Isoquantal Capital Modulation: A Harmonic Modeling Approach to Understanding and Managing the Investment Decision

My doctoral dissertation where I argue that the purpose of business is to employ capital factors in such a manner as to generate value for its customers and profits for its owners. To achieve these ends, management must make at least three distinct decisions -- the operating, investment, and financing decisions. The purpose of this study is to formulate a modeling methodology that harmonically analyzes and explains how the investment decision and capital elasticity influence competitive advantage. The research explores the descriptive literature for the current states of computational modeling, accounting theory and practice, managerial finance, macroeconomics, capital theory, and harmonic analysis in order to provide evidence supporting the content validity of a proposed modeling framework, which encodes, modulates, and transforms raw financial data into waveforms suitable for harmonic analysis. The framework is operationalized algebraically, translated into a high-level computational language, and subsequently tested using simulation methods in order to analyze the computational robustness of the implementation. Finally, empirical testing shows a significant correlation exists between the model's reported results and the profitability of sole proprietorships in the U.S. providing initial evidence of the framework's construct validity. Additional empirical testing shows that the relationship between the model's reported results and net profitability is stronger than results returned from the use of raw capital magnitudes providing evidence of the model's positive capacity for recommending decisions. The study uses extant financial data obtained from the Internal Revenue Service (IRS), which maintains and releases Federal tax information extracted from its archives into the public domain through its Statistics of Income (SOI) programs.

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