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This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The purpose of this paper is to examine the banking industry’s efficiency using the case of Yemen.

The paper utilises two-stage analysis to evaluate the efficiency adopting Data Envelopment Window Analysis (DEWA) in the first stage for the period 1996-2011. Furthermore, the paper addresses, in two-dimensional matrix, the stability and efficiency of the banking sector in order to assess their ability for survival. In the second stage, panel data analysis is applied to regress a set of bank-specific and macro-economic variables on the efficiency of the banking sector in Yemen in a comparative fashion between Islamic and conventional banks.

The findings of the investigation indicate that the Yemeni banking industry in general was on a declining efficiency’s trend with increased instability during the later period of the investigation. In addition, the study shows that most conventional banks were relatively stable, though inefficient, while Islamic banks were more efficient over the time. The results of panel data regression further suggest that efficiency is related to a number of determinants. Loan/financing, and profitability are the common key determinants of efficiency for both Islamic and conventional banks. However, other determinants have impacted differently for Islamic and conventional banks, which could reflect the uniqueness of their operation and structure.

The present study provides a basis for the regulators and bankers to assess the viability of the banking sector and proposes policies to restructure the industry in order to enhance the performance of the whole industry.

The paper presents new empirical findings on the efficiency of Islamic and conventional banks in Yemen.

The aim of the paper is to develop an algorithm based on fuzzy logic (FL) systems for reconstructing cracks shapes, which will be faster and simpler to learn than neural networks, especially in case of large training set.

The inverse model in defectoscopy can be considered as reverse model of the whole measurement process (crack‐sensor‐output). The most important disadvantage of the inverse neural models with dynamic networks is that the performance of training is disappointing due to large training set and many inputs of such networks. The paper proposes the FL as the substitute of neural network. The typical ANFIS networks are sufficient only for simulating systems with small number of inputs. For this reason the paper developed the learning algorithm that produces relatively small number of rules and it can be used in case of systems with hundred of inputs and thousands of training pairs.

This paper provides details about algorithm for reconstructing cracks profiles that produces relatively small number of rules of the fuzzy system. The basis rule of inverse model with moving window for one‐ and multi‐frequency method is described. The results of profile identification in 2D and 3D space for real and simulated data are presented as well.

Generally, the algorithm proposed in this paper can be widely used for simulating multi‐input systems, which are described by a large training set.

The purpose of this paper is to prove the effectiveness of minimum semi‐absolute deviations (MSAD) method in dynamic portfolio investment.

In financial investment, the classical static portfolio theory of Markowitz type lacks the dynamic adaptability to the changing market situations. This paper proposes a dynamic portfolio theory which uses MSAD criterion on a moving window to replace the Markowitz mean‐variance analysis.

Two specific models are developed to test the validity of the MSAD method: the first model constructs a portfolio consisting of Shanghai‐Shenzhen 300 Index and a national debt as two contrarian assets; the second model constructs a portfolio consisting of a complete set of 18 Chinese stock sector indices and a national debt. The empirical results of the test using six‐year monthly data (2005 to 2010) provide significant evidence that the MSAD method is valid, producing superior returns of investment over the stock index during the test period.

The findings in this study clearly highlight the validity of the MSAD method in determining the weights of assets in Chinese stock markets.

In order to resolve the problem of portfolio investment in Chinese stock markets, the MSAD method with stop loss control strategy can be used for investors to obtain the weights of assets and control the risk.

This study analyzes and verifies the effectiveness of the MSAD method in dynamic portfolio investment. The stop loss control strategy designed and used in the MSAD method is a pioneering and exploratory experiment.

Over the last decade or so there has been an increased interest in combining the forecasts from different models. Pooling the forecast outcomes from different models has been shown to improve out‐of‐sample forecast test statistics beyond any of the individual component techniques. The discussion and practice of forecast combination has revolved around the pooling of results from individual forecasting methodologies. A different approach to forecast combination is followed in this paper. A method is used in which negatively correlated forecasts are combined to see if this offers improved out‐of‐sample forecasting performance in property markets. This is compared with the outcome from both the original model and with benchmark naïve forecasts over three 12‐month out‐of‐sample periods. The study will look at securitised property in three international property markets – the USA, the UK and Australia.

This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios.

This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation.

By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others.

This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

A unified application management framework for Linux and Java applications on mobile phones is presented. Although Java‐based applications for mobile phones are in strong demand, the complexity of interaction between these platform independent programs and the core functionality of mobile phones has made software development difficult. The unified framework presented here provides uniform application state management and inter‐application communication between Java based and operating‐system specific applications, allowing native Linux applications to be directly replaced with the equivalent Java application. The framework is described in detail and a trial implementation of the system is evaluated.

The study evaluates the accident-adjusted dynamic efficiency of public bus operators providing bus transportation services in eight major metropolitan cities of India.

The slack-based measure (SBM)–undesirable window analysis approach is used to gauge the dynamic efficiency levels and identify the sources of inefficiency in bus transportation services. This innovative approach integrates the SBM model developed by Tone (2001, 2004) and the window analysis approach of Charnes et al. (1985). The main advantage of this approach is that one can explicitly incorporate the number of accidents in the production technology specification as an undesirable (bad) output and potently handle the issue of the “curse of dimensionality” in a small sample like ours.

The key empirical findings suggest wide variations in average efficiency levels across sample bus operators in metropolitan cities. The Chennai Transport Corporation is observed as the most efficient and consistent bus operator due to its most stable efficiency performance. The results additionally unveil that the role of managerial inefficiency was diminutive, and the scale-related issues were the real cause of sub-optimal or supra-optimal behaviour of sample bus operators in the resource-utilisation process.

There is an urgent requirement for effective policy intercessions to mitigate the sizeable observed inefficiency in the production process and resolve scale-related issues of public bus operators offering transit services in major metropolitan cities of India.

This paper is maybe the first to assess the dynamic efficiency of public bus transit systems in India's major metropolitan cities after treating accidents.

Planer Products has just concluded a distribution agreement with Fuji Electric of Japan covering the company's image processing products.