Implementation of Monte Carlo Simulation to Estimate Investment Risk in Small Projects
DOI:
https://doi.org/10.70577/t90gj719Keywords:
Monte Carlo Simulation, Financial Risk, Net Present Value, Computational Mathematics, SMEs.Abstract
Post-pandemic economic instability has rendered static valuation models for small productive projects obsolete, necessitating stochastic approaches. This research evaluates the financial viability and risk of the Vendistribuidora S.A.S. B.I.C. business project (2019-2024), contrasting a deterministic analysis with a Monte Carlo simulation. Methodologically, the discounted cash flow was algebraically formulated, and goodness-of-fit tests (Kolmogorov-Smirnov) were applied to assign probability distributions to critical variables: demand was modeled with a PERT distribution, and operating costs with a triangular distribution, integrating a Spearman correlation matrix. Computationally, a 5,000-iteration algorithm was executed with a convergence criterion of 1% and a predictive MAPE of 5.2%. While the deterministic model projected an ideal Net Present Value (NPV) of 1,955,575.81 and an Internal Rate of Return (IRR) of 32%, the simulation adjusted the expected average NPV to 1,798,797.12 (with a standard deviation of 846,668.26) and projected an average IRR of 38.48%. The risk analysis revealed a probability of loss of only 3.8% and an extreme scenario of -220,036.64, quantifying the maximum exposure using Value at Risk (VaR) and Conditional Value at Risk (CVaR) at a 95% confidence level. It is concluded that the project has a favorable positive asymmetry (skewness coefficient = 0.41), but linear planning should be replaced by CVaR to size liquidity reserves, which validates computational mathematics as a pillar of financial resilience for SMEs.
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