ABSTRACT
This study analyzes the robustness and efficiency of Internal Rate of Return (IRR) calculations under stochastically fluctuating cash flow conditions. Three numerical methods are compared: Newton–Raphson, Secant, and the Proposed Optimal Numerical Method (PONM). Empirical datasets and lognormal stochastic simulations are used to test algorithmic performance against volatility and random disturbances. Experimental results show that PONM achieves the fastest convergence, with an average of 5.98 iterations, a success ratio of 99.5%, and the smallest deviation . Robustness and sensitivity tests show that PONM has the lowest Coefficient of Variation and Shock Sensitivity, indicating the highest numerical stability. With a fourth-order convergence, PONM proves more efficient and robust to noise than classical methods. These findings confirm the relevance of PONM as an optimal algorithm for IRR calculations in a highly volatile investment environment.
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