Data Science in Economics and Business

2021 ◽  
pp. 544-568
Author(s):  
Mara Madaleno ◽  
João Lourenço Marques ◽  
Muhammad Tufail
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Causal Inference ◽  
Bibliometric Analysis ◽  
Data Science ◽  
State Of The Art ◽  
Research Articles

Economics and business are a great background for data science provided econometricians and data scientists are sets with an intersection, although remaining unknown. In econometrics, data mining is somewhat a monstrous word, a field that traditionally seeks causal inference and results in interpretability. When we go deeper into what data science usually is, the boundaries between more traditional econometrics and even statistics and the hip and cool machine learning become shorter. In economics and business, we find examples and applications of simple and advanced data science techniques. This chapter intends to provide state-of-the-art data science applications in economics and business. The review and bibliometric analysis are limited to the research articles published through Elsevier Scopus. Results allowed the authors to conclude that despite the number of already existent research, a lot more remains to be explored joining both fields of knowledge, data since, and economics and business. This analysis allowed the authors to identify further possible avenues of research critically.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

scholarly journals Towards Spatial Data Science: Bridging the Gap between GIS, Cartography and Data Science

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Jan Wilkening
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Open Source ◽  
Real Time ◽  
Spatial Data ◽  
Data Science ◽  
Spatial Concepts ◽  
Front End ◽  
Gis Tools ◽  
University Curricula

<p><strong>Abstract.</strong> Data is regarded as the oil of the 21st century, and the concept of data science has received increasing attention in the last years. These trends are mainly caused by the rise of big data &amp;ndash; data that is big in terms of volume, variety and velocity. Consequently, data scientists are required to make sense of these large datasets. Companies have problems acquiring talented people to solve data science problems. This is not surprising, as employers often expect skillsets that can hardly be found in one person: Not only does a data scientist need to have a solid background in machine learning, statistics and various programming languages, but often also in IT systems architecture, databases, complex mathematics. Above all, she should have a strong non-technical domain expertise in her field (see Figure 1).</p><p>As it is widely accepted that 80% of data has a spatial component, developments in data science could provide exciting new opportunities for GIS and cartography: Cartographers are experts in spatial data visualization, and often also very skilled in statistics, data pre-processing and analysis in general. The cartographers’ skill levels often depend on the degree to which cartography programs at universities focus on the “front end” (visualisation) of a spatial data and leave the “back end” (modelling, gathering, processing, analysis) to GIScientists. In many university curricula, these front-end and back-end distinctions between cartographers and GIScientists are not clearly defined, and the boundaries are somewhat blurred.</p><p>In order to become good data scientists, cartographers and GIScientists need to acquire certain additional skills that are often beyond their university curricula. These skills include programming, machine learning and data mining. These are important technologies for extracting knowledge big spatial data sets, and thereby the logical advancement to “traditional” geoprocessing, which focuses on “traditional” (small, structured, static) datasets such shapefiles or feature classes.</p><p>To bridge the gap between spatial sciences (such as GIS and cartography) and data science, we need an integrated framework of “spatial data science” (Figure 2).</p><p>Spatial sciences focus on causality, theory-based approaches to explain why things are happening in space. In contrast, the scope of data science is to find similar patterns in big datasets with techniques of machine learning and data mining &amp;ndash; often without considering spatial concepts (such as topology, spatial indexing, spatial autocorrelation, modifiable area unit problems, map projections and coordinate systems, uncertainty in measurement etc.).</p><p>Spatial data science could become the core competency of GIScientists and cartographers who are willing to integrate methods from the data science knowledge stack. Moreover, data scientists could enhance their work by integrating important spatial concepts and tools from GIS and cartography into data science workflows. A non-exhaustive knowledge stack for spatial data scientists, including typical tasks and tools, is given in Table 1.</p><p>There are many interesting ongoing projects at the interface of spatial and data science. Examples from the ArcGIS platform include:</p><ul><li>Integration of Python GIS APIs with Machine Learning libraries, such as scikit-learn or TensorFlow, in Jupyter Notebooks</li><li>Combination of R (advanced statistics and visualization) and GIS (basic geoprocessing, mapping) in ModelBuilder and other automatization frameworks</li><li>Enterprise GIS solutions for distributed geoprocessing operations on big, real-time vector and raster datasets</li><li>Dashboards for visualizing real-time sensor data and integrating it with other data sources</li><li>Applications for interactive data exploration</li><li>GIS tools for Machine Learning tasks for prediction, clustering and classification of spatial data</li><li>GIS Integration for Hadoop</li></ul><p>While the discussion about proprietary (ArcGIS) vs. open-source (QGIS) software is beyond the scope of this article, it has to be stated that a.) many ArcGIS projects are actually open-source and b.) using a complete GIS platform instead of several open-source pieces has several advantages, particularly in efficiency, maintenance and support (see Wilkening et al. (2019) for a more detailed consideration). At any rate, cartography and GIS tools are the essential technology blocks for solving the (80% spatial) data science problems of the future.</p>

scholarly journals Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

scholarly journals Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

2020 ◽  
Vol 36 (4) ◽  
pp. 1769-1801 ◽  
Author(s):  
Yazhou Xie ◽  
Majid Ebad Sichani ◽  
Jamie E Padgett ◽  
Reginald DesRoches
Keyword(s):  
Machine Learning ◽  
Earthquake Engineering ◽  
Structural Control ◽  
Data Science ◽  
State Of The Art ◽  
Hazard Analysis ◽  
Future Research ◽  
Main Stream ◽  
Topic Area ◽  
Data Resource

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

Adaptive Interaction for Mass Customisation

2012 ◽  
pp. 2101-2116
Author(s):  
Gulden Uchyigit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Ubiquitous Computing ◽  
Mass Customization ◽  
New Technologies ◽  
State Of The Art ◽  
Computing Technologies ◽  
One To One ◽  
Marketing Service ◽  
Innovative Techniques

The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.

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