An introduction to machine learning in quantitative finance:
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New Jersey ; London ; Singapore
World Scientific
[2021]
|
| Schriftenreihe: | Advanced textbooks in mathematics
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032522490&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | xxiv, 238 Seiten Illustrationen, Diagramme |
| ISBN: | 9781786349361 9781786349644 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents Preface About the Authors Acknowledgments Disclaimer Listings 1. Overview of Machine Learning and Financial Applications 1 1.1 1.2 1.3 1 2 5 5 1.4 1.5 1.6 1.7 2. vii xi xiii xv xxiii Big Data Era......................................................................... Machine Learning................................................................... Quantitative Finance............................................................. 1.3.1 Challenges of financial data..................................... 1.3.2 Recent development of machinelearning for financial applications.............................................. 1.3.3 The future of quantitative finance........................ Next Generation of Talents in Quantitative Finance ... Outline of the Book ............................................................. Useful Resources................................................................... 1.6.1 Python libraries....................................................... 1.6.2 Books and other online readingmaterials............. Go Beyond This Book.......................................................... 6 7 8 9 10 10 11 13 Supervised Learning 15 2.1 15 17 19 20 30 Framework of Regression........................................................ 2.1.1 Model................................................................ 2.1.2 Loss function............................................................. 2.1.3 Optimization............................................................. 2.1.4 Prediction and validation........................................ xvii
An Introduction to Machine Learning in Quantitative Finance xviii 2.2 2.3 2.4 3. Linear Regression and Regularization 3.1 3.2 3.3 3.4 4. Prom Regression to Classification........................................ 2.2.1 Categorical output.................................................... 2.2.2 Model.......................................................................... 2.2.3 Loss function and optimization............................... 2.2.4 Prediction and validation........................................ Model Ensemble .................................................................... 2.3.1 Intuition of ensemble.............................................. 2.3.2 Homogeneous weak learners ensemble................... 2.3.3 Heterogeneous weak learners ensemble................... Exercises................................................................................... Ordinary Least Squares Method........................................... 3.1.1 Derivation................................................................. 3.1.2 Pros and cons........................................................... Linear Model with Regularization........................................ 3.2.1 Regularization........................................................... 3.2.2 Ridge Regression........................................................ 3.2.3 Lasso Regression........................................................ 3.2.4 Numerical example ................................................. 3.2.5 Connection between Ridge Regression and Lasso
Regression................................................................. Extension of Linear Models: Basis Expansion................... Exercises................................................................................... Tree-based Models 4.1 4.2 4.3 4.4 4.5 Introduction............................................................................. Decision Tree................................................................... 4.2.1 Tree structure........................................................... 4.2.2 Model............................................ 4.2.3 Regression decision tree........................................... 4.2.4 Pruning....................................................................... 4.2.5 Feature importance................................................. Random Forest....................................................................... Gradient Boosting Decision Tree........................................ Numerical Example: Iris Dataset........................................ 4.5.1 Decision tree implementation.................................. 4.5.2 Random forest implementation............................... 33 33 34 35 36 42 43 44 48 50 51 51 51 53 54 54 56 58 59 62 64 65 67 67 68 69 70 72 77 77 78 79 80 81 82
Contents 4.6 5. Neural Networks 5.1 5.2 5.3 5.4 5.5 6. 4.5.3 GBDT implementation........................................... 4.5.4 Comparison of three tree-based methods............ Exercises................................................................................... XIX 83 83 86 87 Basic Terminology........................... 87 5.1.1 Neuron.................. 88 5.1.2 Layer.............................. 88 5.1.3 Activation function................................................. 89 5.1.4 Tensor ...................................................................... 93 Artificial Neural Network.................................................... 94 5.2.1 Shallow neural network........................................... 94 5.2.2 Multi-layer ANN model architecture..................... 97 5.2.3 Optimization............ ................................................. 99 5.2.4 Numerical example:MNIST digit recognition . . . 105 Convolutional Neural Network.............................................. 109 5.3.1 Introduction and motivation.................................. 109 5.3.2 Problem setting and image data............................ 110 5.3.3 Model......................................................................... 112 5.3.4 Optimization............................................................. 122 5.3.5 Numerical example: CifarlO image recognition . . 122 Recurrent Neural Network.................................................... 129 5.4.1 Introduction and motivation.................................. 130 5.4.2 Sequential data
....................................................... 131 5.4.3 Model......................................................................... 132 5.4.4 Optimization: Backpropagation through time . . . 133 5.4.5 Limitation of RNN ................................................. 137 5.4.6 Variants of RNN: LSTM and GRU......................... 138 5.4.7 Numerical example: High frequency financial data prediction................................................ 141 Exercises.................................................................................. 152 Cluster Analysis 153 6.1 6.2 153 154 154 155 156 157 Introduction............................................................................. Clustering Framework................................................ 6.2.1 Data and objective ........................... 6.2.2 Similarity measures................................................. 6.2.3 Clustering methods............... 6.2.4 Clustering validation .................................
xx An Introduction to Machine Learning in Quantitative Finance 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7. Principal Component Analysis 7.1 7.2 7.3 7.4 7.5 8. Aľ-means...................................................................... 6.3.1 Introduction............................................................. 6.3.2 Practical issues.......................................................... 6.3.3 Summary.................................... Hierarchical Clustering................................. 6.4.1 Linkage........................... 6.4.2 Dendrogram............................................................. Density-based Clustering: DBSCAN .................................. 6.5.1 Definition.................................................................... 6.5.2 Determine parameters.................. 6.5.3 Advantage of DBSCAN........................................... Distribution-based Clustering.............................................. 6.6.1 Introduction ............................................................. 6.6.2 Expectation-maximization algorithm.................. 6.6.3 Gaussian mixture model ........................................ Clustering with Python ....................................................... Numerical Example................................................................. Exercises.................................... Dimension Reduction.............................................................. Principal Component Analysis.............................................. 7.2.1 Linear transformation..................... 7.2.2 Singular value
decomposition.................................. 7.2.3 Principal components and covariance................... 7.2.4 Introduction.............................................................. 7.2.5 Practical problems.................................................... Python Implementation....................................................... Application: Term Structure Analysis Using PCA............ 7.4.1 Introduction to fixed income term structure .... 7.4.2 Data and observation.............................................. 7.4.3 PCA on term structure........................................... 7.4.4 PCA for hedging....................................................... 7.4.5 Clustering and PCA................................................. Exercises.................................................... Reinforcement Learning 8.1 8.2 Introduction............................................................................. Recurrent Reinforcement Learning..................................... 158 158 159 162 162 164 165 166 166 168 169 169 169 170 172 172 172 175 177 177 178 178 178 179 180 181 182 183 183 184 186 189 194 195 197 197 200
Contents 8.3 8.4 8.5 9. Link between RRL and RNN.............................................. Numerical Example: Algorithmic Trading......................... Exercises.................................................................................. Case Study in Finance: Home Credit Default Risk 9.1 9.2 9.3 9.4 9.5 9.6 Problem Setup and Data.................. Exploratory Data Analysis.................................................... 9.2.1 Imbalanced data . . . .............................................. 9.2.2 Missing values.......................................................... 9.2.3 Feature grouping....................................................... Building the First Classifier................................................. 9.3.1 Data preprocessing ................................................. 9.3.2 Feature engineering................................................. 9.3.3 Training a model ....................................................... 9.3.4 Out-of-folds prediction ........................................... 9.3.5 Parameter tuning.................................................... Model Stacking...................................................................... Result Submission to Haggle................................................. Exercises................................................................................... 9.6.1 CFM challenge: Volatility forecast........................ 9.6.2 Other Haggle competitions on financial applications ............................................................. Bibliography Index XXI 202
205 211 213 214 216 216 216 216 218 218 219 219 220 221 222 228 229 229 229 231 235
|
| any_adam_object | 1 |
| author | Ni, Hao Dong, Xin Zheng, Jinsong Yu, Guangxi |
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| dewey-full | 332.0285/631 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.0285/631 |
| dewey-search | 332.0285/631 |
| dewey-sort | 3332.0285 3631 |
| dewey-tens | 330 - Economics |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV047116098 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:10:09Z |
| institution | BVB |
| isbn | 9781786349361 9781786349644 |
| language | English |
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| publishDate | 2021 |
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| publisher | World Scientific |
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| series2 | Advanced textbooks in mathematics |
| spellingShingle | Ni, Hao Dong, Xin Zheng, Jinsong Yu, Guangxi An introduction to machine learning in quantitative finance Maschinelles Lernen (DE-588)4193754-5 gnd Mathematische Modellierung (DE-588)7651795-0 gnd Finanzwirtschaft (DE-588)4017214-4 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)7651795-0 (DE-588)4017214-4 |
| title | An introduction to machine learning in quantitative finance |
| title_auth | An introduction to machine learning in quantitative finance |
| title_exact_search | An introduction to machine learning in quantitative finance |
| title_full | An introduction to machine learning in quantitative finance Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China), Guangxi Yu (SWS Research, China) |
| title_fullStr | An introduction to machine learning in quantitative finance Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China), Guangxi Yu (SWS Research, China) |
| title_full_unstemmed | An introduction to machine learning in quantitative finance Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China), Guangxi Yu (SWS Research, China) |
| title_short | An introduction to machine learning in quantitative finance |
| title_sort | an introduction to machine learning in quantitative finance |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Mathematische Modellierung (DE-588)7651795-0 gnd Finanzwirtschaft (DE-588)4017214-4 gnd |
| topic_facet | Maschinelles Lernen Mathematische Modellierung Finanzwirtschaft |
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