Fintech with Artificial Intelligence, Big Data, and Blockchain (Blockchain Technologies)

✍ Scribed by Paul Moon Sub Choi (editor), Seth H. Huang (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 306
• Category: Library

✦ Synopsis

This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain―all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths.

✦ Table of Contents

Contents
Blockchain, Cryptocurrency, and Artificial Intelligence in Finance
1 Introduction
2 Logic of Blockchain
2.1 Introduction to Transactions in Blockchain
2.2 Controversy Regarding Public Blockchain Application: Cryptocurrency
3 Blockchain in the Financial Industry
3.1 Blockchain and Banking Industry
3.2 Blockchain and Corporate Governance
4 Artificial Intelligence and Big Data in Finance
4.1 Fintech and Banking Industry
4.2 AI and Lending Platforms
4.3 AI and Investing
5 Conclusions
References
Alternative Data, Big Data, and Applications to Finance
1 Overview
2 Where Does Alternative Data Come from?
2.1 Sources of Alternative Data
2.2 Types of Alternative Data
2.3 The Market for Alternative Data
2.4 Characteristics of an Appealing Alternative Dataset
3 Economic Framework
3.1 Discretionary Versus Quantitative Trading
3.2 Forecasting
3.3 Nowcasting
3.4 Statistical Modeling and High Dimensional Data
4 Quantitative Trading
4.1 Strategy Decay and the Impetus for Alternative Data
4.2 The Alternative Data Framework for Quantitative Trading
4.3 Additional Uses of Alternative Data
5 Credit Scoring
5.1 The Canonical Credit Scoring Model
5.2 The Advantage of Having Data
5.3 Mobile Data
5.4 Measurement of Predictability
5.5 Digital Footprint Data
5.6 Psychometric Data
6 Application in Macroeconomics
6.1 Statistical Framework
6.2 National Accounts
6.3 Inflation
6.4 The Labor Market
6.5 Uncertainty
7 Additional Opportunities in Alternative Data
7.1 ESG
7.2 Data on Private Firms
7.3 Operations of Financial Institutions
7.4 COVID-19
8 Cautions About Alternative Data
8.1 Pitfalls of Alternative Data
8.2 Legal Consideration
9 Conclusion
References
Application of Big Data with Fintech in Financial Services
1 Introduction
2 Application of Financial Technology in Sub-Saharan Africa
3 Application of Financial Technology in Nigeria
4 The Emergence of Financial Technologies
5 Big Data in Financial Services
6 Financial Technology and Big Data
7 Challenges of Using Big Data in Financial Technology
8 Conclusions
References
Using Machine Learning to Predict the Defaults of Credit Card Clients
1 Introduction
2 Literature Review
2.1 Techniques for Credit Scoring
2.2 Feature Selection Techniques
2.3 Logistic Regression (LR)
2.4 K-Nearest Neighbor Classifier (KNN)
2.5 Support Vector Machine (SVM)
2.6 Naive Bayesian Classifier (NBC)
2.7 Decision Tree (DT)
2.8 Random Forest Classifier (RFC)
3 Methodology
3.1 Data Collecting Processes
3.2 Research Methodology
4 Experimental Result
4.1 Performance Evaluation
4.2 Result
5 Conclusions
References
Artificial Intelligence and Advanced Time Series Classification: Residual Attention Net for Cross-Domain Modeling
1 Introduction
2 Model
2.1 ResNet
2.2 Universal Transformer
3 Experimental Results
4 Conclusion
References
Generating Synthetic Sequential Data for Enhanced Model Training: A Generative Adversarial Net Framework
1 Introduction
2 Model Framework and Evaluation Metrics
2.1 Model Structure: A-GAN and AC-GAN
2.2 The Framework
2.3 ResNet
2.4 Universal Transformer
2.5 Evaluation Metrics
3 Experimental Results
4 Future Works
5 Conclusion
References
A Machine Learning-based Model for the Asymmetric Prediction of Accounting and Financial Information
1 Introduction
2 Information Asymmetry
3 Introduction to Machine Learning in Finance
4 A Prediction Method for Information Asymmetry in Companies Using Machine Learning
5 Conclusions
References
Artificial Intelligence-based Detection and Prediction of Corporate Earnings Management
1 Introduction
2 Literature Survey
2.1 Conventional Models for Detecting Earnings Management
2.2 Artificial Intelligence-based Approach
3 Conclusion
References
Machine Learning Applications in Finance Research
1 Overview
2 Defining Machine Learning in Finance Research
2.1 Inference and Prediction
2.2 Important Topics in Machine Learning
2.3 Machine Learning Models
3 Applications in Finance Research
3.1 Forecasting Stock Returns
3.2 Assisting Financial-Economic Decision-Making Process
3.3 Use of Unstructured Data and Machine Learning Techniques
4 Discussion
References
Price-Bands: A Technical Tool for Stock Trading
1 Introduction
2 Construction of Price-Bands
3 Stochastic Combinatorial Optimization Problem Formulation
4 Binomial Moment Problem Formulation
5 Numerical Examples and Discussion
6 Concluding Remark
References
Informed or Biased? Some Evidence from Listed Fund Trading
1 Introduction
2 A Model for the Discount of Listed Funds
3 Variables, Data, and Preliminary Results
3.1 Variables
3.2 Data
3.3 Preliminary Results
4 Main Results
4.1 Panel Regressions
4.2 Regressions with the Risk Factor Based on the Proportion Measure
4.3 Discussion
5 Co-movement of Fund Pairs
6 Conclusion
References
Information Divide About Mergers: Evidence from Investor Trading
1 Introduction
2 Data and Methodology
3 Empirical Results
4 Implications of Big Data and Artificial Intelligence
4.1 Impact of Big Data on Mergers
4.2 Artificial Intelligence and Asymmetric Information
5 Conclusion
References
Machine Learning and Cryptocurrency in the Financial Markets
1 Introduction
2 Machine Learning in Financial Markets
2.1 Naïve Bayes Classifier
2.2 Random Forest
2.3 Support Vector Machine
2.4 Multilayer Perceptron
2.5 Logistic Regression
3 Optimizing Portfolio—Artificial Intelligence as Optimization Techniques
4 Price Prediction—Learning for Time Series Analysis
5 Options and Bitcoin—Reinforcement Learning
6 Concluding Remarks
References