Search results

This chapter takes a closer look at outliers and extreme outliers identified in the data derived from a complete case treatment of missing values in the European and North American datasets and consistently observe significant negatively skewed distributions with high excess kurtosis across all industries. We then plot the density functions for return on assets (ROA) across different industries in the two datasets and find pervasive observations in the tails where negative returns and outlying observations constitute a frequent and recurring phenomenon. We analyze the persistency of outliers and find noticeable percentages of outlying over- and underperformers hovering around 3–6% dependent on industry context. We further analyze potential size effects associated with extreme negative skewness but do not find that (even sizeable) elimination of extreme values reduce the phenomenon. Finally, we analyze the percentage of firm observations that must be eliminated to reach at distributions that fulfill the characteristics of a normal distribution and reach at a substantial percentage of around 5–10% dependent on industry. To conclude, the often-assumed normally distributed performance outcomes are typically wrong and discards the substantial number of outliers in the samples.

Studies of unicorns and gazelles can offer detailed information about the process of enterprise development but are unrepresentative as examples of entrepreneurial success. In presenting a novel method for outlier analysis, this article combines insights from case studies of unusual organizations with explanatory frameworks that management scholars have applied to broader samples of firms, irrespective of their survival.

The authors illustrate the approach to outlier analysis using a prominent case from economic history: the House of Rothschild, founded during the 18th century, which became the most famous investment bank in Europe. Following the iterative refinement of mechanisms using comparison data on Jewish enclave firms, this analysis sheds light on the sources of dissimilarity in outcomes between Rothschild and the comparison group.

The study results suggest that the House of Rothschild's longevity can be explained via the mechanisms of risk sequencing, intergenerational transfers and spatial brokerage. The authors show that these mechanisms are not idiosyncratic to one enterprise but instead generalize to other family firms.

Outlier analysis encourages a rapprochement between case study and large-N research. The high failure rate of new organizations means that those yielding a large amount of information to researchers tend to be exceptional. By obtaining data on a comparison group of startups founded by similar entrepreneurs, analysts can probe the mechanisms of success identified for unicorns or gazelles.

Schools in resource challenged communities require principal approaches that break patterns of low expectations and low student achievement. This study identifies Alabama’s “outlier” schools that have been consistently successful in attaining higher student outcomes than their neighboring schools despite their similar community conditions. Then, it describes the perspectives and practices of principals leading these outlier schools. The purpose of this paper is to discuss findings on principal leadership in five of Alabama's outlier schools.

In a sequential, explanatory mixed-methods design, the authors first use state administrative data to identify which Alabama schools had better results than their peers as evidenced by standardized testing between 2016 and 2020. Then, through semi-structured interviews, they examine the beliefs and approaches of five principals who are currently leading an outlier school. The frame of contextual leadership provides a deeper understanding of how these principals navigate successful schools in the midst of challenging community influences.

The evidence demonstrated that (1) community factors of low education, high unemployment, single-parent households and generational poverty are associated with considerably lower levels of student growth and achievement; (2) measured school and community factors do not explain student growth and achievement in these outlier schools; (3) outlier principals have a realistic view of their community’s challenges but focus on supporting students through a context sensitive, relational approach that emphasizes assets over limitations.

While research has attended to leadership in turnaround schools and effective schools, there is little literature on principals leading in positive outlier schools. This study contributes to the literature on school leadership in resource challenged contexts by identifying high performing, resource challenged schools and then showing the perspectives and practices of principals who lead in schools that have consistently achieved better than expected student outcomes. It extends the construct of “outlier leadership” in education and connects it to contextual leadership in schools.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

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.

Due to the large-size, non-uniform transactions per day, the money laundering detection (MLD) is a time-consuming and difficult process. The major purpose of the proposed auto-regressive (AR) outlier-based MLD (AROMLD) is to reduce the time consumption for handling large-sized non-uniform transactions.

The AR-based outlier design produces consistent asymptotic distributed results that enhance the demand-forecasting abilities. Besides, the inter-quartile range (IQR) formulations proposed in this paper support the detailed analysis of time-series data pairs.

The prediction of high-dimensionality and the difficulties in the relationship/difference between the data pairs makes the time-series mining as a complex task. The presence of domain invariance in time-series mining initiates the regressive formulation for outlier detection. The deep analysis of time-varying process and the demand of forecasting combine the AR and the IQR formulations for an effective outlier detection.

The present research focuses on the detection of an outlier in the previous financial transaction, by using the AR model. Prediction of the possibility of an outlier in future transactions remains a major issue.

The lack of prior segmentation of ML detection suffers from dimensionality. Besides, the absence of boundary to isolate the normal and suspicious transactions induces the limitations. The lack of deep analysis and the time consumption are overwhelmed by using the regression formulation.

There has been increased interest in expanding the Medicare Prospective Payment System (PPS) to non-Medicare payers to provide incentives for hospitals to contain costs and to concentrate in those Diagnosis-Related Groups (DRGs) which they can provide efficiently. However, this should not force low-volume, low-cost payers to subsidize high cost payers and should not penalize low Length-of-Stay (LOS), low-cost hospitals. This article proposes a new method proportional pricing to expand PPS incentives to non-Medicare payers with equity for payers and hospitals. It would also allow all-payer rate setting and premium price competition among payers to coexist.

Among the growing number of data mining (DM) techniques, outlier detection has gained importance in many applications and also attracted much attention in recent times. In the past, outlier detection researched papers appeared in a safety care that can view as searching for the needles in the haystack. However, outliers are not always erroneous. Therefore, the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care, in particular.

It is a combined DM (clustering and the nearest neighbor) technique for outliers’ detection, which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare safety. The outcomes or the knowledge implicit is vitally essential to a proper clinical decision-making process. The method is important to the semantic, and the novel tactic of patients’ events and situations prove that play a significant role in the process of patient care safety and medications.

The outcomes of the paper is discussing a novel and integrated methodology, which can be inferring for different biological data analysis. It is discussed as integrated DM techniques to optimize its performance in the field of health and medical science. It is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient factors. Based on these facts, outliers are detected as clusters and point events, and novel ideas proposed to empower clinical services in consideration of customers’ satisfactions. It is also essential to be a baseline for further healthcare strategic development and research works.

This paper mainly focussed on outliers detections. Outlier isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not touch. Therefore, the research can be extended more about the hierarchy of patient problems.

DM is a dynamic and successful gateway for discovering useful knowledge for enhancing healthcare performances and patient safety. Clinical data based outlier detection is a basic task to achieve healthcare strategy. Therefore, in this paper, the authors focussed on combined DM techniques for a deep analysis of clinical data, which provide an optimal level of clinical decision-making processes. Proper clinical decisions can obtain in terms of attributes selections that important to know the influential factors or parameters of healthcare services. Therefore, using integrated clustering and nearest neighbors techniques give more acceptable searched such complex data outliers, which could be fundamental to further analysis of healthcare and patient safety situational analysis.

The purpose of this paper is to investigate an empirical solution to dividend policy relevance.

The paper combines measures of firm maturity in a logit regression to define a comprehensive life-cycle model of the likelihood of dividend payment. The valuation of firms that conform to the model is compared to the valuation of firms that do not fit the model. Valuation is measured by the market to book (M/B) ratio.

The analysis indicates that dividend policy is related to firm value. Dividend-paying firms that fit the life-cycle model have a higher median valuation than dividend-paying firms that do not fit the life-cycle model. Similarly, non-paying firms that fit the life-cycle model have a higher median valuation than non-paying firms that do not fit the life-cycle model. The results also provide evidence that the disappearing dividend phenomenon is related to shifts in valuation.

This paper focuses on the payment of dividends. Stock repurchases are not considered.

The results indicate that dividend policy is related to firm value. Approximately 15 percent of sample observations have a dividend policy counter to the life-cycle model.

This paper shows that the relation between a firm’s M/B ratio and dividend policy changes over the firm’s life-cycle. It also shows that the catering motive for dividends is strongest among firms that are outliers in the life-cycle model and firms of intermediate maturity.

Construction contract auctions are characterized by (1) a heavy emphasis on the lowest bid as it is that which usually determines the winner of the auction, (2) anticipated high outliers because of the presence of non‐competitive bids, (3) very small samples, and (4) uncertainty of the appropriate underlying density function model of the bids. This paper describes a method for simultaneously identifying outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness‐of‐fit of the resulting reduced samples with censored normal and lognormal density function. The special importance of the lowest bid value in this context is utilized in the goodness‐of‐fit test by the probability of the lowest bid recorded for each auction as a lowest order statistic. Six different identification strategies are tested empirically by application, both independently and in pooled form, to eight sets of auction data gathered from around the world. The results indicate the most conservative identification strategy to be a multiple of the auction standard deviation assuming a lognormal composite density. Surprisingly, the normal density alternative was the second most conservative solution. The method is also used to evaluate some methods used in practice and to identify potential improvements.