 9 September 2006 is used to illustrate these descriptions.

GCFR also discusses valuation measures that relate financial data to stock price.

We offer a unique approach for rolling up the detailed products of a financial analysis into easy-to-understand quantitative scores that give insight into a company's financial health and valuation. It is thought that this methodology, with some tailoring, is applicable to big and small companies in most, if not all, industries.

Although financial statements include some figures shaped by subjective estimates, and they can (and have been) manipulated by the unscrupulous, we still consider these tables of data to be the best source of quantitative information available free to individual investors. The numbers in these tables and supporting notes can, with some effort, be used to evaluate many aspects of the reporting organization.

In some cases, early warning signs of changing business conditions can be detected. Analysts can use the data provided by a given company to make an independent estimate of the organization's value and to form an opinion of whether the price of its shares might be too high or too low.

This blog is intended to educate and facilitate discussions about alternative methods for analyzing financial data. It does not provide investment advice. The author is intrigued by the challenge of mining valuable information from the deluge of publicly available data. Please see the additional disclosures on the right side of our web site.

Note: This page was originally posted on 22 October 2006. It was revised on 30 June 2008.