{"product_id":"deep-learning-in-quantitative-finance-hardback-9781119685241","title":"Deep Learning in Quantitative Finance (Hardback) 9781119685241","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Learning in Quantitative Finance\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAndrew Green (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119685241, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 March 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e736 pages\u003cbr\u003e27.9 x 22.6 x 4.6 cm, 1.678 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eThe complete and practical guide to one of the hottest topics in quantitative finance\u003c\/b\u003e  \u003cp\u003eDeep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. \u003ci\u003eDeep Learning in Quantitative Finance\u003c\/i\u003e provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly. \u003c\/p\u003e\n\u003cp\u003eThis book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You’ll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts. \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eCovers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques\u003c\/li\u003e \u003cli\u003eOffers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning\u003c\/li\u003e \u003cli\u003eDemonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website\u003c\/li\u003e \u003cli\u003eIntroduces the most important software frameworks for applying deep learning within finance\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003cbr\u003e1.1 What this book is about 3\u003cbr\u003e1.2 The Rise of AI 5\u003cbr\u003e1.3 The Promise of AI in Quantitative Finance 7\u003cbr\u003e1.4 Practicalities 7\u003cbr\u003e1.5 Reading this book 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Feed Forward Neural Networks 13\u003c\/b\u003e\u003cbr\u003e2.1 Introducing Neural Networks 13\u003cbr\u003e2.2 Regression and Classification 18\u003cbr\u003e2.3 Activation Functions 27\u003cbr\u003e2.4 The Universal Function Approximation Theorem 45\u003cbr\u003e2.5 Conclusions 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Training Neural Networks 49\u003c\/b\u003e\u003cbr\u003e3.1 Backpropagation and Adjoint Algorithmic Differentiation 50\u003cbr\u003e3.2 Data Preparation and Scaling 53\u003cbr\u003e3.3 Weight Initialization 57\u003cbr\u003e3.4 The Choice of Loss Function 68\u003cbr\u003e3.5 Optimization Algorithms 82\u003cbr\u003e3.6 Common Training Problems 97\u003cbr\u003e3.7 Batch Normalization 104\u003cbr\u003e3.8 Evaluation and Validation 110\u003cbr\u003e3.9 Sobolev Training Using Function Derivatives 124\u003cbr\u003e3.10 Conclusions 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Regularisation 133\u003c\/b\u003e\u003cbr\u003e4.1 Introduction Regularisation and Generalisation 133\u003cbr\u003e4.2 Weight Decay 134\u003cbr\u003e4.3 Early Stopping 137\u003cbr\u003e4.4 Ensemble Methods and Dropout 138\u003cbr\u003e4.5 Data Augmentation 146\u003cbr\u003e4.6 Other Regularisation Methods 147\u003cbr\u003e4.7 Conclusions Regularisation Strategy 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hyperparameter Optimization 151\u003c\/b\u003e\u003cbr\u003e5.1 Introduction 151\u003cbr\u003e5.2 Manual 155\u003cbr\u003e5.3 Grid Search 155\u003cbr\u003e5.4 Random Search 158\u003cbr\u003e5.5 Bayesian Optimization 159\u003cbr\u003e5.6 Bandit-based 165\u003cbr\u003e5.7 Population Based Training (PBT) 181\u003cbr\u003e5.8 Conclusions 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Convolutional Neural Networks 187\u003c\/b\u003e\u003cbr\u003e6.1 Introduction 187\u003cbr\u003e6.2 Convolutions 188\u003cbr\u003e6.3 Downsampling 203\u003cbr\u003e6.4 Data Augmentation 206\u003cbr\u003e6.5 Transfer Learning Using Pre-trained Networks 211\u003cbr\u003e6.6 Visualising Features 213\u003cbr\u003e6.7 Famous CNNs 223\u003cbr\u003e6.8 Conclusions on CNNs 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Sequence Models 255\u003c\/b\u003e\u003cbr\u003e7.1 Introducing Sequence Models 255\u003cbr\u003e7.2 Recurrent Neural Networks 257\u003cbr\u003e7.3 Neural Natural Language Processing 276\u003cbr\u003e7.4 Conclusions on Sequence Models 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Autoencoders 323\u003c\/b\u003e\u003cbr\u003e8.1 Introduction 323\u003cbr\u003e8.2 Autoencoders and Singular-Valued Decomposition 325\u003cbr\u003e8.3 Shallow and Deep Autoencoders 332\u003cbr\u003e8.4 Regularized and Sparse Autoencoders 336\u003cbr\u003e8.5 Denoising Autoencoders 339\u003cbr\u003e8.6 Autoencoders and Generative Models 341\u003cbr\u003e8.7 Conclusion 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Generative Models 343\u003c\/b\u003e\u003cbr\u003e9.1 Introduction 343\u003cbr\u003e9.2 Evaluating Generative Model Performance 345\u003cbr\u003e9.3 Energy-based Models (EBMs) 348\u003cbr\u003e9.4 Variational Autoencoders (VAEs) 383\u003cbr\u003e9.5 Generative Adversarial Networks (GANs) 396\u003cbr\u003e9.6 Latent Diffusion Models (LDMs) 491\u003cbr\u003e9.7 Conclusions on Generative Models 493\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Deep Reinforcement Learning 495\u003c\/b\u003e\u003cbr\u003e10.1 Introduction 495\u003cbr\u003e10.2 Key Concepts in Reinforcement Learning 496\u003cbr\u003e10.3 Markov Decision Processes (MDPs) and the Bellman Equations 506\u003cbr\u003e10.4 Dynamic Programming and Policy Search 509\u003cbr\u003e10.5 Monte Carlo Methods for RL 516\u003cbr\u003e10.6 TD Learning 535\u003cbr\u003e10.7 Deep Q Networks (DQNs) 546\u003cbr\u003e10.8 Policy Gradient 561\u003cbr\u003e10.9 Actor-Critic Methods 567\u003cbr\u003e10.10 Conclusions 568\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Derivative Valuation using Neural Networks 571\u003c\/b\u003e\u003cbr\u003e11.1 Introduction 571\u003cbr\u003e11.2 Derivative Valuation using Neural Networks trained as Non-parametric Models 572\u003cbr\u003e11.3 Derivative Valuation Function Approximation 584\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 High Dimensional PDE and BSDE Solvers 603\u003c\/b\u003e\u003cbr\u003e12.1 Introduction 603\u003cbr\u003e12.2 Deep Galerkin Method (DGM) 604\u003cbr\u003e12.3 Deep BSDE Solvers 619\u003cbr\u003e12.4 Projection and Martingale Solvers 641\u003cbr\u003e12.5 Deep Path Dependent PDEs (DPPDE) 642\u003cbr\u003e12.6 Physics Informed Neural Networks (PINNs) 644\u003cbr\u003e12.7 Deep Backward Dynamic Programming (DBDP) 646\u003cbr\u003e12.8 Deep Splitting (DS) 647\u003cbr\u003e12.9 Conclusions 649\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Deep Monte Carlo and Optimal Stopping 651\u003c\/b\u003e\u003cbr\u003e13.1 Introduction 651\u003cbr\u003e13.2 Deep Monte Carlo 653\u003cbr\u003e13.3 Deep Optimal Stopping and Applications 685\u003cbr\u003e13.4 Conclusion Deep Monte Carlo 703\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Static Replication using Neural Networks 705\u003c\/b\u003e\u003cbr\u003e14.1 (Semi) Static Replication 705\u003cbr\u003e14.2 Neural Static Replication 708\u003cbr\u003e14.3 Conclusions on Neural Static Replication 716\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Volatility Surfaces 717\u003c\/b\u003e\u003cbr\u003e15.1 Introduction 717\u003cbr\u003e15.2 Volatility Surface Models 718\u003cbr\u003e15.3 Deep Learning Volatility Surfaces 722\u003cbr\u003e15.4 Deep Local Volatility 736\u003cbr\u003e15.5 Conclusions 750\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Model Calibration 751\u003c\/b\u003e\u003cbr\u003e16.1 Introduction 751\u003cbr\u003e16.2 Model Calibration 752\u003cbr\u003e16.3 Conclusion on Deep Calibration 767\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 XVA 769\u003c\/b\u003e\u003cbr\u003e17.1 Introduction 769\u003cbr\u003e17.2 Credit Curve Mapping 771\u003cbr\u003e17.3 Exposure Calculation using Neural Networks 784\u003cbr\u003e17.4 Conclusions on Deep XVA 791\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Generating Realistic Market Data 793\u003c\/b\u003e\u003cbr\u003e18.1 Introduction and Classical Methods 793\u003cbr\u003e18.2 Motivation and Applications of Synthetic Financial Market Data 796\u003cbr\u003e18.3 Time Series Generation 798\u003cbr\u003e18.4 Generating Higher Dimensional Market Data Structures 864\u003cbr\u003e18.5 Completing Market Data - imputing missing values 886\u003cbr\u003e18.6 Conclusions Synthetic Market Data 888\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Deep Hedging 893\u003c\/b\u003e\u003cbr\u003e19.1 Introduction 893\u003cbr\u003e19.2 Approaches to Deep Hedging 894\u003cbr\u003e19.3 Deep Hedging Examples 935\u003cbr\u003e19.4 Conclusion 942\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 The Future Quant 957\u003c\/b\u003e\u003cbr\u003e20.1 Conclusion on Deep Learning 957\u003cbr\u003e20.2 The Future of Quantitative Analytics 959\u003cbr\u003e20.3 The Future Quant 960\u003cbr\u003e20.4 A Final Word 960\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Finance \u0026amp; accounting [\u003ca title=\"See our other books on Finance \u0026amp; accounting\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Finance%20\u0026amp;%20accounting%20%5BKF%5D%22\"\u003eKF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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