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Author: Matei Zatreanu Publisher: Wiley ISBN: 9781119429630 Category : Business & Economics Languages : en Pages : 0
Book Description
Implement a data-driven investment strategy The investing landscape is increasingly driven by big data and artificial intelligence. For most finance professionals, big data, statistics, and programming are outside their comfort zone. Yet, proficiency in these areas is becoming a prerequisite for successful investing. And while there are plenty of resources on these individual topics, what is missing is a framework for combining these disciplines for investment purposes. Data-Driven Investing shows readers how investment decisions can be made or improved through the use of alternative datasets and inference techniques. The author covers artificial intelligence algorithms, data visualization, and data sourcing to show how these components come together to form a more robust investment strategy. The goal is to help finance professionals prepare for an investing landscape increasingly driven by big data and artificial intelligence. Shows how investing wisdom can be harnessed through science and augmented by data Demonstrates how an augmented investing philosophy promises a deeper understanding of future economic performance Is essential reading for fund managers, research analysts, quantitative investors, data scientists, and general finance professionals Includes a companion website with code, data sets, and videos providing more in-depth information on augmented/data-driven investing This book comes at a time of increasing investor anxiety with lackluster hedge fund performance, which is causing many funds to explore data-driven investing as a possible evolution of their strategies.
Author: Matei Zatreanu Publisher: Wiley ISBN: 9781119429630 Category : Business & Economics Languages : en Pages : 0
Book Description
Implement a data-driven investment strategy The investing landscape is increasingly driven by big data and artificial intelligence. For most finance professionals, big data, statistics, and programming are outside their comfort zone. Yet, proficiency in these areas is becoming a prerequisite for successful investing. And while there are plenty of resources on these individual topics, what is missing is a framework for combining these disciplines for investment purposes. Data-Driven Investing shows readers how investment decisions can be made or improved through the use of alternative datasets and inference techniques. The author covers artificial intelligence algorithms, data visualization, and data sourcing to show how these components come together to form a more robust investment strategy. The goal is to help finance professionals prepare for an investing landscape increasingly driven by big data and artificial intelligence. Shows how investing wisdom can be harnessed through science and augmented by data Demonstrates how an augmented investing philosophy promises a deeper understanding of future economic performance Is essential reading for fund managers, research analysts, quantitative investors, data scientists, and general finance professionals Includes a companion website with code, data sets, and videos providing more in-depth information on augmented/data-driven investing This book comes at a time of increasing investor anxiety with lackluster hedge fund performance, which is causing many funds to explore data-driven investing as a possible evolution of their strategies.
Author: Timothy Hayes Publisher: McGraw-Hill Companies ISBN: Category : Business enterprises Languages : en Pages : 328
Book Description
The editor of "Investment Strategy" shows how individual investors can access institutional-quality tools, data, and indicators and consistently beat the market. Hayes presents walk-through examples of a wide variety of investment models based on more than 100 years of stock market data and research from Ned Davis Research to achieve top results. 120 illustrations. 60 tables.
Author: Cris Doloc Publisher: John Wiley & Sons ISBN: 1119550513 Category : Business & Economics Languages : en Pages : 313
Book Description
“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.
Author: Michael Moesgaard Andersen Publisher: Routledge ISBN: 100032916X Category : Business & Economics Languages : en Pages : 117
Book Description
Today, innovation does not just occur in large and incumbent R&D organizations. Instead, it often emerges from the start-up community. In the new innovation economy, the key is to quickly find pieces of innovation, some of which may already be developed. Therefore, there is the need for more advanced means of searching and identifying innovation wherever it may occurs. We point to the importance of data-driven innovation based on digital platforms, as their footprints are growing rapidly and in sync with the shift from analogue to digital innovation workflows. This book offers companies insights on paths to business success and tools that will help them find the right route through the various options when it comes to the digital platforms where innovations may be discovered and from which value may be appropriated. The world hungers for growth and one of the most important vehicles for growth is innovation. In light of the new digital platforms from which data-driven innovation can be extracted, major parts of analogue workflows will be substituted with digital workflows. Data-driven innovation and digital innovation workflows are here to stay. Are you?
Author: Hossein Hassani Publisher: Springer Nature ISBN: 3030313913 Category : Business & Economics Languages : en Pages : 132
Book Description
As technology continues to revolutionise today’s economy, Big Data, Blockchain and Cryptocurrency are rapidly transforming themselves into mainstream functions within the financial services industry. This book examines each concept individually, analysing the opportunities and challenges they bring and exploring the potential for future development. The authors further evaluate the fusion of these three important products of the FinTech revolution, illustrating their combined influence on the digital economy. Providing a comprehensive analysis of three innovative technologies, this timely book will appeal to scholars researching innovation in the finance industry and financial services technology more specifically.
Author: Douglas Cumming Publisher: Oxford University Press ISBN: 0192577719 Category : Business & Economics Languages : en Pages : 560
Book Description
The Oxford Handbook of Hedge Funds provides a comprehensive overview of the hedge fund industry from a global perspective, bringing together insights from theoretical and applied research. The book seeks to both introduce the industry and what it does to scholars and practitioners new to the area, and to provide more advanced insights to those with extensive expertise in the area. The handbook explains the main context in which hedge funds operate, how the raise capital, and their structure and governance. It evaluates the main factors that have affected the operation of hedge funds, including competition from mutual funds, the market environment, and financial regulation, explains key concepts such as hedge fund flows, and core issues of practice, such as hedge fund manager fees. This volume provides insights into the principle head fund strategies and how these have changed over the years. The behavioural dimensions of hedge fund behaviour are evaluated, as are fintech's consequences. The volume evaluates the effects of hedge funds on the firms they invest in, in terms of internal governance, strategy and practice. Furthermore, it explores a range of ethical issues around the operation of hedge funds, how they fit within the wider political economy, and changes in hedge fund regulation and taxation strategies.
Author: Russell Glass Publisher: John Wiley & Sons ISBN: 1118889789 Category : Business & Economics Languages : en Pages : 224
Book Description
Get the expert perspective and practical advice on big data The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits makes the case that big data is for real, and more than just big hype. The book uses real-life examples—from Nate Silver to Copernicus, and Apple to Blackberry—to demonstrate how the winners of the future will use big data to seek the truth. Written by a marketing journalist and the CEO of a multi-million-dollar B2B marketing platform that reaches more than 90% of the U.S. business population, this book is a comprehensive and accessible guide on how to win customers, beat competitors, and boost the bottom line with big data. The marketplace has entered an era where the customer holds all the cards. With unprecedented choice in both the consumer world and the B2B world, it's imperative that businesses gain a greater understanding of their customers and prospects. Big data is the key to this insight, because it provides a comprehensive view of a company's customers—who they are, and who they may be tomorrow. The Big Data-Driven Business is a complete guide to the future of business as seen through the lens of big data, with expert advice on real-world applications. Learn what big data is, and how it will transform the enterprise Explore why major corporations are betting their companies on marketing technology Read case studies of big data winners and losers Discover how to change privacy and security, and remodel marketing Better information allows for better decisions, better targeting, and better reach. Big data has become an indispensable tool for the most effective marketers in the business, and it's becoming less of a competitive advantage and more like an industry standard. Remaining relevant as the marketplace evolves requires a full understanding and application of big data, and The Big Data-Driven Business provides the practical guidance businesses need.
Author: Bernard J. Jansen Publisher: Springer Nature ISBN: 3031022319 Category : Computers Languages : en Pages : 317
Book Description
Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.
Author: Alex Khang Publisher: CRC Press ISBN: 1000802019 Category : Technology & Engineering Languages : en Pages : 289
Book Description
This book focuses on futuristic approaches and designs for real-time systems and applications, as well as the fundamental concepts of including advanced techniques and tools in models of data-driven blockchain ecosystems. The Data-Driven Blockchain Ecosystem: Fundamentals, Applications, and Emerging Technologies discusses how to implement and manage processes for releasing and delivering blockchain applications. It presents the core of blockchain technology, IoT-based and AI-based blockchain systems, and various manufacturing areas related to Industry 4.0. The book illustrates how to apply design principles to develop and manage blockchain networks, and also covers the role that cloud computing plays in blockchain applications. All major technologies involved in blockchain-embedded applications are included in this book, which makes it useful to engineering students, researchers, academicians, and professionals interested in the core of blockchain technology.
Author: Matei Zatreanu
Author: Matei Zatreanu
Author: Mitchell R. Hardy
Author: Timothy Hayes
Author: Cris Doloc
Author: Michael Moesgaard Andersen
Author: Hossein Hassani
Author: Douglas Cumming
Author: Russell Glass
Author: Bernard J. Jansen
Author: Alex Khang