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Independent Film Finance : A Research-Based Guide to Funding Your Movie
For aspiring producers and directors who need to learn film finance from the ground up, this revolutionary new book teaches the fundamentals, through the voices of more than 60 successful independent producers.Using a research and data-based approach, award-winning professor David Offenberg combines the wisdom of well-known and successful producers into one fun, easy-to-follow guide. Within, readers will learn how to talk to potential investors and what those financiers will expect from them in return.The book is also packed with informative anecdotes and examples to enrich each chapter and contextualize the film financing landscape.As the book progresses, equity, debt, revenue, profits, and their role in your movie will be explored.Accessible information about tax incentives and profit participations is included to help emerging filmmakers build out a workable financing plan.The book combines hard numbers and data sets, with direct guidance from successful producers, to construct a holistic overview on how you can turn your new-found financial knowledge into funding for your movie. This ground-breaking book is a must-read for any aspiring producer or director who wishes to gain an informed and easily digestible understanding of film finance.
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The Customer-Funded Business : Start, Finance, or Grow Your Company with Your Customers' Cash
Who needs investors? More than two generations ago, the venture capital community – VCs, business angels, incubators and others – convinced the entrepreneurial world that writing business plans and raising venture capital constituted the twin centerpieces of entrepreneurial endeavor.They did so for good reasons: the sometimes astonishing returns they've delivered to their investors and the astonishingly large companies that their ecosystem has created. But the vast majority of fast-growing companies never take any venture capital.So where does the money come from to start and grow their companies?From a much more agreeable and hospitable source, their customers.That's exactly what Michael Dell, Bill Gates and Banana Republic's Mel and Patricia Ziegler did to get their companies up and running and turn them into iconic brands. In The Customer Funded Business, best-selling author John Mullins uncovers five novel approaches that scrappy and innovative 21st century entrepreneurs working in companies large and small have ingeniously adapted from their predecessors like Dell, Gates, and the Zieglers: Matchmaker models (Airbnb)Pay-in-advance models (Threadless)Subscription models (TutorVista)Scarcity models (Vente Privee)Service-to-product models (GoViral) Through the captivating stories of these and other inspiring companies from around the world, Mullins brings to life the five models and identifies the questions that angel or other investors will – and should! – ask of entrepreneurs or corporate innovators seeking to apply them.Drawing on in-depth interviews with entrepreneurs and investors who have actually put these models to use, Mullins goes on to address the key implementation issues that characterize each of the models: when to apply them, how best to apply them, and the pitfalls to watch out for. Whether you're an aspiring entrepreneur lacking the start-up capital you need, an early-stage entrepreneur trying to get your cash-starved venture into take-off mode, an intrapreneur seeking funding within an established company, or an angel investor or mentor who supports high-potential ventures, this book offers the most sure-footed path to starting, financing, or growing your venture. John Mullins is the author of The New Business Road Test and, with Randy Komisar, the widely acclaimed Getting to Plan B.
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The Customer-Funded Business: Start, Finance, or Grow Your Company with Your Customers' Cash
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Adventure Finance : How to Create a Funding Journey That Blends Profit and Purpose
The venture capital model doesn’t work—at least not for 99% of startups and small businesses.In this 99% are a lot of companies with incredible potential: businesses headed by female founders and those from diverse racial backgrounds, organizations headquartered outside of venture capital hubs, and purpose-driven enterprises that are creating social and environmental impact alongside financial success. Counter to what the press-savvy venture capital world would have you believe, there are a lot of funding options out there for startups and small businesses.Adventure Finance is designed to help you understand some of these options, and walk you through real examples of how other founders and funders have put them to use.In simple, approachable language, the book breaks down the different types of funding options available from revenue-based financing to recoverable grants to redeemable equity to distributed ownership and more.Through a mix of storytelling and frameworks, based on a decade of research and experience in investing in early-stage companies, this book will give you the ability to determine how each of these structures can contribute to your own funding journey. The goal for this book is to shift the conversation about startup funding and help founders and funders widen the spectrum of “mainstream” investment options in order to make the venture financing world more inclusive and purpose-driven.
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Adventure Finance: How to Create a Funding Journey That Blends Profit and Purpose
Adventure Finance: How to Create a Funding Journey That Blends Profit and Purpose
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Tidy Finance with R
This textbook shows how to bring theoretical concepts from finance and econometrics to the data.Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch.We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages.Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database.We reuse these data in all the subsequent chapters, which we keep as self-contained as possible.The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Highlights1. Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copying and pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat.The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper.The exercises can be used for self-studying or as a source of inspiration for teaching exercises.
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Tidy Finance with Python
This textbook shows how to bring theoretical concepts from finance and econometrics to the data.Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch.We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine.Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database.We reuse these data in all the subsequent chapters, which we keep as self-contained as possible.The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Key Features:Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copying and pasting the code we provide. A full-fledged introduction to machine learning with scikit-learn based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. We show how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat, including detailed explanations of the most relevant data characteristics. Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper.The exercises can be used for self-studying or as a source of inspiration for teaching exercises.
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Chau Loan
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