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| financial engineering online course: A Primer for the Mathematics of Financial Engineering Dan Stefanica, 2011 |
| financial engineering online course: 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) |
| financial engineering online course: 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. |
| financial engineering online course: 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. |
| financial engineering online course: 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. |
| financial engineering online course: Financial Engineering Tanya S. Beder, Cara M. Marshall, 2011-06-07 FINANCIAL ENGINEERING Financial engineering is poised for a great shift in the years ahead. Everyone from investors and borrowers to regulators and legislators will need to determine what works, what doesn't, and where to go from here. Financial Engineering part of the Robert W. Kolb Series in Finance has been designed to help you do just this. Comprised of contributed chapters by distinguished experts from industry and academia, this reliable resource will help you focus on established activities in the field, developing trends and changes, as well as areas of opportunity. Divided into five comprehensive parts, Financial Engineering begins with an informative overview of the discipline, chronicling its complete history and profiling potential career paths. From here, Part II quickly moves on to discuss the evolution of financial engineering in major markets fixed income, foreign exchange, equities, commodities and credit and offers important commentary on what has worked and what will change. Part III then examines a number of recent innovative applications of financial engineering that have made news over the past decade such as the advent of securitized and structured products and highly quantitative trading strategies for both equities and fixed income. Thoughts on how risk management might be retooled to reflect what has been learned as a result of the recent financial crisis are also included. Part IV of the book is devoted entirely to case studies that present valuable lessons for active practitioners and academics. Several of the cases explore the risk that has instigated losses across multiple markets, including the global credit crisis. You'll gain in-depth insights from cases such as Countrywide, Société Générale, Barings, Long-Term Capital Management, the Florida Local Government Investment Pool, AIG, Merrill Lynch, and many more. The demand for specific and enterprise risk managers who can think outside the box will be substantial during this decade. Much of Part V presents new ways to be successful in an era that demands innovation on both sides of the balance sheet. Chapters that touch upon this essential topic include Musings About Hedging; Operational Risk; and The No-Arbitrage Condition in Financial Engineering: Its Use and Mis-Use. This book is complemented by a companion website that includes details from the editors' survey of financial engineering programs around the globe, along with a glossary of key terms from the book. This practical guide puts financial engineering in perspective, and will give you a better idea of how it can be effectively utilized in real- world situations. |
| financial engineering online course: 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. |
| financial engineering online course: 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. |
| financial engineering online course: Financial Engineering and Arbitrage in the Financial Markets Robert Dubil, 2011-10-13 A whole is worth the sum of its parts. Even the most complex structured bond, credit arbitrage strategy or hedge trade can be broken down into its component parts, and if we understand the elemental components, we can then value the whole as the sum of its parts. We can quantify the risk that is hedged and the risk that is left as the residual exposure. If we learn to view all financial trades and securities as engineered packages of building blocks, then we can analyze in which structures some parts may be cheap and some may be rich. It is this relative value arbitrage principle that drives all modern trading and investment. This book is an easy-to-understand guide to the complex world of today’s financial markets teaching you what money and capital markets are about through a sequence of arbitrage-based numerical illustrations and exercises enriched with institutional detail. Filled with insights and real life examples from the trading floor, it is essential reading for anyone starting out in trading. Using a unique structural approach to teaching the mechanics of financial markets, the book dissects markets into their common building blocks: spot (cash), forward/futures, and contingent (options) transactions. After explaining how each of these is valued and settled, it exploits the structural uniformity across all markets to introduce the difficult subjects of financially engineered products and complex derivatives. The book avoids stochastic calculus in favour of numeric cash flow calculations, present value tables, and diagrams, explaining options, swaps and credit derivatives without any use of differential equations. |
| financial engineering online course: Mathematics for Finance Marek Capinski, Tomasz Zastawniak, 2006-04-18 This textbook contains the fundamentals for an undergraduate course in mathematical finance aimed primarily at students of mathematics. Assuming only a basic knowledge of probability and calculus, the material is presented in a mathematically rigorous and complete way. The book covers the time value of money, including the time structure of interest rates, bonds and stock valuation; derivative securities (futures, options), modelling in discrete time, pricing and hedging, and many other core topics. With numerous examples, problems and exercises, this book is ideally suited for independent study. |
| financial engineering online course: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| financial engineering online course: 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. |
| financial engineering online course: Machine Learning in Finance Matthew F. Dixon, Igor Halperin, Paul Bilokon, 2020-07-01 This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. |
| financial engineering online course: A First Course in Quantitative Finance Thomas Mazzoni, 2018-03-29 Using stereoscopic images and other novel pedagogical features, this book offers a comprehensive introduction to quantitative finance. |
| financial engineering online course: An Introduction to Financial Markets Paolo Brandimarte, 2018-02-22 COVERS THE FUNDAMENTAL TOPICS IN MATHEMATICS, STATISTICS, AND FINANCIAL MANAGEMENT THAT ARE REQUIRED FOR A THOROUGH STUDY OF FINANCIAL MARKETS This comprehensive yet accessible book introduces students to financial markets and delves into more advanced material at a steady pace while providing motivating examples, poignant remarks, counterexamples, ideological clashes, and intuitive traps throughout. Tempered by real-life cases and actual market structures, An Introduction to Financial Markets: A Quantitative Approach accentuates theory through quantitative modeling whenever and wherever necessary. It focuses on the lessons learned from timely subject matter such as the impact of the recent subprime mortgage storm, the collapse of LTCM, and the harsh criticism on risk management and innovative finance. The book also provides the necessary foundations in stochastic calculus and optimization, alongside financial modeling concepts that are illustrated with relevant and hands-on examples. An Introduction to Financial Markets: A Quantitative Approach starts with a complete overview of the subject matter. It then moves on to sections covering fixed income assets, equity portfolios, derivatives, and advanced optimization models. This book’s balanced and broad view of the state-of-the-art in financial decision-making helps provide readers with all the background and modeling tools needed to make “honest money” and, in the process, to become a sound professional. Stresses that gut feelings are not always sufficient and that “critical thinking” and real world applications are appropriate when dealing with complex social systems involving multiple players with conflicting incentives Features a related website that contains a solution manual for end-of-chapter problems Written in a modular style for tailored classroom use Bridges a gap for business and engineering students who are familiar with the problems involved, but are less familiar with the methodologies needed to make smart decisions An Introduction to Financial Markets: A Quantitative Approach offers a balance between the need to illustrate mathematics in action and the need to understand the real life context. It is an ideal text for a first course in financial markets or investments for business, economic, statistics, engineering, decision science, and management science students. |
| financial engineering online course: 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. |
| financial engineering online course: Hedge Fund Course Stuart A. McCrary, 2004-12-03 A self-study course that reviews the technical and quantitative knowledge necessary to properly manage a hedge fund Today, traditional asset managers are looking to develop their own hedge funds as alternative offerings to their clients. Hedge Fund Course presents all the technical and quantitative knowledge necessary to run a leveraged investment company, and complements the less-technical information presented in the popular, How to Create and Manage a Hedge Fund (0-471-22488-X). Filled with in-depth insight and expert advice, this book represents an executive-level educational program for money managers exploring the launch of alternative investment strategies or entering the hedge fund industry for the first time. Stuart A. McCrary (Winnetka, IL) is a partner with Chicago Partners LLC and specializes in options, mortgage-backed securities, derivatives, and hedge funds. As president of Frontier Asset Management, McCrary managed and ran his own hedge fund before joining Chicago Partners. He received his BA and MBA from Northwestern University. |
| financial engineering online course: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| financial engineering online course: A Course in Financial Calculus Alison Etheridge, 2002-08-15 Finance provides a dramatic example of the successful application of mathematics to the practical problem of pricing financial derivatives. This self-contained text is designed for first courses in financial calculus. Key concepts are introduced in the discrete time framework: proofs in the continuous-time world follow naturally. The second half of the book is devoted to financially sophisticated models and instruments. A valuable feature is the large number of exercises and examples, designed to test technique and illustrate how the methods and concepts are applied to realistic financial questions. |
| financial engineering online course: 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 |
| financial engineering online course: Principles of Financial Engineering Salih N. Neftci, 2008-12-09 Principles of Financial Engineering, Second 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 you 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. 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 Second Edition presents 5 new chapters on structured product engineering, credit markets and instruments, and principle protection techniques, 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 |
| financial engineering online course: Quantitative Financial Risk Management Desheng Dash Wu, 2011-06-25 The bulk of this volume deals with the four main aspects of risk management: market risk, credit risk, risk management - in macro-economy as well as within companies. It presents a number of approaches and case studies directed at applying risk management to diverse business environments. Included are traditional market and credit risk management models such as the Black-Scholes Option Pricing Model, the Vasicek Model, Factor models, CAPM models, GARCH models, KMV models and credit scoring models. |
| financial engineering online course: 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. |
| financial engineering online course: C++ Design Patterns and Derivatives Pricing Mark Suresh Joshi, 2004-08-05 Design patterns are the cutting-edge paradigm for programming in object-oriented languages. Here they are discussed, for the first time in a book, in the context of implementing financial models in C++. Assuming only a basic knowledge of C++ and mathematical finance, the reader is taught how to produce well-designed, structured, re-usable code via concrete examples. Each example is treated in depth, with the whys and wherefores of the chosen method of solution critically examined. Part of the book is devoted to designing re-usable components that are then put together to build a Monte Carlo pricer for path-dependent exotic options. Advanced topics treated include the factory pattern, the singleton pattern and the decorator pattern. Complete ANSI/ISO-compatible C++ source code is included on a CD for the reader to study and re-use and so develop the skills needed to implement financial models with object-oriented programs and become a working financial engineer. Please note the CD supplied with this book is platform-dependent and PC users will not be able to use the files without manual intervention in order to remove extraneous characters. Cambridge University Press apologises for this error. Machine readable files for all users can be obtained from www.markjoshi.com/design. |
| financial engineering online course: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| financial engineering online course: Stochastic Calculus for Finance I Steven Shreve, 2005-06-28 Developed for the professional Master's program in Computational Finance at Carnegie Mellon, the leading financial engineering program in the U.S. Has been tested in the classroom and revised over a period of several years Exercises conclude every chapter; some of these extend the theory while others are drawn from practical problems in quantitative finance |
| financial engineering online course: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| financial engineering online course: How I Became a Quant Richard R. Lindsey, Barry Schachter, 2011-01-11 Praise for How I Became a Quant Led by two top-notch quants, Richard R. Lindsey and Barry Schachter, How I Became a Quant details the quirky world of quantitative analysis through stories told by some of today's most successful quants. For anyone who might have thought otherwise, there are engaging personalities behind all that number crunching! --Ira Kawaller, Kawaller & Co. and the Kawaller Fund A fun and fascinating read. This book tells the story of how academics, physicists, mathematicians, and other scientists became professional investors managing billions. --David A. Krell, President and CEO, International Securities Exchange How I Became a Quant should be must reading for all students with a quantitative aptitude. It provides fascinating examples of the dynamic career opportunities potentially open to anyone with the skills and passion for quantitative analysis. --Roy D. Henriksson, Chief Investment Officer, Advanced Portfolio Management Quants--those who design and implement mathematical models for the pricing of derivatives, assessment of risk, or prediction of market movements--are the backbone of today's investment industry. As the greater volatility of current financial markets has driven investors to seek shelter from increasing uncertainty, the quant revolution has given people the opportunity to avoid unwanted financial risk by literally trading it away, or more specifically, paying someone else to take on the unwanted risk. How I Became a Quant reveals the faces behind the quant revolution, offering you?the?chance to learn firsthand what it's like to be a?quant today. In this fascinating collection of Wall Street war stories, more than two dozen quants detail their roots, roles, and contributions, explaining what they do and how they do it, as well as outlining the sometimes unexpected paths they have followed from the halls of academia to the front lines of an investment revolution. |
| financial engineering online course: C++ Primer Plus Stephen Prata, 2011-10-18 C++ Primer Plus, Sixth Edition New C++11 Coverage C++ Primer Plus is a carefully crafted, complete tutorial on one of the most significant and widely used programming languages today. An accessible and easy-to-use self-study guide, this book is appropriate for both serious students of programming as well as developers already proficient in other languages. The sixth edition of C++ Primer Plus has been updated and expanded to cover the latest developments in C++, including a detailed look at the new C++11 standard. Author and educator Stephen Prata has created an introduction to C++ that is instructive, clear, and insightful. Fundamental programming concepts are explained along with details of the C++ language. Many short, practical examples illustrate just one or two concepts at a time, encouraging readers to master new topics by immediately putting them to use. Review questions and programming exercises at the end of each chapter help readers zero in on the most critical information and digest the most difficult concepts. In C++ Primer Plus, you’ll find depth, breadth, and a variety of teaching techniques and tools to enhance your learning: A new detailed chapter on the changes and additional capabilities introduced in the C++11 standard Complete, integrated discussion of both basic C language and additional C++ features Clear guidance about when and why to use a feature Hands-on learning with concise and simple examples that develop your understanding a concept or two at a time Hundreds of practical sample programs Review questions and programming exercises at the end of each chapter to test your understanding Coverage of generic C++ gives you the greatest possible flexibility Teaches the ISO standard, including discussions of templates, the Standard Template Library, the string class, exceptions, RTTI, and namespaces Table of Contents 1: Getting Started with C++ 2: Setting Out to C++ 3: Dealing with Data 4: Compound Types 5: Loops and Relational Expressions 6: Branching Statements and Logical Operators 7: Functions: C++’s Programming Modules 8: Adventures in Functions 9: Memory Models and Namespaces 10: Objects and Classes 11: Working with Classes 12: Classes and Dynamic Memory Allocation 13: Class Inheritance 14: Reusing Code in C++ 15: Friends, Exceptions, and More 16: The string Class and the Standard Template Library 17: Input, Output, and Files 18: The New C++11 Standard A Number Bases B C++ Reserved Words C The ASCII Character Set D Operator Precedence E Other Operators F The stringTemplate Class G The Standard Template Library Methods and Functions H Selected Readings and Internet Resources I Converting to ISO Standard C++ J Answers to Chapter Reviews |
| financial engineering online course: 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 |
| financial engineering online course: Financial Calculus Martin Baxter, Andrew Rennie, 1996-09-19 A rigorous introduction to the mathematics of pricing, construction and hedging of derivative securities. |
| financial engineering online course: Statistical Foundations of Data Science Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou, 2020-09-21 Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning. |
| financial engineering online course: Introduction to the Economics and Mathematics of Financial Markets Jaksa Cvitanic, Fernando Zapatero, 2004-02-27 An innovative textbook for use in advanced undergraduate and graduate courses; accessible to students in financial mathematics, financial engineering and economics. Introduction to the Economics and Mathematics of Financial Markets fills the longstanding need for an accessible yet serious textbook treatment of financial economics. The book provides a rigorous overview of the subject, while its flexible presentation makes it suitable for use with different levels of undergraduate and graduate students. Each chapter presents mathematical models of financial problems at three different degrees of sophistication: single-period, multi-period, and continuous-time. The single-period and multi-period models require only basic calculus and an introductory probability/statistics course, while an advanced undergraduate course in probability is helpful in understanding the continuous-time models. In this way, the material is given complete coverage at different levels; the less advanced student can stop before the more sophisticated mathematics and still be able to grasp the general principles of financial economics. The book is divided into three parts. The first part provides an introduction to basic securities and financial market organization, the concept of interest rates, the main mathematical models, and quantitative ways to measure risks and rewards. The second part treats option pricing and hedging; here and throughout the book, the authors emphasize the Martingale or probabilistic approach. Finally, the third part examines equilibrium models—a subject often neglected by other texts in financial mathematics, but included here because of the qualitative insight it offers into the behavior of market participants and pricing. |
| financial engineering online course: Financial Instrument Pricing Using C++ Daniel J. Duffy, 2013-10-23 One of the best languages for the development of financial engineering and instrument pricing applications is C++. This book has several features that allow developers to write robust, flexible and extensible software systems. The book is an ANSI/ISO standard, fully object-oriented and interfaces with many third-party applications. It has support for templates and generic programming, massive reusability using templates (?write once?) and support for legacy C applications. In this book, author Daniel J. Duffy brings C++ to the next level by applying it to the design and implementation of classes, libraries and applications for option and derivative pricing models. He employs modern software engineering techniques to produce industrial-strength applications: Using the Standard Template Library (STL) in finance Creating your own template classes and functions Reusable data structures for vectors, matrices and tensors Classes for numerical analysis (numerical linear algebra ?) Solving the Black Scholes equations, exact and approximate solutions Implementing the Finite Difference Method in C++ Integration with the ?Gang of Four? Design Patterns Interfacing with Excel (output and Add-Ins) Financial engineering and XML Cash flow and yield curves Included with the book is a CD containing the source code in the Datasim Financial Toolkit. You can use this to get up to speed with your C++ applications by reusing existing classes and libraries. 'Unique... Let's all give a warm welcome to modern pricing tools.' -- Paul Wilmott, mathematician, author and fund manager |
| financial engineering online course: Optimization Models Giuseppe C. Calafiore, Laurent El Ghaoui, 2014-10-31 This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects. |
| financial engineering online course: Implementing QuantLib. Quantitative Finance in C++: an Inside Look at the Architecture of the QuantLib Library Luigi Ballabio, 2020 |
| financial engineering online course: Statistical Analysis of Financial Data in S-Plus René Carmona, 2006-04-18 This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and to practitioners with financial data analysis concerns. |
| financial engineering online course: Machine Trading Ernest P. Chan, 2017-02-06 Dive into algo trading with step-by-step tutorials and expert insight Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level. Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools. Utilize the newer, easier algorithmic trading platforms Access markets previously unavailable to systematic traders Adopt new strategies for a variety of instruments Gain expert perspective into the human side of trading The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions. |
| financial engineering online course: When Genius Failed Roger Lowenstein, 2001-10-09 “A riveting account that reaches beyond the market landscape to say something universal about risk and triumph, about hubris and failure.”—The New York Times NAMED ONE OF THE BEST BOOKS OF THE YEAR BY BUSINESSWEEK In this business classic—now with a new Afterword in which the author draws parallels to the recent financial crisis—Roger Lowenstein captures the gripping roller-coaster ride of Long-Term Capital Management. Drawing on confidential internal memos and interviews with dozens of key players, Lowenstein explains not just how the fund made and lost its money but also how the personalities of Long-Term’s partners, the arrogance of their mathematical certainties, and the culture of Wall Street itself contributed to both their rise and their fall. When it was founded in 1993, Long-Term was hailed as the most impressive hedge fund in history. But after four years in which the firm dazzled Wall Street as a $100 billion moneymaking juggernaut, it suddenly suffered catastrophic losses that jeopardized not only the biggest banks on Wall Street but the stability of the financial system itself. The dramatic story of Long-Term’s fall is now a chilling harbinger of the crisis that would strike all of Wall Street, from Lehman Brothers to AIG, a decade later. In his new Afterword, Lowenstein shows that LTCM’s implosion should be seen not as a one-off drama but as a template for market meltdowns in an age of instability—and as a wake-up call that Wall Street and government alike tragically ignored. Praise for When Genius Failed “[Roger] Lowenstein has written a squalid and fascinating tale of world-class greed and, above all, hubris.”—BusinessWeek “Compelling . . . The fund was long cloaked in secrecy, making the story of its rise . . . and its ultimate destruction that much more fascinating.”—The Washington Post “Story-telling journalism at its best.”—The Economist |
| financial engineering online course: Principles Ray Dalio, 2018-08-07 #1 New York Times Bestseller “Significant...The book is both instructive and surprisingly moving.” —The New York Times Ray Dalio, one of the world’s most successful investors and entrepreneurs, shares the unconventional principles that he’s developed, refined, and used over the past forty years to create unique results in both life and business—and which any person or organization can adopt to help achieve their goals. In 1975, Ray Dalio founded an investment firm, Bridgewater Associates, out of his two-bedroom apartment in New York City. Forty years later, Bridgewater has made more money for its clients than any other hedge fund in history and grown into the fifth most important private company in the United States, according to Fortune magazine. Dalio himself has been named to Time magazine’s list of the 100 most influential people in the world. Along the way, Dalio discovered a set of unique principles that have led to Bridgewater’s exceptionally effective culture, which he describes as “an idea meritocracy that strives to achieve meaningful work and meaningful relationships through radical transparency.” It is these principles, and not anything special about Dalio—who grew up an ordinary kid in a middle-class Long Island neighborhood—that he believes are the reason behind his success. In Principles, Dalio shares what he’s learned over the course of his remarkable career. He argues that life, management, economics, and investing can all be systemized into rules and understood like machines. The book’s hundreds of practical lessons, which are built around his cornerstones of “radical truth” and “radical transparency,” include Dalio laying out the most effective ways for individuals and organizations to make decisions, approach challenges, and build strong teams. He also describes the innovative tools the firm uses to bring an idea meritocracy to life, such as creating “baseball cards” for all employees that distill their strengths and weaknesses, and employing computerized decision-making systems to make believability-weighted decisions. While the book brims with novel ideas for organizations and institutions, Principles also offers a clear, straightforward approach to decision-making that Dalio believes anyone can apply, no matter what they’re seeking to achieve. Here, from a man who has been called both “the Steve Jobs of investing” and “the philosopher king of the financial universe” (CIO magazine), is a rare opportunity to gain proven advice unlike anything you’ll find in the conventional business press. |
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