scholarly journals Big Data in Finance

Author(s):  
Itay Goldstein ◽  
Chester S Spatt ◽  
Mao Ye

Abstract Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

2017 ◽  
Vol 119 (1) ◽  
pp. 1-6
Author(s):  
Adrienne D. Dixson ◽  
Gloria Ladson-Billings

The articles in this special issue represent both our attempt as editors to survey the field and provide some clarity for practitioners and teacher educators on fundamental ideas that frame CRP, not to limit its implementation or future research directions, but to ensure that as a community of educators and scholars, we share a common understanding of exactly what it means to be culturally relevant. The articles in this special issue provide both that clarity of the field, and vision for the future.


Big Data ◽  
2016 ◽  
pp. 2368-2387
Author(s):  
Hajime Eto

As this book has the limited numbers of chapters and pages, many important issues remain unanalyzed. This chapter picks up and roughly discusses some of them for the future analyses in more analytical ways. The focuses are placed on how to apply the data scientific methods to the analyses of public voice, claims and behaviors of tourists, customers and the general publics by using the big data already acquired and stored somewhere.


Author(s):  
Steven Walczak

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.


Author(s):  
Mondher Feki

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.


2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.


Author(s):  
Suzanne Roff-Wexler

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.


Author(s):  
David Chan

Studies of team-level constructs can produce new insights when researchers explicitly take into account several critical conceptual and methodological issues. This article explicates the conceptual bases for multilevel research on team constructs and discusses specific issues relating to conceptual frameworks, measurement, and data analysis. To advance programmatic research involving team-level constructs, several future research directions concerning issues of substantive content (i.e., changes in the nature of work and teams, member-team fit, linking team-level constructs to higher-level constructs) and strategic approaches (i.e., the construct's theoretical roles, dimensionality and specificity, malleability and changes over time, relationships with Big Data) are proposed.


2020 ◽  
Vol 27 (8) ◽  
pp. 2435-2457 ◽  
Author(s):  
Ricardo Belinski ◽  
Adriana M.M. Peixe ◽  
Guilherme F. Frederico ◽  
Jose Arturo Garza-Reyes

PurposeIndustry 4.0 has been one of the most topics of interest by researches and practitioners in recent years. Then, researches which bring new insights related to the subjects linked to the Industry 4.0 become relevant to support Industry 4.0's initiatives as well as for the deployment of new research works. Considering “organizational learning” as one of the most crucial subjects in this new context, this article aims to identify dimensions present in the literature regarding the relation between organizational learning and Industry 4.0 seeking to clarify how learning can be understood into the context of the fourth industrial revolution. In addition, future research directions are presented as well.Design/methodology/approachThis study is based on a systematic literature review that covers Industry 4.0 and organizational learning based on publications made from 2012, when the topic of Industry 4.0 was coined in Germany, using data basis Web of Science and Google Scholar. Also, NVivo software was used in order to identify keywords and the respective dimensions and constructs found out on this research.FindingsNine dimensions were identified between organizational learning and Industry 4.0. These include management, Industry 4.0, general industry, technology, sustainability, application, interaction between industry and the academia, education and training and competency and skills. These dimensions may be viewed in three main constructs which are essentially in order to understand and manage learning in Industry 4.0's programs. They are: learning development, Industry 4.0 structure and technology Adoption.Research limitations/implicationsEven though there are relatively few publications that have studied the relationship between organizational learning and Industry 4.0, this article makes a material contribution to both the theory in relation to Industry 4.0 and the theory of learning - for its unprecedented nature, introducing the dimensions comprising this relation as well as possible future research directions encouraging empirical researches.Practical implicationsThis article identifies the thematic dimensions relative to Industry 4.0 and organizational learning. The understanding of this relation has a relevant contribution to professionals acting in the field of organizational learning and Industry 4.0 in the sense of affording an adequate deployment of these elements by organizations.Originality/valueThis article is unique for filling a gap in the academic literature in terms of understanding the relation between organizational learning and Industry 4.0. The article also provides future research directions on learning within the context of Industry 4.0.


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