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• Introduction xiii Why You Should Read This Book xiii The Intended Reader xiv Why MATLAB (R)? xiv How to Use This Book xvi Font Conventions xvi About the Author xvii MathWorks Information xviii References xviii Part I MATLAB Conventions and Basic Skills 1
• Chapter 1 Working with MATLAB (R) Data 3 1.1 Introduction 3 1.2 Arrays 3 1.2.1 Numerical Arrays 4 1.2.2 Math Calculations with Scalars, Vectors, and Matrices 10 1.2.3 Statistical Calculations on Vectors and Matrices 16 1.2.4 Extracting Values from Numerical Vectors and Matrices 19 1.2.5 Counting Elements 26 1.2.6 Sorting Vectors and Matrices 28 1.2.7 Relational Expressions and Logical Arrays 31 1.2.8 Dealing with NaNs (Not a Number) 35 1.2.9 Dealing with Missing Data 39 1.3 Character Arrays 40 1.3.1 String Arrays 44 1.4 Flexible Data Structures 46 1.4.1 Cell Arrays 47 1.4.2 Structure ("struct") Arrays 49 1.4.3 Tables 51 References 60 Further Reading 60
• Chapter 2 Working with Dates and Times 61 2.1 Introduction 61 2.2 Finance Background: Why Dates and Times Matter 61 2.2.1 First Challenge: Day Count Conventions 62 2.2.2 Second Challenge: Date Formats 63 2.3 Dates and Times in MATLAB 64 2.3.1 Datetime Variables 64 2.3.2 Date Conversions 73 2.3.3 Date Generation Functions with Serial Number Outputs 79 2.3.4 Duration Arrays 83 2.3.5 Calendar Duration Variables 86 2.3.6 Date Calculations and Operations 89 2.3.7 Plotting Date Variables Introduction 94 References 95
• Chapter 3 Basic Programming with MATLAB (R) 97 3.1 Introduction 97 3.1.1 Algorithms 101 97 3.1.2 Go DIY or Use Built-In Code? 98 3.2 MATLAB Scripts and Functions 99 3.2.1 Scripts 99 3.2.2 Developing Functions 106 3.2.3 If Statements 112 3.2.4 Modular Programming 115 3.2.5 User Message Formats 121 3.2.6 Testing and Debugging 124 References 127
• Chapter 4 Working with Financial Data 129 4.1 Introduction 129 4.2 Accessing Financial Data 129 4.2.1 Closing Prices versus Adjusted Close Prices for Stocks 130 4.2.2 Data Download Examples 131 4.2.3 Importing Data Interactively 133 4.2.4 Automating Data Imports with a Script 138 4.2.5 Automating Data Imports with a Function 140 4.2.6 Importing Data Programmatically 147 4.3 Working with Spreadsheet Data 154 4.3.1 Importing Spreadsheet Data with Import Tool 154 4.3.2 Importing Spreadsheet Data Programmatically 154 4.4 Data Visualization 156 4.4.1 Built-In Plot Functions 156 4.4.2 Using the Plot Tools 158 4.4.3 Plotting with Commands 159 4.4.4 Other Plot Tools 162 4.4.5 Built-In Financial Charts 173 References 176 Part II Financial Calculations with MATLAB 177
• Chapter 5 The Time Value of Money 179 5.1 Introduction 179 5.2 Finance Background 180 5.2.1 Future Value with Single Cash Flows 180 5.2.2 Future Value with Multiple Cash Flows 185 5.2.3 Present Value with Single Cash Flows 187 5.2.4 Present Value with Multiple Variable Cash Flows 188 5.3 MATLAB Time Value of Money Functions 189 5.3.1 Future Value of Fixed Periodic Payments 190 5.3.2 Future Value of Variable Payments 191 5.3.3 Present Value of Fixed Payments 193 5.3.4 Present Value of Variable Payments 194 5.4 Internal Rate of Return 197 5.5 Effective Interest Rates 198 5.6 Compound Annual Growth Rate 198 5.7 Continuous Interest 200 5.8 Loans 200 References 202
• Chapter 6 Bonds 203 6.1 Introduction 203 6.2 Finance Background 204 6.2.1 Bond Classifications 204 6.2.2 Bond Terminology 205 6.3 MATLAB Bond Functions 206 6.3.1 US Treasury Bills 206 6.3.2 Bond Valuation Principles 208 6.3.3 Calculating Bond Prices 209 6.3.4 Calculating Bond Yields 212 6.3.5 Calculating a Bond's Total Return 214 6.3.6 Pricing Discount Bonds 216 6.4 Bond Analytics 216 6.4.1 Interest Rate Risk 217 6.4.2 Measuring Rate Sensitivity 219 6.4.3 Yield Curves 227 6.5 Callable Bonds 229 References 231 Further Reading 231
• Chapter 7 Dealing with Uncertainty and Risk 233 7.1 Introduction 233 7.2 Overview of Financial Risk 234 7.3 Data Insights 234 7.3.1 Visualizing Data 235 7.3.2 Basic Single Series Plots 237 7.3.3 Basic Multiple Series Plots 237 7.3.4 Adding Plot Customization 238 7.3.5 Histograms 239 7.3.6 Measures of Central Location 241 7.3.7 Measures of Data Dispersion 243 7.4 Data Relationships 249 7.4.1 Covariance and Correlation 251 7.4.2 Correlation Coefficients 252 7.5 Creating a Basic Simulation Model 253 7.6 Value at Risk (VaR) 258 References 261 Further Reading 262
• Chapter 8 Equity Derivatives 263 8.1 Introduction 263 8.2 Options 264 8.2.1 Option Quotes 265 8.2.2 Market Mechanics 266 8.2.3 Factors in Option Valuation 267 8.3 Option Pricing Models 268 8.3.1 Arbitrage 269 8.3.2 Binomial Option Pricing 270 8.3.3 Black-Scholes 274 8.4 Options' Uses 276 8.4.1 Hedging 277 8.4.2 Speculation and Leverage 277 8.4.3 Customizing Payoff Profiles 278 8.5 Appendix: Other Types of Derivatives 279 8.5.1 Commodity and Energy 279 8.5.2 Credit 279 8.5.3 Exotic Options 280 References 281 Further Reading 281
• Chapter 9 Portfolios 283 9.1 Introduction 283 9.2 Finance Background 283 9.3 Portfolio Optimization 285 9.4 MATLAB Portfolio Object 286 9.4.1 Object-Oriented Programming (OOP) 286 9.4.2 A Basic Example 287 9.4.3 Using Data Stored in a Table Format 294 References 296
• Chapter 10 Regression and Time Series 297 10.1 Introduction 297 10.2 Basic Regression 297 10.2.1 Understanding Least Squares 300 10.2.2 Model Notation 301 10.2.3 Fitting a Polynomial with polyfit and polyval 303 10.2.4 Linear Regression Methods 305 10.3 Working with Time Series 308 10.3.1 Step 1: Load the Data (Single Series) 308 10.3.2 Step 2: Create the FTS Object 309 10.3.3 Step 3: Using FTS Tools 311 References 314
• Appendix 1 Sharing Your Work 315 A1.1 Introduction 315 A1.2 Publishing a Script 316 A1.2.1 Publishing with Code Sections 317 A1.2.2 futureValueCalc3 319 A1.2.3 Formatting Options 321 A1.2.4 Working with Live Scripts 322 A1.2.5 Editing and Control 325 References 326
• Appendix 2 Reference for Included MATLAB (R) Functions 327 Index 335.
• (source: Nielsen Book Data)

Graduate from Excel to MATLAB(R) to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB(R) is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today's analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB's capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more. Master MATLAB's basic operations including matrices, arrays, and flexible data structures Learn how to build your own customized solutions when the built-ins just won't do Learn how to handle financial data and industry-specific variables including risk and uncertainty Adopt more accurate modeling practices for portfolios, options, time series, and more MATLAB is an integrated development environment that includes everything you need in one well-designed user interface. Available Toolboxes provide tested algorithms that save you hours of code, and the skills you learn using MATLAB make it easier to learn additional languages if you choose to do so. Financial firms are catching up to universities in MATLAB usage, so this is skill set that will follow you throughout your career. When you're ready to step into the new age of finance, Foundations of Computational Finance with MATLAB provides the expert instruction you need to get started quickly.
(source: Nielsen Book Data)

• part, I Computationally Expensive Problems in the Financial Industry
• chapter 1 Computationally Expensive Problems in Investment Banking / Jonathan Rosen Christian Kahl Russell Goyder Mark Gibbs
• chapter 2 Using Market Sentiment to Enhance Second-Order Stochastic Dominance Trading Models / Gautam Mitra Christina Erlwein-Sayer Cristiano Arbex Valle Xiang Yu
• chapter 3 The Alpha Engine: Designing an Automated Trading Algorithm / Anton Golub James B. Glattfelder Richard B. Olsen
• chapter 4 Portfolio Liquidation and Ambiguity Aversion / Álvaro Cartea Ryan Donnelly Sebastian Jaimungal
• chapter 5 Challenges in Scenario Generation: Modeling Market and Non-Market Risks in Insurance / Douglas McLean
• part, II Numerical Methods in Financial High-Performance Computing (HPC)
• chapter 6 Finite Difference Methods for Medium- and High-Dimensional Derivative Pricing PDEs / Christoph Reisinger Rasmus Wissmann
• chapter 7 Multilevel Monte Carlo Methods for Applications in Finance * / Michael B. Giles Lukasz Szpruch
• chapter 8 Fourier and Wavelet Option Pricing Methods / Stefanus C. Maree Luis Ortiz-Gracia Cornelis W. Oosterlee
• chapter 9 A Practical Robust Long-Term Yield Curve Model / M. A. H. Dempster Elena A. Medova Igor Osmolovskiy Philipp Ustinov
• chapter 10 Algorithmic Differentiation / Uwe Naumann Jonathan Hüser Jens Deussen Jacques du Toit
• chapter 11 Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD) / Luca Capriotti Jacky Lee
• chapter 12 Tackling Reinsurance Contract Optimization by Means of Evolutionary Algorithms and HPC / Omar Andres Carmona Cortes Andrew Rau-Chaplin
• chapter 13 Evaluating Blockchain Implementation of Clearing and Settlement at the IATA Clearing House / Sergey Ivliev Yulia Mizgireva Juan Ivan Martin
• part, III HPC Systems: Hardware, Software, and Data with Financial Applications
• chapter 14 Supercomputers / Peter Schober
• chapter 15 Multiscale Dataflow Computing in Finance / Oskar Mencer Brian Boucher Gary Robinson Jon Gregory Georgi Gaydadjiev
• chapter 16 Manycore Parallel Computation / John Ashley Mark Joshi
• chapter 17 Practitioner's Guide on the Use of Cloud Computing in Finance / Binghuan Lin Rainer Wehkamp Juho Kanniainen
• chapter 18 Blockchains and Distributed Ledgers in Retrospective and Perspective / Alexander Lipton
• chapter 19 Optimal Feature Selection Using a Quantum Annealer / Andrew Milne Max Rounds Phil Goddard.

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.
(source: Nielsen Book Data)

These proceedings detail computer technology and the contemporary application of advanced mathematical techniques and concepts to financial and investment problems. Benefits and applications of the breakthroughs are described here with an eye to solving the problems caused by the increasing complexity and size of modern financial systems.
(source: Nielsen Book Data)

• 1. How to use python?
• 2. Using data structures in Python
• 3. Using Data in Python
• 4. Descriptive statistics using Python
• 5. Statistical approach to multiple variables
• 6. Comparing stocks between companies
• 7. Porfolio and Risk
• 8. Value at Risk

This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.
(source: Nielsen Book Data)

• List of Contributors.- Part I. Introduction to Prediction in the Financial Markets: Introduction to the Financial Markets. Univariate and Multivariate Time Series Predictions. Evidence of Predictability in Financial Markets. Bond Pricing and the Yield Curve. Data Selection.- Part II. Theory of Prediction Modelling: General Form of Models of Financial Markets. Overfitting, Generalisation and Regularisation. The Bootstrap, Bagging and Ensembles. Linear Models. Input Selection.- Part III. Theory of Specific Prediction Models: Neural Networks. Learning Trading Strategies for Imperfect Markets. Dynamical Systems Perspective and Embedding. Vector Machines. Bayesian Methods and Evidence.- Part IV. Prediction Model Applications: Yield Curve Modelling. Predicting Bonds Using the Linear Relevance Vector Machine. Artificial Neural Networks. Adaptive Lag Networks. Network Integration. Cointegration. Joint Optimisation in Statistical Arbitrage Trading. Univariate Modelling. Combining Models.- Part V. Optimising and Beyond: Portfolio Optimisation. Multi-Agent Modelling. Finance Prediction Modelling: Summary and Future Avenues.- References.- Subject Index.
• (source: Nielsen Book Data)

This volume looks at financial prediction from a broad range of perspectives. It covers: the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations). It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
(source: Nielsen Book Data)

• The relationship between corporate needs, organizational goals and computerized accounting systems
• automated key accounting procedures
• the missing link - automated financial planning systems
• automated treasury management - an overview
• improving cash management
• automating foreign exchange management
• automating cross-border treasury management systems
• electronic banking - the new frontier
• the not-so-distant future of financial automation.
• (source: Nielsen Book Data)

This book has been designed to give financial managers, controllers and accountants a comprehensive analysis of the current state of global financial management. It is based on a recent study of over 350 corporations which was commissioned by FERF, the research arm of the Financial Executives Institute, by Business International, a consulting company. The book provides a review of the automation of accounting and financial systems, surveys current trends and points the way to the development of sophisticated automated systems which will improve financial decision-making. Topics include accounting and control, financial planning systems and treasury management. The book contains practical aids including checklists, point-by-point programmes and examples.
(source: Nielsen Book Data)

Online transaction has become an important part of the new economy and finance. At the same time, transaction payment fraud also presents an explosive trend.This unique compendium introduces risk control theories technologies of online transaction processes, and applies the traditional security technologies and advanced behavior authentication methods to the trustworthy guarantee of online transaction systems. The comprehensive volume also promotes the development of trustworthy online transaction theory and technologies.This useful reference text will benefit researchers in the field of computer science and technologies, as well as a research reference in the field of online transaction risk prevention and control.
(source: Nielsen Book Data)

• Table of Contents Neural Networks and Gradient-Based Optimization Applying Machine Learning to Structured Data Utilizing Computer Vision Understanding Time Series Parsing Textual Data with Natural Language Processing Using Generative Models Reinforcement Learning for Financial Markets Privacy, Debugging, and Launching Your Products Fighting Bias Bayesian Inference and Probabilistic Programming.
• (source: Nielsen Book Data)

A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine learning and how to put them to work in financial industries Clear explanation and expert discussion of how machine learning works, with an emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement learning Book DescriptionMachine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. What you will learn Apply machine learning to structured data, natural language, photographs, and written text How machine learning can detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Dig deep into neural networks, examine uses of GANs and reinforcement learning Debug machine learning applications and prepare them for launch Address bias and privacy concerns in machine learning Who this book is forThis book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.
(source: Nielsen Book Data)

• Financial machine learning as a distinct subject
• Part 1: Data analysis. Financial data structures
• Labeling
• Sample weights
• Fractionally differentiated features
• Part 2: Modelling. Ensemble methods
• Cross-validation in finance
• Feature importance
• Hyper-parameter tuning with cross-validation
• Part 3: Backtesting. Bet sizing
• The dangers of backtesting
• Backtesting through cross-validation
• Backtesting on synthetic data
• Backtest statistics
• Understanding strategy risk
• Machine learning asset allocation
• Part 4: Useful financial features. Structural breaks
• Entropy features
• Microstructural features
• Part 5: High-performance computing recipes
• Multiprocessing and vectorization
• Brute force and quantum computers
• High-performance computational intelligence and forecasting technologies/ Kesheng Wu and Horst D. Simon.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
(source: Nielsen Book Data)

• Computational Economics in the Era of Natural Computationalism: 50 Years after "The Theory of Self Reproducing Automata" / Shu-Heng Chen, Mak Kaboudan, Ye-Rong Du
• Economic and Financial Modeling with Genetic Programming - A Review / Clı́odhna Tuite, Michael O'Neill, Anthony Brabazon
• Algorithmic Trading Based on Biologically-inspired Algorithms / Vassilios Vassiliadis, Georgios Dounias
• Algorithmic Trading in Practice / Peter Gomber, Kai Zimmermann
• Computational Spatiotemporal Modeling of Southern California Home Prices / Mak Kaboudan
• Business Applications of Fuzzy Logic / Petr Dostál, Chia-Yang Lin
• Modeling of Desirable Socio-economic Networks / Akira Namatame, Takanori Komatsu
• Computational Models of Financial Networks, Risk and Regulatory Policies / Kimmo Soramäki
• From Minority Games to $-Games / Jorgen Vitting Andersen
• An Overview and Evaluation of The CAT Market Design Competition / Tim Miller, Jinzhong Niu, Martin Chapman, Peter McBurney
• Agent-Based Macroeconomic Modeling and Policy Analysis: The Eurace@Unibi Model / Herbert Dawid, Simon Gemkow, Philipp Harting, Sander van der Hoog, Michael Neugart
• Dynamic Stochastic General Equilibrium Models: A Computational Perspective / Michel Juillard
• Agent-Based Models for Economic Policy Design / Michael Neugart, Matteo Richiardi
• Computational Economic Modeling of Migration / Anna Klabunde
• Computational Industrial Economics / Myong-Hun Chang
• Agent-Based Modelling for Financial Markets / Giulia Iori, James Porter
• Agent-Based Models of the Labor Market / Frank Westerhoff, Reiner Franke
• The Emerging Standard Neurobiological Model of Decision Making / Shih-Wei Wu, Paul W. Glimcher
• The Epistemology of Simulation, Computation and Dynamics in Economics / K. Vela Velupillai
• Tax-rate Rules for Reducing Government Debt: An Application of Computational Methods for Macroeconomic Stabilization / G. C. Lim, Paul D. McNelis
• Solving Rational Expectations Models / Jean Barthélemy, Magali Marx
• Computable General Equilibrium Models for Policy Evaluation and Economic Consequence Analysis / Ian Sue Wing, Edward J. Balistreri
• Multifractal Models in Finance: Their Origin, Properties, and Applications / Thomas Lux, Mawuli Segnon
• Particle Filters for Markov Switching Stochastic Volatility Models / Yun Bao, Carl Chiarella, Boda Kang.

The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.
(source: Nielsen Book Data)