The growing need for more relevant detail in financial statements proper to be produced annually, quarterly or monthly, and possibly continuously, translates into an urgent need for more advanced methods and tools for trend analysis. This paper takes a broader view at balance sheet analysis. We observe balance sheet items at the highest level of aggregation and compare them with the next level of detail. This exposes a multidimensional structure produced by all balance sheet items and their time points. This innovative approach to balance sheet analysis provides a new method to determine the relevance and materiality of accounting information. Instead of computing accounting ratios separately, we apply multivariate analysis as to explore the “data space” of the balance sheet of our example company: 3M. We study ten‐years of quarterly balance sheets and discuss some trends by comparing scatter plots with spectral map analysis – spectramap for short – and color coding to expose latent variables hidden in this data. We substantiate that we can explain the larger part of variance present in balance sheets in a more meaningful manner. This paper also seeks to corroborate the generality assumption that underlies the structure of the balance sheet. We strive to increase the usability of balance sheet data and underpin its explanatory power.
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