Machine Learning Approaches in Financial Analytics

• Record Nr.: UNINA9910882887903321
• Autore: Maglaras Leandros A
• Pubbl/distr/stampa: Cham : , : Springer, , 2024; ©2024
• ISBN: 9783031610370; 9783031610363
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (485 pages)
• Collana: Intelligent Systems Reference Library ; ; v.254
• Altri autori (Persone): DasSonali; TripathyNaliniprava; PatnaikSrikanta
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Editorial -- Key Features -- Contents -- Part I Foundations -- 1 Introduction to Optimal Execution -- 1.1 Overview: Financial Market and Execution Problem -- 1.1.1 Electronic Market and System Transition -- 1.1.2 Large Trader and Market (Price) Impact -- 1.1.3 Structure of This Chapter -- 1.2 Notations and Some Remarks -- 1.3 Almgren-Chriss Model for Optimal Execution -- 1.3.1 Market Model and Optimal Execution Strategy -- 1.3.2 Efficient Frontier of Optimal Execution: A Mean-Variance Perspective -- 1.4 A Continuous-time Analog -- 1.4.1 Model -- 1.4.2 Numerical Example -- 1.5 Transient Impact Model with Small Traders' Orders ch1OMSM20QF -- 1.5.1 Market Model -- 1.5.2 Formulation as a Markov Decision Process -- 1.5.3 Dynamics of the Optimal Execution -- 1.5.4 In the Case with Target Close Order -- 1.5.5 Computation Method for Optimal Execution -- 1.6 Bibliographic Notes -- References -- Part II Tools and Techniques -- 2 Python Stack for Design and Visualization in Financial Engineering -- 2.1 Introduction -- 2.2 Design of Interactive Applications: Literature Review -- 2.3 Design Elements -- 2.4 Interactive Python Applications with Jupyter and Matplotlib -- 2.4.1 Interactive Plotting with Matplotlib -- 2.5 Python Implementation -- 2.5.1 The Exemplar Structured Product: A 3-Way Collar -- 2.5.2 Python ; Implementation -- 2.5.3 Black_Scholes: An Object-Oriented Python Module for Designing and Pricing -- 2.5.4 Adding Matplotlib Widgets at Run Time -- 2.5.5 Extensions: An Example with a Barrier Included -- 2.6 Conclusion -- Appendix: Design of 3-Way Collar -- References -- 3 Neurodynamic Approaches to Cardinality-Constrained Portfolio Optimization -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Biconvex Optimization -- 3.2.2 Mean-Variance Portfolio Selection -- 3.2.3 Conditional Value-at-Risk.; 3.2.4 Sharpe Ratio and Conditional Sharpe Ratio -- 3.2.5 Cardinality-Constrained Portfolio Selection -- 3.3 Neurodynamic Models -- 3.4 Neurodynamic Portfolio Selection -- 3.4.1 Collaborative Neurodynamic Approach -- 3.4.2 Two-Timescale Duplex Neurodynamic Approach -- 3.5 Experimental Results -- 3.5.1 Setups -- 3.5.2 Results -- 3.6 Concluding Remarks -- References -- 4 Fully Homomorphic Encrypted Wavelet Neural Network for Privacy-Preserving Bankruptcy Prediction in Banks -- 4.1 Introduction -- 4.2 Overview of Bankruptcy Prediction and Problem Definition -- 4.3 Literature Survey -- 4.4 Proposed Methodology -- 4.4.1 Homomorphic Encryption -- 4.4.2 CKKS Scheme -- 4.4.3 Overview of the Original Unencrypted WNN -- 4.4.4 Proposed Privacy-Preserving Wavelet Neural Network -- 4.5 Datasets Description -- 4.5.1 Qualitative Bankruptcy Dataset -- 4.5.2 Spanish Banks Dataset -- 4.5.3 Turkish Banks Dataset -- 4.5.4 UK Banks Dataset -- 4.6 Results and Discussion -- 4.7 Conclusions and Future Work -- Appendix: Datasets Description -- References -- 5 Tools and Measurement Criteria of Ethical Finance Through Computational Finance -- 5.1 Introduction -- 5.2 Ethical Finance, Principles and Operating Criteria -- 5.3 Computational Finance Critic: Limits and Challenge with Respect to Ethic Finance -- 5.3.1 Some Definition Aspects Considered in This Paragraph -- 5.3.2 The Background Vice: Economic Positivism -- 5.4 Measurement Criteria of Computational Finance with the Principles of Ethical Finance -- 5.5 Some Conclusions -- References -- 6 Data Mining Techniques for Predicting the Non-performing Assets (NPA) of Banks in India -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Research Methodology -- 6.3.1 Sample and Data Collection -- 6.3.2 Experimental Variables -- 6.3.3 Data Mining Methodology -- 6.4 Results and Discussion -- 6.4.1 Random Forest.; 6.4.2 Elastic Net Regression -- 6.4.3 k-NN Algorithm -- 6.5 Conclusion -- Appendix -- References -- 7 Multiobjective Optimization of Mean-Variance-Downside-Risk Portfolio Selection Models -- 7.1 Introduction -- 7.2 Multiobjective Portfolio Optimization Models -- 7.3 Multiobjective Evolutionary Algorithms -- 7.4 Computational Results -- 7.4.1 Computational Experiments on S&amp -- P 100 Index -- 7.4.2 Computational Experiments on a Large-Scale Problem Instance -- 7.4.3 Comparison with Competing Portfolios -- 7.5 Conclusion -- References -- Part III Risk Assessment and Ethical Considerations -- 8 Bankruptcy Forecasting of Indian Manufacturing Companies Post the Insolvency and Bankruptcy Code 2016 Using Machine Learning Techniques -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Data Collection and Methodology -- 8.3.1 Data Collection -- 8.3.2 Methodology -- 8.4 Data Analysis -- 8.5 Empirical Findings -- 8.6 Conclusion -- 8.6.1 Managerial Implication -- 8.6.2 Conclusion -- 8.7 Future Scope -- References -- 9 Ensemble Deep Reinforcement Learning for Financial Trading -- 9.1 Introduction -- 9.1.1 How Reinforcement Learning Works -- 9.2 Problem Statement -- 9.3 Literature Survey -- 9.4 Proposed Methodology -- 9.4.1 Assumptions Made During Stock Trading -- 9.4.2 Stock Market Environment -- 9.4.3 RL Trading Agents -- 9.5 Dataset Description ; and Experimental Setup -- 9.6 Results and Discussion -- 9.7 Conclusions -- References -- Part IV Real-World Applications -- 10 Bibliometric Analysis of Digital Financial Reporting -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Methodology -- 10.4 Data Extraction -- 10.5 Results -- 10.6 Distribution of Annual Trend -- 10.7 Distribution of Authors -- 10.8 Distribution of Top Journals -- 10.9 Distribution of Articles Based on Citations -- 10.10 Distribution of Different Affiliations.; 10.11 Distribution of Publications Among Countries -- 10.12 Distribution of Keyword Analysis -- 10.13 Discussions -- 10.14 Conclusion -- 10.15 Theoretical Implications -- 10.16 Practical Implications -- References -- 11 The Quest for Financing Environmental Sustainability in Emerging Nations: Can Internet Access and Financial Technology Be Crucial? -- 11.1 Background -- 11.2 Schematic Analysis -- 11.3 Situational Analysis -- 11.3.1 Where is African Climate Finance Coming from? -- 11.3.2 The Glimpse of Environmental Sustainability Financing of East Asian Countries -- 11.3.3 Internet Access Situational Analysis -- 11.3.4 Fintech Situational Analysis by Region -- 11.4 Implication of Findings -- 11.5 Policy Outlook -- References -- 12 A Comprehensive Review of Bitcoin's Energy Consumption and Its Environmental Implications -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Bitcoin Mining and Its Implications -- 12.3.1 The Concept of Bitcoin Mining -- 12.3.2 Estimating Energy Consumption of Mining Farms -- 12.3.3 Issues with Bitcoin Mining -- 12.4 The Economies of Bitcoin Mining -- 12.4.1 Profitability of Bitcoin Mining -- 12.4.2 Regulation of Bitcoin Mining -- 12.5 Discussion -- 12.5.1 Calculating Bitcoin's Energy Consumption -- 12.5.2 Current Models Used to Calculate Bitcoin's Energy Consumption -- 12.6 Sustainability and Future of Mining -- 12.6.1 The Discomforts of Switching to Renewable Electricity for Mining -- 12.6.2 Proof-of-Stake as an Alternative Strategy -- 12.6.3 Limitations on Circuit Applications for Reducing Electronic Wastages -- 12.7 Conclusion -- References -- 13 Emerging Economies: Volatility Prediction in the Metal Futures Markets Using GARCH Model -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Data and Methodology -- 13.3.1 GARCH (1,1) -- 13.4 Results and Discussion -- 13.5 Concluding Observation and Managerial Implication.; Appendix -- References -- 14 Constructing a Broad View of Tax Compliance Intentions Based on Big Data -- 14.1 Introduction to Taxes and Tax Compliance Intentions -- 14.2 Introduction to Theory of Planned Behavior (TPB) -- 14.3 The Link Between TPB and Intention to Comply with Taxes -- 14.4 Religiosity and Utilization of e-Filing as a Determinant Factor in Intention to Comply with Taxes Through TPB -- 14.5 Extracting Information in Online Media is Related to Variables that Influence Tax Compliance Intentions -- 14.6 Introduction to Big Data -- 14.7 Modeling Using SEM -- 14.8 Integration of Information Mining Results in Online Media Using DNA with SEM -- 14.9 Research Hypothesis -- 14.10 Conclusion -- References -- 15 Influence of Firm-Specific Variables on Capital Structure Decisions: An Evidence from the Fintech Industry -- 15.1 Introduction -- 15.2 Literature Review and Hypotheses Development -- 15.2.1 Capital Structure (Capstr) Decisions -- 15.2.2 Capital Structure (Capstr) Decisions: Fintech Industry -- 15.2.3 Determinants of Capital Structure -- 15.3 Variables and the Research Model -- 15.3.1 Research Model -- 15.4 Sample and Descriptive Statistics -- 15.5 Results and Discussion -- 15.5.1 Distribution of Capstr (Box Plot Technique) -- 15.5.2 Regression Results -- 15.6 Conclusion and Managerial Contribution -- References -- 16 A Weights Direct Determination Neural Network for Credit Card ; Attrition Analysis -- 16.1 Introduction -- 16.2 The MTA-WASD Model -- 16.2.1 Activation Functions and the WDD Process -- 16.2.2 The Trigonometrically Activated WASD Algorithm -- 16.3 Experiments -- 16.3.1 Attrition Dataset I -- 16.3.2 Attrition Dataset II -- 16.3.3 Attrition Dataset III -- 16.3.4 Collective Performance Comparison -- 16.4 Conclusion -- References -- 17 Stock Market Prediction Using Machine Learning: Evidence from India -- 17.1 Introduction.; 17.2 A Review on Machine Learning Techniques.