An Optimized Newton-Raphson Algorithm for Approximating Internal Rate of Return

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 technique 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 a 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 32.76% faster than the midpoint approach. It also delivered a better average accuracy of 68.53% than the midpoint-based algorithm.

Financial Aid of Higher Education in Malaysia: A Study Loan Case

“…The Newton-Raphson algorithm is practical algorithm because of its quick convergence yet given a certain accuracy level (Nagares et al, 2019;Ypma, 1995). This method is very conventional for its fast rate convergence and improving the convergence quality.…”

“…However, this study aims to focus on solving the interest rate of study loan based on Newton-Raphson method. This method is selected due to its properties that requires fewer iterations to achieve convergence and this method is found much faster, more reliable, and produces accurate results (Nagares et al, 2019). In this study, a mathematical model of ordinary annuity to find interest rate of study loan is shown in Equation ( 1).…”

An Efficient Hybrid Conjugate Gradient Coefficient u nder Inexact Line Search

“…The convergence analysis of these algorithms with different was studied by several academics. (see Zoutendijk [10], Powell [11,12], Z. Wei [13], Zhi-Feng Dai [14], Al-Baali [15], Dai and Yuan [16], Narene et al, [29]).…”