| computer science and finance: Computational Finance Argimiro Arratia, 2014-05-08 The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described. |
| computer science and finance: Financial Software Engineering Kevin Lano, Howard Haughton, 2019-05-02 In this textbook the authors introduce the important concepts of the financial software domain, and motivate the use of an agile software engineering approach for the development of financial software. They describe the role of software in defining financial models and in computing results from these models. Practical examples from bond pricing, yield curve estimation, share price analysis and valuation of derivative securities are given to illustrate the process of financial software engineering. Financial Software Engineering also includes a number of case studies based on typical financial engineering problems: *Internal rate of return calculation for bonds * Macaulay duration calculation for bonds * Bootstrapping of interest rates * Estimation of share price volatility * Technical analysis of share prices * Re-engineering Matlab to C# * Yield curve estimation * Derivative security pricing * Risk analysis of CDOs The book is suitable for undergraduate and postgraduate study, and for practitioners who wish to extend their knowledge of software engineering techniques for financial applications |
| computer science and finance: 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. |
| computer science and finance: High-Performance Computing in Finance M. A. H. Dempster, Juho Kanniainen, John Keane, Erik Vynckier, 2018-02-21 High-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computing– that can be used without much expertise and expense – to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Wave’s quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems. |
| computer science and finance: Machine Learning for Financial Engineering György Ottucsák, Harro Walk, 2012 Preface v 1 On the History of the Growth-Optimal Portfolio M.M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth A. Urbán 153 5 Nonparametric Sequential Prediction of Stationary Time Series L. Györfi Gy. Ottucsák 179 6 Empirical Pricing American Put Options L. Györfi A. Telcs 227 Index 249. |
| computer science and finance: Professional Financial Computing Using Excel and VBA Donny C. F. Lai, Humphrey K. K. Tung, Michael C. S. Wong, 2011-12-28 Professional Financial Computing Using Excel and VBA is an admirable exposition that bridges the theoretical underpinnings of financial engineering and its application which usually appears as a black-box software application. The book opens the black-box and reveals the architecture of risk-modeling and financial engineering based on industry-standard stochastic models by utilizing Excel and VBA functionality to create a robust and practical modeling tool-kit. Financial engineering professionals who purchase this book will have a jumpstart advantage for their customized financial engineering and modeling needs. Dr. Cameron Wicentowich Vice President, Treasury Analytics Canadian Imperial Bank of Commerce (CIBC) Spreadsheet modeling for finance has become a standard course in the curriculum of many Quantitative Finance programs since the Excel-based Visual Basic programming is now widely used in constructing optimal portfolios, pricing structured products and managing risks. Professional Financial Computing Using Excel and VBA is written by a unique team of finance, physics and computer academics and practitioners. It is a good reference for those who are studying for a Masters degree in Financial Engineering and Risk Management. It can also be useful for financial engineers to jump-start a project on designing structured products, modeling interest term structure or credit risks. Dr. Jin Zhang Director of Master of Finance Program and Associate Professor The University of Hong Kong Excel has been one of the most powerful tools for financial planning and computing over the last few years. Most users utilize a fraction of its capabilities. One of the reasons is the limited availability of books that cover the advanced features of Excel for Finance. Professional Financial Computing Using Excel and VBA goes the extra mile and deals with the Excel tools many professionals call for. This book is a must for professionals or students dealing with financial engineering, financial risk management, computational finance or mathematical finance. I loved the way the authors covered the material using real life, hands-on examples. Dr. Isaac Gottlieb Temple University Author, Next Generation Excel: Modeling in Excel for Analysts and MBAs |
| computer science and finance: 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. |
| computer science and finance: Fintech For Finance Professionals David Kuo Chuen Lee, Joseph Lim, Kok Fai Phoon, Yu Wang, 2021-11-29 As technologies such as artificial intelligence, big data, cloud computing, and blockchain have been applied to various areas in finance, there is an increasing demand for finance professionals with the skills and knowledge related to fintech. Knowledge of the technologies involved and finance concepts is crucial for the finance professional to understand the architecture of technologies as well as how they can be applied to solve various aspects of finance.This book covers the main concepts and theories of the technologies in fintech which consist of big data, data science, artificial intelligence, data structure and algorithm, computer network, network security, and Python programming. Fintech for Finance Professionals is a companion volume to the book on finance that covers the fundamental concepts in the field. Together, these two books form the foundation for a good understanding of finance and fintech applications which will be covered in subsequent volumes.Bundle set: Global Fintech Institute-Chartered Fintech Professional Set I |
| computer science and finance: The Ultimate Algorithmic Trading System Toolbox + Website George Pruitt, 2016-06-20 The accessible, beneficial guide to developing algorithmic trading solutions The Ultimate Algorithmic Trading System Toolbox is the complete package savvy investors have been looking for. An integration of explanation and tutorial, this guide takes you from utter novice to out-the-door trading solution as you learn the tools and techniques of the trade. You'll explore the broad spectrum of today's technological offerings, and use several to develop trading ideas using the provided source code and the author's own library, and get practical advice on popular software packages including TradeStation, TradersStudio, MultiCharts, Excel, and more. You'll stop making repetitive mistakes as you learn to recognize which paths you should not go down, and you'll discover that you don't need to be a programmer to take advantage of the latest technology. The companion website provides up-to-date TradeStation code, Excel spreadsheets, and instructional video, and gives you access to the author himself to help you interpret and implement the included algorithms. Algorithmic system trading isn't really all that new, but the technology that lets you program, evaluate, and implement trading ideas is rapidly evolving. This book helps you take advantage of these new capabilities to develop the trading solution you've been looking for. Exploit trading technology without a computer science degree Evaluate different trading systems' strengths and weaknesses Stop making the same trading mistakes over and over again Develop a complete trading solution using provided source code and libraries New technology has enabled the average trader to easily implement their ideas at very low cost, breathing new life into systems that were once not viable. If you're ready to take advantage of the new trading environment but don't know where to start, The Ultimate Algorithmic Trading System Toolbox will help you get on board quickly and easily. |
| computer science and finance: Computational Finance 1999 Yaser S. Abu-Mostafa, 2000 This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business. |
| computer science and finance: A Primer for the Mathematics of Financial Engineering Dan Stefanica, 2011 |
| computer science and finance: 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. |
| computer science and finance: 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. |
| computer science and finance: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on the Growth of Computer Science Undergraduate Enrollments, 2018-04-28 The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. |
| computer science and finance: Stochastic Processes in Science, Engineering and Finance Frank Beichelt, 2006-02-22 This book presents a self-contained introduction to stochastic processes with emphasis on their applications in science, engineering, finance, computer science, and operations research. It provides theoretical foundations for modeling time-dependent random phenomena in these areas and illustrates their application by analyzing numerous practical examples. The treatment assumes few prerequisites, requiring only the standard mathematical maturity acquired by undergraduate applied science students. It includes an introductory chapter that summarizes the basic probability theory needed as background. Numerous exercises reinforce the concepts and techniques discussed and allow readers to assess their grasp of the subject. Solutions to most of the exercises are provided in an appendix. While focused primarily on practical aspects, the presentation includes some important proofs along with more challenging examples and exercises for those more theoretically inclined. Mastering the contents of this book prepares readers to apply stochastic modeling in their own fields and enables them to work more creatively with software designed for dealing with the data analysis aspects of stochastic processes. |
| computer science and finance: Practical C++ Financial Programming Carlos Oliveira, 2015-03-12 Practical C++ Financial Programming is a hands-on book for programmers wanting to apply C++ to programming problems in the financial industry. The book explains those aspects of the language that are more frequently used in writing financial software, including the STL, templates, and various numerical libraries. The book also describes many of the important problems in financial engineering that are part of the day-to-day work of financial programmers in large investment banks and hedge funds. The author has extensive experience in the New York City financial industry that is now distilled into this handy guide. Focus is on providing working solutions for common programming problems. Examples are plentiful and provide value in the form of ready-to-use solutions that you can immediately apply in your day-to-day work. You’ll learn to design efficient, numerical classes for use in finance, as well as to use those classes provided by Boost and other libraries. You’ll see examples of matrix manipulations, curve fitting, histogram generation, numerical integration, and differential equation analysis, and you’ll learn how all these techniques can be applied to some of the most common areas of financial software development. These areas include performance price forecasting, optimizing investment portfolios, and more. The book style is quick and to-the-point, delivering a refreshing view of what one needs to master in order to thrive as a C++ programmer in the financial industry. Covers aspects of C++ especially relevant to financial programming. Provides working solutions to commonly-encountered problems in finance. Delivers in a refreshing and easy style with a strong focus on the practical. |
| computer science and finance: Financial Engineering and Computation Yuh-Dauh Lyuu, 2002 A comprehensive text and reference, first published in 2002, on the theory of financial engineering with numerous algorithms for pricing, risk management, and portfolio management. |
| computer science and finance: Introduction to C++ for Financial Engineers Daniel J. Duffy, 2013-10-24 This book introduces the reader to the C++ programming language and how to use it to write applications in quantitative finance (QF) and related areas. No previous knowledge of C or C++ is required -- experience with VBA, Matlab or other programming language is sufficient. The book adopts an incremental approach; starting from basic principles then moving on to advanced complex techniques and then to real-life applications in financial engineering. There are five major parts in the book: C++ fundamentals and object-oriented thinking in QF Advanced object-oriented features such as inheritance and polymorphism Template programming and the Standard Template Library (STL) An introduction to GOF design patterns and their applications in QF Applications The kinds of applications include binomial and trinomial methods, Monte Carlo simulation, advanced trees, partial differential equations and finite difference methods. This book includes a companion website with all source code and many useful C++ classes that you can use in your own applications. Examples, test cases and applications are directly relevant to QF. This book is the perfect companion to Daniel J. Duffy’s book Financial Instrument Pricing using C++ (Wiley 2004, 0470855096 / 9780470021620) |
| computer science and finance: Data Science and Intelligent Systems Radek Silhavy, Petr Silhavy, Zdenka Prokopova, 2021-11-16 This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results |
| computer science and finance: Modern Computational Finance Antoine Savine, 2018-11-20 Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book. |
| computer science and finance: Differential Equations and Numerical Analysis Valarmathi Sigamani, John J. H. Miller, Ramanujam Narasimhan, Paramasivam Mathiazhagan, Franklin Victor, 2016-08-18 This book offers an ideal introduction to singular perturbation problems, and a valuable guide for researchers in the field of differential equations. It also includes chapters on new contributions to both fields: differential equations and singular perturbation problems. Written by experts who are active researchers in the related fields, the book serves as a comprehensive source of information on the underlying ideas in the construction of numerical methods to address different classes of problems with solutions of different behaviors, which will ultimately help researchers to design and assess numerical methods for solving new problems. All the chapters presented in the volume are complemented by illustrations in the form of tables and graphs. |
| computer science and finance: 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 |
| computer science and finance: Computational Finance and Financial Econometrics Eric Zivot, 2017-01-15 This book presents mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. The tools are used to model asset returns, measure risk, and construct optimized portfolios using the open source R programming language and Microsoft Excel. The author explains how to build probability models for asset returns, to apply statistical techniques to evaluate if asset returns are normally distributed, to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and to use optimization methods to construct efficient portfolios. |
| computer science and finance: The 2009 What Color is Your Parachute? Richard Nelson Bolles, 2009 A guide to discovering personal goals and interests explains how to apply that information toward obtaining satisfying employment |
| computer science and finance: Computational Finance and Its Applications II M. Costantino, C. A. Brebbia, 2006 Featuring papers from the Second International Conference on Computational Finance and its Applications, the text includes papers that encompass a wide range of topics such as risk management, derivatives pricing, credit risk, trading strategies, portfolio management and asset allocation, and market analysis. |
| computer science and finance: Computer Science Robert Sedgewick, Kevin Wayne, 2016-06-17 Named a Notable Book in the 21st Annual Best of Computing list by the ACM! Robert Sedgewick and Kevin Wayne’s Computer Science: An Interdisciplinary Approach is the ideal modern introduction to computer science with Java programming for both students and professionals. Taking a broad, applications-based approach, Sedgewick and Wayne teach through important examples from science, mathematics, engineering, finance, and commercial computing. The book demystifies computation, explains its intellectual underpinnings, and covers the essential elements of programming and computational problem solving in today’s environments. The authors begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, they turn to functions, introducing key modular programming concepts, including components and reuse. They present a modern introduction to object-oriented programming, covering current programming paradigms and approaches to data abstraction. Building on this foundation, Sedgewick and Wayne widen their focus to the broader discipline of computer science. They introduce classical sorting and searching algorithms, fundamental data structures and their application, and scientific techniques for assessing an implementation’s performance. Using abstract models, readers learn to answer basic questions about computation, gaining insight for practical application. Finally, the authors show how machine architecture links the theory of computing to real computers, and to the field’s history and evolution. For each concept, the authors present all the information readers need to build confidence, together with examples that solve intriguing problems. Each chapter contains question-and-answer sections, self-study drills, and challenging problems that demand creative solutions. Companion web site (introcs.cs.princeton.edu/java) contains Extensive supplementary information, including suggested approaches to programming assignments, checklists, and FAQs Graphics and sound libraries Links to program code and test data Solutions to selected exercises Chapter summaries Detailed instructions for installing a Java programming environment Detailed problem sets and projects Companion 20-part series of video lectures is available at informit.com/title/9780134493831 |
| computer science and finance: Information And Complexity Mark Burgin, Cristian S Calude, 2016-11-28 The book is a collection of papers of experts in the fields of information and complexity. Information is a basic structure of the world, while complexity is a fundamental property of systems and processes. There are intrinsic relations between information and complexity.The research in information theory, the theory of complexity and their interrelations is very active. The book will expand knowledge on information, complexity and their relations representing the most recent and advanced studies and achievements in this area.The goal of the book is to present the topic from different perspectives — mathematical, informational, philosophical, methodological, etc. |
| computer science and finance: Financial Signal Processing and Machine Learning Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry M. Malioutov, 2016-04-21 The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community. |
| computer science and finance: 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. |
| computer science and finance: Entrepreneurial Finance and Accounting for High-Tech Companies Frank J. Fabozzi, 2016-11-10 Financial aspects of launching and operating a high-tech company, including risk analysis, business models, U.S. securities law, financial accounting, tax issues, and stock options, explained accessibly. This book offers an accessible guide to the financial aspects of launching and operating a high-tech business in such areas as engineering, computing, and science. It explains a range of subjects—from risk analysis to stock incentive programs for founders and key employees—for students and aspiring entrepreneurs who have no prior training in finance or accounting. The book begins with the rigorous analysis any prospective entrepreneur should undertake before launching a business, covering risks associated with a new venture, the reasons startup companies fail, and the stages of financing. It goes on to discuss business models and their components, business plans, and exit planning; forms of business organization, and factors to consider in choosing one; equity allocation to founders and employees; applicable U.S. securities law; and sources of equity capital. The book describes principles of financial accounting, the four basic financial statements, and financial ratios useful in assessing management performance. It also explains financial planning and the use of budgets; profit planning; stock options and other option-type awards; methodologies for valuing a private company; economic assessment of a potential investment project; and the real options approach to risk and managerial flexibility. Appendixes offer case studies of Uber and of the valuation of Tentex. |
| computer science and finance: Python for Algorithmic Trading Yves Hilpisch, 2020-11-12 Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms |
| computer science and finance: Natural Language Processing: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2019-11-01 As technology continues to become more sophisticated, a computer’s ability to understand, interpret, and manipulate natural language is also accelerating. Persistent research in the field of natural language processing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror natural language processes that have existed for centuries. Natural Language Processing: Concepts, Methodologies, Tools, and Applications is a vital reference source on the latest concepts, processes, and techniques for communication between computers and humans. Highlighting a range of topics such as machine learning, computational linguistics, and semantic analysis, this multi-volume book is ideally designed for computer engineers, computer and software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of natural language processing. |
| computer science and finance: Computer Science and Education Wenxing Hong, Yang Weng, 2023-06-16 This three-volume set constitues selected papers presented during the 17th International Conference on Computer Science and Education, ICCSE 2022, held in Ningbo, China, in August 2022. The 168 full papers and 43 short papers presented were thoroughly reviewed and selected from the 510 submissions. They focus on a wide range of computer science topics, especially AI, data science, and engineering, and technology-based education, by addressing frontier technical and business issues essential to the applications of data science in both higher education and advancing e-Society. |
| computer science and finance: Codes of Finance Vincent Antonin Lépinay, 2011-08-08 A behind-the-scenes account of the derivatives business at a major investment bank The financial industry's invention of complex products such as credit default swaps and other derivatives has been widely blamed for triggering the global financial crisis of 2008. In Codes of Finance, Vincent Antonin Lépinay, a former employee of one of the world’s leading investment banks, takes readers behind the scenes of the equity derivatives business at the bank before the crisis, providing a detailed firsthand account of the creation, marketing, selling, accounting, and management of these financial instruments—and of how they ultimately created havoc inside and outside the bank. |
| computer science and finance: Optimized C++ Kurt Guntheroth, 2016-04-27 In today’s fast and competitive world, a program’s performance is just as important to customers as the features it provides. This practical guide teaches developers performance-tuning principles that enable optimization in C++. You’ll learn how to make code that already embodies best practices of C++ design run faster and consume fewer resources on any computer—whether it’s a watch, phone, workstation, supercomputer, or globe-spanning network of servers. Author Kurt Guntheroth provides several running examples that demonstrate how to apply these principles incrementally to improve existing code so it meets customer requirements for responsiveness and throughput. The advice in this book will prove itself the first time you hear a colleague exclaim, “Wow, that was fast. Who fixed something?” Locate performance hot spots using the profiler and software timers Learn to perform repeatable experiments to measure performance of code changes Optimize use of dynamically allocated variables Improve performance of hot loops and functions Speed up string handling functions Recognize efficient algorithms and optimization patterns Learn the strengths—and weaknesses—of C++ container classes View searching and sorting through an optimizer’s eye Make efficient use of C++ streaming I/O functions Use C++ thread-based concurrency features effectively |
| computer science and finance: So Good They Can't Ignore You Cal Newport, 2012-09-18 In an unorthodox approach, Georgetown University professor Cal Newport debunks the long-held belief that follow your passion is good advice, and sets out on a quest to discover the reality of how people end up loving their careers. Not only are pre-existing passions rare and have little to do with how most people end up loving their work, but a focus on passion over skill can be dangerous, leading to anxiety and chronic job hopping. Spending time with organic farmers, venture capitalists, screenwriters, freelance computer programmers, and others who admitted to deriving great satisfaction from their work, Newport uncovers the strategies they used and the pitfalls they avoided in developing their compelling careers. Cal reveals that matching your job to a pre-existing passion does not matter. Passion comes after you put in the hard work to become excellent at something valuable, not before. In other words, what you do for a living is much less important than how you do it. With a title taken from the comedian Steve Martin, who once said his advice for aspiring entertainers was to be so good they can't ignore you, Cal Newport's clearly written manifesto is mandatory reading for anyone fretting about what to do with their life, or frustrated by their current job situation and eager to find a fresh new way to take control of their livelihood. He provides an evidence-based blueprint for creating work you love, and will change the way you think about careers, happiness, and the crafting of a remarkable life. |
| computer science and finance: Solutions Manual to Accompany Corporate Finance Stephen A. Ross, Joseph Smolira, Randolph Westerfield, Jeffrey F. Jaffe, 2009-10 The Solutions Manual contains detailed, worked-out solutions for all of the problems in the end of chapter material. It has also been revised for accuracy by multiple sources. It is also available for purchase by students. The Solutions Manual is prepared by Joseph Smolira, Belmont University |
| computer science and finance: Wages in New York City , 1980-05 |
| computer science and finance: Hands-On Python for Finance Krish Naik, 2019-03-29 Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python What you will learn Clean financial data with data preprocessing Visualize financial data using histograms, color plots, and graphs Perform time series analysis with pandas for forecasting Estimate covariance and the correlation between securities and stocks Optimize your portfolio to understand risks when there is a possibility of higher returns Calculate expected returns of a stock to measure the performance of a portfolio manager Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow Who this book is for This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you're looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python programming language is necessary. |
| computer science and finance: Valuation McKinsey & Company Inc., Tim Koller, Marc Goedhart, David Wessels, 2020-05-21 McKinsey & Company's bestselling guide to teaching corporate valuation - the fully updated seventh edition Valuation: Measuring and Managing the Value of Companies, University Edition is filled with the expert guidance from McKinsey & Company that students and professors have come to rely on for over nearly three decades. Now in its seventh edition, this acclaimed volume continues to help financial professionals and students around the world gain a deep understanding of valuation and help their companies create, manage, and maximize economic value for their shareholders. This latest edition has been carefully revised and updated throughout, and includes new insights on topics such as digital, ESG (environmental, social and governance), and long-term investing, as well as fresh case studies. For thirty years, Valuation has remained true to its basic principles and continues to offer a step-by-step approach to teaching valuation fundamentals, including: Analyzing historical performance Forecasting performance Estimating the cost of capital Interpreting the results of a valuation in context Linking a company's valuation multiples to core performance drivers The University Edition contains end-of-chapter review questions to help students master key concepts from the book. Wiley also offers an Online Instructor's Manual with a full suite of learning resources to complement valuation classroom instruction. |
How do I get into Finance with a CS degree? : r/FinancialCare…
Mar 21, 2022 · After 5 years in finance, I realized I probably would have been more marketable/able to get pretty …
Computer Science student wanting to get into quant - Re…
Feb 15, 2021 · Any suggestions on how a senior CS student can get into quant research / quant trading? Only …
Computer science and finance jobs? : r/FinancialCareers - Re…
Mar 13, 2022 · Computer science and finance jobs? Why do I see so many people talking about double …
Computer Science and Finance? - College Confidential Forums
Jul 15, 2016 · Finance and investing is an excellent career choice. Finance professionals need to know …
Transitioning from a CS Degree to Finance career? - Math/Co…
Aug 4, 2015 · Hi, Im a sophomore pursuing a CS degree from my university and I have always been …
How do I get into Finance with a CS degree? : r/FinancialCareers
Mar 21, 2022 · After 5 years in finance, I realized I probably would have been more marketable/able to get pretty much any finance job sooner if I had a petroleum engineering degree. In the same …
Computer Science student wanting to get into quant - Reddit
Feb 15, 2021 · Any suggestions on how a senior CS student can get into quant research / quant trading? Only professional experience is as a Project Engineer Intern for a general contractor so …
Computer science and finance jobs? : r/FinancialCareers - Reddit
Mar 13, 2022 · Computer science and finance jobs? Why do I see so many people talking about double majoring in CS and finance. So many people talk about it making you competitive with …
Computer Science and Finance? - College Confidential Forums
Jul 15, 2016 · Finance and investing is an excellent career choice. Finance professionals need to know spreadsheets and analytical packages, statistics and accounting formulas. This is all …
Transitioning from a CS Degree to Finance career?
Aug 4, 2015 · Hi, Im a sophomore pursuing a CS degree from my university and I have always been torn by the Finance vs Tech issue when it comes to career opportunities. How hard(or easy) is it …
Computer Science to Investment Banking? - Wall Street Oasis
May 19, 2017 · I went to Waterloo and graduated from a dual-degree in computer science and accounting & finance. I worked in programming and software development for most of my earlier …
Finance+Computer Science vs Finance+MIS - College Confidential …
Dec 31, 2009 ·
Hi, I just have a question regarding double major vs major+minor. I’m currently major in Finance and I was just wondering which one would be more helpful for my future, and …
Finance major with computer science minor? : r/college - Reddit
Jul 9, 2016 · I recommend majoring in Computer Science and a minor in Finance if you can. You can still get finance jobs and there's the backup of going developer otherwise.
Should I Get a Finance and Computer Science Dual Degree?
Mar 26, 2011 · If not, which single degree should I choose? Finance Computer Science Double Major For Trading? Generally speaking, our users shared that getting a double major in computer …
computer science + finance dual major. type of finance/investment …
Mar 23, 2011 · Hello all i am a highschooler and I am looking to dual major in CS and Finance. I hear to have great high paying finance jobs you must do something different from the crowd.
