Enterprise applications, markets and services in the finance industry : 10th International Workshop, FinanceCom 2020, Helsinki, Finland, August 18, 2020 : revised selected papers

This book constitutes the revised selected papers from the 10th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2020, held in Helsinki, Finland, in August 2020. Due to the COVID-19 pandemic the conference took place virtually. The 6 full papers presented together with 1 extended abstract in this volume were carefully reviewed and selected from a total of 14 submissions to the workshop. They are grouped in topical sections named Machine Learning Applications in Trading and Financial Markets, Fraud Detection and Information Generation in Finance, and Alternative Trading and Investment Offerings by FinTechs. The workshop spans multiple disciplines, including analytical, technical, service, economic, sociological and behavioral sciences Preface Organization Contents Machine Learning Applications in Trading and Financial Markets State-of-the-Art in Applying Machine Learning to Electronic Trading 1 Introduction 2 Background 2.1 Components of an Electronic Trading System 2.2 Upstream Decision Making 2.3 Analysing Information Sources 2.4 Trading Decision Making and Order Execution Management 2.5 Pre-trade and Post-trade Analytics 3 Survey Methodology 4 Results Per Categories 4.1 Analysing Data Patterns 4.2 Information Processing and Analysis 4.3 Trading Decision Making and Order Execution Management 5 Summary, Conclusions and Further Work References Using Machine Learning to Predict Short-Term Movements of the Bitcoin Market 1 Introduction 2 Related Work 2.1 Market Efficiency and Financial Market Prediction 2.2 Bitcoin Market Prediction via Machine Learning 3 Methodology 3.1 Data 3.2 Generation of Training, Validation and Test Sets 3.3 Features 3.4 Targets 3.5 Prediction Models 3.6 Evaluation 4 Results 5 Discussion 6 Conclusion A Supplemental Tables B Supplemental Graphical Material References Fraud Detection and Information Generation in Finance Scalable and Imbalance-Resistant Machine Learning Models for Anti-money Laundering: A Two-Layered Approach 1 Introduction 2 Related Work 3 Data 4 Methodology 4.1 Two-Layered Model Concept 4.2 Framework 4.3 Feature Extraction 4.4 Tackling Class Imbalance 5 Experimental Setup 6 Results 6.1 First Layer Results 6.2 Second Layer Results 6.3 Overall Results 7 Conclusions and Future Work A Appendix References Leveraging Textual Analyst Sentiment for Investment 1 Introduction 2 Data 3 Methodology 3.1 Trading Signals 3.2 Portfolio Construction 3.3 Impact of Forecast Error 3.4 Performance Evaluation 4 Results 5 Discussion and Conclusion Appendix References Alternative Trading and Investment Offerings by FinTechs Portfolio Rankings on Social Trading Platforms in Uncertain Times 1 Introduction 2 Theoretical Foundations 2.1 Social Trading 2.2 Rankings on Online Platforms 2.3 Rankings in the Context of Social Trading Platforms 3 Dataset and Descriptive Statistics 4 Analysis 5 Discussion 6 Conclusion References What Do Robo-Advisors Recommend? - An Analysis of Portfolio Structure, Performance and Risk 1 Introduction 2 Foundations and Related Research on Robo-Advisors 2.1 Robo-Advisor Definition and Process 2.2 Research on Robo-Advisor Portfolio Structures 3 Methodology 3.1 Analysis Approach 3.2 Analysis Measures and Statistical Test Procedures 4 Findings 4.1 Portfolio Products and Allocation 4.2 Portfolio Performance and Risk 5 Discussion 5.1 Implications 5.2 Limitations and Future Research 6 Conclusion Appendix References Invited Talk The Financial Viability of eHealth and mHealth 1 Introduction 2 eHealth/mHealth Costs 3 Occupation Therapy Case Study 3.1 Actual OT Paper-Based Assessment Process 3.2 Proposed mHealth-Based OT Assessment Process 3.3 Tentative Direct Cost Analysis Model 4 Conclusions Reference Author Index