Data Analytics Applications in Emerging Markets

Description

This book analyzes the impact of technology in emerging markets by considering conditions and the history of how it has changed the way of working and market development in such contexts. The book delves into key areas such as fintech enterprises, artificial intelligence, pension funds, stock markets, and energy markets though applied studies and research. This book is a useful read for practitioners and scholars interested in how technology has and continues to change the way in which development is defined and achieved, particularly in emerging markets.  Foreword Introduction Contents Financial Technologies in the Emerging Markets 1 Fintech Trends in Emerging Markets 1.1 Fintech Determinant Factors in Emerging Markets 1.2 Tech-Savvy Populations 1.3 Proliferation of Mobile Phones 1.4 Internet Connectivity 1.5 Financial Inclusion Goals 1.6 Fintech Consumers and Business Opportunities 1.7 Challenges for Fintech in Emerging Markets 1.8 Global Trends 1.9 A Closer Look to the Mexican Fintech Market Appendix 1 References Financial System: Challenges and Opportunities of Digital Transformation in Mexico 1 Evolution of Technology in the Financial System 1.1 The Fintech Ecosystem 1.2 The Role of Authorities: Digital Financial Inclusion 1.3 Financial Technologies in the Financial System in Mexico 1.4 Regulatory Framework: Fintech Law 1.5 Challenges for Deepening the Digital Transformation of the Financial System 1.6 Risks in the Face of a New Paradigm in the Financial System 1.7 Final Remarks References Machine Learning Models, Risk Management Current Regulation and Perspectives 1 Introduction 1.1 The Global and Systemic Profile of Basel Regulation 1.2 Brief Summary of Basel Implementation and the Paradox of Internal Risk Models and the Systemic Component 1.3 Two Relevant Documents for the Purpose of This Chapter 1.4 Internal Rating Models 1.5 Relevant Changes in Basel IV, the Framework ML and AIRB Have to Deal with 1.6 The Basel IV Document on IRB Approach Minimum Requirements 1.7 Idiosyncratic and Systemic Risk in Internal Risk Models 1.8 Machine Learning Spreading, Models Undue Complexity, Dealing with Black Boxes 1.9 What the Pandemic Has Evidenced About Internal Models (IRB) 1.10 Conclusions References Financial Emerging Markets Revisited 1 Introduction 2 Emerging Markets 3 A First Glance 4 Modeling Volatility 5 Breaking Down the Time Series 6 Unwanted Fluctuations References Disruptive Monetary Phenomenon, Challenges and Complexities (Cryptocurrencies) 1 Discussion 2 Conclusions References Pension Funds in Emerging Markets: A Projection of Mexican Pension Assets 1 Introduction 2 Literature Review 3 Pension Systems in Emerging Countries Versus Developed Countries 4 The Retirement Savings System in Mexico 5 Data 6 Methodology 6.1 Asset’s Projection of the Retirement Savings System 7 Results 8 Conclusions References Relationship Between Economic Growth and Oil Production in Emerging Countries for the Period 2020–2050 1 Introduction 2 Accelerated Technological Change 3 Estimation Model and Method 4 Estimates and Results 5 Discussion and Conclusions Annex 1 Annex 2 Annex 3 References Hedging and Optimization of Energy Asset Portfolios 1 Introduction 1.1 Organization of the Study 1.2 State of the Art 2 Methodology 2.1 Data Adjustments and Simulation 3 Risk Measures 3.1 Standard Deviation, CVaR and MAD 3.2 Optimization of Functions 3.3 Portfolio Hedging 3.4 Portfolio Optimization 4 Data and Results 4.1 Data 4.2 Results 5 Advances in Machine Learning in Economics and Finance 6 Conclusions and Final Considerations Annex 1 ‘Stepzero’. Code in MATLAB® that Generates a Series of Simulated Returns from the Historical Price Data of the Portfolio Assets Annex 2 ‘Stephedge’. Code in MATLAB® that Calculates the Efficient Frontier Values, Including the One with Minimum Risk for the Hedging Portfolio. It also Calculates the Values of Alternative Risk Measures for Optimal Hedge Solutions Annex 3 ‘Stepone’. Code in MATLAB® that Calculates the Efficient Frontier Values for a Securities Portfolio Under Different Risk Measures References Artificial Intelligence and Its Application in the Study of the Legal Complexity of the Value Added Tax Act in Mexico 1 Introduction 2 Artificial Intelligence: Definition, Background, and Areas of Study 2.1 Definition of AI 2.2 AI Historical Background 3 Text Mining 3.1 What is Text Mining? 3.2 Text Mining Process 4 Research Article. Legal Complexity of the Value Added Tax Act and Its Impact on Taxpayers’ Payment of Taxes from 1978 to 2016 5 Conclusions References