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Artificial Intelligence for Asset Management and Investment

Al Naqvi

Author : Al Naqvi
Publisher : John Wiley & Sons
Page : 323 pages
File Size : 49,9 Mb
Release : 2021-02-09
Category : Business & Economics
ISBN : 9781119601821

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Artificial Intelligence for Asset Management and Investment by Al Naqvi Pdf

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.

Artificial Intelligence in Asset Management

Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari

Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 46,9 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034

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Artificial Intelligence in Asset Management by Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari Pdf

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.

Machine Learning for Asset Managers

Marcos M. López de Prado

Author : Marcos M. López de Prado
Publisher : Cambridge University Press
Page : 152 pages
File Size : 45,7 Mb
Release : 2020-04-22
Category : Business & Economics
ISBN : 9781108879729

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Machine Learning for Asset Managers by Marcos M. López de Prado Pdf

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.

The AI Book

Ivana Bartoletti,Anne Leslie,Shân M. Millie

Author : Ivana Bartoletti,Anne Leslie,Shân M. Millie
Publisher : John Wiley & Sons
Page : 782 pages
File Size : 47,6 Mb
Release : 2020-04-09
Category : Business & Economics
ISBN : 9781119551928

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The AI Book by Ivana Bartoletti,Anne Leslie,Shân M. Millie Pdf

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

Investment Analytics In The Dawn Of Artificial Intelligence

Bernard Lee

Author : Bernard Lee
Publisher : World Scientific
Page : 265 pages
File Size : 46,6 Mb
Release : 2019-07-24
Category : Business & Economics
ISBN : 9789814725378

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Investment Analytics In The Dawn Of Artificial Intelligence by Bernard Lee Pdf

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)

Handbook of Artificial Intelligence and Big Data Applications in Investments

Larry Cao

Author : Larry Cao
Publisher : CFA Institute Research Foundation
Page : 258 pages
File Size : 42,7 Mb
Release : 2023-04-24
Category : Business & Economics
ISBN : 9781952927348

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Handbook of Artificial Intelligence and Big Data Applications in Investments by Larry Cao Pdf

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.

Artificial Intelligence in Financial Markets

Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos

Author : Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Publisher : Springer
Page : 349 pages
File Size : 44,9 Mb
Release : 2016-11-21
Category : Business & Economics
ISBN : 9781137488800

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Artificial Intelligence in Financial Markets by Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos Pdf

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.

Machine Learning for Asset Management

Emmanuel Jurczenko

Author : Emmanuel Jurczenko
Publisher : John Wiley & Sons
Page : 460 pages
File Size : 41,5 Mb
Release : 2020-10-06
Category : Business & Economics
ISBN : 9781786305442

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Machine Learning for Asset Management by Emmanuel Jurczenko Pdf

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.

Intelligent Asset Management

Frank Xing,Erik Cambria,Roy Welsch

Author : Frank Xing,Erik Cambria,Roy Welsch
Publisher : Springer Nature
Page : 149 pages
File Size : 48,9 Mb
Release : 2019-11-13
Category : Medical
ISBN : 9783030302634

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Intelligent Asset Management by Frank Xing,Erik Cambria,Roy Welsch Pdf

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.

Artificial Intelligence in Finance & Investing

Robert R. Trippi,Jae K. Lee

Author : Robert R. Trippi,Jae K. Lee
Publisher : McGraw Hill Professional
Page : 280 pages
File Size : 55,8 Mb
Release : 1996
Category : Business & Economics
ISBN : 1557388687

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Artificial Intelligence in Finance & Investing by Robert R. Trippi,Jae K. Lee Pdf

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.

Asset Management: Tools And Issues

Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov

Author : Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov
Publisher : World Scientific
Page : 514 pages
File Size : 44,7 Mb
Release : 2020-12-02
Category : Business & Economics
ISBN : 9789811225765

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Asset Management: Tools And Issues by Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov Pdf

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.

Machine Learning for Asset Management and Pricing

Henry Schellhorn,Tianmin Kong

Author : Henry Schellhorn,Tianmin Kong
Publisher : SIAM
Page : 267 pages
File Size : 54,5 Mb
Release : 2024-03-26
Category : Computers
ISBN : 9781611977905

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Machine Learning for Asset Management and Pricing by Henry Schellhorn,Tianmin Kong Pdf

This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.

Smart(er) Investing

Elisabetta Basilico,Tommi Johnsen

Author : Elisabetta Basilico,Tommi Johnsen
Publisher : Springer Nature
Page : 162 pages
File Size : 44,5 Mb
Release : 2019-12-11
Category : Business & Economics
ISBN : 9783030266929

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Smart(er) Investing by Elisabetta Basilico,Tommi Johnsen Pdf

This book identifies and discusses the most successful investing practices with an emphasis on the academic articles that produced them and why this research led to popular adoption and growth in $AUM. Investors are bombarded with ideas and prescriptions for successful investing every day. Given the steady stream of information on stock tips, sector timing, asset allocation, etc., how do investors decide? How do they judge the quality and reliability of the investment advice they are given on a day-to-day basis? This book identifies which academic articles turned investment ideas were the most innovative and influential in the practice of investment management. Each article is discussed in terms of the asset management process: strategy, portfolio construction, portfolio implementation, and risk management. Some examples of topics covered are factor investing, the extreme growth of trading instruments like Exchange Traded Funds, multi-asset investing, socially responsible investing, big data, and artificial intelligence. This book analyzes a curated selection of peer-reviewed academic articles identified among those published by the scientific investment community. The book briefly describes each of the articles, how and why each one changed the way we think about investing in that specific asset class, and provides insights as to the nuts and bolts of how to take full advantage of this successful investment idea. It is as timely as it is informative and will help each investor to focus on the most successful strategies, ideas, and implementation that provide the basis for the efficient accumulation and management of wealth.

Implementing Machine Learning for Finance

Tshepo Chris Nokeri

Author : Tshepo Chris Nokeri
Publisher : Apress
Page : 182 pages
File Size : 49,7 Mb
Release : 2021-05-27
Category : Computers
ISBN : 1484271092

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Implementing Machine Learning for Finance by Tshepo Chris Nokeri Pdf

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)