Download This PaperAn Optimized Newton-Raphson Algorithm for Approximating Internal Rate of Return
International Journal of Advanced Trends in Computer Science and Engineering Volume 8, No.6, 2019
4 Pages Posted: 8 Jun 2020
Date Written: December 2019
Abstract
The internal rate of return (IRR) is the most widely-used method in measuring the rate of return on investment (RROI), which helps investors decide whether an investment is viable or not. Iterative root-finding algorithms are the most efficient techniques in calculating IRR, amongst which, the Newton-Raphson algorithm is the most popular and the fastest algorithm. However, when the primary unknown, which is provided by the user, is far from the actual root, the result of the algorithm, oftentimes, does not converge to the root. This problem is addressed by the midpoint-based Newton-Raphson algorithm. Nevertheless, said algorithm could further be improved in terms of proximity, speed, and accuracy. This study presents a novelty in estimating IRR using the centroid-based Newton-Raphson algorithm. The experimental results show that the presented algorithm is 30.77% faster than the midpoint approach. It also delivered an average error reduction of 90.96% than the midpoint-based algorithm in calculating the initial IRR.
Keywords: root-finding algorithm, Newton-Raphson algorithm, IRR, convergence
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