Investment valuation and the performance of companies with modified internal rate of return: A simulation markov chain

Abstract

An investment is considered worthwhile if it generates value for its owners. Thus, it important for an investor to have a deeper look at their equity performance in the stock market to improve their investment decisions. Therefore, this study attempts to determine the required number of companies to evaluate the performance of a particular sector in the long-run using Markov chain simulations. Accordingly, two different performance metrics are applied to determine the sample size needed to perform the analysis. This study also aimed to assess the long-run performance of the Malaysian Industrial Product and Services sector (MIPS) based on the modified internal rate of return (MIRR) utilizing stock market data of 147 publicly listed companies in the MIPS sector over the period 2007 – 2018. The study applies a two-state Markov chain model, which are either Good or Bad states to estimate the transition probability matrix of the MIRR. The findings from this study indicate that one should have at least 37 companies to assess the performance of a particular sector in the long-run. Furthermore, the findings reveal that the MIPS sector has a good performance in the long-run over the study period with a probability of 50.6%. Finally, the findings from this study are useful for potential investors and the company’s board to improve their future investment decisions.