A NON-LINEAR PROGRAMMING MODEL FOR BUDGETING DECISION ON NEW FIRMS

Access to market information and difficulties to obtain external financing are traditional barriers to financial planning exercises that reflect the growth opportunities aimed by new high-tech companies from emerging markets. There is also a lack of studies that propose a robust methodological approach to deal with these issues. This article develops two decision support models based on the nonlinear programming algorithm GRG (Generalized Reduced Gradient). The model was applied in the context of capital budgeting in high-tech small and medium enterprises. The models use input information from three basic financial statements, which are the income statement, the cash flow statement and the balance sheet. This study provided insights into the trade-offs of capital-structure and value in high-tech firms operating in developing countries. The discussions and results obtained in the case study carried in this work can be helpful to the community of entrepreneurs, financial analysts, investors, and policy makers involved in the design of new financing mechanisms for startups.