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Essentials of Machine Learning in Finance and Accounting [Paperback / softback]

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Edited by (University of New Orleans, USA), Edited by (University of Pardubice, Czech Republic), Edited by (University of Illinois Springfield, USA), Edited by (HSTU, Bangladesh)
  • Format: Paperback / softback, 234 pages, height x width: 246x174 mm, weight: 660 g, 27 Tables, black and white; 47 Line drawings, black and white; 5 Halftones, black and white; 52 Illustrations, black and white
  • Series: Routledge Advanced Texts in Economics and Finance
  • Pub. Date: 21-Jun-2021
  • Publisher: Routledge
  • ISBN-10: 0367480816
  • ISBN-13: 9780367480813
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Essentials of Machine Learning in Finance and AccountingLarger Image
  • Format: Paperback / softback, 234 pages, height x width: 246x174 mm, weight: 660 g, 27 Tables, black and white; 47 Line drawings, black and white; 5 Halftones, black and white; 52 Illustrations, black and white
  • Series: Routledge Advanced Texts in Economics and Finance
  • Pub. Date: 21-Jun-2021
  • Publisher: Routledge
  • ISBN-10: 0367480816
  • ISBN-13: 9780367480813
Other books in subject:
Permanent link: https://www.kriso.lv/db/9780367480813.html
Keywords:
"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 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"--

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.

Reviews

"This book will serve as a valuable source for the digital transformation of the financial industry."

Dr. Zamir Iqbal, VP Finance and Chief Financial Officer (CFO), Islamic Development Bank (IsDB)

"An essential resource for financial accounting managers and students of financial management."

Professor Mehmet Huseyin Bilgin, Istanbul Medeniyet University, Turkey

"A comprehensive coverage of emerging intelligent technologies in finance."

Professor Ohaness Paskelian, University of Houston-Downtown, USA

List of figures
xiii
List of tables
xvii
Notes on contributors xix
1 Machine Learning In Finance And Accounting
1(6)
Mohammad Zoynul Abedin
M. Kabir Hassan
Petr Hajek
Mohammed Mohi Uddin
1.1 Introduction
1(1)
1.2 Motivation
2(1)
1.3 Brief overview of chapters
3(1)
References
4(3)
2 Decision Trees And Random Forests
7(30)
Roberto Casarin
Alessandro Facchinetti
Domenico Sorice
Stefano Tonellato
2.1 Introduction
7(1)
2.2 Classification trees
8(6)
2.2.1 Impurity and binary splitting
9(1)
2.2.1.1 Specification of the impurity function
10(1)
2.2.1.2 Labeling the leaves
11(1)
2.2.1.3 Tree size and stopping rules
12(1)
2.2.2 Performance estimation
12(1)
2.2.2.1 Resubstitution estimate
13(1)
2.2.2.2 Test-sample estimate
13(1)
2.3 Regression trees
14(2)
2.3.1 Regression
14(1)
2.3.2 Performance assessment and optimal size of the tree
15(1)
2.3.2.1 Resubstitution estimate of MSE(T)
15(1)
2.3.2.2 Test-sample estimate of MSE(T)
15(1)
2.4 Issues common to classification and regression trees
16(3)
2.4.1 Surrogate splits
16(1)
2.4.1.1 Handling of missing values
17(1)
2.4.1.2 Ranking of input variables
18(1)
2.4.1.3 Input combination
18(1)
2.4.2 Advantages and disadvantages of decision trees
18(1)
2.5 Random forests
19(5)
2.5.1 Prediction error bias-variance decomposition
19(2)
2.5.2 Bias-variance decomposition for randomized trees ensembles
21(1)
2.5.3 From trees ensembles to random forests
22(1)
2.5.4 Partial dependence function
23(1)
2.6 Forecasting bond returns using macroeconomic variables
24(4)
2.7 Default prediction based on accountancy data
28(2)
2.8 Appendix: R source codes for the applications in this chapter
30(5)
2.8.1 Application to US B of A Index
31(3)
2.8.2 SME default risk application
34(1)
References
35(2)
3 Improving Longevity Risk Management Through Machine Learning
37(20)
Susanna Levantesi
Andrea Nigri
Gabriella Piscopo
3.1 Introduction
37(2)
3.2 The mortality models
39(2)
3.3 Modeling mortality with machine learning
41(2)
3.4 Numerical application
43(5)
3.4.1 Mortality models by comparison: an empirical analysis
43(3)
3.4.2 Longevity management for life insurance: sample cases
46(2)
3.5 Conclusions
48(1)
3.6 Appendix
49(6)
Note
55(1)
References
55(2)
4 Kernel Switching Ridge Regression In Business Intelligence Systems
57(18)
Md. Ashad Alam
Osamu Komori
Md. Ferdush Rahman
4.1 Introduction
57(2)
4.2 Method
59(7)
4.2.1 Switching regression
59(1)
4.2.2 Switching ridge regression
60(1)
4.2.3 Dual form of the ridge regression
60(1)
4.2.4 Basic notion of kernel methods
61(1)
4.2.5 Alternative derivation to use ridge regression in the feature space
61(1)
4.2.6 Kernel ridge regression
62(1)
4.2.7 Kernel ridge regression: duality
63(2)
4.2.8 Kernel switching ridge regression
65(1)
4.3 Experimental results
66(4)
4.3.1 Simulation
66(1)
4.3.2 Application in business intelligence
67(3)
4.4 Discussion
70(1)
4.5 Conclusion and future research
70(1)
4.6 Appendix: Kernel switching ridge regression: an R code
71(1)
References
72(3)
5 Predicting Stock Return Volatility Using Sentiment Analysis Of Corporate Annual Reports
75(22)
Petr Hajek
Renata Myskova
Vladimir Olej
5.1 Introduction
75(1)
5.2 Related literature
76(2)
5.3 Research methodology
78(8)
5.3.1 Financial data and indicators
79(1)
5.3.2 Textual data and linguistic indicators
80(1)
5.3.3 Machine learning methods
81(5)
5.4 Experimental results
86(7)
5.5 Conclusions
93(1)
Acknowledgments
93(1)
References
93(4)
6 Random Projection Methods In Economics And Finance
97(26)
Roberto Casarin
Veronica Veggente
6.1 Introduction
97(3)
6.2 Dimensionality reduction
100(3)
6.2.1 Principal component analysis (PCA)
101(1)
6.2.2 Factor analysis
102(1)
6.2.3 Projection pursuit
103(1)
6.3 Random projection
103(3)
6.3.1 Johnson-Lindenstrauss lemma
104(1)
6.3.2 Projection matrices' specification
105(1)
6.4 Applications of random projection
106(12)
6.4.1 A compressed linear regression model
106(2)
6.4.2 Tracking the S&P 500 Index
108(3)
6.4.3 Forecasting S&P 500 returns
111(3)
6.4.4 Forecasting energy trading volumes
114(4)
6.5 Appendix: Matlab code
118(2)
Notes
120(1)
References
120(3)
7 The Future Of Cloud Computing In Financial Services: A Machine Learning And Artificial Intelligence Perspective
123(16)
Richard L. Harmon
Andrew Psaltis
7.1 Introduction
123(1)
7.2 The role of machine learning and artificial intelligence in financial services
124(2)
7.3 The enterprise data cloud
126(1)
7.4 Data contextuality: machine learning-based entity analytics across the enterprise
127(4)
7.5 Identifying Central Counterparty (CCP) risk using ABM simulations
131(3)
7.6 Systemic risk and cloud concentration risk exposures
134(3)
7.7 How should regulators address these challenges?
137(1)
Notes
137(1)
References
138(1)
8 Prospects And Challenges Of Using Artificial Intelligence In The Audit Process
139(18)
Emon Kalyan Chowdhury
8.1 Introduction
139(2)
8.1.1 Background and relevant aspect of auditing
140(1)
8.2 Literature review
141(1)
8.3 Artificial intelligence in auditing
142(1)
8.3.1 Artificial intelligence
142(1)
8.3.2 Use of expert systems in auditing
143(1)
8.3.3 Use of neural network in auditing
143(1)
8.4 Framework for including AI in auditing
143(3)
8.4.1 Components
144(1)
8.4.1.1 AI strategy
144(1)
8.4.1.2 Governance
144(1)
8.4.1.3 Human factor
144(1)
8.4.2 Elements
145(1)
8.4.2.1 Cyber resilience
145(1)
8.4.2.2 AI competencies
145(1)
8.4.2.3 Data quality
145(1)
8.4.2.4 Data architecture and infrastructure
145(1)
8.4.2.5 Measuring performance
145(1)
8.4.2.6 Ethics
145(1)
8.4.2.7 Black box
146(1)
8.5 Transformation of the audit process
146(3)
8.5.1 Impact of digitalization on audit quality
147(1)
8.5.2 Impact of digitalization on audit firms
147(1)
8.5.3 Steps to transform manual audit operations to AI-based
148(1)
8.6 Applications of artificial intelligence in auditing -- few examples
149(1)
8.6.1 KPMG
149(1)
8.6.2 Deloitte
149(1)
8.6.3 PwC
149(1)
8.6.4 Ernst and Young (EY)
150(1)
8.6.5 K.Coe Isom
150(1)
8.6.6 Doeren Mayhew
150(1)
8.6.7 CohnReznick
150(1)
8.6.8 The Association of Certified Fraud Examiners (ACFE)
150(1)
8.7 Prospects of an Al-based audit process in Bangladesh
150(2)
8.7.1 General aspects
151(1)
8.7.2 Audit firm specific aspects
151(1)
8.7.3 Business organization aspects
152(1)
8.8 Conclusion
152(1)
Bibliography
153(4)
9 Web Usage Analysis: Pillar 3 Information Assessment In Turbulent Times
157(24)
Anna Pilkova
Michal Munk
Petra Blazekova
Lubomir Benko
9.1 Introduction
157(1)
9.2 Related work
158(3)
9.3 Research methodology
161(3)
9.4 Results
164(8)
9.5 Discussion and conclusion
172(3)
Acknowledgements
175(1)
Disclosure statement
175(1)
References
175(6)
10 Machine Learning In The Fields Of Accounting, Economics And Finance: The Emergence Of New Strategies
181(18)
Maha Radwan
Salma Drissi
Silvana Secinaro
10.1 Introduction
181(1)
10.2 General overview on machine learning
182(1)
10.3 Data analysis process and main algorithms used
183(6)
10.3.1 Supervised models
184(2)
10.3.2 Unsupervised models
186(1)
10.3.3 Semi-supervised models
187(1)
10.3.4 Reinforcement learning models
188(1)
10.4 Machine learning uses: cases in the fields of economics, finance and accounting
189(5)
10.4.1 Algorithmic trading
189(1)
10.4.2 Insurance pricing
190(1)
10.4.3 Credit risk assessment
191(1)
10.4.4 Financial fraud detection
192(2)
10.5 Conclusions
194(1)
References
194(5)
11 Handling Class Imbalance Data In Business Domain
199(12)
Md. Shajalal
Mohammad Zoynul Abedin
Mohammed Mohi Uddin
11.1 Introduction
199(1)
11.2 Data imbalance problem
200(1)
11.3 Balancing techniques
201(2)
11.3.1 Random sampling-based method
201(1)
11.3.2 SMOTE oversampling
201(1)
11.3.3 Borderline-SMOTE
202(1)
11.3.4 Class weight boosting
203(1)
11.4 Evaluation metrics
203(3)
11.5 Case study: credit card fraud detection
206(2)
11.6 Conclusion
208(1)
References
208(3)
12 Artificial Intelligence (Ai) In Recruiting Talents: Recruiters' Intention And Actual Use Of Ai
211(22)
Md. Aftab Uddin
Mohammad Sarwar Alam
Md. Kaosar Hossain
Tarikul Islam
Md. Shah Azizul Hoque
12.1 Introduction
211(2)
12.2 Theory and hypothesis development
213(5)
12.2.1 Technology anxiety and intentions to use
214(1)
12.2.2 Performance expectancy and intentions to use
214(1)
12.2.3 Effort expectancy and intentions to use
214(1)
12.2.4 Social influence and intention to use
215(1)
12.2.5 Resistance to change and intentions to use
215(1)
12.2.6 Facilitating conditions and intentions to use
215(1)
12.2.7 Behavioral intention to use and actual use
216(1)
12.2.8 Moderating effects of age status
216(2)
12.3 Research design
218(5)
12.3.1 Survey design
218(1)
12.3.2 Data collection procedure and participants' information
218(1)
12.3.3 Measurement tools
218(1)
12.3.4 Results and hypotheses testing
219(1)
12.3.4.1 Analytical technique
219(1)
12.3.4.2 Measurement model evaluation
219(2)
12.3.4.3 Structural model evaluation
221(1)
12.3.4.4 Testing of direct effects
222(1)
12.3.4.5 Testing of moderating effects
222(1)
12.4 Discussion and conclusion
223(3)
12.4.1 Limitation of study and future research directions
225(1)
References
226(7)
Index 233
Mohammad Zoynul Abedin is an associate professor of Finance at the Hajee Mohammad Danesh Science and Technology University, Bangladesh. Dr. Abedin continuously publishes academic papers in refereed journals. Moreover, Dr. Abedin served as an ad hoc reviewer for many academic journals. His research interest includes data analytics and business intelligence.

M. Kabir Hassan is a professor of Finance at the University of New Orleans, USA. Prof. Hassan has over 350 papers (225 SCOPUS, 108 SSCI, 58 ESCI, 227 ABDC, 161 ABS) published as book chapters and in top refereed academic journals. According to an article published in Journal of Finance, the number of publications would put Prof. Hassan in the top 1% of peers who continue to publish one refereed article per year over a long period of time.

Petr Hajek is currently an associate professor with the Institute of System Engineering and Informatics, University of Pardubice, Czech Republic. He is the author or co-author of four books and more than 60 articles in leading journals. His current research interests include business decision making, soft computing, text mining, and knowledge-based systems.

Mohammed Mohi Uddin is an assistant professor of Accounting at the University of Illinois Springfield, USA. His primary research interests concern accountability, performance management, corporate social responsibility, and accounting data analytics. Dr. Uddin published scholarly articles in reputable academic and practitioners journals.