Enhanced Newton-Raphson Algorithm in Estimating Internal Rate of Return (IRR)
Nerio S. Pascual1, Ariel M. Sison2, Ruji P. Medina3
1Nerio S. Pascual, Graduate Programs, Technological Institute, Philippines, Quezon City, Philippines.
2Dr. Ariel M. Sison, Graduate Programs, Technological Institute, Philippines, Quezon City, Philippines.
3Dr. Ruji P. Medina, Graduate Programs, Technological Institute, Philippines, Quezon City, Philippines.
Manuscript received on 25 May 2019 | Revised Manuscript received on 03 June 2019 | Manuscript Published on 22 June 2019 | PP: 389-392 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C10810283S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Internal Rate of Return (IRR) is the most compelling performance metric among measures for profitability of investments. The most efficient way to estimate it is by using iterative methods, four of the most popular of which are false position, bisection, secant and Newton-Raphson algorithms. Although Newton-Raphson method is the quickest among them, it does not, however, converge to the root if the user’s guess initial input value is far from the true value of IRR. This study proposes an enhancement that gets rid of such user’s guess input and makes input automatically generated, improves accuracy, lessens the number of iterations and shortens the runtime. The study finds that said enhancement, indeed, does not require user guess input and allows the algorithm to converge to the root with higher accuracy, fewer iterations and shorter runtime.
Keywords: IRR, Root-finding Algorithm, Newton-Raphson Algorithm, Convergence, Divergence.
Scope of the Article: Algorithm Engineering