Financial Analytics with R Building a Laptop Laboratory for Data Science 1st edition by Mark Bennett, Dirk Hugen ISBN 1107150752 978-1107150751

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ISBN 10: 1107150752
ISBN 13: 978-1107150751
Author: Mark Bennett, Dirk Hugen 

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.

Financial Analytics with R. Building a Laptop Laboratory for Data Science 1st Table of contents:

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

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

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

A 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

A.11 The 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

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