Deep Learning For Finance
DOWNLOAD
Download Deep Learning For Finance PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning For Finance book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Deep Learning For Finance
DOWNLOAD
Author : Sofien Kaabar
language : en
Publisher:
Release Date : 2024-02-20
Deep Learning For Finance written by Sofien Kaabar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-20 with categories.
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create, trade, and back-test trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents out-of-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Create and understand machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in trading Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the profitability and the predictability of the models to understand their limitations and potential
Deep Learning For Finance
DOWNLOAD
Author : Sofien Kaabar
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-01-08
Deep Learning For Finance written by Sofien Kaabar and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-08 with Business & Economics categories.
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential
Machine Learning In Finance
DOWNLOAD
Author : Matthew F. Dixon
language : en
Publisher: Springer Nature
Release Date : 2020-07-01
Machine Learning In Finance written by Matthew F. Dixon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Business & Economics categories.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Hands On Deep Learning For Finance
DOWNLOAD
Author : Luigi Troiano
language : en
Publisher:
Release Date : 2020-02-28
Hands On Deep Learning For Finance written by Luigi Troiano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Computers categories.
The Essentials Of Machine Learning In Finance And Accounting
DOWNLOAD
Author : Mohammad Zoynul Abedin
language : en
Publisher: Routledge
Release Date : 2021-06-20
The Essentials Of Machine Learning In Finance And Accounting written by Mohammad Zoynul Abedin and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-20 with Business & Economics categories.
This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Machine Learning And Ai In Finance
DOWNLOAD
Author : German Creamer
language : en
Publisher: Routledge
Release Date : 2021-04-05
Machine Learning And Ai In Finance written by German Creamer and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-05 with Business & Economics categories.
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
Machine Learning For Finance
DOWNLOAD
Author : Jannes Klaas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30
Machine Learning For Finance written by Jannes Klaas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Computers categories.
A guide to advances in machine learning for financial professionals, with working Python code Key FeaturesExplore advances in machine learning and how to put them to work in financial industriesClear explanation and expert discussion of how machine learning works, with an emphasis on financial applicationsDeep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learningBook Description Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learnApply machine learning to structured data, natural language, photographs, and written textHow machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and moreImplement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlowDig deep into neural networks, examine uses of GANs and reinforcement learningDebug machine learning applications and prepare them for launchAddress bias and privacy concerns in machine learningWho this book is for This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
Deep Learning For Finance
DOWNLOAD
Author : J.B. Heaton
language : en
Publisher:
Release Date : 2019
Deep Learning For Finance written by J.B. Heaton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems - such as those presented in designing and pricing securities, constructing portfolios, and risk management - often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.
Deep Learning For Quant Finance
DOWNLOAD
Author : Victor Trex
language : en
Publisher: HiTeX Press
Release Date : 2025-12-10
Deep Learning For Quant Finance written by Victor Trex and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-10 with Business & Economics categories.
"Deep Learning for Quant Finance: Transformers, LSTMs, and Reinforcement Learning" Deep learning is transforming quantitative finance, from intraday alpha generation to market making and derivatives hedging. This book is written for quantitative researchers, data scientists, and technically inclined practitioners who want to move beyond toy examples and build serious, production-grade models. Blending financial intuition with modern machine learning, it shows how to connect neural architectures directly to PnL, risk, and execution objectives in real markets. You will progress from mathematical and market microstructure foundations to a full deep learning stack tailored to financial time series. The book covers sequence models (RNNs, LSTMs, TCNs), attention and Transformers for irregular, high-frequency data, and reinforcement learning for trading, execution, and market making. Along the way, you will learn how to design finance-aware loss functions and evaluation metrics, manage walk-forward validation and leakage, and integrate predictive models into portfolio construction, risk management, and option pricing workflows. Assuming comfort with Python and basic probability, the text is self-contained in its treatment of the required math, optimization, and ML concepts. Throughout, it emphasizes robustness, MLOps, distribution shift, and explainability, culminating in end-to-end case studies. The result is a practical, rigorous guide to building deep learning systems that matter in a professional quantitative finance environment.
Machine Learning For Economics And Finance In Tensorflow 2
DOWNLOAD
Author : Isaiah Hull
language : en
Publisher: Apress
Release Date : 2020-11-26
Machine Learning For Economics And Finance In Tensorflow 2 written by Isaiah Hull and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Computers categories.
Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.