Identifying and treating outliers in finance

Adams, John C.; Hayunga, Darren K.; Mansi, Sattar; Reeb, David M.; Verardi, Vincenzo

doi:10.1111/fima.12269

Cited by 62 publications

(33 citation statements)

References 87 publications

(251 reference statements)

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“…This is so as the values of skewness, kurtosis and Jaque-Bera have confirmed so as presented earlier. This finding is not far from the truth as it concurs with both [93,94] findings. Data being not normally distributed can be due to data can never be normal because of asymmetries, discreteness, boundedness and existence of outliers.…”

Section: Resultssupporting

confidence: 82%

Seasonal Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya

Charles¹,

Tabitha²,

Tobias³

2020

JFE

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Stock market generally accepted as security exchange has immensely attracted so much interests from various stakeholders around the world as they endow exceedingly to the growth of the world economy. Kenya's stock market, Nairobi Securities Exchange, being an emerging market, this study focused on seasonal anomalies as a type of market anomalies which is operationalized monthly. When there is seasonal anomalies, firms tend to exhibit unhealthy financial position which is financial distress. Financial distress takes a huge chunk of challenges which firms face in their day to day operations and is measured by Z-Score. This study takes a departure from the previous studies and assesses listed firms which are trading, under suspension or delisted from the stock markets while at the same time relating seasonal anomalies to financial distress which created a scholarly gap. This study adopts descriptive research design. It also embraced secondary data from 2007 to 2017 from a target population of 67 listed firms which had been licensed by the Capital Market Authority. It was found that there was indeed a relationship between seasonal anomalies and firms' financial distress. The study recommends that the management must have well thought risk mitigants so as to avoid the harsh repercussions of the seasonal anomalies effects. Investors ought to engage finance experts on investment decisions so as to only invest in financially healthy firms. Policy makers and regulators should have the capability and capacity of unearthing the infamous practice of tax evasion or tax fraud by firms. They should also make sure that information dissemination is done in accordance and within the precincts of the law so as to benefit of all the stakeholders.

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Section: Resultssupporting

confidence: 82%

Seasonal Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya

Charles¹,

Tabitha²,

Tobias³

2020

JFE

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“…These parameters are statistically significant at the 1% level, with the Table 1. The data were winsorized at the 1% and 99% percentiles (Adams et al 2019;Tukey 1962). The AIC and BIC tests confirmed that winsorized data explain better the models models explaining around 60% of variance.…”

Section: Regression Resultsmentioning

confidence: 87%

“…We winsorized the data at the 1% and 99% percentiles to minimize the potential effect of outliers (Tukey 1962). A recent review of financial studies shows that the majority of financial studies use winsorizing to treat outliers (Adams et al 2019).…”

Section: Datamentioning

confidence: 99%

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The impact of US sugar prices on the financial performance of US sugar-using firms

et al. 2020

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The effect of the United States (US) sugar program on sugar-using firm profitability from 2000 to 2017 is examined using firm financial data and the relative US-to-world sugar price ratio. Return on assets and market-to-book ratio proxy for firm financial performance. The regression results provide statistical evidence that as the US sugar price increases relative to the world sugar price, sugar-using firm financial performance improves. This is likely a result of sugar-using firms passing higher sugar costs on to consumers. An ex post analysis indicates that the statistical tests have adequate power. Findings provide guidelines for future analyses investigating the relationship between the US sugar program and sugar-using firm financial performance.

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“…One of these patterns results from the presence of outliers, defined as abnormal values that can have an extreme effect on the analysis (Acock, 2014;Irizarry & Love, 2015). It is not always easy to identify these outliers correctly, nor to estimate their real influence on data behavior (Adams, Hayunga, Mansi, Reeb, & Verardi, 2019;Loperfido, 2020). For this reason, researchers often follow protocols that recommend excluding these outliers, treating them as a problem to be solved before starting the core of the analysis (Hair, Black, Babin, & Anderson, 2018;Malhotra, 2018).…”

Section: Introduction Introductionmentioning

confidence: 99%

Cluster Analysis in Practice: Dealing with Outliers in Managerial Research

Lopes

Gosling

2021

Rev. adm. contemp.

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Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explain that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Results: our analyzes indicate that researchers have distinct clustering techniques for dealing with outliers accordingly. Conclusion: we concluded that researchers need to have a more diversified repertoire of clustering techniques. After all, this would give them two relevant empirical alternatives: choose the most appropriate technique for their research objectives or adopt a multi-method approach.

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Identifying and treating outliers in finance

Cited by 62 publications

References 87 publications

Seasonal Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya

Seasonal Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya

The impact of US sugar prices on the financial performance of US sugar-using firms

Cluster Analysis in Practice: Dealing with Outliers in Managerial Research

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