The essentials of machine learning in finance and accounting:
Gespeichert in:
| Weitere beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London ; New York
Routledge
2021
|
| Ausgabe: | First published |
| Schriftenreihe: | Routledge advanced texts in economics and finance
36 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032778731&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | xxiv, 234 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367480813 9780367480837 |
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Datensatz im Suchindex
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| adam_text | Contents List of figures ..........................................................................................................................................xiii List of tables............................................................................................................................................ xvii Notes on contributors ..........................................................................................................................six 1 Machine learning İn finance and accounting ........................................................................ 1 MOHAMMAD ZOYNUL ABEDIN, M. KABİR HASSAN, PETR HAJEK, AND MOHAMMED MOHI UDDIN 2 1.1 Introduction........................................................................................................................... 1.2 Motivation.............................................................................................................................. 1.3 Brief overview of chapters.................................................................................................... References.......................................................................................................................................... 1 2 3 4 Decision trees and random forests............................................................................................ 7 ROBERTO CASARIN, ALESSANDRO FACCHINETTI, DOMENICO SORICE, AND STEFANO TONELLATO 2.1 2.2 Introduction...........................................................................................................................
Classification trees................................................................................................................. 2.2.1 Impuri։} 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 Resubscittmon 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 Resubscicution estimace ofMSE(7՜).................................................... 2.3.2.2 Tesc-sample estimate of MSEfT՝)........................................................ 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.................................................................................. 7 8 9 10 H 12 12 13 13 14 H 15 15 15 16 16 17 18 18 vii
viii ■ Contents 2.4.2 Advantages and disadvantages of decision trees.................................................. 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........................................................................................................................................... 2.5 3 18 19 19 21 22 23 24 28 30 31 34 35 Improving longevity rtsk management through machine learning ................................ 37 SUSANNA LEVANTES!, ANDREA NIGRI, AND GABRIELLA PISCOPO 3-1 Յ.2 Յ.Յ 3.4
Introduction........................................................................................................................... The mortality models.............................................................................................................. Modeling mortality with machine learning...................................................................... 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............................................................................................................................. Յ.6 Appendix................................................................................................................................. Note..................................................................................................................................................... References........................................................................................................................................... 37 39 41 43 43 46 48 49 55 55 4 Kernel switching ridge regression in business intelligence systems ................................ 57 MD. ASHAD ALAM, OSAMU KOMORI, AND MD. FERDUSH RAHMAN 4.1 4.2 4.3 4.4
Introduction............................................................................................................................ Method...................................................................................................................................... 4.2.1 Switching regression................................................................................................. 4.2.2 Switching ridge regression....................................................................................... 4.2.Յ 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 tidge regression.......................................................................... Experimental results............................................................................................................... 4.З.І Simulation.................................................................................................................. 4.3-2 Application in business
intelligence..................................................................... Discussion................................................................................................................................ 57 59 59 60 60 6! 61 62 63 65 66 66 67 70
Contente U ¡x 4.5 Conclusion and future research.......................................................................................... 70 4.6 Appendix: Kernel switching ridge regression: an R code............................................... 71 References......................................................................................................................................... 72 5 Predicting stock return volatility using sentiment analysis of corporate annual reports................................................................................................................................ 75 PETR HAJEK, RENATA MYSKOVA, AND VLADIMIR OLEJ 5.1 5.2 5-3 Introduction........................................................................................................................... Related literature.................................................................................................................... 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 75 76 78 79 80 81 86 93 93 93 Random projection methods in economics and finance ................................................... 97 ROBERTO CASARINAND VERONICA VEGGENTE 6.1 6.2 Introduction........................................................................................................................... 97 Dimensionality reduction..................................................................................................... 100 6.2.1 Principal component analysis (PCA).................................................................... 101 6.2.2 Factor analysis...........................................................................................................102 6.2.3 Projection pursuit..................................................................................................... 103 6.3 Random projection.................................................................................................................103 6.3.1 Johnson-Lindenstrauss lemma................................................................................104 6.3.2 Projection matrices’
specification..........................................................................105 6.4 Applications of random projection......................................................................................106 6.4.1 A compressed linear regression model..................................................................106 6.4.2 Tracking the S P500 index...................................................................................108 6.4.3 Forecasting S P500 returns................................................................................. Ill 6.4.4 Forecasting energy trading volumes...................................................................... 114 6.5 Appendix: Matlab code..........................................................................................................118 Notes................................................................................................................................................... 120 References.......................................................................................................................................... 120 7 The future of cloud computing in financial services: a machine learning and artificial intelligence perspective ..................................................123 RICHARD L. HARMON AND ANDREW PSALTIS 7.1 7.2 7.3 Introduction............................................................................................................................ 123 The role of machine learning and artificial intelligence in financial services.............124 The enterprise data
cloud.......................................................................................................126
■ 7.4 7.5 Contents Data concextuality: machine learning-based enriry analytics across rhe enterprise... 127 Identifying Central Counterparty (CCP) risk using ABM simulations......................131 7.6 Systemic risk and cloud concentration risk exposures.................................................... 134 7.7 How should regulators address these challenges?.............................................................137 Notes.................................................................................................................................................... 137 References............................................................................................................................................138 Prospects and challenges of using artificial intelligence in the audit process...............139 EMON KALYAN CHOWDHURY 8.1 Introduction............................................................................................................................139 8.1.1 Background and relevant aspect of auditing........................................................ 140 Literature review...................................................................................................................... 141 Artificial intelligence in auditing...........................................................................................142 8.3.1 Artificial intelligence.................................................................................................142 8.3.2 Use of expert systems in
auditing........................................................................... 143 8.3.3 Use of neural network in auditing..........................................................................143 8.4 Framework for including AI in auditing.............................................................................143 8.4.1 Components............................................................................................................... 144 8.4.1.1 .31 strategy..................................................................................................144 8.4.1.2 Governance................................................................................................ 144 8.4.1.3 Human factor............................................................................................144 8.4.2 Elements...................................................................................................................... 145 8.4.2.1 Cyber resilience......................................................................................... 145 8.4.2.2 AI competencies........................................................................................145 8.4.2.3 Data quality............................................................................................... 145 8.4.2.4 Data architecture and infrastructure......................................................145 8.4.2.5 Measuring performance........................................................................... 145 8.4.2.6
Ethics.......................................................................................................... 145 8.4.2.7 Black box..................................................................................................... 146 8.5 Transformation of the audit process..................................................................................... 146 8.5.1 Impact of digitalization on audit quality ..............................................................147 8.5-2 Impact of digitalization on audit firms...................................................................147 8.5.3 Steps to transform manual audit operations to Al-based...................................148 8.6 Applications of artificial intelligence in auditing — few examples...................................149 8.6.1 KPMG.........................................................................................................................149 8.6.2 Deloitte....................................................................................................................... 149 8.6.3 PwC......................................................................... ..149 8.6.4 Ernst and Young (EY)...............................................................................................150 8.6.5 K.Coe Isom................................................................................................................ 150 8.6.6 Doeren Mayhew.........................................................................................................150 8.6.7
CohnReznick............................................................................................................. 150 8.6.8 The Association of Certified Fraud Examiners (ACFE).................................... 150 8.7 Prospects of an Al-based audit process in Bangladesh..................................................... 150 8.7.1 General aspects............................................................................................................ 151 8.2 8.3
Contents U 8.7.2 Audit հրա specific aspects.......................................................................................15) 8.7.3 Business organization aspects................................................................................. 152 8.8 Conclusion..............................................................................................................................152 Bibliography....................................................................................................................................... 153 9 Web usage analysis: pillar 3 informationassessment in turbulent times ........................ 157 ANNA PILKOVÁ, MICHAL MUNK, PETRA BLAZEKOVA AND LUBOMIR BENKO 9.1 Introduction............................................................................................................................ 157 9-2 Related work...........................................................................................................................158 9.Յ Research methodology..........................................................................................................161 9.4 Results...................................................................................................................................... 164 9-5 Discussion and conclusion...................................................................................................172 Acknowledgements...........................................................................................................................175 Disclosure
statement............................................................... 175 References.......................................................................................................................................... 175 10 Machine (earning in the helds ofaccounting, economics and finance: the emergence of new strategies .................................................................................................181 MAHA RADWAN, SAIMA DRISSI AND SILVANA SECİNARO 10.1 Introduction............................................................................................................................ 181 10.2 General overview on machine learning.............................................................................. 182 10.3 Data analysis process and main algorithms used...............................................................183 10.3.1 Supervised models................................................................................................... 184 10.3.2 Unsupervised models................................................................................................186 10.3.3 Semi-supervised models..........................................................................................187 10.3.4 Reinforcement learning models.............................................................................188 10.4 Machine learning uses: cases in the fields of economics, finance and accounting.... 189 10.4.1 Algorithmic trading.................................................................................................. 189 10.4.2 Insurance
pricing..................... 190 10.4.3 Credit risk assessment..............................................................................................191 10.4.4 Financial fraud detection........................................................................................ 192 10.5 Conclusions..............................................................................................................................194 References.......................................................................................................................................... 194 11 Handling class imbalance data inbusiness domain .............................................................. 199 MD. SHAJALAL, MOHAMMAD ZOYNUL ABEDIN AND MOHAMMED MOHI UDDIN 11.1 Introduction............................................................................................................................199 11.2 Data imbalance problem.......................................................................................................200 11.3 Balancing techniques..............................................................................................................201 11.3.1 Random sampling-based mechod..........................................................................201 11.3.2 SMOTE oversampling.............................................................................................201 11.3.3 Borderiine-SMOTE.................................................................................................202 11.3.4 Class weight
boosting.............................................................................................. 203 11.4 Evaluation metrics.................................................................................................................. 203
xii ■ Contents 11.5 Case study: credit card fraud detection................................................................................206 11.6 Conclusion.................................................................................................................................208 References...........................................................................................................................................208 12 Artificial intelligence (AI) in recruiting talents: recruiters’ intention and actual use of AI ............................................................................................................................... 211 MD. AFTAB UDDIN, MOHAMMAD SAKWARALAM, MD. KAOSAR HOSSAIN, TÁJUKUL ISLAM, AND MD. SHAH AZIZUL HOQUE 12.1 Introduction............................................................................................................................ 211 12.2 Theory and hypothesis development....................................................................................2)3 12.2.1 Technology anxiety and intentions to use.............................................................214 12.2.2 Performance expectancy and intentions to use.................................................... 214 12.2.3 Effort expectancy and intentions to use................................................................214 12.2.4 Social influence and intention to use......................................................................215 12.2.5 Resistance to change and intentions to
use.......................................................... 215 12.2.6 Facilitating conditions and intentions to use....................................................... 215 12.2.7 Behavioral intention to use and actual use........................................................... 216 12.2.8 Moderating effects of age status.............................................................................. 216 12.3 Research design......................................................................................................................... 218 12.3.1 Survey design..............................................................................................................218 12.3.2 Data collection procedure and participants1 information..................................218 12.3.3 Measurement tools..................................................................................................... 218 12.3.4 Results and hypotheses testing................................................................................219 12.3.4.1 Analytical technique................................................................................219 12.3.4.2 Measurement model evaluation........................................................... 219 12.3.4.3 Structural model evaluation...................................................................221 12.3.4.4 Testing of direct effects........................................................................... 222 12-3-4,5 Testing of moderating effects...............................................................222 12.4 Discussion
and conclusion....................................................................................................223 12.4.1 Limitation of study and future research directions............................................225 References.......................................................................................................................................... 226 iudex ........................................................................................................................................................ 233
|
| any_adam_object | 1 |
| author2 | Abedin, Mohammad Zoynul Hassan, M. Kabir 1963- Hájek, Petr 1940- Uddin, Mohammed Mohi |
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| author_facet | Abedin, Mohammad Zoynul Hassan, M. Kabir 1963- Hájek, Petr 1940- Uddin, Mohammed Mohi |
| building | Verbundindex |
| bvnumber | BV047377056 |
| callnumber-first | H - Social Science |
| callnumber-label | HG104 |
| callnumber-raw | HG104 |
| callnumber-search | HG104 |
| callnumber-sort | HG 3104 |
| callnumber-subject | HG - Finance |
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| ctrlnum | (OCoLC)1231954182 (DE-599)KXP174446247X |
| dewey-full | 332.0285/631 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
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| dewey-search | 332.0285/631 |
| dewey-sort | 3332.0285 3631 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | First published |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047377056 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:17:46Z |
| institution | BVB |
| isbn | 9780367480813 9780367480837 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032778731 |
| oclc_num | 1231954182 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xxiv, 234 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Routledge |
| record_format | marc |
| series | Routledge advanced texts in economics and finance |
| series2 | Routledge advanced texts in economics and finance |
| spellingShingle | The essentials of machine learning in finance and accounting Routledge advanced texts in economics and finance Finanzierung (DE-588)4017182-6 gnd Unsicherheit (DE-588)4186957-6 gnd Risikomanagement (DE-588)4121590-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Rechnungslegung (DE-588)4128343-0 gnd |
| subject_GND | (DE-588)4017182-6 (DE-588)4186957-6 (DE-588)4121590-4 (DE-588)4193754-5 (DE-588)4128343-0 (DE-588)4143413-4 |
| title | The essentials of machine learning in finance and accounting |
| title_auth | The essentials of machine learning in finance and accounting |
| title_exact_search | The essentials of machine learning in finance and accounting |
| title_full | The essentials of machine learning in finance and accounting edited by Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, and Mohammed Mohi Uddin |
| title_fullStr | The essentials of machine learning in finance and accounting edited by Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, and Mohammed Mohi Uddin |
| title_full_unstemmed | The essentials of machine learning in finance and accounting edited by Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, and Mohammed Mohi Uddin |
| title_short | The essentials of machine learning in finance and accounting |
| title_sort | the essentials of machine learning in finance and accounting |
| topic | Finanzierung (DE-588)4017182-6 gnd Unsicherheit (DE-588)4186957-6 gnd Risikomanagement (DE-588)4121590-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Rechnungslegung (DE-588)4128343-0 gnd |
| topic_facet | Finanzierung Unsicherheit Risikomanagement Maschinelles Lernen Rechnungslegung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032778731&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV037241432 |
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