In capital budgeting practices, investment project evaluations based on the net present value (NPV) and the internal rate of return (IRR) represent the traditional evaluation techniques. Compared with the traditional methods, the modified internal rate of return (MIRR) gives the opportunity to evaluate an investment in certain projet, while taking the changes in cash flows over time and issuing shares such as dividing shares, bonuses, and dividend for each end of the investment year into account. Therefore, this study aims to evaluate an investment in the Malaysian construction sector utilizing financial data for 39 public listed companies operating in the Malaysian construction sector over the period from Jan 1, 2007, to December 30, 2018, based on the MIRR method. Stochastic was studied in this study to estimate the estimated probability by applying the Markov chain model to the MIRR method where the transition matrix has two possible movements of either Good (G) or Bad (B). it is found that the long-run probability of getting a good investment is higher than the probability of getting a bad investment in the long-run, where were the probabilities of good and bad are 0.5119, 0.4881, respectively. Hence, investment in the Malaysian construction sector is recommended.

The fluctuations in stock prices produce a high risk that makes investors uncertain about their investment decisions. The present paper provides a methodology to forecast the long-term behavior of five randomly selected equities operating in the Malaysian construction sector. The method used in this study involves Markov chains as a stochastic analysis, assuming that the price changes have the proparty of Markov dependency with their transition probabilities. We identified a three-state Markov model (i.e., increase, stable, fall) and a two-state Markov model (i.e., increase and fall). The findings suggested that the chains had limiting distributions. The mean return time was computed for respective equities as well as to determine the average duration to return to a stock price increase. The analysis might aid investors in improving their investment knowledge, and they will be able to make better decisions when an equity portfolio possesses higher transition probabilities, higher limiting distribution, and lowest mean return time in response to a price increase. Finally, our investigations suggest that investors are more likely to invest in the GKent based on the three-state model, while VIZIONE seems to be a better investment choice based on a two-state model.

This paper developed long-term investment of stock cash flow activities comprising of how yearly investment contribution turns to share units and vice versa, how the series of dividends pay out are declared and finally, how the growth of share units are generated over the years of investing period. These investment model activities form cash inflows and outflows and hence, in return, the performance of this investment model can be evaluated. In addition, this model also constrained that, the yearly dividends obtained were being reinvested together with the annual contributions in accumulating shares. Besides presenting the computation of purchasing and selling of share units and the amount of dividend obtained, this paper contributed in the computation of the growth of the shares in a year, based on share issuance such as share split or consolidation, as well as bonus share rewards. Based on these activities, the net present value (NPV) was derived and the modified internal rate of return (MIRR) was determined by setting up zero-valued of NPV. We illustrated the computation of MIRR by looking at the investment activity towards Prolexus Berhad from the year 2011 to 2015. The increasing of company share prices through years, the encouraging series of dividend rates and generous of the company in issuing shares to the shareholders, were also clearly figured that determined attractive MIRR.

This paper deals with the problem of determining the sufficient sample size needed to estimate the transition matrix in the Markov chain. In particular, this paper focuses on systems with insufficient data or a short frequency of time caused by the difficulty of acquiring data. This study developed a Markov chain simulation technique that achieves a sufficient sample and can be used to estimate the size of the transition probability, despite having a short frequency of time. It also shows how this technique can be used in the short-, medium-, and long-term, and how a sufficient sample size can be found in these three situations. More specifically, this study illustrates the proposed simulation Markov chain model that estimates the transition probability matrix of the return of assets (ROA) in the industrial sector in Malaysia between 2007 and 2018. In this study, we present a method of determining an adequate sample size using a Markov chain simulation model. This model uses data from a number of companies in the industrial sector in Malaysia in order to study the performance of ROA and assist investors in making investment decisions. However, each company only has yearly ROA values. In other words, the frequency of the values is low, which makes studying the performance of ROA in the industrial sector more difficult. This could be the case because companies don't publish financial yearly reports, or because they are emerging companies that don't have adequate financial reports to calculate their ROA. This study was able to compensate for the lack of data through the number of companies used. Contribution/Originality: This study is one of very few studies that has investigated how to determine an adequate sample size using a Markov chain simulation model. It presents a selection of companies, in order to study the performance of ROA in Malaysia's industrial sector, and to assist investors in their decision making.

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