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This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors.

The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data.

Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models.

The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it.

The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage.

The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.

The purpose of this paper is to propose models of duration for maturity gap risk management in Islamic banks.

A thorough review of literature on duration modeling, duration measurement in Islamic banks and Shariah compliance has been conducted to set parameters to develop Shariah-compliant maturity gap risk management mechanism.

Models based on durations of earning assets and return bearing liabilities using various rates of return earned and paid, benchmark rates and industry standards commonly used by Islamic and conventional banks.

Increased Shariah compliance has threefold impact. Firstly, it will increase trust of customers. Secondly, it will help improve profitability by reducing non-Shariah compliance penalties from the regulators. And finally, it will enhance market capitalization and returns stability to investors because of enhanced customer base, increased level of trust and increased profitability.

This research proposes Shariah-compliant maturity gap risk management models based on the concept of duration according to recommendations of Bank for International Settlements. As there is no such maturity gap risk management mechanism that meets the requirements of Shariah using benchmarks that are common between Islamic and conventional banks; therefore, this research presents risk management solutions that can be applied simultaneously in the entire banking sector.

Construction duration is referred to as one of the most crucial success elements for a construction project along with quality and cost. Modelling construction duration supports decision making at the early stages of a project. Exploring the complex structure of construction duration is necessary; hence this forms a basis to develop a predictive model. Therefore the main aim of this study is to determine the subset of variables for a descriptive model that explains the most substantial part of the variation in construction duration.

Secondary data set which includes 1,695 observations with 30 quantitative and four qualitative variables was utilized. Multiple linear regression was employed to define the relationship between explanatory variables and construction duration. General procedure for variable selection was presented in detail for data sets that suffer multicollinearity and singularity.

Analysis indicated that gross external floor area and cost of construction works are the major variables for describing the construction duration. In addition, categorisation with respect to type of facility, project location, availability of construction area, and market conditions was justified to be statistically significant.

The study was limited to the population: German building industry. Besides, the affect of various qualitative variables, identified in the literature, could not be assessed due to lack of data availability.

To the authors' knowledge, the study is the first to assess construction duration for German building projects. Moreover, it provides a method for describing variable selection routine transparently.

Determining the duration for road construction projects represents a problem for construction professionals in Ghana. The purpose of this paper is to develop an artificial neural network (ANN) model for determining the duration for rural bituminous surfaced road projects.

Data for 22 completed bituminous surfaced road projects from the Department of Feeder Roads (rural road agency) were collected and analyzed using the principal component analysis (PCA) and ANN techniques. The data collected were final payment certificates which contained payment bill of quantities (BOQ) of work items executed for the selected completed road projects. The executed quantities in the BOQ were the total quantities of work items for site clearance, earthworks, in-situ concrete, reinforcement, formwork, gravel sub-base/base, bitumen, road line markings and furniture, length of road and actual durations for each of the completed projects. The PCA was first employed to reduce the data in order to identify a smaller number of variables (or significant quantities) that constitute 81.58 percent of the total variance of the collected data. The ANN was then used to develop the network using the identified significant quantities as input variables and the actual durations as output variables.

The coefficient of correlation (R) and determination (R²) as well as the mean absolute percentage error (MAPE) obtained show that construction professionals can use the developed ANN model for determining duration. The study shows that the best neural network is the multi-layer perceptron with a structure 3-38-1 based on a back propagation feed forward algorithm. The developed network produces good results with an MAPE of 17.56 percent or an average accuracy of 82.44 percent.

Apart from the fact that the sample size was small, the developed model does not incorporate the implications of other likely factors that may affect contract duration.

The outcome of this study is to help construction professionals to fix realistic contract duration for road construction projects before signing a contract. Such realistic contract duration would help reduce time overruns as well as the payment of liquidated and ascertained damages by contractors for late completion.

This paper proposes an alternative way of determining the duration for road construction projects using the total quantities of work items in a final payment BOQ. The approach is based on the PCA and ANN model of quantities of work items of completed road projects.

The purpose of this research is to propose a framework for research on Macaulay duration and establish future research directions.

Thematic, bibliometric and content analyses have been used to review 168 research papers published between 1938 and 2019 taken from ISI Web of Science and Scopus contributed by leading authors, journals and regulatory bodies.

Identification and integration of themes of duration theory, duration model development and duration model implementation leading to unattended research gaps, and framework for research on Macaulay duration.

The study is based on an extensive review of the literature to extract important themes, research gaps and frameworks. It does not empirically investigate significance of Macaulay duration and various sectors.

This research has several aspects that are helpful for practitioners. Macaulay duration has been the subject of empirical research only without any guiding framework. This research provides a platform to initiate profound researches in various areas of finance. Various proposed models are required to be tested under holistic approach in conventional and emerging fields, especially in Islamic settings.

This research highlights, research themes leading to framework, research gaps and factors that are crucial in developing, extending and testing duration models leading to enhancement of theoretical base of Macaulay duration.

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

The purpose of this study is to develop, test and examine econometric methodology for Sharīʿah-compliant duration models of Islamic banks.

The research evaluates all existing duration models from Sharīʿah’s perspective and develops a four-stage framework for testing Sharīʿah-compliant duration models. The econometric methodology consists of multiple regression, Johansen co-integration, error correction model, vector error correction model (VECM) and threshold vector error models (TVECM).

Regressions analysis suggests that returns on earning assets and interbank offered rates are significant factors for calculating the duration of earning assets, whereas returns paid on return bearing liabilities and average interbank rates of deposits are significant factors for duration of return bearing liabilities. VECM suggests that short run duration converges into long run duration and TVECM suggests that management of assets and liabilities also plays a significant role that can bring about a change of about 15% in respective durations.

Sharīʿah-compliant duration models will improve risk and Sharīʿah efficiency, which will ultimately improve market capitalization and returns stability of Islamic banks in the long run.

Sharīʿah-compliant duration models testing provides insight into how various factors, namely, rates of return, benchmark rates and managerial skills of Islamic bank risk managers impact durations of assets and liabilities. It also explains the future course of action for Sharīʿah-compliant duration model testing.

The first paper of this two‐paper series has developed an understanding of neurofuzzy concept modelling techniques. This second paper demonstrates the power and versatility of neurofuzzy methods when applied to the determination of construction project duration. The paper explains data selection and preprocessing, the modelling process and optimization of developed models. The paper also presents and discusses the results generated by the developed model.

The purpose of this paper is to explain what information is contained in mutual funds' trading behaviors and to try to further assess the impact on the stock market.

The objective is achieved by an empirical examination using the high‐frequency intraday data. The main methods used for the research are the autoregressive conditional duration model and the UHF‐GARCH model.

This paper gives an empirical study of mutual funds' behavior on two aspects. The first aspect is the direct impact on micro variables. The results show that mutual funds changing their positions will have different influences to the spread, adding position broadens the spread, while decreasing position makes the spread narrow; behaviors of funds change the clustering characteristic of the duration. The second aspect is the impact on the relationships among micro variables. The results indicate that trading started by liquidity buyers will make volatility larger.

This paper supposes funds as informed traders and individual investors as liquidity traders in China's stock market. If it is not true, some interpretations of empirical results would be wrong. The authors' results may help researchers to understand the information content of funds' trading behaviors in the microstructure aspect.

The paper is an original work, which will be interesting to scholars in market microstructure and to practitioners in the Chinese stock market. The main contributions of the paper are: the use of high‐frequency data to study funds' behaviors and combine the trading duration and investors' trading behavior to analyze the information content of trading behaviors; second, the use of 14 stock samples in the Shanghai Stock Exchange to do the empirical study, which ensures the reliability of the results.