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Deep Dive Into Financial Models
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Book Synopsis Deep Dive into Financial Models by : Mathieu Le Bellac
Download or read book Deep Dive into Financial Models written by Mathieu Le Bellac and published by World Scientific Publishing Company. This book was released on 2016-11-14 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since 2007, the repeated financial crises around the world have brought to the headlines financial practices and models considered to fuel the economic instabilities. Deep Dive into Financial Models: Modeling Risk and Uncertainty comes handy in demystifying the underlying quantitative finance concepts. With a limited use of mathematical formalism, the book explains thoroughly the models, their hypotheses, principles and other building blocks. A particular care is given to model limitations and their misuse for investment strategies, asset pricing, or risk management. Its reader-friendly nature provides readers with a head start in quantitative finance. Request Inspection Copy Contents:Interest RatesCredit Risk ModelingPortfolio Management TheoriesNo-arbitrage TheoryThe Black-Scholes ModelVolatility ModelsNumerical MethodsValue at Risk (VaR)Non-Gaussian Models Readership: Undergraduate and graduate students who are taking up Quantitative Finance courses and those who possess college mathematical background.
Book Synopsis Financial Modeling and Valuation by : Paul Pignataro
Download or read book Financial Modeling and Valuation written by Paul Pignataro and published by John Wiley & Sons. This book was released on 2013-07-10 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by the Founder and CEO of the prestigious New York School of Finance, this book schools you in the fundamental tools for accurately assessing the soundness of a stock investment. Built around a full-length case study of Wal-Mart, it shows you how to perform an in-depth analysis of that company's financial standing, walking you through all the steps of developing a sophisticated financial model as done by professional Wall Street analysts. You will construct a full scale financial model and valuation step-by-step as you page through the book. When we ran this analysis in January of 2012, we estimated the stock was undervalued. Since the first run of the analysis, the stock has increased 35 percent. Re-evaluating Wal-Mart 9months later, we will step through the techniques utilized by Wall Street analysts to build models on and properly value business entities. Step-by-step financial modeling - taught using downloadable Wall Street models, you will construct the model step by step as you page through the book. Hot keys and explicit Excel instructions aid even the novice excel modeler. Model built complete with Income Statement, Cash Flow Statement, Balance Sheet, Balance Sheet Balancing Techniques, Depreciation Schedule (complete with accelerating depreciation and deferring taxes), working capital schedule, debt schedule, handling circular references, and automatic debt pay downs. Illustrative concepts including detailing model flows help aid in conceptual understanding. Concepts are reiterated and honed, perfect for a novice yet detailed enough for a professional. Model built direct from Wal-Mart public filings, searching through notes, performing research, and illustrating techniques to formulate projections. Includes in-depth coverage of valuation techniques commonly used by Wall Street professionals. Illustrative comparable company analyses - built the right way, direct from historical financials, calculating LTM (Last Twelve Month) data, calendarization, and properly smoothing EBITDA and Net Income. Precedent transactions analysis - detailing how to extract proper metrics from relevant proxy statements Discounted cash flow analysis - simplifying and illustrating how a DCF is utilized, how unlevered free cash flow is derived, and the meaning of weighted average cost of capital (WACC) Step-by-step we will come up with a valuation on Wal-Mart Chapter end questions, practice models, additional case studies and common interview questions (found in the companion website) help solidify the techniques honed in the book; ideal for universities or business students looking to break into the investment banking field.
Book Synopsis Advanced Modelling in Finance using Excel and VBA by : Mary Jackson
Download or read book Advanced Modelling in Finance using Excel and VBA written by Mary Jackson and published by John Wiley & Sons. This book was released on 2006-08-30 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new and unique book demonstrates that Excel and VBA can play an important role in the explanation and implementation of numerical methods across finance. Advanced Modelling in Finance provides a comprehensive look at equities, options on equities and options on bonds from the early 1950s to the late 1990s. The book adopts a step-by-step approach to understanding the more sophisticated aspects of Excel macros and VBA programming, showing how these programming techniques can be used to model and manipulate financial data, as applied to equities, bonds and options. The book is essential for financial practitioners who need to develop their financial modelling skill sets as there is an increase in the need to analyse and develop ever more complex 'what if' scenarios. Specifically applies Excel and VBA to the financial markets Packaged with a CD containing the software from the examples throughout the book Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Book Synopsis Financial Modelling with Jump Processes by : Peter Tankov
Download or read book Financial Modelling with Jump Processes written by Peter Tankov and published by CRC Press. This book was released on 2003-12-30 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: WINNER of a Riskbook.com Best of 2004 Book Award! During the last decade, financial models based on jump processes have acquired increasing popularity in risk management and option pricing. Much has been published on the subject, but the technical nature of most papers makes them difficult for nonspecialists to understand, and the mathematic
Book Synopsis The Art and Science of Financial Modeling by : Anurag Singal
Download or read book The Art and Science of Financial Modeling written by Anurag Singal and published by Business Expert Press. This book was released on 2018-09-10 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book will help readers dive deep into the vocabulary and the syntax, the art and science of financial modeling and valuation. To use a cliché, we live in a volatile uncertain complex and ambiguous (VUCA) world. Organizations simply cannot afford to try out new strategies in reality and correct mistakes, once they’ve occurred. The stakes are too high. Thus emerges the utility of this technique across functions like financial planning and risk management. Financial models help a business manager simulate the future and see the impact of their change, without risking costly setbacks of real world trials and errors. Mastering the art of financial modeling is imperative for those who want to enter the ultra-competitive world of corporate finance, investment banking, private equity, or equity research. Only those who excel (pun intended) in modeling early on are often the most successful long- term. Readers will be able to prepare/use existing models more competently, interpret the results and have greater comfort over the integrity and accuracy of the model’s calculations.
Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado
Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Book Synopsis The bond market: a deep dive into debt investment by : George Wilton
Download or read book The bond market: a deep dive into debt investment written by George Wilton and published by Az Boek. This book was released on 2024-04-27 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Beloved Brands by : Graham Robertson
Download or read book Beloved Brands written by Graham Robertson and published by Createspace Independent Publishing Platform. This book was released on 2018-01-06 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Beloved Brands is a book every CMO or would-be CMO should read." Al Ries With Beloved Brands, you will learn everything you need to know so you can build a brand that your consumers will love. You will learn how to think strategically, define your brand with a positioning statement and a brand idea, write a brand plan everyone can follow, inspire smart and creative marketing execution, and be able to analyze the performance of your brand through a deep-dive business review. Marketing pros and entrepreneurs, this book is for you. Whether you are a VP, CMO, director, brand manager or just starting your marketing career, I promise you will learn how to realize your full potential. You could be in brand management working for an organization or an owner-operator managing a branded business. Beloved Brands provides a toolbox intended to help you every day in your job. Keep it on your desk and refer to it whenever you need to write a brand plan, create a brand idea, develop a creative brief, make advertising decisions or lead a deep-dive business review. You can even pass on the tools to your team, so they can learn how to deliver the fundamentals needed for your brands. This book is also an excellent resource for marketing professors, who can use it as an in-class textbook to develop future marketers. It will challenge communications agency professionals, who are looking to get better at managing brands, including those who work in advertising, public relations, in-store marketing, digital advertising or event marketing. "Most books on branding are really for the MARCOM crowd. They sound good, but you find it's all fluff when you try to take it from words to actions. THIS BOOK IS DIFFERENT! Graham does a wonderful job laying out the steps in clear language and goes beyond advertising and social media to show how branding relates to all aspects of GENERAL as well as marketing management. Make no mistake: there is a strong theoretical foundation for all he says...but he spares you the buzzwords. Next year my students will all be using this book." Kenneth B. (Ken) Wong, Queen's University If you are an entrepreneur who has a great product and wants to turn it into a brand, you can use this book as a playbook. These tips will help you take full advantage of branding and marketing, and make your brand more powerful and more profitable. You will learn how to think, define, plan, execute and analyze, and I provide every tool you will ever need to run your brand. You will find models and examples for each of the four strategic thinking methods, looking at core strength, competitive, consumer and situational strategies. To define the brand, I will provide a tool for writing a brand positioning statement as well as a consumer profile and a consumer benefits ladder. I have created lists of potential functional and emotional benefits to kickstart your thinking on brand positioning. We explore the step-by-step process to come up with your brand idea and bring it all together with a tool for writing the ideal brand concept. For brand plans, I provide formats for a long-range brand strategy roadmap and the annual brand plan with definitions for each planning element. From there, I show how to build a brand execution plan that includes the creative brief, innovation process, and sales plan. I provide tools for how to create a brand calendar and specific project plans. To grow your brand, I show how to make smart decisions on execution around creative advertising and media choices. When it comes time for the analytics, I provide all the tools you need to write a deep-dive business review, looking at the marketplace, consumer, channels, competitors and the brand. Write everything so that it is easy to follow and implement for your brand. My promise to help make you smarter so you can realize your full potential.
Book Synopsis Operational Risk Modeling in Financial Services by : Patrick Naim
Download or read book Operational Risk Modeling in Financial Services written by Patrick Naim and published by John Wiley & Sons. This book was released on 2019-03-28 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transform your approach to oprisk modelling with a proven, non-statistical methodology Operational Risk Modeling in Financial Services provides risk professionals with a forward-looking approach to risk modelling, based on structured management judgement over obsolete statistical methods. Proven over a decade’s use in significant banks and financial services firms in Europe and the US, the Exposure, Occurrence, Impact (XOI) method of operational risk modelling played an instrumental role in reshaping their oprisk modelling approaches; in this book, the expert team that developed this methodology offers practical, in-depth guidance on XOI use and applications for a variety of major risks. The Basel Committee has dismissed statistical approaches to risk modelling, leaving regulators and practitioners searching for the next generation of oprisk quantification. The XOI method is ideally suited to fulfil this need, as a calculated, coordinated, consistent approach designed to bridge the gap between risk quantification and risk management. This book details the XOI framework and provides essential guidance for practitioners looking to change the oprisk modelling paradigm. Survey the range of current practices in operational risk analysis and modelling Track recent regulatory trends including capital modelling, stress testing and more Understand the XOI oprisk modelling method, and transition away from statistical approaches Apply XOI to major operational risks, such as disasters, fraud, conduct, legal and cyber risk The financial services industry is in dire need of a new standard — a proven, transformational approach to operational risk that eliminates or mitigates the common issues with traditional approaches. Operational Risk Modeling in Financial Services provides practical, real-world guidance toward a more reliable methodology, shifting the conversation toward the future with a new kind of oprisk modelling.
Book Synopsis Financial Modeling by : Anurag Singal
Download or read book Financial Modeling written by Anurag Singal and published by . This book was released on 2018-09-07 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: To use a cliché, we live in a volatile uncertain complex and ambiguous (VUCA) world.Organizations simply cannot afford to try out new strategies in reality and correct mistakes, once they've occurred.The stakes are too high. Thus emerges the utility of this technique across functions like financial planning and risk management. Financial models help a business manager simulate the future and see the impact of their change, without risking costly setbacks of real world trials and errors.Mastering the art of financial modeling is imperative for those who want to enter the ultra-competitive world of corporate finance, investment banking, private equity, or equity research. Only those who excel (pun intended) in modeling early on are often the most successful long-term.The book will help readers dive deep into the vocabulary and the syntax, the art and science of financial modeling and valuation. Readers will be able to prepare/use existing models more competently, interpret the results and have greater comfort over the integrity and accuracy of the model's calculations.
Book Synopsis A Practical Guide for Startup Valuation by : Sinem Derindere Köseoğlu
Download or read book A Practical Guide for Startup Valuation written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2023-09-25 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sheds new light on the most important contemporary and emerging startup valuation topics. Drawing on the first-hand professional experience of practitioners, professionals, and startup experts from various fields of finance, combined with a sound academic foundation, it offers a practical guide to startup valuation and presents applications, practical examples, and case studies of real startup ecosystems. The book discusses pressing questions, such as: Why are startups in California are higher valued than those in New York? Or why do startups based in London receive higher valuations than those in Paris, Berlin, or Milan, even when they are based in similarly-sized economies, share the same industries, and often even have the same investors? Answering these questions, the authors present key topics, such as hierarchical and segmented approaches to startup valuation, business plans, and sensitivity analysis, many methods such as venture capital valuation, first Chicago valuation, scorecard valuation, Dave Berkus valuation, risk factor summation valuation, and discounted cash flow valuation, in addition to business valuation by data envelopment analysis and real options analysis, as well as critical conceptual issues in the valuation such as expected returns of the venture capital and price versus value concepts, among others. The book will help angel investors, venture capitalists, institutional investors, crowd-based fractional investors, and investment fund professionals understand how to use basic and advanced analytics for a more precise valuation that helps them craft their long-term capital-raising strategy and keep their funding requests in perspective. It will also appeal to students and scholars of finance and business interested in a better understanding of startup valuation.
Book Synopsis A Quick Start Guide to Financial Forecasting by : Philip Campbell
Download or read book A Quick Start Guide to Financial Forecasting written by Philip Campbell and published by Grow and Succeed Publishing. This book was released on 2017-07-12 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning in Finance by : Matthew F. Dixon
Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Book Synopsis Practical Applications of Data Processing, Algorithms, and Modeling by : Whig, Pawan
Download or read book Practical Applications of Data Processing, Algorithms, and Modeling written by Whig, Pawan and published by IGI Global. This book was released on 2024-04-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's data-driven era, the persistent gap between theoretical understanding and practical implementation in data science poses a formidable challenge. As we navigate through the complexities of harnessing data, deciphering algorithms, and unleashing the potential of modeling techniques, the need for a comprehensive guide becomes increasingly evident. This is the landscape explored in Practical Applications of Data Processing, Algorithms, and Modeling. This book is a solution to the pervasive problem faced by aspiring data scientists, seasoned professionals, and anyone fascinated by the power of data-driven insights. From the web of algorithms to the strategic role of modeling in decision-making, this book is an effective resource in a landscape where data, without proper guidance, risks becoming an untapped resource. The objective of Practical Applications of Data Processing, Algorithms, and Modeling is to address the pressing issue at the heart of data science – the divide between theory and practice. This book seeks to examine the complexities of data processing techniques, algorithms, and modeling methodologies, offering a practical understanding of these concepts. By focusing on real-world applications, the book provides readers with the tools and knowledge needed to bridge the gap effectively, allowing them to apply these techniques across diverse industries and domains. In the face of constant technological advancements, the book highlights the latest trends and innovative approaches, fostering a deeper comprehension of how these technologies can be leveraged to solve complex problems. As a practical guide, it empowers readers with hands-on examples, case studies, and problem-solving scenarios, aiming to instill confidence in navigating data challenges and making informed decisions using data-driven insights.
Book Synopsis Dive Into Deep Learning by : Joanne Quinn
Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
Book Synopsis Python for Finance Cookbook by : Eryk Lewinson
Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Book Synopsis Machine Learning for Financial Risk Management with Python by : Abdullah Karasan
Download or read book Machine Learning for Financial Risk Management with Python written by Abdullah Karasan and published by "O'Reilly Media, Inc.". This book was released on 2021-12-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
