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.
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.
In the years since the now-classic Pioneering Portfolio Management was first published, the global investment landscape has changed dramatically -- but the results of David Swensen's investment strategy for the Yale University endowment have remained as impressive as ever. Year after year, Yale's portfolio has trumped the marketplace by a wide margin, and, with over $20 billion added to the endowment under his twenty-three-year tenure, Swensen has contributed more to Yale's finances than anyone ever has to any university in the country. What may have seemed like one among many success stories in the era before the Internet bubble burst emerges now as a completely unprecedented institutional investment achievement. In this fully revised and updated edition, Swensen, author of the bestselling personal finance guide Unconventional Success, describes the investment process that underpins Yale's endowment. He provides lucid and penetrating insight into the world of institutional funds management, illuminating topics ranging from asset-allocation structures to active fund management. Swensen employs an array of vivid real-world examples, many drawn from his own formidable experience, to address critical concepts such as handling risk, selecting advisors, and weathering market pitfalls. Swensen offers clear and incisive advice, especially when describing a counterintuitive path. Conventional investing too often leads to buying high and selling low. Trust is more important than flash-in-the-pan success. Expertise, fortitude, and the long view produce positive results where gimmicks and trend following do not. The original Pioneering Portfolio Management outlined a commonsense template for structuring a well-diversified equity-oriented portfolio. This new edition provides fund managers and students of the market an up-to-date guide for actively managed investment portfolios.
While technology advances at a high pace in the age of machine learning, there is a lack of clear intent and framing of acceptable ethical standards. This book brings together the complex topic of "good" technology in a cross-functional way, alternating between theory and practice.The authors address the ever-expanding discussion on Artificial Intelligence (AI) and ethics by providing an orientation. Pragmatic and recent issues are especially taken into account such as the collateral effects of the COVID19 pandemic. An up-to-date overview of digitization - already a very broad field in itself - is presented along with an analysis of the approaches of AI from an ethical perspective. Furthermore, concrete approaches to consider appropriate ethical principles in AI-based solutions are offered. The book will be appealing to academics, from humanities or business or technical disciplines, as well as practitioners who are looking for an introduction to the topic and an orientation with concrete questions and assistance.
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.
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.
Behavioral finance presented in this book is the second-generation of behavioral finance. The first generation, starting in the early 1980s, largely accepted standard finance’s notion of people’s wants as “rational” wants—restricted to the utilitarian benefits of high returns and low risk. That first generation commonly described people as “irrational”—succumbing to cognitive and emotional errors and misled on their way to their rational wants. The second generation describes people as normal. It begins by acknowledging the full range of people’s normal wants and their benefits—utilitarian, expressive, and emotional—distinguishes normal wants from errors, and offers guidance on using shortcuts and avoiding errors on the way to satisfying normal wants. People’s normal wants include financial security, nurturing children and families, gaining high social status, and staying true to values. People’s normal wants, even more than their cognitive and emotional shortcuts and errors, underlie answers to important questions of finance, including saving and spending, portfolio construction, asset pricing, and market efficiency.
Emerging markets are increasingly facing significant challenges, from a slowdown in productivity, rising debt, and trade tensions to the adverse effects of proliferating global uncertainty on domestic financial systems. This incisive Handbook examines the ongoing dynamics of global financial markets and institutions within the context of such rising uncertainty and provides a comprehensive overview of innovative models in banking and finance.
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
This book tells the story of how the convergence between corporate sustainability and sustainable investing is now becoming a major force driving systemic market changes. The idea and practice of corporate sustainability is no longer a niche movement. Investors are increasingly paying attention to sustainability factors in their analysis and decision-making, thus reinforcing market transformation. In this book, high-level practitioners and academic thought leaders, including contributions from John Ruggie, Fiona Reynolds, Johan Rockström, and Paul Polman, explain the forces behind these developments. The contributors highlight (a) that systemic market change is influenced by various contextual factors that impact how sustainable investing is perceived and practiced; (b) that the integration of ESG factors in investment decisions is impacting markets on a large scale and hence changes practices of major market players (e.g. pension funds); and (c) that technology and the increasing datafication of sustainability act as further accelerators of such change. The book goes beyond standard economic theory approaches to sustainable investing and emphasizes that capitalism founded on more real-world (complex) economics and cooperation can strengthen ESG integration. Aimed at both investment professionals and academics, this book gives the reader access to more practitioner-relevant information and it also discusses implementation issues. The reader will gain insights into how "mainstream" financial actors relate to sustainable investing.
