Call for Papers: Big Data in Finance – Opportunities and Challenges of Financial Digitalization -- An edited collection to be published by Palgrave-Macmillan

Description

Editors:
Thomas Walker, Professor of Finance and Concordia University Research Chair in Emerging Risk Management, John Molson School of Business, Concordia University
Fred Davis, Associate Professor, Department of Finance, John Molson School of Business, Concordia University
Tyler Schwartz, Research Assistant, John Molson School of Business, Concordia University

Book description:
Big data, as a discipline, is what allows AI to develop complex pattern-detecting algorithms. The massive troves of information collected can be anything from customer information for banking, historical prices of stocks for investing, or past fraudulent transactions for fraud detection. The data can be structured (organized, classified data), unstructured (text, social media activity), or semi-structured (incorporating both structured and unstructured elements). Currently, many critical questions dominate the big data space, including how data is collected (consumer privacy and ethical concerns), how it is stored (environmental impacts), how it is secured, and how it is analyzed and used.

Big Data in Finance – Opportunities and Challenges of Financial Digitalization is an edited collection that will explore the unique risks, opportunities, challenges, and societal implications associated with big data developments within the field of finance, both for today and for the future. While a general use of big data has often been the subject of discussion, this book will take a more focused look at big data applications in the financial sector. Additionally, the edited book will not only focus on the positive opportunities of these new developments but will also critically explore and assess the potential risks and challenges involved in their implementation. These include the ethical and storage issues involved in collecting and storing big data as well as the barriers to implementation in financial institutions. The book will explore the possible policy solutions to these questions and will propose strategies to overcome these barriers.

POTENTIAL TOPICS FOR CHAPTERS:
1 Financial Markets
1.1 High frequency trading
1.2 Automated (algorithmic) trading
1.3 Factor models
1.4 Historical analysis
1.5 Forecasting
1.6 Fixed income
1.7 Social investing (ESG scores)
1.8 Portfolio management (Robo-advisors)

2 Financial Services
2.1 The application programming interface (API) economy
2.2 Online banking
2.3 Digital/mobile payments
2.4 Digital/mobile lending (credit scoring, micro finance)
2.5 Insurance
2.6 Digital currencies

3 Operational Applications
3.1 Risk management (fraud detection, behavioral biometrics, etc.)
3.2 Customer services (chatbots, virtual assistants)

4 Challenges and Opportunities
4.1 Barriers to implementation
4.2 Storage and computational costs
4.3 Environmental impact
4.4 Privacy regulations
4.5 Unorganized data
4.6 Personal biases in algorithms
4.7 Computation advancements
4.8 Technological innovations
4.9 Financial inclusion

The editors are accepting contributions by experts in both the academic and practitioner communities in finance, big data and artificial intelligence, as well as related fields such as economics, computer science, business technology management, supply chain management, policy, sustainability, and entrepreneurship. The editors are inviting contributions that: 1) Review and critically analyze new developments at the intersection of big data and finance, 2) Explore the theory and mechanisms behind the algorithms using big data, and exploring their use in a finance context, 3) Explain and demonstrate the predictive capabilities of big data in finance using different model types, and/or 4) Present recent advancements made in deep learning and how they can be leveraged in combination with big data to innovate the financial sector in different aspects.

Moreover, because the use of big data in finance has many implications that go beyond their use in financial institutions, the co-editors will also be accepting chapters that go beyond the fields of artificial intelligence, big data, and finance. These fields will include both policy and sustainability, where contributors from these fields will look at possible policy and sustainability-oriented solutions and implications of the use of big data in finance. In addition, chapters that use case studies or comparative studies (between different solutions, applications in different industries, or variations between regions) are strongly encouraged. The submissions will be reviewed with an open mind and with a particular focus on the relevance of the chapter with respect to big data in finance.

Submitted chapters must be original and exclusively prepared for the book, with no part of the article having been published elsewhere.

Submissions:
Researchers and practitioners are invited to submit abstracts of no more than 500 words, a bibliography for their proposed chapter, and a CV to the email address below.

Timeframe:
• Abstract and CV submission deadline – September 30, 2021
• Selection of abstracts and notification to successful contributors – October 31, 2021
• Full chapter submission – January 31, 2021
• Revised chapter submission – March 31, 2022

Contact:
For inquiries, and to submit your abstract, please email big.data@concordia.ca