Financial Applications of the Mahalanobis Distance

Stöckl, Sebastian; Hanke, Michael

doi:10.11114/aef.v1i2.511

Cited by 12 publications

(8 citation statements)

References 20 publications

(20 reference statements)

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“…We established a threshold value, epsilon (ε), against which f(X) is compared. Data points where f(X) is less than ε are considered anomalous (Stöckl & Hanke, 2014).…”

Section: Anomalies Detectionmentioning

confidence: 99%

Financial Anomalies and Creditworthiness: A Python-Driven Machine Learning Approach Using Mahalanobis Distance for ISE-Listed Companies in the Production and Manufacturing Sector

Demirhan

2024

JFRM

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This paper investigated the effect of financial ratio anomalies on the creditworthiness of companies in the production and manufacturing sectors listed on the Istanbul Stock Exchange. Financial ratios are crucial in assessing creditworthiness as they reflect potential financial risks, but anomalies in these ratios can skew credit evaluations. The statistical analysis is carried out using the Mahalanobis distance, a Python-driven machine learning technique, with financial data from 254 companies over the period of 2014 to 2023. The findings show that 89.61% of the observations were identified as normal, falling into low and moderate risk categories, whereas 10.39% were considered anomalies or high-risk, indicating that a substantial proportion of companies maintained financial indices within expected ranges. Besides the study observed significant risk category variations, particularly in the latter half of each year, likely reflecting differing reporting and auditing practices. Farther, Principal Component (PCA) analysis indicated that Asset Profitability, EBITDA to Total Debts, Short-Term Debt to Current Assets, Net Sales to Short-Term Debts, and Current Ratio are the most influential financial ratios. On the other hand, the least influential financial ratios in PCA include Net Working Capital Turnover, Operating Expenses to Average Assets, Market Value to EBITDA, Efficiency Ratio (Assets Turnover Ratio), and Change in Depreciation Expenses Ratio (annual difference in depreciation expense). In conclusion, this study underscores the complex role of financial ratio anomalies in Turkish industries in assessing creditworthiness.How to cite this paper: Demirhan, H.

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“…We established a threshold value, epsilon (ε), against which f(X) is compared. Data points where f(X) is less than ε are considered anomalous (Stöckl & Hanke, 2014).…”

Section: Anomalies Detectionmentioning

confidence: 99%

Financial Anomalies and Creditworthiness: A Python-Driven Machine Learning Approach Using Mahalanobis Distance for ISE-Listed Companies in the Production and Manufacturing Sector

Demirhan

2024

JFRM

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“…Note that the 2 Hz response is omitted from the later analysis as it is generally present [1,53,54] in the suckling data of all infants. its many variations have been used in applications from finance [56] and neurocomputing [57] to medical diagnosis [58]. The Mahalanobis distance may be determined from…”

Section: E Anomaly Detection Using Robust and Mahalanobis Distancementioning

confidence: 99%

Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior

Truong,

Walsh,

Scott

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices. Background. While it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of motherchild dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices. Materials and Methods: A non-nutritive suckling vacuum measurement system, previously developed by the authors [1], is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia. Results: In a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements. Conclusions: Statistical analysis (Mahalanobis Distance) and machine learning (KNN) can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum. This approach for identifying those at risk for breastfeeding complications is crucial to complement complex emerging clinical evaluation technology. Clinical Impact: By analyzing non-nutritive suckling using computational methods, we demonstrate the ability to detect abnormal and normal behavior in infant suckling that can inform breastfeeding intervention pathways in clinic. Clinical and Translational Impact Statement:The work serves to shed light on the lack of consensus for determining appropriate intervention pathways for infant oral dysfunction. We demonstrate using statistical analysis and machine learning that normal and abnormal infant suckling can be identified and used in determining if surgical intervention is a necessary solution to resolve infant feeding difficulties.

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“…Boudt et al [26] rely on it at a more granular level to filter out outliers from the return series that are used to fit prediction models. Stöckl and Hanke [27] also identify a number of related use cases in their study on the financial applications of the Mahalanobis distance. These authors discuss the detection of structural changes over time; the identification of deviations from model prices and checks for arbitrage opportunities, and forecast evaluation.…”

Section: Approachmentioning

confidence: 99%

Resampled Efficient Frontier Integration for MOEAs

Quintana

Moreno

2021

Entropy

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Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.

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Financial Applications of the Mahalanobis Distance

Cited by 12 publications

References 20 publications

Financial Anomalies and Creditworthiness: A Python-Driven Machine Learning Approach Using Mahalanobis Distance for ISE-Listed Companies in the Production and Manufacturing Sector

Financial Anomalies and Creditworthiness: A Python-Driven Machine Learning Approach Using Mahalanobis Distance for ISE-Listed Companies in the Production and Manufacturing Sector

Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior

Resampled Efficient Frontier Integration for MOEAs

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