UID:
almafu_9960119058002883
Format:
1 online resource (xvi, 377 pages) :
,
digital, PDF file(s).
ISBN:
1-316-77609-3
,
1-316-77763-4
,
1-316-58446-1
Content:
Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.
Note:
Title from publisher's bibliographic system (viewed on 27 Oct 2016).
,
Cover -- Half-title page -- Title page -- Copyright page -- Dedication -- Contents -- Preface -- Acknowledgments -- 1 Analytical Thinking -- 1.1 What Is Financial Analytics? -- 1.2 What Is the Laptop Laboratory for Data Science? -- 1.3 What Is R and How Can It Be Used in the Professional Analytics World? -- 1.4 Exercises -- 2 The R Language for Statistical Computing -- 2.1 Getting Started with R -- 2.2 Language Features: Functions, Assignment, Arguments, and Types -- 2.3 Language Features: Binding and Arrays -- 2.4 Error Handling -- 2.5 Numeric, Statistical, and Character Functions -- 2.6 Data Frames and Input-Output -- 2.7 Lists -- 2.8 Exercises -- 3 Financial Statistics -- 3.1 Probability -- 3.2 Combinatorics -- 3.3 Mathematical Expectation -- 3.4 Sample Mean, Standard Deviation, and Variance -- 3.5 Sample Skewness and Kurtosis -- 3.6 Sample Covariance and Correlation -- 3.7 Financial Returns -- 3.8 Capital Asset Pricing Model -- 3.9 Exercises -- 4 Financial Securities -- 4.1 Bond Investments -- 4.2 Stock Investments -- 4.3 The Housing Crisis -- 4.4 The Euro Crisis -- 4.5 Securities Datasets and Visualization -- 4.6 Adjusting for Stock Splits -- 4.7 Adjusting for Mergers -- 4.8 Plotting Multiple Series -- 4.9 Securities Data Importing -- 4.10 Securities Data Cleansing -- 4.11 Securities Quoting -- 4.12 Exercises -- 5 Dataset Analytics and Risk Measurement -- 5.1 Generating Prices from Log Returns -- 5.2 Normal Mixture Models of Price Movements -- 5.3 Sudden Currency Price Movement in 2015 -- 5.4 Exercises -- 6 Time Series Analysis -- 6.1 Examining Time Series -- 6.2 Stationary Time Series -- 6.3 Auto-Regressive Moving Average Processes -- 6.4 Power Transformations -- 6.5 The TSA Package -- 6.6 Auto-Regressive Integrated Moving Average Processes -- 6.7 Case Study: Earnings of Johnson & -- Johnson -- 6.8 Case Study: Monthly Airline Passengers.
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6.9 Case Study: Electricity Production -- 6.10 Generalized Auto-Regressive Conditional Heteroskedasticity -- 6.11 Case Study: Volatility of Google Stock Returns -- 6.12 Exercises -- 7 The Sharpe Ratio -- 7.1 Sharpe Ratio Formula -- 7.2 Time Periods and Annualizing -- 7.3 Ranking Investment Candidates -- 7.4 The Quantmod Package -- 7.5 Measuring Income Statement Growth -- 7.6 Sharpe Ratios for Income Statement Growth -- 7.7 Exercises -- 8 Markowitz Mean-Variance Optimization -- 8.1 Optimal Portfolio of Two Risky Assets -- 8.2 Quadratic Programming -- 8.3 Data Mining with Portfolio Optimization -- 8.4 Constraints, Penalization, and the Lasso -- 8.5 Extending to High Dimensions -- 8.6 Case Study: Surviving Stocks of the S& -- P 500 Index from 2003 to 2008 -- 8.7 Case Study: Thousands of Candidate Stocks from 2008 to 2014 -- 8.8 Case Study: Exchange-Traded Funds -- 8.9 Exercises -- 9 Cluster Analysis -- 9.1 K-Means Clustering -- 9.2 Dissecting the K-Means Algorithm -- 9.3 Sparsity and Connectedness of Undirected Graphs -- 9.4 Covariance and Precision Matrices -- 9.5 Visualizing Covariance -- 9.6 The Wishart Distribution -- 9.7 Glasso: Penalization for Undirected Graphs -- 9.8 Running the Glasso Algorithm -- 9.9 Tracking a Value Stock through the Years -- 9.10 Regression on Yearly Sparsity -- 9.11 Regression on Quarterly Sparsity -- 9.12 Regression on Monthly Sparsity -- 9.13 Architecture and Extension -- 9.14 Exercises -- 10 Gauging the Market Sentiment -- 10.1 Markov Regime Switching Model -- 10.2 Reading the Market Data -- 10.3 Bayesian Reasoning -- 10.4 The Beta Distribution -- 10.5 Prior and Posterior Distributions -- 10.6 Examining Log Returns for Correlation -- 10.7 Momentum Graphs -- 10.8 Exercises -- 11 Simulating Trading Strategies -- 11.1 Foreign Exchange Markets -- 11.2 Chart Analytics -- 11.3 Initialization and Finalization.
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11.4 Momentum Indicators -- 11.5 Bayesian Reasoning within Positions -- 11.6 Entries -- 11.7 Exits -- 11.8 Profitability -- 11.9 Short-Term Volatility -- 11.10 The State Machine -- 11.11 Simulation Summary -- 11.12 Exercises -- 12 Data Exploration Using Fundamentals -- 12.1 The RSQLite Package -- 12.2 Finding Market-to-Book Ratios -- 12.3 The Reshape2 Package -- 12.4 Case Study: Google -- 12.5 Case Study: Walmart -- 12.6 Value Investing -- 12.7 Lab: Trying to Beat the Market -- 12.8 Lab: Financial Strength -- 12.9 Exercises -- 13 Prediction Using Fundamentals -- 13.1 Best Income Statement Portfolio -- 13.2 Reformatting Income Statement Growth Figures -- 13.3 Obtaining Price Statistics -- 13.4 Combining the Income Statement with Price Statistics -- 13.5 Prediction Using Classification Trees and Recursive Partitioning -- 13.6 Comparing Prediction Rates among Classifiers -- 13.7 Exercises -- 14 Binomial Model for Options -- 14.1 Applying Computational Finance -- 14.2 Risk-Neutral Pricing and No Arbitrage -- 14.3 High Risk-Free Rate Environment -- 14.4 Convergence of Binomial Model for Option Data -- 14.5 Put-Call Parity -- 14.6 From Binomial to Log-Normal -- 14.7 Exercises -- 15 Black-Scholes Model and Option-Implied Volatility -- 15.1 Geometric Brownian Motion -- 15.2 Monte Carlo Simulation of Geometric Brownian Motion -- 15.3 Black-Scholes Derivation -- 15.4 Algorithm for Implied Volatility -- 15.5 Implementation of Implied Volatility -- 15.6 The Rcpp Package -- 15.7 Exercises -- Appendix Probability Distributions and Statistical Analysis -- A.1 Distributions -- A.2 Bernoulli Distribution -- A.3 Binomial Distribution -- A.4 Geometric Distribution -- A.5 Poisson Distribution -- A.6 Functions for Continuous Distributions -- A.7 The Uniform Distribution -- A.8 Exponential Distribution -- A.9 Normal Distribution -- A.10 Log-Normal Distribution.
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A.11 The t[sub(ν)] Distribution -- A.12 Multivariate Normal Distribution -- A.13 Gamma Distribution -- A.14 Estimation via Maximum Likelihood -- A.15 Central Limit Theorem -- A.16 Confidence Intervals -- A.17 Hypothesis Testing -- A.18 Regression -- A.19 Model Selection Criteria -- A.20 Required Packages -- References -- Index.
Additional Edition:
ISBN 1-107-15075-2
Language:
English
Subjects:
Computer Science
,
Economics
URL:
Volltext
(URL des Erstveröffentlichers)
URL:
https://doi.org/10.1017/CBO9781316584460
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