This paper aims to use coordinated financial statements' system properties that include exogenous and endogenous variables to answer important questions. These questions include the following: What is the financial condition of the firm? What if there is a change in the firm's exogenous variable(s) – how will the financial condition of the firm change? And, how much of a change in the firm's exogenous variable(s) is required for the firm to reach its financial goal(s)?
This paper uses coordinated financial statements to construct solvency, profitability, efficiency, liquidity and leverage (SPELL) ratios to answer the question: what is the financial condition of the firm? It answers what-if questions by changing an exogenous variable(s) and recalculating SPELL ratios. It answers how-much questions by using Excel's Goal Seek algorithm to find the required change in an exogenous variable to reach a firm's goal.
The authors find that coordinated financial statements' system properties can be used to answer important what-is, what-if and how-much questions about the firm.
The usefulness of coordinated financial statements' system properties to answer what-is, what-if and how-much questions about the firm depends – mostly on the accuracy of exogenous data used to represent the firm's external financial environment. Furthermore, the usefulness of what-if and how-much analysis depends on how appropriate the changes are in exogenous variables used to represent alternative scenarios.
Using coordinated financial statements' system properties to answer what-is, what-if and how-much questions provides the firm's financial manager the tools to not only asses the firm's current financial condition but also to assess its ability to respond to opportunities and threats posed by future scenarios.
The ability to assess the financial condition of a firm and to assess its strengths and weaknesses in key to making sound financial decisions. In addition, the consistency imposed on coordinated financial statements makes it an effective tool for discovering errors in its data.
The authors know of no similar work.
