Data Driven Investing


Book Description




Data-Driven Investing, + Website


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.




Data Driven


Book Description

Poor data quality costs the United States $3.1 trillion dollars every year. Data Driven: Solving the Biggest Problems in Startup Investing explores how new venture capitalists and data scientists can leverage data to invest in startups more efficiently and successfully. Author Amal Bhatnagar aims to teach you how to make better investment decisions by creating your own data-driven organization. You'll hear stories from industry leaders like: David Coats, the Managing Director at Correlation Ventures, who created the world's most complete and accurate database of US-based venture capital financings Will Bricker, a Principal at the Hustle Fund, who built systems to handle 40 percent of all startup investment opportunities without human intervention Tim Harsch, the Chief Executive Officer of Owler, who created data on 13 million+ companies and the world's second largest business community Jonathan Hsu, Tribe Capital's Co-Founder, who uses data science techniques to handle more than $1.3 billion assets under management. This book is a must-read if you are an aspiring investor who wants to make better startup investment decisions or data scientist who wants to build financial products. Here is the first step on the path to building a data-driven competitive edge and a more successful data-driven leadership.




The Book of Alternative Data


Book Description

The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.




The Research Driven Investor


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.




Data Mesh


Book Description

Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.




Data-Driven Business Models for the Digital Economy


Book Description

Today the fastest growing companies have no physical assets. Instead, they create innovative digital products and new data-driven business models. They capture huge market share fast and their capitalizations skyrocket. The success of these digital giants is pushing all companies to rethink their business models and to start digitizing their products and services. Whether you are a new start-up building a digital product or service, or an employee of an established company that is transitioning to digital, you need to consider how digitization has transformed every aspect of management. Data-driven business models scale not through asset accumulation and product standardization, but through disaggregation of supply and demand. The winners in the new economy master the demand for one and the supply to millions. Throughout the book the author illustrates with examples and use cases how the market competition has changed and how companies adept to the new rules of the game. The economic levers of scale and scope are also different in the digital economy and companies have to learn new tactics how to achieve and sustain their competitive advantage. While data is at the core of all digital business models, the monetization strategies vary across products, services and business models. Our Monetization Matrix is a model that helps managers, marketers, sales professionals, and technical product designers to align the digital product design with the data-driven business model.




Python for Finance


Book Description

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.




Merger Arbitrage


Book Description

A wave of corporate mergers, acquisitions, restructuring, and similar transactions has created unprecedented opportunities for those versed in contemporary risk arbitrage techniques. At the same time, the nature of the merger wave has lent such transactions a much higher degree of predictability than ever before, making risk arbitrage more attractive to investors. Surprisingly, there is little transparency and instruction for investors interested in learning the latest risk arbitrage techniques. Merger Arbitrage – A Fundamental Approach to Event-Driven Investing helps readers understand the inner workings of the strategy and hedge funds which engaged in this investment strategy. Merger arbitrage is one of the most commonly used strategies but paradoxically one of the least known. This book puts it in the spotlight and explains how fund managers are able to benefit from mergers and acquisitions. It describes how to implement this strategy, located at the crossroad of corporate finance and asset management, and where its risks lie through numerous topical examples. The book is split into three parts. The first part, examining the basis of merger arbitrage, looks at the key role of the market in takeover bids. It also assesses the major changes in the financial markets over recent years and their impact on M&A. Various M&A risk and return factors are also discussed, alongside the historical profitability of merger arbitrage, the different approaches used by fund managers and the results of academic studies on the subject. The second part of the book deals with the risk of an M&A transaction failing in terms of financing risk, competition issues, the legal aspects of merger agreements and administrative and political risks. The third part of the book examines specificities of M&A transactions, comprehensively covering hostile takeovers and leveraged buyouts. Each part contains many recent examples and case studies in order to show how the various theories and notions are put into practice. From researching prospects and determining positions, to hedging and trading tactics, Lionel Melka and Amit Shabi present the full complement of sophisticated risk arbitrage techniques, making Merger Arbitrage a must read for finance and investment professionals who want to take advantage of the nearly limitless opportunities afforded by today’s rapidly changing global business environment. The book builds on its authors’ diverse backgrounds and common experience managing a merger arbitrage fund, providing readers with an enriching inside view on M&A operations. Translated by Andrew Fanko and Frances Thomas




Data-Driven Marketing Content


Book Description

This practical content guide empowers businesses to understand, identify and act on big-data opportunities, producing superior business insights for prolific marketing gains.