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| data science investment banking: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| data science investment banking: Python for Finance Yves J. Hilpisch, 2018-12-05 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. |
| data science investment banking: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| data science investment banking: Principles of Financial Engineering Robert Kosowski, Salih N. Neftci, 2014-11-26 Principles of Financial Engineering, Third Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. A solutions manual enhances the text by presenting additional cases and solutions to exercises. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Third Edition presents three new chapters on financial engineering in commodity markets, financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles and how to incorporate counterparty risk into derivatives pricing, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The solutions manual enhances the text by presenting additional cases and solutions to exercises |
| data science investment banking: The Best Book On Investment Banking Careers Donna Khalife, 2012-07-24 Whether you’re an undergraduate prepping for your first internship, or seeking a new career in investment banking, knowing the ins and outs of the industry can help you make your big break. In this eBook, Donna Khalife shares an insider’s perspective to the investment banking industry and helps prepare readers for their chance at landing their dream job. |
| data science investment banking: Suits Nina Godiwalla, 2011-02-28 A fiercely ambitious woman from the Persian-Indian community ventures from Houston to New York to follow her dream of working in the world of banking and finance in pursuit of success, honor, and family pride. |
| data science investment banking: Data Science for Financial Econometrics Nguyen Ngoc Thach, Vladik Kreinovich, Nguyen Duc Trung, 2020-11-13 This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. |
| data science investment banking: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| data science investment banking: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| data science investment banking: Investment Banks, Hedge Funds, and Private Equity David P. Stowell, 2012-09-01 The dynamic environment of investment banks, hedge funds, and private equity firms comes to life in David Stowell's introduction to the ways they challenge and sustain each other. Capturing their reshaped business plans in the wake of the 2007-2009 global meltdown, his book reveals their key functions, compensation systems, unique roles in wealth creation and risk management, and epic battles for investor funds and corporate influence. Its combination of perspectives—drawn from his industry and academic backgrounds—delivers insights that illuminate the post-2009 reinvention and acclimation processes. Through a broad view of the ways these financial institutions affect corporations, governments, and individuals, Professor Stowell shows us how and why they will continue to project their power and influence. - Emphasizes the needs for capital, sources of capital, and the process of getting capital to those who need it - Integrates into the chapters ten cases about recent transactions, along with case notes and questions - Accompanies cases with spreadsheets for readers to create their own analytical frameworks and consider choices and opportunities |
| data science investment banking: Big Data Science in Finance Irene Aldridge, Marco Avellaneda, 2021-01-08 Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners. |
| data science investment banking: Investment Banking For Dummies Matthew Krantz, Robert R. Johnson, 2020-07-14 Wrap your head around the complicated world of investment banking with this understandable and comprehensive resource The celebrated authors of Investment Banking For Dummies, 2nd Edition have updated and modernized their best-selling book to bring readers an invaluable and accessible volume about the investment banking industry. Written in the straightforward and approachable tone the For Dummies series is known for the world over, authors Matthew Krantz and Robert Johnson have created an indispensable resource for students and professionals new to investment banking. The book covers all the crucial topics required to understand the fundamentals of the industry, including: Strategies for different types of risk management: market, credit, operating, reputation, legal, and funding The key investment banking operations: venture capital, buyouts, M&A, equity underwriting, debt, and more The relationship between leverages buyout funds, hedge funds, and corporate and institutional clients Investment Banking For Dummies, 2nd Edition offers, for the first time, a brand-new chapter devoted to cryptocurrencies, and new content on “unicorn” IPOs, including Uber, Lyft, and Airbnb. |
| data science investment banking: Investment Banking Joshua Rosenbaum, Joshua Pearl, 2020-03-20 A timely update to the global bestselling book on investment banking and valuation – this new edition reflects valuable contributions from Nasdaq and the global law firm Latham & Watkins LLP plus access to the online valuation models and course. In the constantly evolving world of finance, a solid technical foundation is an essential tool for success. Due to the fast-paced nature of this world, however, no one was able to take the time to properly codify its lifeblood--namely, valuation and dealmaking. Rosenbaum and Pearl originally responded to this need in 2009 by writing the first edition of the book that they wish had existed when they were trying to break into Wall Street. Investment Banking: Valuation, LBOs, M&A, and IPOs, 3rd Edition is a highly accessible and authoritative book written by investment bankers that explains how to perform the valuation work and financial analysis at the core of Wall Street – comparable companies, precedent transactions, DCF, LBO, M&A analysis...and now IPO analytics and valuation. Using a step-by-step, how-to approach for each methodology, the authors build a chronological knowledge base and define key terms, financial concepts, and processes throughout the book. The genesis for the original book stemmed from the authors' personal experiences as students interviewing for investment banking positions. As they both independently went through the rigorous process, they realized that their classroom experiences were a step removed from how valuation and financial analysis were performed in real-world situations. Consequently, they created this book to provide a leg up to those individuals seeking or beginning careers on Wall Street – from students at undergraduate universities and graduate schools to career changers looking to break into finance. Now, over 10 years after the release of the first edition, the book is more relevant and topical than ever. It is used in over 200 universities globally and has become a go-to resource for investment banks, private equity, investment firms, and corporations undertaking M&A transactions, LBOs, IPOs, restructurings, and investment decisions. While the fundamentals haven't changed, the environment must adapt to changing market developments and conditions. As a result, Rosenbaum and Pearl have updated their widely adopted book accordingly, turning the latest edition of Investment Banking: Valuation, LBOs, M&A, and IPOs into a unique and comprehensive training package, which includes: Two new chapters covering IPOs plus insightful contributions from Nasdaq, the leading U.S. exchange and technology provider for IPOs and new listings, and global law firm Latham & Watkins LLP Access to six downloadable valuation model templates, including Comparable Companies Analysis, Precedent Transactions Analysis, Discounted Cash Flow Analysis, Leveraged Buyout Analysis, M&A Analysis, and IPO Valuation Six-month access to online Wiley Investment Banking Valuation Course featuring bite-sized lessons, over five hours of video lectures, 100+ practice questions, and other investment banking study tools Launch your career on Wall Street and hone your financial expertise with Rosenbaum and Pearl’s real-world knowledge and forward-looking guidance in the latest edition of Investment Banking: Valuation, LBOs, M&A, and IPOs. |
| data science investment banking: Artificial Intelligence in Banking Introbooks, 2020-04-07 In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, In a world focused on using AI in new ways, we're focused on using it wisely and responsibly. |
| data science investment banking: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data science investment banking: Distressed Investment Banking Henry Furlow Owsley, Peter S. Kaufman, 2005 The definitive work on the role of the investment banker in a troubled company situation. |
| data science investment banking: How to Be an Investment Banker Andrew Gutmann, 2013-03-26 A top-notch resource for anyone who wants to break into the demanding world of investment banking For undergraduates and MBA students, this book offers the perfect preparation for the demanding and rigorous investment banking recruitment process. It features an overview of investment banking and careers in the field, followed by chapters on the core accounting and finance skills that make up the necessary framework for success as a junior investment banker. The book then moves on to address the kind of specific technical interview and recruiting questions that students will encounter in the job search process, making this the ideal resource for anyone who wants to enter the field. The ideal test prep resource for undergraduates and MBA students trying to break into investment banking Based on author Andrew Gutmann's proprietary 24 to 30-hour course Features powerful learning tools, including sample interview questions and answers and online resources For anyone who wants to break into investment banking, How to Be an Investment Banker is the perfect career-making guide. |
| data science investment banking: Corporate and Investment Banking Fidelio Tata, 2020-07-19 This book provides unique information to prepare graduates and newly hired corporate and investment banking professionals for a career in the global markets environment of large universal and international investment banks. It shows the interrelationship between the three specific business functions of sales, trading, and research, as well as the interaction with corporate and institutional clients. The book fills a gap in the available literature by linking financial market theory to the practical aspects of day-to-day operations on a trading floor and offers a taxonomy of the current banking business, providing an in-depth analysis of the main market participants in the global markets ecosystem. Engaging the reader with case studies, anecdotes, and industry color, the book addresses the risks and opportunities of the global markets business in today’s global financial markets both from a theoretical and from a practitioner’s perspective and focuses on the most important fixed-income financial instruments from a pricing, risk-management, and client-marketing perspective. |
| data science investment banking: Practical Data Analytics for BFSI Bharat Sikka, Dr. Priyender Yadav, Dr. Prashant Verma, 2023-09-02 Revolutionizing BFSI with Data Analytics KEY FEATURES ● Real-world examples and exercises will ground you in the practical application of analytics techniques specific to BFSI. ● Master Python for essential coding, SQL for data manipulation, and industry-leading tools like IBM SPSS and Power BI for sophisticated analyses. ● Understand how data-driven strategies generate profits, mitigate risks, and redefine customer support dynamics within the BFSI sphere. DESCRIPTION Are you looking to unlock the transformative potential of data analytics in the dynamic world of Banking, Financial Services, and Insurance (BFSI)? This book is your essential guide to mastering the intricate interplay of data science and analytics that underpins the BFSI landscape. Designed for intermediate-level practitioners, as well as those aspiring to join the ranks of BFSI analytics professionals, this book is your compass in the data-driven realm of banking. Address the unique challenges and opportunities of the BFSI sector using Artificial Intelligence and Machine Learning models for a data driven analysis. This book is a step by step guide to utilize tools like IBM SPSS and Microsoft Power BI. Hands-on examples that utilize Python and SQL programming languages make this an essential guide. The book features numerous case studies that illuminate various use cases of Analytics in BFSI. Each chapter is enriched with practical insights and concludes with a valuable multiple-choice questionnaire, reinforcing understanding and engagement. This book will uncover how these solutions not only pave the way for increased profitability but also navigate risks with precision and elevate customer support to unparalleled heights. WHAT WILL YOU LEARN ● Delve into the world of Data Science, including Artificial Intelligence and Machine Learning, with a focus on their application within BFSI. ● Explore hands-on examples and step-by-step tutorials that provide practical solutions to real-world challenges faced by banking institutions. ● Develop skills in essential programming languages such as Python (fundamentals) and SQL (intermediate), crucial for effective data manipulation and analysis. ● Gain insights into how businesses adapt data-driven strategies to make informed decisions, leading to improved operational efficiency. ● Stay updated on emerging trends, technologies, and innovations shaping the future of data analytics in the BFSI industry. WHO IS THIS BOOK FOR? This book is tailored for professionals already engaged in or seeking roles within Data Analytics in the BFSI industry. Additionally, it serves as a strategic resource for business leaders and upper management, guiding them in shaping data platforms and products within their organizations. The book also serves as a starting point for individuals interested in the BFSI sector. Prior experience with coding tools such as Python, SQL, Power BI is beneficial but not required as it covers all dimensions from the basics. TABLE OF CONTENTS 1. Introduction to BFSI and Data Driven Banking 2. Introduction to Analytics and Data Science 3. Major Areas of Analytics Utilization 4. Understanding Infrastructures behind BFSI for Analytics 5. Data Governance and AI/ML Model Governance in BFSI 6. Domains of BFSI and team planning 7. Customer Demographic Analysis and Customer Segmentation 8. Text Mining and Social Media Analytics 9. Lead Generation Through Analytical Reasoning and Machine Learning 10. Cross Sell and Up Sell of Products through Machine Learning 11. Pricing Optimization 12. Data Envelopment Analysis 13. ATM Cash Forecasting 14. Unstructured Data Analytics 15. Fraud Modelling 16. Detection of Money Laundering and Analysis 17. Credit Risk and Stressed Assets 18. High Performance Architectures: On-Premises and Cloud 19. Growing Trends in the Data-Driven Future of BFSI |
| data science investment banking: Middle Market M & A Kenneth H. Marks, Robert T. Slee, Christian W. Blees, Michael R. Nall, 2012-01-10 In-depth coverage in a single handbook of the middle market based on the body of knowledge of the Certified M&A Advisor credential program M&A advisors have an unprecedented opportunity in the middle market with the generational transfer of wealth and capital being deployed by private equity and corporate investors. Middle Market M&A: Handbook for Investment Banking and Business Consulting is a must-read for investment bankers, M&A intermediaries and specialists, CPAs and accountants, valuation experts, deal and transaction attorneys, wealth managers and investors, corporate development leaders, consultants and advisors, CEOs, and CFOs. Provides a holistic overview and guide on mergers, acquisitions, divestitures and strategic transactions of companies with revenues from $5 million to $500 million Encompasses current market trends, activities, and strategies covering pre, during, and post transaction Addresses the processes and core subject areas required to successfully navigate and close deals in the private capital market Includes content on engagement and practice management for those involved in the M&A business This practical guide and reference is also an excellent primer for those seeking to obtain their FINRA Series 79 license. |
| data science investment banking: Data Smart Jordan Goldmeier, 2023-09-22 A straightforward and engaging approach to data science that skips the jargon and focuses on the essentials In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel’s easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful artificial intelligence concepts, focusing on how to learn by doing. You’ll also find: Four-color data visualizations that highlight and illustrate the concepts discussed in the book Tutorials explaining complicated data science using just Microsoft Excel How to take what you’ve learned and apply it to everyday problems at work and life A must-read guide to data science for every day, non-technical professionals, Data Smart will earn a place on the bookshelves of students, analysts, data-driven managers, marketers, consultants, business intelligence analysts, demand forecasters, and revenue managers. |
| data science investment banking: Network Models for Data Science Alan Julian Izenman, 2023-01-05 This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component. |
| data science investment banking: The Quants Scott Patterson, 2011-01-25 With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris, and an ominous warning about Wall Street’s future. In March of 2006, four of the world’s richest men sipped champagne in an opulent New York hotel. They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them. They were accustomed to risking billions. On that night, these four men and their cohorts were the new kings of Wall Street. Muller, Griffin, Asness, and Weinstein were among the best and brightest of a new breed, the quants. Over the prior twenty years, this species of math whiz--technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers--had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino. The quants helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse. Few realized, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster. Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize--and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast. |
| data science investment banking: Big Data and Machine Learning in Quantitative Investment Tony Guida, 2018-12-12 Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how. |
| data science investment banking: Python for Finance Yves Hilpisch, 2014-12-11 The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through 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, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies |
| data science investment banking: A Pragmatist’s Guide to Leveraged Finance Robert S. Kricheff, 2021-05-25 The high-yield leveraged bond and loan market is now valued at $4+ trillion in North America, Europe, and emerging markets. What’s more the market is in a period of significant growth. To successfully issue, evaluate, and invest in high-yield debt, financial professionals need credit and bond analysis skills specific to these instruments. This fully revised and updated edition of A Pragmatist’s Guide to Leveraged Finance is a complete, practical, and expert tutorial and reference book covering all facets of modern leveraged finance analysis. Long-time professional in the field, Bob Kricheff, explains why conventional analysis techniques are inadequate for leveraged instruments, clearly defines the unique challenges sellers and buyers face, walks step-by-step through deriving essential data for pricing and decision-making, and demonstrates how to apply it. Using practical examples, sample documents, Excel worksheets, and graphs, Kricheff covers all this, and much more: yields, spreads, and total return; ratio analysis of liquidity and asset value; business trend analysis; modeling and scenarios; potential interest rate impacts; evaluating leveraged finance covenants; how to assess equity (and why it matters); investing on news and events; early-stage credit; bankruptcy analysis and creating accurate credit snapshots. This second edition includes new sections on fallen angels, environmental, social and governance (ESG) investment considerations, interaction with portfolio managers, CLOs, new issues, and data science. A Pragmatist’s Guide to Leveraged Finance is an indispensable resource for all investment and underwriting professionals, money managers, consultants, accountants, advisors, and lawyers working in leveraged finance. It also teaches credit analysis skills that will be valuable in analyzing a wide variety of higher-risk investments, including growth stocks. |
| data science investment banking: Advances in Data Science and Management Samarjeet Borah, Sambit Kumar Mishra, Brojo Kishore Mishra, Valentina Emilia Balas, Zdzislaw Polkowski, 2022-02-13 This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands. |
| data science investment banking: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science investment banking: Data Science Uncovering the Reality Pulkit Bansal, Kunal Kishore, Pankaj Gupta, Srijan Saket, Neeraj Kumar, 2020-04-15 Data Science has become a popular field of work today. However a good resource to understand applied Data Science is still missing. In Data Science Uncovering the Reality, a group of IITians unravel how Data Science is done in the industry. They have interviewed Data Science and technology leaders at top companies in India and presented their learnings here. This book will give you honest answers to questions such as: How to build a career in Data Science? How A.I. is used in the world’s most successful companies. How Data Science leaders actually work and the challenges they face. |
| data science investment banking: Banking 4.0 Mohan Bhatia, 2022-05-21 This book shows banking professionals how to leverage the best practices in the industry to build a structured and coordinated approach towards the digitization of banking processes. It provides a roadmap and templates in order to industrialize the financial services firm over iterative cycles. To achieve the planned business and revenue results at the optimal costs, the digital transformation has to be calibrated and coordinated across both the front and back office, scaled and timed against external innovation benchmarks and Fintechs. To this end, data collection and evaluation must be ingrained, banking-specific artificial intelligence methods must be included, and all digitization approaches must be harmonized on an iterative basis with the experience gained. Spread over several chapters, this book provides a calibration and coordination framework for the delivery of the digital bank 4.0. |
| data science investment banking: Hands-On Artificial Intelligence for Banking Jeffrey Ng, Subhash Shah, 2020-07-10 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. |
| data science investment banking: Data Science and Data Analytics Amit Kumar Tyagi, 2021-09-22 Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity. |
| data science investment banking: MarketPsych Richard L. Peterson, Frank F. Murtha, 2010-07-30 An investor's guide to understanding the most elusive (yet most important) aspect of successful investing - yourself. Why is it that the investing performance of so many smart people reliably and predictably falls short? The answer is not that they know too little about the markets. In fact, they know too little about themselves. Combining the latest findings from the academic fields of behavioral finance and experimental psychology with the down-and-dirty real-world wisdom of successful investors, Drs. Richard Peterson and Frank Murtha guide both new and experienced investors through the psychological learning process necessary to achieve their financial goals. In an easy and entertaining style that masks the book’s scientific rigor, the authors make complex scientific insights readily understandable and actionable, shattering a number of investing myths along the way. You will gain understanding of your true investing motivations, learn to avoid the unseen forces that subvert your performance, and build your investor identity - the foundation for long-lasting investing success. Replete with humorous games, insightful self-assessments, entertaining exercises, and concrete planning tools, this book goes beyond mere education. MarketPsych: How to Manage Fear and Build Your Investor Identity functions as a psychological outfitter for your unique investing journey, providing the tools, training and equipment to help you navigate the right paths, stay on them, and see your journey through to success. |
| data science investment banking: Vault Career Guide to Investment Banking Tom Lott, Derek Loosvelt, 2005 One of Vault's bestselling titles, this guide covers the basics of financial markets, including walk-throughs of equity and fixed income offerings, M&A, and private placements. |
| data science investment banking: Investment Banking Workbook Joshua Rosenbaum, Joshua Pearl, Joseph Gasparro, 2021-02-23 The ideal companion to Investment Banking Investment Banking WORKBOOK is the ideal complement to Investment Banking: Valuation, LBOs, M&A, and IPOs, Third Edition—enabling you to truly master and refine the core skills at the center of the world of finance. This comprehensive study guide provides an invaluable opportunity to explore your understanding of the strategies and techniques covered in the main text before putting them to work in real-world situations. The WORKBOOK, which parallels the main book chapter by chapter, contains over 500 problem-solving exercises and multiple-choice questions. Topics reviewed include: – Valuation and its various forms of analysis, including comparable companies, precedent transactions, and DCF analysis –Leveraged buyouts—from the fundamentals of LBO economics and structure to detailed modeling and valuation –M&A sell-side tools and techniques, including an overview of an organized M&A sale process –M&A buy-side strategy and analysis, including a comprehensive merger consequences analysis that includes accretion/dilution and balance sheet effects –IPOs, including valuation, structure, and process, as well as SPACs and direct listings The lessons found within will help you successfully navigate the dynamic world of investment banking, LBOs, M&A, IPOs, and professional investing. Investment Banking WORKBOOK will enable you to take your learning to the next level in terms of understanding and applying the critical financial tools necessary to be an effective finance professional. |
| data science investment banking: The Accidental Investment Banker Jonathan A. Knee, 2006-08-15 Jonathan A. Knee had a ringside seat during the go-go, boom-and-bust decade and into the 21st century, at the two most prestigious investment banks on Wall Street--Goldman Sachs and Morgan Stanley. In this candid and irreverent insider's account of an industry in free fall, Knee captures an exhilarating era of fabulous deal-making in a free-wheeling Internet economy--and the catastrophe that followed when the bubble burst. Populated with power players, back stabbers, celebrity bankers, and godzillionaires, here is a vivid account of the dramatic upheaval that took place in investment banking. Indeed, Knee entered an industry that was typified by the motto first-class business in a first-class way and saw it transformed in a decade to a free-for-all typified by the acronym IBG, YBG (I'll be gone, you'll be gone). Increasingly mercenary bankers signed off on weak deals, knowing they would leave them in the rear-view mirror. Once, investment bankers prospered largely on their success in serving the client, preserving the firm, and protecting the public interest. Now, in the financial supermarket era, bankers felt not only that each day might be their last, but that their worth was tied exclusively to how much revenue they generated for the firm on that day--regardless of the source. Today, most young executives feel no loyalty to their firms, and among their clients, Knee finds an unprecedented but understandable level of cynicism and distrust of investment banks. Brimming with insight into what investment bankers actually do, and told with biting humor and unflinching honesty, The Accidental Investment Banker offers a fascinating glimpse behind the scenes of the most powerful companies on Wall Street. |
| data science investment banking: Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes Cornelis W Oosterlee, Lech A Grzelak, 2019-10-29 This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry. |
| data science investment banking: Inside the Yield Book Sidney Homer, Martin L. Leibowitz, 1972 |
| data science investment banking: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| data science investment banking: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
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