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• Introduction and Descriptive Statistics.- Probability and Important Distributions.- Statistical Inferences Based on Samples.- Regression and Correlation: Relating Two or More Variables.- Selected Topics in Statistical Analysis for Business and Economics.- Appendices.- Index.
• (source: Nielsen Book Data)

Statistics for Business and Financial Economics, 3rd edition is the definitive Business Statistics book to use Finance, Economics, and Accounting data throughout the entire book. Therefore, this book gives students an understanding of how to apply the methodology of statistics to real world situations. In particular, this book shows how descriptive statistics, probability, statistical distributions, statistical inference, regression methods, and statistical decision theory can be used to analyze individual stock price, stock index, stock rate of return, market rate of return, and decision making. In addition, this book also shows how time-series analysis and the statistical decision theory method can be used to analyze accounting and financial data. In this fully-revised edition, the real world examples have been reconfigured and sections have been edited for better understanding of the topics. On the Springer page for the book, the solution manual, test bank and powerpoints are available for download.
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• Preface ix
• Chapter 1 Introduction 1
• Chapter 2 The Normal Probability Distribution 7 Standard deviation in a financial market 8 The impact of volatility and time on the standard deviation 8
• Chapter 3 Volatility 11 The probability distribution of the value of a Future after one year of trading 11 Normal distribution versus log-normal distribution 11 Calculating the annualised volatility traditionally 15 Calculating the annualised volatility without 17 Calculating the annualised volatility applying the 16% rule 19 Variation in trading days 20 Approach towards intraday volatility 20 Historical versus implied volatility 23
• Chapter 4 Put Call Parity 25 Synthetically creating a Future long position, the reversal 29 Synthetically creating a Future short position, the conversion 30 Synthetic options 31 Covered call writing 34 Short note on interest rates 35
• Chapter 5 Delta 37 Change of option value through the delta 38 Dynamic delta 40 Delta at different maturities 41 Delta at different volatilities 44 20 80 Delta region 46 Delta per strike 46 Dynamic delta hedging 47 The at the money delta 50 Delta changes in time 53
• Chapter 6 Pricing 55 Calculating the at the money straddle using Black and Scholes formula 57 Determining the value of an at the money straddle 59
• Chapter 7 Delta II 61 Determining the boundaries of the delta 61 Valuation of the at the money delta 64 Delta distribution in relation to the at the money straddle 65 Application of the delta approach, determining the delta of a call spread 68
• Chapter 8 Gamma 71 The aggregate gamma for a portfolio of options 73 The delta change of an option 75 The gamma is not a constant 76 Long term gamma example 77 Short term gamma example 77 Very short term gamma example 78 Determining the boundaries of gamma 79 Determining the gamma value of an at the money straddle 80 Gamma in relation to time to maturity, volatility and the underlying level 82 Practical example 85 Hedging the gamma 87 Determining the gamma of out of the money options 89 Derivatives of the gamma 91
• Chapter 9 Vega 93 Different maturities will display different volatility regime changes 95 Determining the vega value of at the money options 96 Vega of at the money options compared to volatility 97 Vega of at the money options compared to time to maturity 99 Vega of at the money options compared to the underlying level 99 Vega on a 3-dimensional scale, vega vs maturity and vega vs volatility 101 Determining the boundaries of vega 102 Comparing the boundaries of vega with the boundaries of gamma 104 Determining vega values of out of the money options 105 Derivatives of the vega 108 Vomma 108
• Chapter 10 Theta 111 A practical example 112 Theta in relation to volatility 114 Theta in relation to time to maturity 115 Theta of at the money options in relation to the underlying level 117 Determining the boundaries of theta 118 The gamma theta relationship 120 Theta on a 3-dimensional scale, theta vs maturity and theta vs volatility 125 Determining the theta value of an at the money straddle 126 Determining theta values of out of the money options 127
• Chapter 11 Skew 129 Volatility smiles with different times to maturity 131 Sticky at the money volatility 133
• Chapter 12 Spreads 135 Call spread (horizontal) 135 Put spread (horizontal) 137 Boxes 138 Applying boxes in the real market 139 The Greeks for horizontal spreads 140 Time spread 146 Approximation of the value of at the money spreads 148 Ratio spread 149
• Chapter 13 Butterfly 155 Put call parity 158 Distribution of the butterfly 159 Boundaries of the butterfly 161 Method for estimating at the money butterfly values 163 Estimating out of the money butterfly values 164 Butterfly in relation to volatility 165 Butterfly in relation to time to maturity 166 Butterfly as a strategic play 166 The Greeks of a butterfly 167 Straddle strangle or the Iron fly 171
• Chapter 14 Strategies 173 Call 173 Put 174 Call spread 175 Ratio spread 176 Straddle 177 Strangle 178 Collar (risk reversal, fence) 178 Gamma portfolio 179 Gamma hedging strategies based on Monte Carlo scenarios 180 Setting up a gamma position on the back of prevailing kurtosis in the market 190 Excess kurtosis 191 Benefitting from a platykurtic environment 192 The mesokurtic market 193 The leptokurtic market 193 Transition from a platykurtic environment towards a leptokurtic environment 194 Wrong hedging strategy: Killergamma 195 Vega convexity/Vomma 196 Vega convexity in relation to time/Veta 202 Index 205.
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A unique, in-depth guide to options pricing and valuing their greeks, along with a four dimensional approach towards the impact of changing market circumstances on options How to Calculate Options Prices and Their Greeks is the only book of its kind, showing you how to value options and the greeks according to the Black Scholes model but also how to do this without consulting a model. You'll build a solid understanding of options and hedging strategies as you explore the concepts of probability, volatility, and put call parity, then move into more advanced topics in combination with a four-dimensional approach of the change of the P&L of an option portfolio in relation to strike, underlying, volatility, and time to maturity. This informative guide fully explains the distribution of first and second order Greeks along the whole range wherein an option has optionality, and delves into trading strategies, including spreads, straddles, strangles, butterflies, kurtosis, vega-convexity , and more. Charts and tables illustrate how specific positions in a Greek evolve in relation to its parameters, and digital ancillaries allow you to see 3D representations using your own parameters and volumes. The Black and Scholes model is the most widely used option model, appreciated for its simplicity and ability to generate a fair value for options pricing in all kinds of markets. This book shows you the ins and outs of the model, giving you the practical understanding you need for setting up and managing an option strategy. Understand the Greeks, and how they make or break a strategy See how the Greeks change with time, volatility, and underlying Explore various trading strategies Implement options positions, and more Representations of option payoffs are too often based on a simple two-dimensional approach consisting of P&L versus underlying at expiry. This is misleading, as the Greeks can make a world of difference over the lifetime of a strategy. How to Calculate Options Prices and Their Greeks is a comprehensive, in-depth guide to a thorough and more effective understanding of options, their Greeks, and (hedging) option strategies.
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• Introduction Introduction to financial derivatives Financial derivatives-what's the big deal? Stylized facts Overview
• Fundamentals Interest rates Cash flows Continuously compounded interest rates Interest rate options: caps and floors
• Discrete-Time Finance The binomial one period model The one period model The multi period model
• Linear Time Series Models Introduction Linear systems in the time domain Linear stochastic processes Linear processes with a rational transfer function Autocovariance functions Prediction in linear processes
• Non-Linear Time Series Models Introduction The aim of model building Qualitative properties of the models Parameter estimation Parametric models Model identification Prediction in non-linear models Applications of non-linear models
• Kernel Estimators in Time Series Analysis Non-parametric estimation Kernel estimators for time series Kernel estimation for regression Applications of kernel estimators
• Stochastic Calculus Dynamical systems The Wiener process Stochastic Integrals Ito stochastic calculus Extensions to jump processes
• Stochastic Differential Equations Stochastic differential equations Analytical solution methods Feynman-Kac representation Girsanov measure transformation
• Continuous-Time Security Markets From discrete to continuous time Classical arbitrage theory Modern approach using martingale measures Pricing Model extensions Computational methods
• Stochastic Interest Rate Models Gaussian one-factor models A general class of one-factor models Time-dependent models Multifactor and stochastic volatility models
• The Term Structure of Interest Rates Basic concepts The classical approach The term structure for specific models Heath-Jarrow-Morton framework Credit models Estimation of the term structure-curve-fitting
• Discrete-Time Approximations Stochastic Taylor expansion Convergence Discretization schemes Multilevel Monte Carlo Simulation of SDEs
• Parameter Estimation in Discretely Observed SDEs Introduction High frequency methods Approximate methods for linear and non-linear models State dependent diffusion term MLE for non-linear diffusions Generalized method of moments (GMM) Model validation for discretely observed SDEs
• Inference in Partially Observed Processes Introduction The model Exact filtering Conditional moment estimators Kalman filter Approximate filters State filtering and prediction The unscented Kalman filter A maximum likelihood method Sequential Monte Carlo filters Application of non-linear filters
• Appendix A: Projections in Hilbert Spaces Appendix B: Probability Theory
• Bibliography
• Problems appear at the end of each chapter.
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Statistics for Finance develops students' professional skills in statistics with applications in finance. Developed from the authors' courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Ito's formula, the Black-Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students' financial reasoning skills.
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• Heavy tails in finance for independent or multifractal price increments / Benoit B. Mandelbrot
• Financial risk and heavy tails / Brendan O. Bradley and Murad S. Taqqu
• Modeling financial data with stable distributions / John P. Nolan
• Statistical issues in modeling multivariate stable portfolios / Tomasz J. Kozubowski, Anna K. Panorska and Svetlozar T. Rachev
• Jump-diffusion models / Wolfgang J. Runggaldier
• Hyperbolic processes in finance / Bo Martin Bibby and Michael Sørensen
• Stable modeling of market and credit value at risk / Svetlozar T. Rachev, Eduardo S. Schwartz and Irina Khindanova
• Modelling dependence with copulas and applications to risk management / Paul Embrechts, Filip Lindskog and Alexander McNeil
• Prediction of financial downside-risk with heavy-tailed conditional distributions / Stefan Mittnik and Marc S. Paolella
• Stable non-Gaussian models for credit risk management / Bernhard Martin, Svetlozar T. Rachev and Eduardo S. Schwartz
• Multifactor stochastic variance models in risk management : maximum entropy approach and Lévy processes / Alexander Levin and Alexander Tchernitser
• Modelling the term structure of monetary rates / Luisa Izzi
• Asset liability management : a review and some new results in the presence of heavy tails / Yesim Tokat, Svetlozar T. Rachev and Eduardo S. Schwartz
• Portfolio choice theory with non-Gaussian distributed returns / Sergio Ortobelli [and three others]
• Portfolio modeling with heavy tailed random vectors / Mark M. Meerschaert and Hans-Peter Scheffler
• Long range dependence in heavy tailed stochastic processes / Borjana Racheva-Iotova and Gennaday Samorodnitsky.

The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series. This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.
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• Recent Developments in GARCH Modeling.- An Introduction to Univariate GARCH Models.- Stationarity, Mixing, Distributional Properties and Moments of GARCH(p, q)-Processes.- ARCH( ) Models and Long Memory Properties.- A Tour in the Asymptotic Theory of GARCH Estimation.- Practical Issues in the Analysis of Univariate GARCH Models.- Semiparametric and Nonparametric ARCH Modeling.- Varying Coefficient GARCH Models.- Extreme Value Theory for GARCH Processes.- Multivariate GARCH Models.- Recent Developments in Stochastic Volatility Modeling.- Stochastic Volatility: Origins and Overview.- Probabilistic Properties of Stochastic Volatility Models.- Moment-Based Estimation of Stochastic Volatility Models.- Parameter Estimation and Practical Aspects of Modeling Stochastic Volatility.- Stochastic Volatility Models with Long Memory.- Extremes of Stochastic Volatility Models.- Multivariate Stochastic Volatility.- Topics in Continuous Time Processes.- An Overview of Asset-Price Models.- Ornstein-Uhlenbeck Processes and Extensions.- Jump-Type Levy Processes.- Levy-Driven Continuous-Time ARMA Processes.- Continuous Time Approximations to GARCH and Stochastic Volatility Models.- Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance.- Parametric Inference for Discretely Sampled Stochastic Differential Equations.- Realized Volatility.- Estimating Volatility in the Presence of Market Microstructure Noise: A Review of the Theory and Practical Considerations.- Option Pricing.- An Overview of Interest Rate Theory.- Extremes of Continuous-Time Processes..- Topics in Cointegration and Unit Roots.- Cointegration: Overview and Development.- Time Series with Roots on or Near the Unit Circle.- Fractional Cointegration.- Special Topics - Risk.- Different Kinds of Risk.- Value-at-Risk Models.- Copula-Based Models for Financial Time Series.- Credit Risk Modeling.- Special Topics - Time Series Methods.- Evaluating Volatility and Correlation Forecasts.- Structural Breaks in Financial Time Series.- An Introduction to Regime Switching Time Series Models.- Model Selection.- Nonparametric Modeling in Financial Time Series.- Modelling Financial High Frequency Data Using Point Processes.- Special Topics - Simulation Based Methods.- Resampling and Subsampling for Financial Time Series.- Markov Chain Monte Carlo.- Particle Filtering.
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The Handbook of Financial Time Series, edited by Andersen, Davis, Kreiss and Mikosch, is an impressive collection of survey articles by many of the leading contributors to the ?eld. These articles are mostly very clearly wr- ten and present a sweep of the literature in a coherent pedagogical manner. The level of most of the contributions is mathematically sophisticated, and I imagine many of these chapters will ?nd their way onto graduate reading lists in courses in ?nancial economics and ?nancial econometrics. In reading through these papers, I found many new insights and presentations even in areas that I know well. The book is divided into ?ve broad sections: GARCH-Modeling, Stoch- tic Volatility Modeling, Continuous Time Processes, Cointegration and Unit Roots, and Special Topics. These correspond generally to classes of stoch- tic processes that are applied in various ?nance contexts. However, there are otherthemesthatcutacrosstheseclasses.Thereareseveralpapersthatca - fully articulate the probabilistic structure of these classes, while others are morefocusedonestimation.Stillothersderivepropertiesofextremesfor each class of processes, and evaluate persistence and the extent of long memory. Papers in many cases examine the stability of the process with tools to check for breaks and jumps. Finally there are applications to options, term str- ture, credit derivatives, risk management, microstructure models and other forecasting settings.
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• Preface xxiii
• 1 Introduction 1 1.1 Statistical Finance 2 1.2 Risk Management 3 1.3 Portfolio Management 5 1.4 Pricing of Securities 6 Part I Statistical Finance 11
• 2 Financial Instruments 13 2.1 Stocks 13 2.2 Fixed Income Instruments 19 2.3 Derivatives 23 2.4 Data Sets 27
• 3 Univariate Data Analysis 33 3.1 Univariate Statistics 34 3.2 Univariate Graphical Tools 42 3.3 Univariate ParametricModels 55 3.4 Tail Modeling 61 3.5 Asymptotic Distributions 83 3.6 Univariate Stylized Facts 91
• 4 Multivariate Data Analysis 95 4.1 Measures of Dependence 95 4.2 Multivariate Graphical Tools 103 4.3 Multivariate ParametricModels 107 4.4 Copulas 111
• 5 Time Series Analysis 121 5.1 Stationarity and Autocorrelation 122 5.2 Model Free Estimation 128 5.3 Univariate Time Series Models 135 5.4 Multivariate Time Series Models 157 5.5 Time Series Stylized Facts 160
• 6 Prediction 163 6.1 Methods of Prediction 164 6.2 Forecast Evaluation 170 6.3 Predictive Variables 175 6.4 Asset Return Prediction 182 Part II Risk Management 193
• 7 Volatility Prediction 195 7.1 Applications of Volatility Prediction 197 7.2 Performance Measures for Volatility Predictors 199 7.3 Conditional Heteroskedasticity Models 200 7.4 Moving Average Methods 205 7.5 State Space Predictors 211
• 8 Quantiles and Value-at-Risk 219 8.1 Definitions of Quantiles 220 8.2 Applications of Quantiles 223 8.3 Performance Measures for Quantile Estimators 227 8.4 Nonparametric Estimators of Quantiles 233 8.5 Volatility Based Quantile Estimation 240 8.6 Excess Distributions in Quantile Estimation 258 8.7 Extreme ValueTheory in Quantile Estimation 288 8.8 Expected Shortfall 292 Part III Portfolio Management 297
• 9 Some Basic Concepts of Portfolio Theory 299 9.1 Portfolios and Their Returns 300 9.2 Comparison of Return andWealth Distributions 312 9.3 Multiperiod Portfolio Selection 326
• 10 Performance Measurement 337 10.1 The Sharpe Ratio 338 10.2 Certainty Equivalent 346 10.3 Drawdown 347 10.4 Alpha and Conditional Alpha 348 10.5 Graphical Tools of Performance Measurement 356
• 11 Markowitz Portfolios 367 11.1 Variance Penalized Expected Return 369 11.2 Minimizing Variance under a Sufficient Expected Return 372 11.3 Markowitz Bullets 375 11.4 Further Topics in Markowitz Portfolio Selection 381 11.5 Examples of Markowitz Portfolio Selection 383
• 12 Dynamic Portfolio Selection 385 12.1 Prediction in Dynamic Portfolio Selection 387 12.2 Backtesting Trading Strategies 393 12.3 One Risky Asset 394 12.4 Two Risky Assets 405 Part IV Pricing of Securities 419
• 13 Principles of Asset Pricing 421 13.1 Introduction to Asset Pricing 422 13.2 Fundamental Theorems of Asset Pricing 430 13.3 Evaluation of Pricing and Hedging Methods 456
• 14 Pricing by Arbitrage 459 14.1 Futures and the Put-Call Parity 460 14.2 Pricing in Binary Models 466 14.3 Black-Scholes Pricing 485 14.4 Black-Scholes Hedging 505 14.5 Black-Scholes Hedging and Volatility Estimation 515
• 15 Pricing in IncompleteModels 521 15.1 Quadratic Hedging and Pricing 522 15.2 Utility Maximization 523 15.3 Absolutely Continuous Changes of Measures 530 15.4 GARCH Market Models 534 15.5 Nonparametric Pricing Using Historical Simulation 545 15.6 Estimation of the Risk-Neutral Density 551 15.7 Quantile Hedging 555
• 16 Quadratic and Local Quadratic Hedging 557 16.1 Quadratic Hedging 558 16.2 Local Quadratic Hedging 583 16.3 Implementations of Local Quadratic Hedging 595
• 17 Option Strategies 615 17.1 Option Strategies 616 17.2 Profitability of Option Strategies 625
• 18 Interest Rate Derivatives 649 18.1 Basic Concepts of Interest Rate Derivatives 650 18.2 Interest Rate Forwards 659 18.3 Interest Rate Options 666 18.4 Modeling Interest Rate Markets 669 References 673 Index 681.
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An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end. Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications. Written for the leading edge of finance, Nonparametric Finance: - Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction - Provides risk management guidance through volatility prediction, quantiles, and value-at-risk - Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more - Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles - Provides supplementary R code and numerous graphics to reinforce complex content Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage. Jussi Klemel , PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.
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• Preface ix Acknowledgments xi CHAPTER 1 Setting the Stage 1 Why Is This Book Different? 2 Road Map of the Book 3 References 5 CHAPTER 2 Building Zero Curves 7 Market Instruments 8 Linear Interpolation 16 Cubic Splining 25 Appendix: Finding Swap Rates Using a Floating Coupon Bond Approach 41 References 43 CHAPTER 3 Valuing Vanilla Options 45 Black-Scholes Formulae 47 Adaptations of the Black-Scholes Formulae 53 Limitations of the Black-Scholes Formulae 70 Application in Currency Risk Management 74 Appendix 78 References 80 CHAPTER 4 Simulations 81 Uniform Number Generation 82 Non-Uniform Number Generation 86 Applications of Simulations 93 Variance Reduction Techniques 100 References 104 CHAPTER 5 Valuing Exotic Options 107 Valuing Path-Independent, European-Style Options on a Single Variable 108 Valuing Path-Dependent, European-Style Options on a Single Variable 114 Valuing Path-Independent, European-Style Options on Two Variables 135 Valuing Path-Dependent, European-Style Options on Multiple Variables 152 References 157 CHAPTER 6 Estimating Model Parameters 159 Calibration of Parameters in the Black-Scholes Model 161 Using Implied Black-Scholes Volatility Surface and Zero Rate Term Structure to Value Options 169 Using Volatility Surface 178 Calibration of Interest Rate Option Model Parameters 190 Statistical Estimation 196 References 203 CHAPTER 7 The Effectiveness of Hedging Strategies 205 Delta Hedging 206 Assumptions Underlying Delta Hedging 216 Beyond Delta Hedging 223 Testing Hedging Strategies 230 Analysis Associated with the Hedging of a European-Style Vanilla Put Option 235 References 244 CHAPTER 8 Valuing Variable Annuity Guarantees 245 Basic GMDB 246 Death Benefit Riders 261 Other Details Associated with GMDB Products 269 Improving Modeling Assumptions 273 Living Benefit Riders 276 References 279 CHAPTER 9 Real Options 281
• Surrendering a GMAB Rider 282 Adding Servers in a Queue 300 References 314 CHAPTER 10 Parting Thoughts 315 About the Author 317 About the Website 319 Index 321.
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Learn how quantitative models can help fight client problems head-on Before financial problems can be solved, they need to be fully understood. Since in-depth quantitative modeling techniques are a powerful tool to understanding the drivers associated with financial problems, one would need a solid grasp of these techniques before being able to unlock their full potential of the methods used. In The Mathematics of Financial Models, the author presents real world solutions to the everyday problems facing financial professionals. With interactive tools such as spreadsheets for valuation, pricing, and modeling, this resource combines highly mathematical quantitative analysis with useful, practical methodologies to create an essential guide for investment and risk-management professionals facing modeling issues in insurance, derivatives valuation, and pension benefits, among others. In addition to this, this resource also provides the relevant tools like matrices, calculus, statistics and numerical analysis that are used to build the quantitative methods used. Financial analysts, investment professionals, risk-management professionals, and graduate students will find applicable information throughout the book, and gain from the self-study exercises and the refresher course on key mathematical topics. Equipped with tips and information, The Mathematics of Financial Models * Provides practical methodologies based on mathematical quantitative analysis to help analysts, investment and risk-management professionals better navigate client issues * Contains interactive tools that demonstrate the power of analysis and modeling * Helps financial professionals become more familiar with the challenges across a range of industries * Includes a mathematics refresher course and plenty of exercises to get readers up to speed The Mathematics of Financial Models is an in-depth guide that helps readers break through common client financial problems and emerge with clearer strategies for solving issues in the future.
(source: Nielsen Book Data)

• Introduction.- Probability and Statistical Models.- Returns.- Time Series Models.- Portfolio Theory.- Regression.- The Capital Asset Pricing Model.- Options Pricing.- Fixed Income Securities.- Resampling.- Value-at-Risk.- GARCH models.- Nonparametric Regression and Splines.- Behavioral Finance.
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This book emphasizes the applications of statistics and probability to finance. The basics of these subjects are reviewed and more advanced topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as needed. The book covers the classical methods of finance and it introduces the newer area of behavioral finance. Applications and use of MATLAB and SAS software are stressed. The book will serve as a text in courses aimed at advanced undergraduates and masters students. Those in the finance industry can use it for self-study.
(source: Nielsen Book Data)

• INTRODUCTORY CONCEPTS AND DEFINITIONS Review of Basic Statistics What Is Statistics? Characterizing Data Measures of Central Tendency Measures of Variability Higher Moments Summarizing Distributions Bivariate Data Three Variables Two-Way Tables
• Stock Price Series and Rates of Return Introduction Sharpe Ratio Value-at-Risk Distributions for RORs
• Several Stocks and Their Rates of Return Introduction Review of Covariance and Correlation Two Stocks Three Stocks m Stocks
• REGRESSION Simple Linear Regression-- CAPM and Beta Introduction Simple Linear Regression Estimation Inference Concerning the Slope Testing Equality of Slopes of Two Lines through the Origin Linear Parametric Functions Variances Dependent upon X A Financial Application: CAPM and "Beta" Slope and Intercept
• Multiple Regression and Market Models Multiple Regression Models Market Models Models with Both Numerical and Dummy Explanatory Variables Model Building
• PORTFOLIO ANALYSIS Mean-Variance Portfolio Analysis Introduction Two Stocks Three Stocks m Stocks m Stocks and a Risk-Free Asset Value-at-Risk Selling Short Market Models and Beta
• Utility-Based Portfolio Analysis Introduction Single-Criterion Analysis
• TIME SERIES ANALYSIS Introduction to Time Series Analysis Introduction Control Charts Moving Averages Need for Modeling Trend, Seasonality, and Randomness Models with Lagged Variables Moving-Average Models Identification of ARIMA Models Seasonal Data Dynamic Regression Models Simultaneous Equations Models
• Regime Switching Models Introduction Bull and Bear Markets
• Appendix A: Vectors and Matrices Appendix B: Normal Distributions Appendix C: Lagrange Multipliers Appendix D: Abbreviations and Symbols
• Index
• A Summary, Exercises, and Bibliography appear at the end of each chapter.
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Taking a data-driven approach, A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance. The book begins with a review of basic statistics, including descriptive statistics, kinds of variables, and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models, such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis. Providing the connection between elementary statistics courses and quantitative finance courses, this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.
(source: Nielsen Book Data)

• Cover; Contents; PART I: PRELIMINARIES; 1 Preliminaries; 1.1 Interest rates and compounding; 1.2 Zero coupon bonds and discounting; 1.3 Annuities; 1.4 Daycount conventions; 1.5 An abridged guide to stocks, bonds and FX; 1.6 Exercises; PART II: FORWARDS, SWAPS AND OPTIONS; 2 Forward contracts and forward prices; 2.1 Derivative contracts; 2.2 Forward contracts; 2.3 Forward on asset paying no income; 2.4 Forward on asset paying known income; 2.5 Review of assumptions; 2.6 Value of forward contract; 2.7 Forward on stock paying dividends and on currency; 2.8 Physical versus cash settlement.

• 2.9 Summary2.10 Exercises; 3 Forward rates and libor; 3.1 Forward zero coupon bond prices; 3.2 Forward interest rates; 3.3 Libor; 3.4 Forward rate agreements and forward libor; 3.5 Valuing floating and flxed cashflows; 3.6 Exercises; 4 Interest rate swaps; 4.1 Swap definition; 4.2 Forward swap rate and swap value; 4.3 Spot-starting swaps; 4.4 Swaps as difference between bonds; 4.5 Exercises; 5 Futures contracts; 5.1 Futures definition; 5.2 Futures versus forward prices; 5.3 Futures on libor rates; 5.4 Exercises; 6 No-arbitrage principle; 6.1 Assumption of no-arbitrage.

• 6.2 Monotonicity theorem6.3 Arbitrage violations; 6.4 Exercises; 7 Options; 7.1 Option definitions; 7.2 Put-call parity; 7.3 Bounds on call prices; 7.4 Call and put spreads; 7.5 Butterflies and convexity of option prices; 7.6 Digital options; 7.7 Options on forward contracts; 7.8 Exercises; PART III: REPLICATION, RISK-NEUTRALITY AND THE FUNDAMENTAL THEOREM; 8 Replication and risk-neutrality on the binomial tree; 8.1 Hedging and replication in the two-state world; 8.2 Risk-neutral probabilities; 8.3 Multiple time steps; 8.4 General no-arbitrage condition; 8.5 Exercises.

• 9 Martingales, numeraires and the fundamental theorem9.1 Definition of martingales; 9.2 Numeraires and fundamental theorem; 9.3 Change of numeraire on binomial tree; 9.4 Fundamental theorem: a pragmatic example; 9.5 Fundamental theorem: summary; 9.6 Exercises; 10 Continuous-time limit and Black-Scholes formula; 10.1 Lognormal limit; 10.2 Risk-neutral limit; 10.3 Black-Scholes formula; 10.4 Properties of Black-Scholes formula; 10.5 Delta and vega; 10.6 Incorporating random interest rates; 10.7 Exercises; 11 Option price and probability duality.

• 11.1 Digitals and cumulative distribution function11.2 Butterflies and risk-neutral density; 11.3 Calls as spanning set; 11.4 Implied volatility; 11.5 Exercises; PART IV: INTEREST RATE OPTIONS; 12 Caps, floors and swaptions; 12.1 Caplets; 12.2 Caplet valuation and forward numeraire; 12.3 Swaptions and swap numeraire; 12.4 Summary; 12.5 Exercises; 13 Cancellable swaps and Bermudan swaptions; 13.1 European cancellable swaps; 13.2 Callable bonds; 13.3 Bermudan swaptions; 13.4 Bermudan swaption exercise criteria; 13.5 Bermudan cancellable swaps and callable bonds; 13.6 Exercises.

The worlds of Wall Street and The City have always held a certain allure, but in recent years have left an indelible mark on the wider public consciousness and there has been a need to become more financially literate. The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types, whether for general interest or because of the enormous monetary rewards on offer. An Introduction to Quantitative Finance concerns financial derivatives - a derivative being a contract between two entities whose value derives from the price of an unde.

• Part I. Python and Finance; Part II. Mastering the Basics; Part III. Financial Data Science; Part IV. Algorithmic Trading; Part V. Derivatives Analytics; Appendix A. Dates and Times; Appendix B. BSM Option Class.

"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."--ProQuest

• Introduction
• Efficient market hypothesis
• Random walk
• Levy stochastic processes and limit theorems
• Scales in financial data
• Stationarity and time correlation
• Time correlation in financial time series
• Stochastic models of price dynamics
• Scaling and its breakdown
• ARCH and GARCH processes
• Financial markets and turbulence
• Correlation and anticorrelation between stocks
• Taxonomy of a stock portfolio
• Options in idealized markets
• Options in real markets.

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.
(source: Nielsen Book Data)

• Introduction
• Efficient market hypothesis
• Random walk
• Levy stochastic processes and limit theorems
• Scales in financial data
• Stationarity and time correlation
• Time correlation in financial time series
• Stochastic models of price dynamics
• Scaling and its breakdown
• ARCH and GARCH processes
• Financial markets and turbulence
• Correlation and anticorrelation between stocks
• Taxonomy of a stock portfolio
• Options in idealized markets
• Options in real markets.

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.
(source: Nielsen Book Data)

Game-theoretic probability and finance come of age Glenn Shafer and Vladimir Vovk Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research, Game-Theoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory. Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context. Praise from early readers ver since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic. In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades. Peter Gr130 0Wiley series in probability and statisticsnwald, CWI and University of Leiden hafer and Vovk have thoroughly re-written their 2001 book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors. Ioannis Karatzas, Columbia University.

• Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters).
• (source: Nielsen Book Data)

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
(source: Nielsen Book Data)

• Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
• (source: Nielsen Book Data)

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
(source: Nielsen Book Data)

• Table of Contents Machine Learning for Trading Market and Fundamental Data Alternative Data for Finance Alpha Factor Research Strategy Evaluation The Machine Learning Process Linear Models Time Series Models Bayesian Machine Learning Decision Trees and Random Forests Gradient Boosting Machines Unsupervised Learning Working with Text Data Topic Modeling Word Embeddings Next Steps.
• (source: Nielsen Book Data)

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
(source: Nielsen Book Data)

This highly praised introductory treatment describes the parallels between statistical physics and finance - both those established in the 100-year long interaction between these disciplines, as well as new research results on financial markets. The random-walk technique, well known in physics, is also the basic model in finance, upon which are built, for example, the Black-Scholes theory of option pricing and hedging, plus methods of portfolio optimization. Here the underlying assumptions are assessed critically. Using empirical financial data and analogies to physical models such as fluid flows, turbulence, or superdiffusion, the book develops a more accurate description of financial markets based on random walks. With this approach, novel methods for derivative pricing and risk management can be formulated. Computer simulations of interacting-agent models provide insight into the mechanisms underlying unconventional price dynamics. It is shown that stock exchange crashes can be modelled in ways analogous to phase transitions and earthquakes, and sometimes have even been predicted successfully. This third edition of "The Statistical Mechanics of Financial Markets" especially stands apart from other treatments because it offers new chapters containing a practitioner's treatment of two important current topics in banking: the basic notions and tools of risk management and capital requirements for financial institutions, including an overview of the new Basel II capital framework which may well set the risk management standards in scores of countries for years to come.
(source: Nielsen Book Data)

• I: Informatics and Baysesian Networks
• Introduction to Informatics
• Basics of Probability and Statistics
• Algorithms for Bayesian Networks
• Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering
• Targeted Advertising
• Market Basket Analysis
• Venture Capital Decision Making
• Measuring Operational Risk
• Credit Scoring
• Applications to Investment Science. Appendices.
• (source: Nielsen Book Data)

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science.
(source: Nielsen Book Data)

• I: Informatics and Baysesian Networks
• Introduction to Informatics
• Basics of Probability and Statistics
• Algorithms for Bayesian Networks
• Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering
• Targeted Advertising
• Market Basket Analysis
• Venture Capital Decision Making
• Measuring Operational Risk
• Credit Scoring
• Applications to Investment Science. Appendices.
• (source: Nielsen Book Data)

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science.
(source: Nielsen Book Data)