Data Science Technologies in Economics and Finance: A Gentle Walk-In

Barbaglia, Luca; Consoli, Sergio; Manzan, Sebastiano; Recupero, Diego Reforgiato; Saisana, Michaela; Pezzoli, Luca Tiozzo

doi:10.1007/978-3-030-66891-4_1

Cited by 9 publications

(8 citation statements)

References 40 publications

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“…Barbaglia L., Consoli S., Manzan S., Reforgiato Recupero D., Saisana M., Tiozzo Pezzoli L. provide an introduction to the application of data science in economics and finance, exploring how big data and advanced computational technologies are utilized to create effective and personalized economic models. It discusses both the benefits and technical challenges such as data handling, protection, and the integration of data science methods into economic forecasting [6].…”

Section: економічний простірmentioning

confidence: 99%

Data Science Methods and Models in Modern Economy

Reznikov,

Turlakova

2024

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In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting economic trends, and informing policy decisions. This article reviews the most commonly used data science models in economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model. It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article concludes with future research directions, such as advanced neural network architectures, large language models, generative AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.

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Section: економічний простірmentioning

confidence: 99%

Data Science Methods and Models in Modern Economy

Reznikov,

Turlakova

2024

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“…We focus on the real-world Australian credit approval dataset, which can be publicly obtained from the UCI ML Repository. 6 There are 690 cases in this dataset [63], of which 307 involve creditworthy applicants and 383 involve uncreditworthy ones. Each instance has class labels (accepted or rejected), eight numeric attributes, and six nominal attributes.…”

Section: Credit Approvalmentioning

confidence: 99%

“…The F1 score is utilized to evaluate classification techniques and is recommended in the case of unbalanced data as it focuses on the recognition of the minority class. The harmonic mean of a classifier's precision and recall is used to calculate the F1 score, which integrates both metrics into a single value [6]. In particular:…”

Section: Evaluating Metricsmentioning

confidence: 99%

“…They apply a resampling process at the data level which aims at balancing minority and majority data samples before the training of ML and DL approaches on the data. These resampling techniques can be mainly categorised into under-sampling and over-sampling approaches [2,6]. Undersampling is commonly performed by randomly removing data samples from the majority class of the training set [25].…”

Section: Introductionmentioning

confidence: 99%

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Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach

Ranjbaran,

Reforgiato Recupero,

Lombardo

et al. 2023

EPJ Data Sci.

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Modern financial markets produce massive datasets that need to be analysed using new modelling techniques like those from (deep) Machine Learning and Artificial Intelligence. The common goal of these techniques is to forecast the behaviour of the market, which can be translated into various classification tasks, such as, for instance, predicting the likelihood of companies’ bankruptcy or in fraud detection systems. However, it is often the case that real-world financial data are unbalanced, meaning that the classes’ distribution is not equally represented in such datasets. This gives the main issue since any Machine Learning model is trained according to the majority class mainly, leading to inaccurate predictions. In this paper, we explore different data augmentation techniques to deal with very unbalanced financial data. We consider a number of publicly available datasets, then apply state-of-the-art augmentation strategies to them, and finally evaluate the results for several Machine Learning models trained on the sampled data. The performance of the various approaches is evaluated according to their accuracy, micro, and macro F1 score, and finally by analyzing the precision and recall over the minority class. We show that a consistent and accurate improvement is achieved when data augmentation is employed. The obtained classification results look promising and indicate the efficiency of augmentation strategies on financial tasks. On the basis of these results, we present an approach focused on classification tasks within the financial domain that takes a dataset as input, identifies what kind of augmentation technique to use, and then applies an ensemble of all the augmentation techniques of the identified type to the input dataset along with an ensemble of different methods to tackle the underlying classification.

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“…In addition, traditional econometrics is difficult to analyse massive data. Developing analysis models or methods suitable for oversized data is a major challenge to promote the development of data economy (Barbaglia et al, 2021).…”

mentioning

confidence: 99%

GA‐based feature selection method for oversized data analysis in digital economy

Lv,

Liu,

Wang

et al. 2023

Expert Systems

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With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy.

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Data Science Technologies in Economics and Finance: A Gentle Walk-In

Cited by 9 publications

References 40 publications

Data Science Methods and Models in Modern Economy

Data Science Methods and Models in Modern Economy

Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach

GA‐based feature selection method for oversized data analysis in digital economy

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