scholarly journals A MARKOV CHAIN MODEL FOR ISLAMIC MICRO-FINANCING

2019 ◽  
Vol 5 (4) ◽  
pp. 763-784
Author(s):  
Djaffar Lessy ◽  
Fouad Khoudjeti ◽  
Marc Diener ◽  
Francine Diener
Keyword(s):  
Markov Chain ◽  
Asymmetric Information ◽  
Moral Hazard ◽  
Adverse Selection ◽  
Markov Chain Model ◽  
The Other ◽  
Chain Model ◽  
Business Sector ◽  
Islamic Banks ◽  
Strategic Default

            This paper introduces a Markov chain model for Islamic micro-financing, especially mudarabah  and murababah contract. Mudarabah and murabahah  are two Islamic micro-financing contracts that have enormous potential in creating a balance between the monetary and sharia sector because these two products are moving to manage the business sector which undoubtedly adds value to the economic movement directly.  On the other hand, these two contracts have the potential to cause problems in their implementation. The most common problem of the two contracts is asymmetric information, which consists of adverse selection and moral hazard. We propose the Markov chain model as a solution for the Islamic banks to reduce the risk because of adverse selection and moral hazard in mudarabah  and murabahah  contract. In our model, we also propose a mechanism to avoid strategic default in mudarabah contract. We observed two different probabilities of an applicant to become a beneficiary to find the solution to the problems. The results of this study, the bank can decrease the probability of an applicant to become a beneficiary to reduce the adverse selection and moral hazard in mudarabah  and murabahah contract.

scholarly journals Overdominant Alleles in a Population of Variable Size

Genetics ◽  
1999 ◽  
Vol 152 (2) ◽  
pp. 775-781 ◽  
Author(s):  
Montgomery Slatkin ◽  
Christina A Muirhead
Keyword(s):  
Markov Chain ◽  
Approximate Method ◽  
Population Size ◽  
Transition Matrix ◽  
Markov Chain Model ◽  
Population Bottleneck ◽  
The Other ◽  
Chain Model ◽  
Variable Size ◽  
Constant Size

Abstract An approximate method is developed to predict the number of strongly overdominant alleles in a population of which the size varies with time. The approximation relies on the strong-selection weak-mutation (SSWM) method introduced by J. H. Gillespie and leads to a Markov chain model that describes the number of common alleles in the population. The parameters of the transition matrix of the Markov chain depend in a simple way on the population size. For a population of constant size, the Markov chain leads to results that are nearly the same as those of N. Takahata. The Markov chain allows the prediction of the numbers of common alleles during and after a population bottleneck and the numbers of alleles surviving from before a bottleneck. This method is also adapted to modeling the case in which there are two classes of alleles, with one class causing a reduction in fitness relative to the other class. Very slight selection against one class can strongly affect the relative frequencies of the two classes and the relative ages of alleles in each class.

scholarly journals Asymmetric Information and Islamic Financial Contracts

2018 ◽  
Vol 11 (1) ◽  
pp. 96 ◽  
Author(s):  
Abdelhafid Benamraoui ◽  
Yousef Alwardat
Keyword(s):  
Asymmetric Information ◽  
Moral Hazard ◽  
Adverse Selection ◽  
Comparative Advantage ◽  
Research Paper ◽  
Islamic Finance ◽  
Islamic Banks ◽  
Financial Contracts ◽  
Net Profit ◽  
Conventional Banks

This research paper aims to examine the relevance of asymmetric information to the two main financial contracts used by Islamic banks or conventional banks with Islamic windows, mudaraba and musharaka. We use theoretical proofs to explain how asymmetric information affects mudaraba and musharaka contract in terms of bank cost and yield and how to account for the adverse selection and moral hazard costs when calculating bank net profit or loss. We also provide suggestions supported by key modern theories including signalling, comparative advantage and incentives to resolve asymmetric information problems in the Islamic financial contracts. The research paper shows that asymmetric information is relevant to both mudaraba and musharaka contracts and directly affects Islamic banks and conventional banks with Islamic windows cost and yield. The paper also reveals that signalling and incentives are effective tools to deal with asymmetric information in Islamic financial contracts. Finally, the paper shows that Islamic finance providers need to opt for more secure financing, particularly with small borrowers.

AGENCY PROBLEM DALAM PENERAPAN PEMBIAYAAN AKAD MUDHARABAH PADA PERBANKAN SYARIAH

ALQALAM ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 46
Author(s):  
Aswadi Lubis
Keyword(s):  
High Risk ◽  
Asymmetric Information ◽  
Human Resources ◽  
Moral Hazard ◽  
Adverse Selection ◽  
Agency Problem ◽  
Agency Problems ◽  
Islamic Banking ◽  
The Public

The purpose of writing this article is to describe the agency problems that arise in the application of the financing with mudharabah on Islamic banking. In this article the author describes the use of the theory of financing, asymetri information, agency problems inside of financing. The conclusion of this article is that the financing is asymmetric information problems will arise, both adverse selection and moral hazard. The high risk of prospective managers (mudharib) for their moral hazard and lack of readiness of human resources in Islamic banking is among the factors that make the composition of the distribution of funds to the public more in the form of financing. The limitations that can be done to optimize this financing is among other things; owners of capital supervision (monitoring) and the customers themselves place restrictions on its actions (bonding).

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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