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.

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