Search results

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Purpose – This study aims to determine the results of productivity index, profitability and improvement of company prices and to understand the relationship between partial input factors and productivity, profitability, and price fixing.

Design/Methodology/Approach – In this work, the productivity at the palm oil factory PT Sayaukath Sejahtera was measured and evaluated by using The American Productivity Center (APC) model approach.

Findings/Results – The results showed that each index that has been analyzed has a 5.143% decrease in the productivity index per year with a profitability equal to 0.286% per year and an increase in the price improvement index of 5.143% per year. Thus, it is concluded that from each index that has been analyzed, there is a decrease in the productivity index and profitability per year and there is an annual increase in the price improvement index.

Research Limitations/Implications (if applicable) –

Practical Implications (if applicable) –

Originality/Value –

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

The objective of this paper is twofold. First, to study the safe-haven characteristic of the Islamic stock indexes and Ṣukūk during the crises time. Second, to evaluate this property in the last pandemic. This study employs the daily dataset from June 15, 2015, to June 15, 2020, for the most affected countries by the earlier disease.

This study uses the Markov-switching Capital Asset Pricing Model (CAPM) approach and the basic CAPM for the main analysis and the safe haven index (SHI) recently developed by Baur and Dimpfl (2021) for the robustness test.

Based on Baur and Lucey's (2010) definition, empirical findings indicate that Islamic stock indexes cannot be a refuge throughout the crisis regime for all selected conventional markets. However, Ṣukūk are a strong refuge in Brazilian, Russian and Malaysian markets. For the remainder countries, except Italy, the USA and Spain, the Ṣukūk index offers weak protection against serious conventional market downturns. Similar conclusions are obtained during the COVID-19 global crisis period. Finally, results are confirmed by using the SHI.

To the best of the authors’ knowledge, this paper is the first study that evaluates the safe haven effectiveness of the Islamic index and Ṣukūk using the SHI in the most impacted countries by the COVID-19 outbreak.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The outbreak and the spreading of the COVID-19 pandemic have impacted the global financial sector, including the alternative clean and renewable energy sector. This paper aims to assess the impact of the pandemic, COVID-19 on the stock market indices of the clean energy sector using quantile regression methods.

This study utilized daily data sets on the four major categories of stocks: (1) Morgan Stanley Capital International Global Alternative Energy Index, (2) WilderHill Clean Energy Index, (3) Renewable Energy Industrial Index (RENIXX) and (4) the S&P 500 Global Clean Index. The study adopts a multifactor capital asset pricing model.

Clean and alternative energy stocks are powerful instruments for diversification. However, the impact of the volatility index induced by infectious disease is negative and significant across quantiles.

For investors and policymakers, considering how the uncertainty caused by COVID-19 and the geopolitical index influences renewable energy markets is of great practical importance. For investors, it throws insights into portfolio diversification. For policy makers, it helps to devise strategies to reboot the economy along the lines of the deployment of renewables. This study sheds light on a global green-energy transition and has practical implications for renewable energy resilience in post-pandemic times.

This paper can be considered as a pioneer that explores the nexus between oil prices, interest rates, volatility index, and geopolitical risk upon the stock indices of clean and alternative sources of (renewable) energy in the COVID-19 pandemic situation. The results have important insights into the area of energy and policy decision-making. Additionally, the paper's novelty lies in using the explanatory variables associated with the Covid 19 pandemic.

The purpose of this paper is to focus on measuring financial inclusion (FI) level for the developing countries.

By using a two-stage principal component analysis method, we construct a composite FI index to measure the degree of FI. Data are collected through secondary sources including World Bank and IMF reports for the period 2012–2018.

We have built an overall FI index which is considered as a comprehensive measure of FI, a useful tool for policymaking and policy evaluation. Comparison with other studies shows that our FI index corroborates with them.

Building a good FI measurement method is important for developing countries. It helps to assess and compare the level of FI of each country and between countries together, made easily and accurately.

This study emphasizes the important role of FI in the economy. From there, an FI solution is integrated into the construction and calculation of its impact on other factors. This will help policymakers to take effective measures to increase FI levels to achieve sustainable economic growth.

The study examines not only the methods for eliminating stale or abnormal prices but also strategies for enhancing liquidity in the KOSPI 200 index options market, for compensating the defects of V-KOSPI 200.

First, introducing market making scheme in the KOSPI 200 options market can be the direct solution to prevent temporary fluctuations and spikes of the index arising from abnormal orders and to alleviate unnatural low variability (level) of the index through decreasing the use of stale market prices (model prices).

Second, if weekly options underlying KOSPI 200 index are available for trading and investor interest in the weeklys are surged, Korea Exchange can enhance V-KOSPI 200 to include series of KOSPI 200 weekly options. The inclusion for at least 5~6 weekly options available for trading allow V-KOSPI 200 to be calculated with KOSPI 200 index option series that most precisely match the 30-day target time-frame for expected volatility that the Index is intended to represent.

Along with these strategies for enhancing liquidity in the KOSPI 200 index options market, the study suggests the methodology which can prevents temporary fluctuations and spikes of the index by substituting stale or abnormal prices for normal prices.

The purpose of this paper is to illustrate a profitable and original index options trading strategy.

The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented.

The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading.

The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.