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In this chapter, we first examine the distribution characteristics of firm performance across different competitive industry contexts and periodic economic conditions of growth, recession, and recovery. There is mounting evidence that the contours of accounting-based economic returns consistently display (extreme) left-skewed leptokurtic distributions with negative risk-return relationships, which implies the existence of many negative performance outliers and some positive outliers. We note how negative skewness, excess kurtosis, and inverse risk-return relationships prevail in industries with more intense competition and in economic growth scenarios where more innovative initiatives compete. As the study of outliers typically is ignored in mainstream management studies, we extract a total of 23 extreme performers using a conventional winsorization technique that identifies 16 negative and 7 positive outliers. We study the performance trajectories of these firms over the full period and find that negative performers typically operate in capital-intensive innovative industries whereas positive performers operate in activities that cater to prevailing demand conditions and expand the business in a balanced manner. The firms that under- and over-perform as measured by the financial return ratio both constitute smaller firms compared to the total sample and show how relative movements in the ratio numerator and denominator affect the recorded return measure. However, the negative outliers generally use their public listing to access capital for investment in more risky development efforts that require a certain scale to succeed and thereby limits their flexibility. The positive outliers appear to expand their business activities in incremental responses to evolving market demands as a way to enhance maneuverability and secure competitive advantage by honing their unique firm-specific capabilities.

This chapter first analyzes how the data-cleaning process affects the share of missing values in the extracted European and North American datasets. It then moves on to examine how three different approaches to treat the issue of missing values, Complete Case, Multiple Imputation Chained Equations (MICE), and K-Nearest Neighbor (KNN) imputations affect the number of firms and their average lifespan in the datasets compared to the original sample and assessed across different SIC industry divisions. This is extended to consider implied effects on the distribution of a key performance indicator, return on assets (ROA), calculating skewness and kurtosis measures for each of the treatment methods and across industry contexts. This consistently shows highly negatively skewed distributions with high positive excess kurtosis across all the industries where the KNN imputation treatment creates results with distribution characteristics that are closest to the original untreated data. We further analyze the persistency of the (extreme) left-skewed tails measured in terms of the share of outliers and extreme outliers, which shows consistent and rather high percentages of outliers around 15% of the full sample and extreme outliers around 7.5% indicating pervasive skewness in the data. Of the three alternative approaches to deal with missing values, the KNN imputation treatment is found to be the method that generates final datasets that most closely resemble the original data even though the Complete Case approach remains the norm in mainstream studies. One consequence of this is that most empirical studies are likely to underestimate the prevalence of extreme negative performance outcomes.

This chapter outlines the major analytical efforts performed as part of the overarching research project with the aim to investigate the organizational and environmental circumstances around the extreme negatively skewed performance outcomes regularly observed across firms. It presents the collection and treatment of comprehensive European and North American datasets where subsequent analyses reproduce the contours of performance distributions observed in prior empirical studies. Key theoretical perspectives engaged in prior studies of performance data and the implied risk-return relationships are presented and these point to emerging commonalities between empirical findings in the management and finance fields. The results from extended analyses of more fine-grained data from North American manufacturing firms uncover the subtle effects of leadership and structural features, and computational simulations demonstrate how the implied adaptive processes can lead to the empirically observed performance distributions. Finally, the findings from the analytical project activities are set in context and the implications of the observed results are discussed to reach at a final conclusion.

Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

This paper aims to examine how banks’ accounting disclosure policies affect information content in stock prices and stock crash risk.

This paper uses 1996-2013 as the sample period. The final sample includes 10,045 observations in 37 countries. This paper uses stock return synchronicity to measure information content in stock prices. This study uses the frequency difference between extremely negative and positive stock returns to measure stock crash risk. To measure the level of bank accounting disclosure, this research follows Nier and Baumann (2006) to construct an aggregate disclosure index based on inclusions and omissions of a series of items in a bank’s annual accounting reports.

This paper finds that banks’ stocks have lower stock return synchronicity and fewer extremely negative returns if banks have higher levels of financial statement disclosure. These results suggest that banks’ stocks have higher information content and lower crash risk if banks’ information environment is more transparent.

Overall, this paper provides new insight about how to increase banks’ transparency and the safety of the banking industry, which is beneficial to economic growth. To increase banks’ transparency and reduce the possibility of extremely negative stock returns, one way to regulate banks is to increase their accounting disclosure. In addition, the extant literature (Chen et al., 2006, Durnev et al., 2003, 2004; Wurgler, 2000) demonstrates that firms with lower stock return synchronicity have more transparent information environments and higher investment efficiency. Thus, this paper finds that higher levels of bank accounting disclosure are associated with lower stock return synchronicity, which further reduces banks’ opacity and increases banks’ investment efficiency. Finally, compared to business firms, stock crash risk has much direr consequences because one bank’s stock crash will affect overall financial stability. Thus, it is important for authorities to know the effects of accounting disclosure on bank stock crash risk.

There is a vast amount of literature which identifies characteristics of effective schools and effective classrooms. This paper examines selected studies and their findings and provides an organizing framework which tries to relate these findings to one another and to the school and its environment. A number of implications for school improvement are discussed.

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.

Purpose – The purpose of this study is to explore to what extent global disparities in the wealth and poverty of nations can be explained by the evolved human diversity measured by the average intelligence of nations (national IQ).

Design/methodology/approach – It is hypothesized that nations with a higher average intelligence are able to produce better living conditions for their members than nations with a lower average intelligence. The hypothesis is tested by empirical evidence of national IQs measuring the average intelligence of nations and by indicators of per capita income, poverty, and human development measuring the wealth of nations from different perspectives. The study covers 187 contemporary countries.

Findings – The results of correlation analysis support the hypothesis. The correlation between national IQ and per capita income is 0.506, between national IQ and Population below $2 a day % it is −0.733, and between national IQ and human development it is 0.830. Regression analysis was used to illustrate the relationship between national IQ and income and human development at the level of single countries.

Practical implications – Because significant parts of global disparities in the wealth and poverty of nations can be traced to evolved human diversity measured by national IQ, human chances to remove or even to decrease those disparities are quite limited. We should learn to accept the inevitable social consequences of the evolved human diversity.

Originality/value – This study provides for social scientists a new perspective to explore the problems of global inequalities in human conditions.