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This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study.

Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility.

Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 10¹⁷ architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled.

This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions.

The purpose of this paper is to show that major reversals of an index (specifically BIST-30 index) can be detected uniquely on the date of reversal by checking the extreme outliers in the rate of change series using daily closing prices.

The extreme outliers are determined by checking if either the rate of change series or the volatility of the rate of change series displays more than two standard deviations on the date of reversal. Furthermore; wavelet analysis is also utilized for this purpose by checking the extreme outlier characteristics of the A1 (approximation level 1) and D3 (detail level 3) wavelet components.

Paper investigates ten major reversals of BIST-30 index during a five year period. It conclusively shows that all these major reversals are characterized by extreme outliers mentioned above. The paper also checks if these major reversals are unique in the sense of being observed only on the date of reversal but not before. The empirical results confirm the uniqueness. The paper also demonstrates empirically the fact that extreme outliers are associated only with major reversals but not minor ones.

The results are important for fund managers for whom the timely identification of the initial phase of a major bullish or bearish trend is crucial. Such timely identification of the major reversals is also important for the hedging applications since a major issue in the practical implementation of the stock index futures as a hedging instrument is the correct timing of derivatives positions.

To the best of the author’ knowledge; this is the first study dealing with the issue of major reversal identification. This is evidently so for the BIST-30 index and the use of extreme outliers for this purpose is also a novelty in the sense that neither the use of rate of change extremity nor the use of wavelet decomposition for this purpose was addressed before in the international literature.

This study focused on identifying measurable constructs of a restorative classroom and appropriate metrics to measure those constructs through content validity analysis of a direct observation tool. The tool was designed to assess restorative practices implementation in the classroom in the context of professional development supporting teachers in a fundamental reorientation towards non-punitive discipline.

The authors administered a 30-item survey to a panel of 14 experts in restorative practices implementation in schools asking them to provide quantitative and qualitative feedback on the tool's content, metrics, and utility for building teachers' skill and confidence in promoting a restorative classroom. The authors calculated item-level content validity indices and scale-level content validity indices. To interpret findings, the authors applied acceptability criteria recommended in the literature. The authors used qualitative coding to analyze qualitative responses and contextualize quantitative findings.

Quantitative results indicated that the tool's structure and measures of teacher behavior were acceptable. The student behavior scale did not meet the acceptability criterion. Qualitative feedback indicated that observation and later co-reflection on teachers' use of specific restorative skills was deemed helpful to teacher implementation of restorative practices. Observations of student behaviors, however, needed to be broadened to emphasize student voice and agency and the quality of student interactions.

Novel approaches to measurement are needed to facilitate teacher implementation of restorative practices as schools adopt those practices to promote equitable student agency, engagement and belonging in a pivotal shift from existing punitive discipline systems.

This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries.

Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent.

Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring.

Of particular note is that the paper addresses the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015 through data analytics. This paper considers various open-source, readily available data for inclusion in multiple models of identified Arab Spring nations in addition to implementing a novel imputation methodology useful to conflict prediction studies in the future.

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.

In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.

In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.

The study is based on actual historical data and is purely data driven.

The study demonstrates the utility of the analytical methodology but carries not implementation recommendations.

This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

This study aims to examine what underlies the estimated relation between idiosyncratic volatility and realized return.

Idiosyncratic volatility has a dual effect on stock pricing: it not only affects investors' expected return but also affects the efficiency of stock price in reflecting its value. Therefore, the estimated relation between idiosyncratic volatility and realized return captures its relations with both expected return and the mispricing-related component due to its dual effect on stock pricing. The sign of its relation with the mispricing-related component is indeterminate.

The estimated relation between idiosyncratic volatility and realized return decreases and switches from positive to negative as the estimation sample consists of proportionately more ex ante overvalued observations; it increases and switches from negative to positive as the estimation sample consists of proportionately more ex post overvalued observations. In sum, the relation of idiosyncratic volatility with the mispricing-related component dominates its relation with expected return in its estimated relation with realized return. Moreover, its estimated relation with realized return varies with research design choices and even switches sign due to their effects on its relation with the mispricing-related component.

The novelty of the study is evident in the implication of its findings that one cannot infer the sign of the relation of idiosyncratic volatility with expected return from its estimated relation with realized return.

This study explores the arbitrage profitability of the KOSPI200 futures spread, using intraday data during 10 days prior to the expiration day of each contract for the 9/3/2001 ∼ 6/912005 period. The theoretical frameworks for arbitrage strategies were developed for the analysis. Our results show that 97.36% of the total 8.633 observations were fairly priced. 1.46% (126 observations) were underpriced, and 1.18% (102 observations) were overpriced, in the ex post arbitrage profitability analysis between the futures spread and the calendar spread. Also, in the arbitrage profitability analysis based on the mispricing of the KOSPI200 futures spread against the theoretical price. 90.39% of the total 10.054 observations were fairly priced and 9.61 % (966 observations) were underpriced. There was no overpriced observation. The ajority of those underpriced observations were concentrated in the 3rd Quarter of 2001 and the 1st quarter of 2003. Overall, there were very few arbitrage opportunities except for the introductory period and some contracts with high uncertainty, implying that the KOSPI200 futures spread market has been generally efficient.

This paper aims to address the following questions: is good liner shipping connectivity a requisite for merchandise imports plus exports? What is the average of merchandise imports plus exports of the countries neighboring China? Do the merchandise imports plus exports of these countries correspond to each country’s own merchandise imports plus exports or liner shipping connectivity index (LSCI)?

The authors spatially analyze liner shipping connectivity and merchandise imports plus exports using 2016 data and a common framework for linear regression to establish the relationship amongst a country’s LSCI and its merchandise imports plus exports and between its merchandise imports plus exports and those of its neighbors. Merchandise imports plus exports of countries are not necessarily independent of each other, and countries that are contiguous may produce similar observations.

North America and Western Europe comprised clusters of countries that participated more actively in the international trading system, while Africa’s countries had less international trade than average. The study identifies and quantifies the geographical ripple of transport infrastructure on merchandise trade from a national perspective. Notably, a spatially lagged term improved the model’s ability to account for variations in merchandise imports plus exports across countries.

The spatial lag of merchandise imports plus exports can contribute to specifying the spread of merchandise imports plus exports beyond what the authors would anticipate from a country’s network of liner shipping.

The purpose of this paper is to show how data analytics can be used to identify areas of potential cost savings for category managers of installation-level services. Using integrated solid waste management (ISWM) as a test case, the authors also examine the impact of small business set-asides on price and contractor performance.

The authors use data analytics, specifically sequential regression, the Wilcoxon rank-sum test and ordered logistic regression to investigate the influence of service- and contracting-related variables on price and contractor performance.

The authors find that service- and contracting-related variables influence price. Specifically, they identify that a service-related variable, number of containers, significantly affects price, and that two contracting-related variables, one type of small business set-aside and the number of offers received, also significantly affect price. The authors quantify the price premiums paid for using various types of small business set-asides.

Although the findings were significant, the authors believe that the robustness of the conclusions could be enhanced if the Air Force captured more data. Additional observations would increase the generalizability of the results.

This empirical experiment demonstrates that detailed analyses are required to gain insights into services’ price drivers to craft more appropriate category management strategies for installation-level services.

This empirical study shows how historical data can be used to assess price drivers of installation-level services. It is also one of the first to quantify the impact that small business set-asides have on price.