Multivariate Data Analysis and Machine-Learning Models in Financial Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 9391

Special Issue Editors


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Guest Editor
Department of Financial and Economic Analysis and Valuation, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: financial analysis; business valuation; business financial strategies

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Guest Editor
Department of Business Administration, Faculty of Economic Studies, University “Lucian Blaga” of Sibiu, Calea Dumbravii, No. 17, 550324 Sibiu, Romania
Interests: business performance; financial modelling; statistics

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Guest Editor
Department of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Interests: financial performance; risk analysis; foreign direct investments; financial models; quantitative methods
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Special Issue Information

Dear Colleagues,

The complexity of financial decisions in an interconnected world has steadily increased in recent years and is accompanied by increased uncertainty driven by the pandemic and the unfolding of the post-pandemic realities. Thus, financial decision making, among other aspects, needs to consider multiple variables, measures and metrics and rely on advanced models for data comprehension and analysis. On the bright side, quantitative approaches in the field of multivariate data analysis and machine learning developed in recent decades have led the way towards improvements in addressing financial assessments and decisions. This Special Issue welcomes theoretical and empirical research papers that explore the multi-faceted financial analysis behind investing, financing and risk assessment and management in corporations and financial institutions alike, as well as providing an opportunity for scholars and the financial analysis industry to bring their insight and novel approaches to data analysis to light. Of particular interest are contributions on the concrete application of multivariate data analysis techniques and machine learning models to specific topics in financial analysis.

Prof. Dr. Stefania Cristina Curea
Prof. Dr. Lucian Belascu
Prof. Dr. Alexandra Horobet
Guest Editors

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Keywords

  • financial analysis
  • financial assessment
  • financial decisions
  • multivariate data analysis
  • machine learning
  • algorithms

Published Papers (3 papers)

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Research

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17 pages, 1488 KiB  
Article
Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model
by Surjeet Dalal, Bijeta Seth, Magdalena Radulescu, Carmen Secara and Claudia Tolea
Mathematics 2022, 10(24), 4679; https://doi.org/10.3390/math10244679 - 09 Dec 2022
Cited by 8 | Viewed by 2576
Abstract
Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as [...] Read more.
Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as rules engines and machine learning. In this research, we introduce a unique hybrid technique for identifying financial payment fraud by combining nature-inspired-based Hyperparameter tuning with several supervised classifier models, as implemented in a modified version of the XGBoost Algorithm. At the outset, we split out a sample of the full financial payment dataset to use as a test set. We use 70% of the data for training and 30% for testing. Records that are known to be illegitimate or fraudulent are predicted, while those that raise suspicion are further investigated using a number of machine learning algorithms. The models are trained and validated using the 10-fold cross-validation technique. Several tests using a dataset of actual financial transactions are used to demonstrate the effectiveness of the proposed approach. Full article
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16 pages, 1057 KiB  
Article
Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods
by Stéphane C. K. Tékouabou, Ștefan Cristian Gherghina, Hamza Toulni, Pedro Neves Mata and José Moleiro Martins
Mathematics 2022, 10(14), 2379; https://doi.org/10.3390/math10142379 - 06 Jul 2022
Cited by 15 | Viewed by 4540
Abstract
The diversity of data collected on both social networks and digital interfaces is extremely increased, raising the problem of heterogeneous variables that are not often favourable to classification algorithms. Despite the significant improvement in machine learning (ML) and predictive analysis efficiency for classification [...] Read more.
The diversity of data collected on both social networks and digital interfaces is extremely increased, raising the problem of heterogeneous variables that are not often favourable to classification algorithms. Despite the significant improvement in machine learning (ML) and predictive analysis efficiency for classification in customer relationship management systems (CRM), their performance remains very limited by heterogeneous data processing, class imbalance, and feature scales. This impact turned out to be more important for simple ML methods which in addition often suffer from over-fitting. This paper proposes a succinct and detailed ML model building process including cross-validation of the combination of SMOTE to balance data and ensemble methods for modelling. From the conducted experiments, the random forest (RF) model yielded the best performance of 0.86 in terms of accuracy and f1-scoreusing balanced data. It confirms the literature summary about this topic which shows that RF was among the most effective algorithms for customer predictive classification issues. The constructed and optimized models were interpreted by Shapley values and feature importance analysis which shows that the “age” feature was the most significant while “HasCrCard” was the less one. This process has proven effective in bridging previously reported research gaps and the resulting model should be used for supporting bank customer loyalty decision-making. Full article
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Review

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15 pages, 613 KiB  
Review
Forecasting Applied to the Electricity, Energy, Gas and Oil Industries: A Systematic Review
by Ivan Borisov Todorov and Fernando Sánchez Lasheras
Mathematics 2022, 10(21), 3930; https://doi.org/10.3390/math10213930 - 23 Oct 2022
Cited by 1 | Viewed by 1275
Abstract
This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables [...] Read more.
This paper presents a literature review in which methodologies employed for the forecast of the price of stock companies and raw materials in the fields of electricity, oil, gas and energy are studied. This research also makes an analysis of which data variables are employed for training the forecasting models. Three scientific databases were consulted to perform the present research: The Directory of Open Access Journals, the Multidisciplinary Digital Publishing Institute and the Springer Link. After running the same query in the three databases and considering the period from January 2017 to December 2021, a total of 1683 articles were included in the analysis. Of these, only 13 were considered relevant for the topic under study. The results obtained showed that when compared with other areas, few papers focus on the forecasting of the prices of raw materials and stocks of companies in the field under study. Furthermore, most make use of either machine learning methodologies or time series analysis. Finally, it is also remarkable that some not only make use of existing algorithms but also develop and test new methodologies. Full article
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