Neural Networks In Finance

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Neural Networks in Finance

Author : Paul D. McNelis
Publisher : Academic Press
ISBN 13 : 0124859674
Total Pages : 262 pages
Book Rating : 4.1/5 (248 download)

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Book Synopsis Neural Networks in Finance by : Paul D. McNelis

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Academic Press. This book was released on 2005-01-05 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Neural Networks in Finance and Investing

Author : Robert R. Trippi
Publisher : Irwin Professional Publishing
ISBN 13 :
Total Pages : 872 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Neural Networks in Finance and Investing by : Robert R. Trippi

Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1996 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.

Artificial Neural Networks in Finance and Manufacturing

Author : Kamruzzaman, Joarder
Publisher : IGI Global
ISBN 13 : 1591406722
Total Pages : 299 pages
Book Rating : 4.5/5 (914 download)

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Book Synopsis Artificial Neural Networks in Finance and Manufacturing by : Kamruzzaman, Joarder

Download or read book Artificial Neural Networks in Finance and Manufacturing written by Kamruzzaman, Joarder and published by IGI Global. This book was released on 2006-03-31 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.

Machine Learning in Finance

Author : Matthew F. Dixon
Publisher : Springer Nature
ISBN 13 : 3030410684
Total Pages : 565 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Machine Learning in Finance by : Matthew F. Dixon

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Financial Prediction Using Neural Networks

Author : Joseph S. Zirilli
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Financial Prediction Using Neural Networks by : Joseph S. Zirilli

Download or read book Financial Prediction Using Neural Networks written by Joseph S. Zirilli and published by . This book was released on 1997 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on approaches to performing trend analysis through the use of neural nets, this book comparess the results of experiments on various types of markets, and includes a review of current work in the area. It appeals to students in both neural computing and finance as well as to financial analysts and academic and professional researchers in the field of neural network applications.

Neural Network Time Series

Author : E. Michael Azoff
Publisher :
ISBN 13 :
Total Pages : 224 pages
Book Rating : 4.3/5 (97 download)

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Book Synopsis Neural Network Time Series by : E. Michael Azoff

Download or read book Neural Network Time Series written by E. Michael Azoff and published by . This book was released on 1994-09-27 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.

Neural Networks in Finance

Author : Paul D. McNelis
Publisher : Elsevier
ISBN 13 : 0080479650
Total Pages : 256 pages
Book Rating : 4.0/5 (84 download)

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Book Synopsis Neural Networks in Finance by : Paul D. McNelis

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Elsevier. This book was released on 2005-01-20 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website

Advances in Financial Machine Learning

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
ISBN 13 : 1119482119
Total Pages : 400 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular 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.

Artificial Intelligence in Finance

Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492055387
Total Pages : 478 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Artificial Intelligence in Finance by : Yves Hilpisch

Download or read book Artificial Intelligence in Finance written by Yves Hilpisch and published by "O'Reilly Media, Inc.". This book was released on 2020-10-14 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about

Big Data Science in Finance

Author : Irene Aldridge
Publisher : John Wiley & Sons
ISBN 13 : 1119602971
Total Pages : 336 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Big Data Science in Finance by : Irene Aldridge

Download or read book Big Data Science in Finance written by Irene Aldridge and published by John Wiley & Sons. This book was released on 2021-01-08 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.

Biologically Inspired Algorithms for Financial Modelling

Author : Anthony Brabazon
Publisher : Springer Science & Business Media
ISBN 13 : 3540313079
Total Pages : 276 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Biologically Inspired Algorithms for Financial Modelling by : Anthony Brabazon

Download or read book Biologically Inspired Algorithms for Financial Modelling written by Anthony Brabazon and published by Springer Science & Business Media. This book was released on 2006-03-28 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.

Artificial Higher Order Neural Networks for Economics and Business

Author : Zhang, Ming
Publisher : IGI Global
ISBN 13 : 1599048981
Total Pages : 542 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Artificial Higher Order Neural Networks for Economics and Business by : Zhang, Ming

Download or read book Artificial Higher Order Neural Networks for Economics and Business written by Zhang, Ming and published by IGI Global. This book was released on 2008-07-31 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.

Computational Finance 1999

Author : Yaser S. Abu-Mostafa
Publisher : MIT Press
ISBN 13 : 9780262511070
Total Pages : 744 pages
Book Rating : 4.5/5 (11 download)

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Book Synopsis Computational Finance 1999 by : Yaser S. Abu-Mostafa

Download or read book Computational Finance 1999 written by Yaser S. Abu-Mostafa and published by MIT Press. This book was released on 2000 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business.

Machine Learning for Finance

Author : Jannes Klaas
Publisher :
ISBN 13 : 9781789136364
Total Pages : 456 pages
Book Rating : 4.1/5 (363 download)

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Book Synopsis Machine Learning for Finance by : Jannes Klaas

Download or read book Machine Learning for Finance written by Jannes Klaas and published by . This book was released on 2019-05-30 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plan and build useful machine learning systems for financial services, with full working Python code Key Features Build machine learning systems that will be useful across the financial services industry Discover how machine learning can solve finance industry challenges Gain the machine learning insights and skills fintech companies value most Book Description Machine learning skills are essential for anybody working in financial data analysis. Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will see how to use machine learning to automate manual tasks, identify and address systemic bias, and find new insights and patterns hidden in available data. Machine Learning for Finance encourages and equips you to find new ways to use data to serve an organization's business goals. Broad in scope yet deeply practical in approach, Machine Learning for Finance will help you to apply machine learning in all parts of a financial organization's infrastructure. If you work or plan to work in fintech, and want to gain one of the most valuable skills in the sector today, this book is for you. What you will learn Practical machine learning for the finance sector Build machine learning systems that support the goals of financial organizations Think creatively about problems and how machine learning can solve them Identify and reduce sources of bias from machine learning models Apply machine learning to structured data, natural language, photographs, and written text related to finance Use machine learning to detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Who this book is for Machine Learning for Finance is for financial professionals who want to develop and apply machine learning skills, and for students entering the field. You should be comfortable with Python and the basic data science stack, such as NumPy, pandas, and Matplotlib, to get the most out of this book.

Neural Networks in the Capital Markets

Author : Apostolos-Paul Refenes
Publisher : Wiley
ISBN 13 : 9780471943648
Total Pages : 392 pages
Book Rating : 4.9/5 (436 download)

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Book Synopsis Neural Networks in the Capital Markets by : Apostolos-Paul Refenes

Download or read book Neural Networks in the Capital Markets written by Apostolos-Paul Refenes and published by Wiley. This book was released on 1995-03-28 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on original papers which represent new and significant research, developments and applications in finance and investment. The author takes a pragmatic view of neural networks, treating them as computationally equivalent to well-understood, non-parametric inference methods in decision science. The author also makes comparisons with established techniques where appropriate.

Neural Smithing

Author : Russell Reed
Publisher : MIT Press
ISBN 13 : 0262181908
Total Pages : 359 pages
Book Rating : 4.2/5 (621 download)

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Book Synopsis Neural Smithing by : Russell Reed

Download or read book Neural Smithing written by Russell Reed and published by MIT Press. This book was released on 1999-02-17 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

Python for Finance Cookbook

Author : Eryk Lewinson
Publisher : Packt Publishing Ltd
ISBN 13 : 1789617324
Total Pages : 426 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Python for Finance Cookbook by : Eryk Lewinson

Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.