Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

Sarsour, Wajeeh Mustafa; Sabri, Shamsul Rijal Muhammad

doi:10.33889/ijmems.2020.5.2.024

Cited by 6 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Internal rate of investment return (IRR) is usually used as a tool capable of evaluating the behaviour of cash flows (Sabri & Sarsour, 2019;Sarsour & Sabri, 2020a;Sarsour & Sabri, 2020b). Investigating the behaviour of investment return can be a challenging task due to its non-linearity, non-stationary, and high uncertainty.…”

Section: Incorporating Growth Rate Parameter On Weibull Distributionmentioning

confidence: 99%

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Abubakar¹,

Sabri²

2021

JST

View full text Add to dashboard Cite

The Weibull distribution is one of the most popular statistical models extensively applied to lifetime data analysis such as survival data, reliability data, wind speed, and recently in financial data, due to itsts flexibility to adaptably imitate different families of statistical distributions. This study proposed a modified version of the two-parameter Weibull distribution by incorporating additional parameters in the internal rate of return and insurance claims data. The objective is to examine the behaviour of investment return on the assumption of the proposed model. The proposed and the existing Weibull distribution parameters have been estimated via a simulated annealing algorithm. Experimental simulations have been conducted mimicking the internal rate of return (IRR) data for both short time (small sample) and long-term investment periods (large samples). The performance of the proposed model has been compared with the existing two-parameter Weibull distribution model in terms of their R-square (R2), mean absolute error (MAE), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the Kolmogorov-Smirnov test (KS). The numerical simulation revealed that the proposed model outperformed the existing two-parameter Weibull distribution model in terms of accuracy, robustness, and sensitivity. Therefore, it can be concluded that the proposed model is entirely suitable for the long-term investment period. The study will be extended using the internal rate of return real data set. Furthermore, a comparison of the various Weibull distribution parameter estimators such as metaheuristics or evolutionary algorithms based on the proposed model will be carried out.

show abstract

Section: Incorporating Growth Rate Parameter On Weibull Distributionmentioning

confidence: 99%

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Abubakar¹,

Sabri²

2021

JST

View full text Add to dashboard Cite

show abstract

“…Specifically, ω in Equation 5 is a probability measure of distribution stationarity of the chain that has an ergodic property (Sarsour and Sabri, 2020).…”

Section: { }mentioning

confidence: 99%

Evaluating the Investment in the Malaysian Construction Sector in the Long-run Using the Modified Internal Rate of Return: A Markov Chain Approach

Sarsour¹,

Sabri²

2020

JAFEB

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The majority of Markov models are based on a sufficient data set, either in terms of sample size or frequency of time, as demonstrated by Fama (1965); Zhang and Zhang (2009); Mettle, Quaye, and Laryea (2014); Sarsour and Sabri (2020). However, these models would be difficult to calibrate for data that are characterized by a short frequency of time, which results in an unreliable estimation of the transition probability matrix.…”

Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

Sarsour

Sabri

2020

Asian Economic and Financial Review

View full text Add to dashboard Cite

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.

show abstract

Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

Cited by 6 publications

References 0 publications

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Evaluating the Investment in the Malaysian Construction Sector in the Long-run Using the Modified Internal Rate of Return: A Markov Chain Approach

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

Contact Info

Product

Resources

About