Download or read book Artificial Intelligence for Asset Management and Investment written by Al Naqvi and published by John Wiley & Sons. This book was released on 2021-02-09 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

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.

Download or read book Machine Learning for Asset Managers written by Marcos M. López de Prado and published by Cambridge University Press. This book was released on 2020-04-22 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The AI Book written by Ivana Bartoletti and published by John Wiley & Sons. This book was released on 2020-04-09 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important

Download or read book Artificial Intelligence in Financial Markets written by Christian L. Dunis and published by Springer. This book was released on 2016-11-21 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

Download or read book Artificial Intelligence in Finance Investing written by Robert R. Trippi and published by McGraw Hill Professional. This book was released on 1996 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Artificial Intelligence in Finance and Investing, authors Robert Trippi and Jae Lee explain this fascinating new technology in terms that portfolio managers, institutional investors, investment analysis, and information systems professionals can understand. Using real-life examples and a practical approach, this rare and readable volume discusses the entire field of artificial intelligence of relevance to investing, so that readers can realize the benefits and evaluate the features of existing or proposed systems, and ultimately construct their own systems. Topics include using Expert Systems for Asset Allocation, Timing Decisions, Pattern Recognition, and Risk Assessment; overview of Popular Knowledge-Based Systems; construction of Synergistic Rule Bases for Securities Selection; incorporating the Markowitz Portfolio Optimization Model into Knowledge-Based Systems; Bayesian Theory and Fuzzy Logic System Components; Machine Learning in Portfolio Selection and Investment Timing, including Pattern-Based Learning and Fenetic Algorithms; and Neural Network-Based Systems. To illustrate the concepts presented in the book, the authors conclude with a valuable practice session and analysis of a typical knowledge-based system for investment management, K-FOLIO. For those who want to stay on the cutting edge of the "application" revolution, Artificial Intelligence in Finance and Investing offers a pragmatic introduction to the use of knowledge-based systems in securities selection and portfolio management.

Download or read book Handbook of Artificial Intelligence and Big Data Applications in Investments written by Larry Cao and published by CFA Institute Research Foundation. This book was released on 2023-04-24 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) and big data have their thumbprints all over the modern asset management firm. Like detectives investigating a crime, the practitioner contributors to this book put the latest data science techniques under the microscope. And like any good detective story, much of what is unveiled is at the same time surprising and hiding in plain sight. Each chapter takes you on a well-guided tour of the development and application of specific AI and big data techniques and brings you up to the minute on how they are being used by asset managers. Given the diverse backgrounds and affiliations of our authors, this book is the perfect companion to start, refine, or plan the next phase of your data science journey.

Download or read book Investment Analytics In The Dawn Of Artificial Intelligence written by Bernard Lee and published by World Scientific. This book was released on 2019-07-24 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: A class of highly mathematical algorithms works with three-dimensional (3D) data known as graphs. Our research challenge focuses on applying these algorithms to solve more complex problems with financial data, which tend to be in higher dimensions (easily over 100), based on probability distributions, with time subscripts and jumps. The 3D research analogy is to train a navigation algorithm when the way-finding coordinates and obstacles such as buildings change dynamically and are expressed in higher dimensions with jumps.Our short title 'ia≠ai' symbolizes how investment analytics is not a simplistic reapplication of artificial intelligence (AI) techniques proven in engineering. This book presents best-of-class sophisticated techniques available today to solve high dimensional problems with properties that go deeper than what is required to solve customary problems in engineering today.Dr Bernard Lee is the Founder and CEO of HedgeSPA, which stands for Sophisticated Predictive Analytics for Hedge Funds and Institutions. Previously, he was a managing director in the Portfolio Management Group of BlackRock in New York City as well as a finance professor who has taught and guest-lectured at a number of top universities globally.Related Link(s)

Download or read book Machine Learning for Asset Management written by Emmanuel Jurczenko and published by John Wiley & Sons. This book was released on 2020-10-06 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.

Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Download or read book Intelligent Asset Management written by Frank Xing and published by Springer Nature. This book was released on 2019-11-13 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas. In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures. This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance.

Download or read book The Digitalization of Financial Markets written by Adam Marszk and published by Routledge. This book was released on 2021-10-10 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides deep insight into theoretical and empirical evidence on information and communication technologies (ICT) as an important factor affecting financial markets. It is focused on the impact of ICT on stock markets, bond markets, and other categories of financial markets, with the additional focus on the linked FinTech services and financial institutions. Financial markets shaped by the adoption of the new technologies are labeled ‘digital financial markets’. With a wide-ranging perspective at both the local and global levels from countries at varying degrees of economic development, this book addresses an important gap in the extant literature concerning the role of ICT in the financial markets. The consequences of these processes had until now rarely been considered in a broader economic and social context, particularly when the impact of FinTech services on financial markets is taken into account. The book’s theoretical discussions, empirical evidence and compilation of different views and perspectives make it a valuable and complex reference work. The principal audience of the book will be scholars in the fields of finance and economics. The book also targets professionals in the financial industry who are directly or indirectly linked to the new technologies on the financial markets, in particular various types of FinTech services. Chapters 2, 5 and 10 of this book are available for free in PDF format as Open Access from the individual product page at www.routledge.com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Download or read book Asset Management Tools And Issues written by Frank J Fabozzi and published by World Scientific. This book was released on 2020-12-02 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Long gone are the times when investors could make decisions based on intuition. Modern asset management draws on a wide-range of fields beyond financial theory: economics, financial accounting, econometrics/statistics, management science, operations research (optimization and Monte Carlo simulation), and more recently, data science (Big Data, machine learning, and artificial intelligence). The challenge in writing an institutional asset management book is that when tools from these different fields are applied in an investment strategy or an analytical framework for valuing securities, it is assumed that the reader is familiar with the fundamentals of these fields. Attempting to explain strategies and analytical concepts while also providing a primer on the tools from other fields is not the most effective way of describing the asset management process. Moreover, while an increasing number of investment models have been proposed in the asset management literature, there are challenges and issues in implementing these models. This book provides a description of the tools used in asset management as well as a more in-depth explanation of specialized topics and issues covered in the companion book, Fundamentals of Institutional Asset Management. The topics covered include the asset management business and its challenges, the basics of financial accounting, securitization technology, analytical tools (financial econometrics, Monte Carlo simulation, optimization models, and machine learning), alternative risk measures for asset allocation, securities finance, implementing quantitative research, quantitative equity strategies, transaction costs, multifactor models applied to equity and bond portfolio management, and backtesting methodologies. This pedagogic approach exposes the reader to the set of interdisciplinary tools that modern asset managers require in order to extract profits from data and processes.

Download or read book Artificial Intelligence for Audit Forensic Accounting and Valuation written by Al Naqvi and published by John Wiley & Sons. This book was released on 2020-07-24 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategically integrate AI into your organization to compete in the tech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform accounting and auditing professions, yet its current application within these areas is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation accounting. Artificial Intelligence for Audit, Forensic Accounting, and Valuation provides a strategic viewpoint on how AI can be comprehensively integrated within audit management, leading to better automated models, forensic accounting, and beyond. No other book on the market takes such a wide-ranging approach to using AI in audit and accounting. With this guide, you’ll be able to build an innovative, automated accounting strategy, using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for audit and accounting firms. With better AI comes better results. If you aren’t integrating AI and automation in the strategic DNA of your business, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of integrated, automated audit and accounting services Learn how to build AI into your organization to remain competitive in the era of automation Go beyond siloed AI implementations to modernize and deliver results across the organization Understand and overcome the governance and leadership challenges inherent in AI strategy Accounting and auditing firms need a comprehensive framework for intelligent, automation-centric modernization. Artificial Intelligence for Audit, Forensic Accounting, and Valuation delivers just that—a plan to evolve legacy firms by building firmwide AI capabilities.

Download or read book Big Data and Machine Learning in Quantitative Investment written by Tony Guida and published by John Wiley & Sons. This book was released on 2019-03-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.

Download or read book Implementing Machine Learning for Finance written by Tshepo Chris Nokeri and published by Apress. This book was released on 2021-05-27 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems. What You Will Learn Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management Know the concepts of feature engineering, data visualization, and hyperparameter optimization Design, build, and test supervised and unsupervised ML and DL models Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk Who This Book Is For Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)