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.
Download or read book Foundations of Reinforcement Learning with Applications in Finance written by Ashwin Rao and published by CRC Press. This book was released on 2022-12-16 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
Download or read book Applications of Reinforcement Learning in Finance written by Rubi Pence and published by Independently Published. This book was released on 2023-12-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the dynamic intersection of finance and cutting-edge technology with "Applications of Reinforcement Learning in Finance." This comprehensive guide explores the transformative impact of reinforcement learning on various facets of the financial industry, offering readers a profound understanding of how this revolutionary approach is reshaping decision-making processes. In this insightful book, you'll embark on a journey through the intricate applications of reinforcement learning, unraveling its potential to optimize trading strategies, risk management, portfolio construction, and more. With real-world examples and practical insights, this book equips both finance professionals and tech enthusiasts with the knowledge needed to leverage reinforcement learning algorithms effectively.
Download or read book Statistical Machine Learning with Applications written by Gordon Ritter and published by . This book was released on 2021-07-30 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.
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.
Download or read book Recent Advances in Reinforcement Learning written by Leslie Pack Kaelbling and published by Springer. This book was released on 2007-08-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Download or read book Machine Learning for Finance written by Saurav Singla and published by BPB Publications. This book was released on 2021-01-05 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Download or read book Machine Learning and AI in Finance written by German Creamer and published by Routledge. This book was released on 2021-04-06 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book An Introduction To Machine Learning In Quantitative Finance written by Hao Ni and published by World Scientific. This book was released on 2021-04-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 authorsFeatured 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!
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Machine Learning for Finance written by Jannes Klaas and published by Packt Publishing Ltd. This book was released on 2019-05-30 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book An Introduction to Deep Reinforcement Learning written by Vincent Francois-Lavet and published by Foundations and Trends (R) in Machine Learning. This book was released on 2018-12-20 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.
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.
Download or read book Financial Signal Processing and Machine Learning written by Ali N. Akansu and published by John Wiley & Sons. This book was released on 2016-04-21 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
