9. Fundamental Analysis
by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs,
R: Data Analysis and Visualization
- R: Data Analysis and Visualization
- Table of Contents
- R: Data Analysis and Visualization
- I. Module 1: Data Analysis with R
- 1. RefresheR
- 2. The Shape of Data
- 3. Describing Relationships
- 4. Probability
- 5. Using Data to Reason About the World
- 6. Testing Hypotheses
- 7. Bayesian Methods
- 8. Predicting Continuous Variables
- 9. Predicting Categorical Variables
- 10. Sources of Data
- 11. Dealing with Messy Data
- 12. Dealing with Large Data
- 13. Reproducibility and Best Practices
- II. Module 2: R Graphs
- 1. R Graphics
- 2. Basic Graph Functions
- Introduction
- Creating basic scatter plots
- Creating line graphs
- Creating bar charts
- Creating histograms and density plots
- Creating box plots
- Adjusting x and y axes' limits
- Creating heat maps
- Creating pairs plots
- Creating multiple plot matrix layouts
- Adding and formatting legends
- Creating graphs with maps
- Saving and exporting graphs
- 3. Beyond the Basics – Adjusting Key Parameters
- Introduction
- Setting colors of points, lines, and bars
- Setting plot background colors
- Setting colors for text elements – axis annotations, labels, plot titles, and legends
- Choosing color combinations and palettes
- Setting fonts for annotations and titles
- Choosing plotting point symbol styles and sizes
- Choosing line styles and width
- Choosing box styles
- Adjusting axis annotations and tick marks
- Formatting log axes
- Setting graph margins and dimensions
- 4. Creating Scatter Plots
- Introduction
- Grouping data points within a scatter plot
- Highlighting grouped data points by size and symbol type
- Labeling data points
- Correlation matrix using pairs plots
- Adding error bars
- Using jitter to distinguish closely packed data points
- Adding linear model lines
- Adding nonlinear model curves
- Adding nonparametric model curves with lowess
- Creating three-dimensional scatter plots
- Creating Quantile-Quantile plots
- Displaying the data density on axes
- Creating scatter plots with a smoothed density representation
- 5. Creating Line Graphs and Time Series Charts
- Introduction
- Adding customized legends for multiple-line graphs
- Using margin labels instead of legends for multiple-line graphs
- Adding horizontal and vertical grid lines
- Adding marker lines at specific x and y values using abline
- Creating sparklines
- Plotting functions of a variable in a dataset
- Formatting time series data for plotting
- Plotting the date or time variable on the x axis
- Annotating axis labels in different human-readable time formats
- Adding vertical markers to indicate specific time events
- Plotting data with varying time-averaging periods
- Creating stock charts
- 6. Creating Bar, Dot, and Pie Charts
- Introduction
- Creating bar charts with more than one factor variable
- Creating stacked bar charts
- Adjusting the orientation of bars – horizontal and vertical
- Adjusting bar widths, spacing, colors, and borders
- Displaying values on top of or next to the bars
- Placing labels inside bars
- Creating bar charts with vertical error bars
- Modifying dot charts by grouping variables
- Making better, readable pie charts with clockwise-ordered slices
- Labeling a pie chart with percentage values for each slice
- Adding a legend to a pie chart
- 7. Creating Histograms
- Introduction
- Visualizing distributions as count frequencies or probability densities
- Setting the bin size and the number of breaks
- Adjusting histogram styles – bar colors, borders, and axes
- Overlaying a density line over a histogram
- Multiple histograms along the diagonal of a pairs plot
- Histograms in the margins of line and scatter plots
- 8. Box and Whisker Plots
- Introduction
- Creating box plots with narrow boxes for a small number of variables
- Grouping over a variable
- Varying box widths by the number of observations
- Creating box plots with notches
- Including or excluding outliers
- Creating horizontal box plots
- Changing the box styling
- Adjusting the extent of plot whiskers outside the box
- Showing the number of observations
- Splitting a variable at arbitrary values into subsets
- 9. Creating Heat Maps and Contour Plots
- 10. Creating Maps
- 11. Data Visualization Using Lattice
- Introduction
- Creating bar charts
- Creating stacked bar charts
- Creating bar charts to visualize cross-tabulation
- Creating a conditional histogram
- Visualizing distributions through a kernel-density plot
- Creating a normal Q-Q plot
- Visualizing an empirical Cumulative Distribution Function
- Creating a boxplot
- Creating a conditional scatter plot
- 12. Data Visualization Using ggplot2
- 13. Inspecting Large Datasets
- 14. Three-dimensional Visualizations
- 15. Finalizing Graphs for Publications and Presentations
- Introduction
- Exporting graphs in high-resolution image formats – PNG, JPEG, BMP, and TIFF
- Exporting graphs in vector formats – SVG, PDF, and PS
- Adding mathematical and scientific notations (typesetting)
- Adding text descriptions to graphs
- Using graph templates
- Choosing font families and styles under Windows, Mac OS X, and Linux
- Choosing fonts for PostScripts and PDFs
- III. Module 3: Learning Data Mining with R
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- An overview of associations and patterns
- Market basket analysis
- Hybrid association rules mining
- Mining sequence dataset
- The R implementation
- High-performance algorithms
- 3. Classification
- Classification
- Generic decision tree induction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Identify spam e-mail and Naïve Bayes classification
- Rule-based classification of player types in computer games and rule-based classification
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- Customer categorization analysis of e-commerce and DBSCAN
- Clustering web pages and OPTICS
- Visitor analysis in the browser cache and DENCLUE
- Recommendation system and STING
- Web sentiment analysis and CLIQUE
- Opinion mining and WAVE clustering
- User search intent and the EM algorithm
- Customer purchase data analysis and clustering high-dimensional data
- SNS and clustering graph and network data
- 7. Outlier Detection
- Credit card fraud detection and statistical methods
- Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
- Intrusion detection and density-based methods
- Intrusion detection and clustering-based methods
- Monitoring the performance of the web server and classification-based methods
- Detecting novelty in text, topic detection, and mining contextual outliers
- Collective outliers on spatial data
- Outlier detection in high-dimensional data
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
- IV. Module 4: Mastering R for Quantitative Finance
- 1. Time Series Analysis
- 2. Factor Models
- 3. Forecasting Volume
- 4. Big Data – Advanced Analytics
- 5. FX Derivatives
- 6. Interest Rate Derivatives and Models
- 7. Exotic Options
- A general pricing approach
- The role of dynamic hedging
- How R can help a lot
- A glance beyond vanillas
- Greeks – the link back to the vanilla world
- Pricing the Double-no-touch option
- Another way to price the Double-no-touch option
- The life of a Double-no-touch option – a simulation
- Exotic options embedded in structured products
- References
- 8. Optimal Hedging
- 9. Fundamental Analysis
- 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios
- 11. Asset and Liability Management
- 12. Capital Adequacy
- 13. Systemic Risks
- V. Module 5: Machine Learning with R module
- 1. Introducing Machine Learning
- 2. Managing and Understanding Data
- R data structures
- Managing data with R
- Exploring and understanding data
- Exploring the structure of data
- Exploring numeric variables
- Measuring the central tendency – mean and median
- Measuring spread – quartiles and the five-number summary
- Visualizing numeric variables – boxplots
- Visualizing numeric variables – histograms
- Understanding numeric data – uniform and normal distributions
- Measuring spread – variance and standard deviation
- Exploring categorical variables
- Exploring relationships between variables
- 3. Lazy Learning – Classification Using Nearest Neighbors
- 4. Probabilistic Learning – Classification Using Naive Bayes
- 5. Divide and Conquer – Classification Using Decision Trees and Rules
- Understanding decision trees
- Example – identifying risky bank loans using C5.0 decision trees
- Understanding classification rules
- Example – identifying poisonous mushrooms with rule learners
- 6. Forecasting Numeric Data – Regression Methods
- Understanding regression
- Example – predicting medical expenses using linear regression
- Understanding regression trees and model trees
- Example – estimating the quality of wines with regression trees and model trees
- 7. Black Box Methods – Neural Networks and Support Vector Machines
- 8. Finding Patterns – Market Basket Analysis Using Association Rules
- 9. Finding Groups of Data – Clustering with k-means
- 10. Evaluating Model Performance
- 11. Improving Model Performance
- 12. Specialized Machine Learning Topics
- Working with proprietary files and databases
- Working with online data and services
- Working with domain-specific data
- Improving the performance of R
- Managing very large datasets
- Learning faster with parallel computing
- GPU computing
- Deploying optimized learning algorithms
- Building bigger regression models with biglm
- Growing bigger and faster random forests with bigrf
- Training and evaluating models in parallel with caret
- A. Reflect and Test Yourself Answers
- Module 1: Data Analysis with R
- Chapter 1: RefresheR
- Chapter 2: The Shape of Data
- Chapter 3: Describing Relationships
- Chapter 4: Probability
- Chapter 5: Using Data to Reason About the World
- Chapter 6: Testing Hypotheses
- Chapter 7: Bayesian Methods
- Chapter 8: Predicting Continuous Variables
- Chapter 9: Predicting Categorical Variables
- Chapter 10: Sources of Data
- Chapter 11: Dealing with Messy Data
- Chapter 12: Dealing with Large Data
- Module 2: R Graphs
- Chapter 1: R Graphics
- Chapter 2: Basic Graph Functions
- Chapter 3: Beyond the Basics – Adjusting Key Parameters
- Chapter 4: Creating Scatter Plots
- Chapter 5: Creating Line Graphs and Time Series Charts
- Chapter 6: Creating Bar, Dot, and Pie Charts
- Chapter 7: Creating Histograms
- Chapter 8: Box and Whisker Plots
- Chapter 9: Creating Heat Maps and Contour Plots
- Module 4: Mastering R for Quantitative Finance
- Module 5: Machine Learning with R
- Chapter 1: Introducing Machine Learning
- Chapter 2: Managing and Understanding Data
- Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
- Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
- Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
- Chapter 6: Forecasting Numeric Data – Regression Methods
- Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
- Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
- Module 1: Data Analysis with R
- B. Bibliography
- Index
Now that the global financial crisis seems to come to an end, most of the investors are moving back to equity markets. By doing so, you face the problem of choosing the stocks that will outperform the other shares during the upcoming time period. To find the right investment asset to purchase, you have two basic options. On one hand, you may rely on any trends and patterns in the development of the historical prices. When developing an investment recommendation based on trends and patterns, you do a technical analysis. On the other hand, you may try to figure out which firms will exceed the market by analyzing their financial performance, strategic position, or future plans. This is called fundamental analysis.
This chapter provides you an aid on how to use R to identify successful fundamental trading strategies for equity investments. We will start by applying basic statistical methods and move on to advanced and more complex ones while we cover how to translate your fundamental investment ideas into statistically testable hypotheses.
When looking for possible investment assets, a wide range of choices is offered to you by the market. You may pick bonds, pieces of art, real estate, currencies, commodities, derivatives, or probably, the most well-known asset class, equity. Equities represent ownership right over a certain part of the given firm (issuer).
However, which shares shall we buy? When should we purchase and sell them? These decisions are of key importance as they will determine the return on your portfolio. There are two different views out there on these problems.
Technical analysis is built on historical price developments and believes that certain patterns may be identified that help predict the future movements of the quotes. Fundamental analysis, on the contrary, focuses on the firm and the value of the ownership right itself rather than on the market price of it. Here, we believe that sooner or later, market price has to reflect the fair value of the share that can be calculated from the future cash flows we collect when owning it, just like in the case of any other kind of investments.
While technical analysis focuses on how investors' behavior might push prices in the future based on historical patterns, fundamental analysis identifies the trends that prices should follow due to the predicted future performance of the firm. So, when performing fundamental analysis, we have to recall our corporate finance and accounting knowledge.
Even when checking for the fair price of just one given share, we may spend several days on modeling future performance and estimating sales growth, expenses, investments, changes in financing strategy, and cost of capital to get a valid discount rate for our cash-flow prediction. When developing a trading strategy, we need to review several thousands of potential investments, so there is no chance we could do such an in-depth analysis. Even trying it may be tricky. If you create large spreadsheet models for all equities, by the time you finish, your assumptions for the first firm could be outdated, and you have to restart the process without even considering your results from the first version of the model. So, instead of really predicting future financial statements, we have to build on historical experience to identify good investment patterns. We will try to connect previous fundamental ratios to historical price developments and expect that these connections will also hold in the future.
This is the key to understand that we do not want to find good companies to invest in; we rather have to find shares that are very likely to be mispriced. So, we want to find undervalued stocks to buy, or if shorting is allowed on the market, we want to find overvalued ones to sell. For the rest of this chapter, we will focus only on the upward potential, but you may use exactly the same techniques to identify shares to sell that have a huge downward potential. Finding the fundamental characteristics of firms for which we have seen the share price increase during the last 12 months may help us identify good investments for the next year based on the current financial statements.
So, when building a fundamental equity strategy, we need to follow these steps:
- Collect financial statement data for possible equity investments.
- Calculate fundamental ratios to standardize data.
- Identify connections between ratios and future price development.
- Follow the testing strategy that is, calculate results on another set of possible equities for the same period and/or same set of shares for a different period of time.
It is not enough to perform these steps once in a lifetime. Applying a strategy that would have performed well during the last year(s) assumes that there were no radical changes, neither within the firm nor in the economy that the company is active in. As markets tend to change, firm have to do so too. This means that what was the best practice last year may be just fine or average now. As a result of this, even if our investment strategy worked well for several years, we may see a gradual or even radical change in its effectiveness. So, a regular recheck and update is vital.
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