The essentials of machine learning in finance and accounting:
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; New York
Routledge
2021
|
| Ausgabe: | First published |
| Schriftenreihe: | Routledge advanced texts in economics and finance
36 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xxiv, 234 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367480813 9780367480837 |
Internformat
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| 245 | 1 | 0 | |a The essentials of machine learning in finance and accounting |c edited by Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, and Mohammed Mohi Uddin |
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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
|
| adam_txt |
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 |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047377056 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:46:29Z |
| indexdate | 2024-07-10T09:10:26Z |
| 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 |
| spelling | The essentials of machine learning in finance and accounting edited by Mohammad Zoynul Abedin, M. Kabir Hassan, Petr Hajek, and Mohammed Mohi Uddin First published London ; New York Routledge 2021 xxiv, 234 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Routledge advanced texts in economics and finance 36 Includes bibliographical references and index Finanzierung (DE-588)4017182-6 gnd rswk-swf Unsicherheit (DE-588)4186957-6 gnd rswk-swf Risikomanagement (DE-588)4121590-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Rechnungslegung (DE-588)4128343-0 gnd rswk-swf Finance / Data processing Finance / Mathematical models Accounting / Data processing Machine leaarning (DE-588)4143413-4 Aufsatzsammlung gnd-content Finanzierung (DE-588)4017182-6 s Rechnungslegung (DE-588)4128343-0 s Maschinelles Lernen (DE-588)4193754-5 s Risikomanagement (DE-588)4121590-4 s Unsicherheit (DE-588)4186957-6 s b DE-604 Abedin, Mohammad Zoynul (DE-588)1219939609 edt Hassan, M. Kabir 1963- (DE-588)133341070 edt Hájek, Petr 1940- (DE-588)143699024 edt Uddin, Mohammed Mohi (DE-588)1196810168 edt Erscheint auch als Online-Ausgabe 978-1-003-03790-3 Routledge advanced texts in economics and finance 36 (DE-604)BV037241432 36 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis |
| 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_exact_search_txtP | 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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