Machine Learning For Financial Engineering

Author : László Györfi,György Ottucsák,Harro Walk
Publisher : World Scientific
Page : 260 pages
File Size : 49,7 Mb
Release : 2012-03-14
Category : Business & Economics
ISBN : 9781908977663

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Machine Learning for Financial Engineering by László Györfi,György Ottucsák,Harro Walk Pdf

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering. Contents:On the History of the Growth-Optimal Portfolio (M M Christensen)Empirical Log-Optimal Portfolio Selections: A Survey (L Györfi, Gy Ottucsák & A Urbán)Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs (L Györfi & H Walk)Growth-Optimal Portfolio Selection with Short Selling and Leverage (M Horváth & A Urbán)Nonparametric Sequential Prediction of Stationary Time Series (L Györfi & G Ottuscák)Empirical Pricing American Put Options (L Györfi & A Telcs) Readership: Researchers, academics and graduate students in artificial intelligence/machine learning, and mathematical finance/quantitative finance. Keywords:Log-Optimal Portfolio;Growth-Optimal Portfolio;Sequential Investment Strategies for Financial MarketsKey Features:Covers machine learning algorithms for the aggregation of elementary investment strategiesHighlights multi-period and multi-asset tradingFocuses on nonparametric estimation of the underlying distributions in the market process

Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
Page : 565 pages
File Size : 41,6 Mb
Release : 2020-07-01
Category : Business & Economics
ISBN : 9783030410681

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Machine Learning in Finance by Matthew F. Dixon,Igor Halperin,Paul Bilokon Pdf

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.

Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
Publisher : John Wiley & Sons
Page : 312 pages
File Size : 51,7 Mb
Release : 2016-04-21
Category : Technology & Engineering
ISBN : 9781118745632

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Financial Signal Processing and Machine Learning by Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov Pdf

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Page : 400 pages
File Size : 48,8 Mb
Release : 2018-01-23
Category : Business & Economics
ISBN : 9781119482116

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Advances in Financial Machine Learning by Marcos Lopez de Prado Pdf

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.

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Page : 406 pages
File Size : 50,9 Mb
Release : 2018-02-21
Category : Business & Economics
ISBN : 9781119482086

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Advances in Financial Machine Learning by Marcos Lopez de Prado Pdf

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.

Author : Hao Ni, (le
Publisher : Unknown
Page : 128 pages
File Size : 47,5 Mb
Release : 2020-12
Category : Electronic
ISBN : 178634937X

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An Introduction to Machine Learning in Quantitative Finance by Hao Ni, (le Pdf

Author : Hao Ni,Jinsong Zheng
Publisher : Advanced Textbooks In Mathematics
Page : 265 pages
File Size : 47,7 Mb
Release : 2021
Category : Finance
ISBN : 1786349647

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An Introduction to Machine Learning in Quantitative Finance by Hao Ni,Jinsong Zheng Pdf

In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it. An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authors Provide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems. Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning. Summarize key contents of each section in the tables as a cheat sheet. Include ample examples of financial applications. Showcase how to tackle an exemplar ML project on financial data end-to-end. Supplement Python codes of all the methods/examples in a GitHub repository. Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data! The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https: //github.com/deepintomlf/mlfbook.git

Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 48,7 Mb
Release : 2016-05-31
Category : Technology & Engineering
ISBN : 9781118745670

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Financial Signal Processing and Machine Learning by Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov Pdf

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Author : Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
Publisher : Routledge
Page : 259 pages
File Size : 47,5 Mb
Release : 2021-06-20
Category : Business & Economics
ISBN : 9781000394115

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The Essentials of Machine Learning in Finance and Accounting by Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin Pdf

• A useful guide to financial product modeling and to minimizing business risk and uncertainty • Looks at wide range of financial assets and markets and correlates them with enterprises’ profitability • Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets • Real world applicable examples to further understanding

Author : Jannes Klaas
Publisher : Packt Publishing Ltd
Page : 457 pages
File Size : 49,7 Mb
Release : 2019-05-30
Category : Computers
ISBN : 9781789134698

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Machine Learning for Finance by Jannes Klaas Pdf

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.

Author : Hitoshi Iba,Claus C. Aranha
Publisher : Springer Science & Business Media
Page : 248 pages
File Size : 49,8 Mb
Release : 2012-02-15
Category : Technology & Engineering
ISBN : 9783642276484

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Practical Applications of Evolutionary Computation to Financial Engineering by Hitoshi Iba,Claus C. Aranha Pdf

“Practical Applications of Evolutionary Computation to Financial Engineering” presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionary Computation. This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within. The book is structured so that each chapter can be read independently from the others. Chapters 1 and 2 describe evolutionary computation. The third chapter is an introduction to financial engineering problems for readers who are unfamiliar with this area. The following chapters each deal, in turn, with a different problem in the financial engineering field describing each problem in detail and focusing on solutions based on evolutionary computation. Finally, the two appendixes describe software packages that implement the solutions discussed in this book, including installation manuals and parameter explanations.

Author : Mohammad Zoynul Abedin,Petr Hajek
Publisher : Springer Nature
Page : 235 pages
File Size : 40,7 Mb
Release : 2023-03-01
Category : Business & Economics
ISBN : 9783031185526

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Novel Financial Applications of Machine Learning and Deep Learning by Mohammad Zoynul Abedin,Petr Hajek Pdf

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.

Author : György Ottucsák,Harro Walk
Publisher : World Scientific
Page : 261 pages
File Size : 44,6 Mb
Release : 2012
Category : Business & Economics
ISBN : 9781848168138

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Machine Learning for Financial Engineering by György Ottucsák,Harro Walk Pdf

Preface v 1 On the History of the Growth-Optimal Portfolio M.M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth A. Urbán 153 5 Nonparametric Sequential Prediction of Stationary Time Series L. Györfi Gy. Ottucsák 179 6 Empirical Pricing American Put Options L. Györfi A. Telcs 227 Index 249.

Author : Sio-Iong Ao,Burghard B. Rieger,Mahyar Amouzegar
Publisher : Springer Science & Business Media
Page : 607 pages
File Size : 49,7 Mb
Release : 2010-10-05
Category : Technology & Engineering
ISBN : 9789048194193

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Machine Learning and Systems Engineering by Sio-Iong Ao,Burghard B. Rieger,Mahyar Amouzegar Pdf

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

Author : Gerasimos G. Rigatos
Publisher : Springer
Page : 310 pages
File Size : 46,8 Mb
Release : 2017-04-04
Category : Technology & Engineering
ISBN : 9783319528663

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State-Space Approaches for Modelling and Control in Financial Engineering by Gerasimos G. Rigatos Pdf

The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in financial systems when these are described in the form of nonlinear ordinary differential equations. It then addresses problems associated with the control and estimation of financial systems governed by partial differential equations (e.g. the Black–Scholes partial differential equation (PDE) and its variants). Lastly it an offers optimal solution to the problem of statistical validation of computational models and tools used to support financial engineers in decision making. The application of state-space models in financial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for finance can be eliminated. It also allows methods of fault-free performance and optimality in the management of assets and capitals and methods assuring stability in the functioning of financial systems to be established. Covering the following key areas of financial engineering: (i) control and stabilization of financial systems dynamics, (ii) state estimation and forecasting, and (iii) statistical validation of decision-making tools, the book can be used for teaching undergraduate or postgraduate courses in financial engineering. It is also a useful resource for the engineering and computer science community