Novel Financial Applications Of Machine Learning And Deep Learning
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Novel Financial Applications Of Machine Learning And Deep Learning
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Novel Financial Applications Of Machine Learning And Deep Learning written by Mohammad Zoynul Abedin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Business & Economics categories.
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Machine Learning In Finance
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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.
Advances In Financial Machine Learning
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Author : Marcos Lopez de Prado
language : en
Publisher: John Wiley & Sons
Release Date : 2018-02-21
Advances In Financial Machine Learning written by Marcos Lopez de Prado and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Business & Economics categories.
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Machine Learning For Finance
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Author : Saurav Singla
language : en
Publisher: BPB Publications
Release Date : 2021-01-05
Machine Learning For Finance written by Saurav Singla and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with Computers categories.
Understand the essentials of Machine Learning and its impact in financial sector KEY FEATURESÊ _Explore the spectrum of machine learning and its usage. _Understand the NLP and Computer Vision and their use cases. _Understand the Neural Network, CNN, RNN and their applications. _ÊUnderstand the Reinforcement Learning and their applications. _Learn the rising application of Machine Learning in the Finance sector. Ê_Exposure to data mining, data visualization and data analytics. DESCRIPTION The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation.Ê Ê The book demonstrates how to solve some of the most common issues in the financial industry.Ê The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Nave Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Ê Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. WHAT WILL YOU LEARN _ Ê Ê Ê You will grasp the most relevant techniques of Machine Learning for everyday use. _ Ê Ê Ê You will be confident in building and implementing ML algorithms. _ Ê Ê Ê Familiarize the adoption of Machine Learning for your business need. _ Ê Ê Ê Discover more advanced concepts applied in banking and other sectors today. _ Ê Ê Ê Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. WHO THIS BOOK IS FORÊ Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. TABLE OF CONTENTS 1.Introduction 2.Naive Bayes, Normal Distribution and Automatic Clustering Processes 3.Machine Learning for Data Structuring 4.Parsing Data Using NLP 5.Computer Vision 6.Neural Network, GBM and Gradient Descent 7.Sequence Modeling 8.Reinforcement Learning For Financial Markets 9.Finance Use Cases 10.Impact of Machine Learning on Fintech 11.Machine Learning in Finance 12.eKYC and Anti-Fraud Policy 13.Uses of Data Mining and Data Visualization 14.Advantages and Disadvantages of Machine Learning 15.Applications of Machine Learning in Other Industries 16.Ethical considerations in Artificial Intelligence 17.Artificial Intelligence in Banking 18.Common Machine Learning Algorithms 19.Frequently Asked Questions
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan 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 2021-12-07 with Computers categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Machine Learning For Economics And Finance In Tensorflow 2
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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.
Machine Learning For Asset Managers
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Author : Marcos M. López de Prado
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-22
Machine Learning For Asset Managers written by Marcos M. López de Prado and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-22 with Business & Economics categories.
Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
Data Mining In Finance
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Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-11
Data Mining In Finance written by Boris Kovalerchuk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-11 with Computers categories.
Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
Hands On Artificial Intelligence For Banking
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Author : Jeffrey Ng
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-10
Hands On Artificial Intelligence For Banking written by Jeffrey Ng 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 2020-07-10 with Computers categories.
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.