The Essentials of Machine Learning in Finance and Accounting 1st Edition by Mohammad Zoynul Abedin, Kabir Hassan, Petr Hajek, Mohammed Mohi Uddin 103297365X 9781032973654

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ISBN 10: 103297365X

ISBN 13: 9781032973654

Author: Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, Mohammed Mohi Uddin 

This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

The Essentials of Machine Learning in Finance and Accounting 1st Table of contents:

1 Machine learning in finance and accounting

1.1 Introduction

1.2 Motivation

1.3 Brief overview of chapters

References

2 Decision trees and random forests

2.1 Introduction

2.2 Classification trees

2.2.1 Impurity and binary splitting

2.2.1.1 Specification of the impurity function

2.2.1.2 Labeling the leaves

2.2.1.3 Tree size and stopping rules

2.2.2 Performance estimation

2.2.2.1 Resubstitution estimate

2.2.2.2 Test-sample estimate

2.3 Regression trees

2.3.1 Regression

2.3.2 Performance assessment and optimal size of the tree

2.3.2.1 Resubstitution estimate of MSE(T)

2.3.2.2 Test-sample estimate of MSE(T)

2.4 Issues common to classification and regression trees

2.4.1 Surrogate splits

2.4.1.1 Handling of missing values

2.4.1.2 Ranking of input variables

2.4.1.3 Input combination

2.4.2 Advantages and disadvantages of decision trees

2.5 Random forests

2.5.1 Prediction error bias-variance decomposition

2.5.2 Bias-variance decomposition for randomized trees ensembles

2.5.3 From trees ensembles to random forests

2.5.4 Partial dependence function

2.6 Forecasting bond returns using macroeconomic variables

2.7 Default prediction based on accountancy data

2.8 Appendix: R source codes for the applications in this chapter

2.8.1 Application to US BofA index

2.8.2 SME default risk application

References

3 Improving longevity risk management through machine learning

3.1 Introduction

3.2 The mortality models

3.3 Modeling mortality with machine learning

3.4 Numerical application

3.4.1 Mortality models by comparison: an empirical analysis

3.4.2 Longevity management for life insurance: sample cases

3.5 Conclusions

3.6 Appendix

Note

References

4 Kernel switching ridge regression in business intelligence systems

4.1 Introduction

4.2 Method

4.2.1 Switching regression

4.2.2 Switching ridge regression

4.2.3 Dual form of the ridge regression

4.2.4 Basic notion of kernel methods

4.2.5 Alternative derivation to use ridge regression in the feature space

4.2.6 Kernel ridge regression

4.2.7 Kernel ridge regression: duality

4.2.8 Kernel switching ridge regression

4.3 Experimental results

4.3.1 Simulation

4.3.2 Application in business intelligence

4.4 Discussion

4.5 Conclusion and future research

4.6 Appendix: Kernel switching ridge regression: an R code

References

5 Predicting stock return volatility using sentiment analysis of corporate annual reports

5.1 Introduction

5.2 Related literature

5.3 Research methodology

5.3.1 Financial data and indicators

5.3.2 Textual data and linguistic indicators

5.3.3 Machine learning methods

5.4 Experimental results

5.5 Conclusions

Acknowledgments

References

6 Random projection methods in economics and finance

6.1 Introduction

6.2 Dimensionality reduction

6.2.1 Principal component analysis (PCA)

6.2.2 Factor analysis

6.2.3 Projection pursuit

6.3 Random projection

6.3.1 Johnson-Lindenstrauss lemma

6.3.2 Projection matrices’ specification

6.4 Applications of random projection

6.4.1 A compressed linear regression model

6.4.2 Tracking the S&P500 index

6.4.3 Forecasting S&P500 returns

6.4.4 Forecasting energy trading volumes

6.5 Appendix: Matlab code

Notes

References

7 The future of cloud computing in financial services: a machine learning and artificial intelligence perspective

7.1 Introduction

7.2 The role of machine learning and artificial intelligence in financial services

7.3 The enterprise data cloud

7.4 Data contextuality: machine learning-based entity analytics across the enterprise

7.5 Identifying Central Counterparty (CCP) risk using ABM simulations

7.6 Systemic risk and cloud concentration risk exposures

7.7 How should regulators address these challenges?

Notes

References

8 Prospects and challenges of using artificial intelligence in the audit process

8.1 Introduction

8.1.1 Background and relevant aspect of auditing

8.2 Literature review

8.3 Artificial intelligence in auditing

8.3.1 Artificial intelligence

8.3.2 Use of expert systems in auditing

8.3.3 Use of neural network in auditing

8.4 Framework for including AI in auditing

8.4.1 Components

8.4.1.1 AI strategy

8.4.1.2 Governance

8.4.1.3 Human factor

8.4.2 Elements

8.4.2.1 Cyber resilience

8.4.2.2 AI competencies

8.4.2.3 Data quality

8.4.2.4 Data architecture and infrastructure

8.4.2.5 Measuring performance

8.4.2.6 Ethics

8.4.2.7 Black box

8.5 Transformation of the audit process

8.5.1 Impact of digitalization on audit quality

8.5.2 Impact of digitalization on audit firms

8.5.3 Steps to transform manual audit operations to AI-based

8.6 Applications of artificial intelligence in auditing – few examples

8.6.1 KPMG

8.6.2 Deloitte

8.6.3 PwC

8.6.4 Ernst and Young (EY)

8.6.5 K.Coe Isom

8.6.6 Doeren Mayhew

8.6.7 CohnReznick

8.6.8 The Association of Certified Fraud Examiners (ACFE)

8.7 Prospects of an AI-based audit process in Bangladesh

8.7.1 General aspects

8.7.2 Audit firm specific aspects

8.7.3 Business organization aspects

8.8 Conclusion

Bibliography

9 Web usage analysis: pillar 3 information assessment in turbulent times

9.1 Introduction

9.2 Related work

9.3 Research methodology

9.4 Results

9.5 Discussion and conclusion

Acknowledgements

Disclosure statement

References

10 Machine learning in the fields of accounting, economics and finance: the emergence of new strategies

10.1 Introduction

10.2 General overview on machine learning

10.3 Data analysis process and main algorithms used

10.3.1 Supervised models

10.3.2 Unsupervised models

10.3.3 Semi-supervised models

10.3.4 Reinforcement learning models

10.4 Machine learning uses: cases in the fields of economics, finance and accounting

10.4.1 Algorithmic trading

10.4.2 Insurance pricing

10.4.3 Credit risk assessment

10.4.4 Financial fraud detection

10.5 Conclusions

References

11 Handling class imbalance data in business domain

11.1 Introduction

11.2 Data imbalance problem

11.3 Balancing techniques

11.3.1 Random sampling-based method

11.3.2 SMOTE oversampling

11.3.3 Borderline-SMOTE

11.3.4 Class weight boosting

11.4 Evaluation metrics

11.5 Case study: credit card fraud detection

11.6 Conclusion

References

12 Artificial intelligence (AI) in recruiting talents: recruiters’ intention and actual use of AI

12.1 Introduction

12.2 Theory and hypothesis development

12.2.1 Technology anxiety and intentions to use

12.2.2 Performance expectancy and intentions to use

12.2.3 Effort expectancy and intentions to use

12.2.4 Social influence and intention to use

12.2.5 Resistance to change and intentions to use

12.2.6 Facilitating conditions and intentions to use

12.2.7 Behavioral intention to use and actual use

12.2.8 Moderating effects of age status

12.3 Research design

12.3.1 Survey design

12.3.2 Data collection procedure and participants’ information

12.3.3 Measurement tools

12.3.4 Results and hypotheses testing

12.3.4.1 Analytical technique

12.3.4.2 Measurement model evaluation

12.3.4.3 Structural model evaluation

12.3.4.4 Testing of direct effects

12.3.4.5 Testing of moderating effects

12.4 Discussion and conclusion

12.4.1 Limitation of study and future research directions

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