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Category Archives: Financial

TreeMap of the Market

TreeMap of the Market

SmartMoney has an interactive visual tool on their website called “Map of the Market”. It is an application of the TreeMap concept developed by Ben Shneiderman which I have blogged about before here.

The map lets you watch more than 500 stocks at once, with data updated every 15 minutes. Each colored rectangle in the map represents an individual company. The rectangle’s size reflects the company’s market cap and the color shows price performance. (Green means the stock price is up; red means it’s down. Dark colors are neutral). Move the mouse over a company rectangle and a little panel will pop up with more information.

Map Of The Market (Source: SmartMoney website)

For example, the above map shows the 26 week performance with the Top 5 Losers highlighted (hovered over RIMM). More information from the corresponding Map Instructions page.

This map is also quite similar in concept to the StockTouch iPad app which I covered here. StockTouch displays 900 companies, grouped into 9 sectors. The above Map of the Market is a free service, with an available upgrade to one showing 1000 companies for a subscription fee. While interesting in its own right, however, this is not about the business model of how to monetize the use of such information.

It might be interesting to put together a time-lapse video showing this map for every close of business day throughout one year. Not only would one see the up and down movement by color, but also the gradual shifts in the cumulative size of various sectors due to the area in the tree map.

Another fascinating set of tree map uses is on display at the Gallery of the Hive Group website. Their interactive tree map product HoneyComb has been used in many different industries. The Gallery shows many examples, ranging from sales performance to manufacturing / quality applications to public interest uses such as browsing Olympic Games results or data on Earthquakes. See the following example screenshot (click to interact on the Hive Group website):

TreeMap of Earthquakes (Source: HiveGroup)

While you won’t get the full benefit of seeing the details of all 540 items in one view, you can filter using the panel controls on the right or change the grouping and size and color attributes. This shows for example that the most powerful earthquakes are generally not the most deadly ones and vice versa.

Interacting with these sample tree maps again drives home the fundamental notion that interactive visualizations lead to quicker grasp and better understanding of data sets. This is similar to how walking around and seeing an object from different perspectives gives you a better idea of it’s 3-D structure than seeing it just in one 2-D picture. With multiple ways of interacting it feels almost as if you’re walking inside the data set to see it from multiple angles and perspectives. You have to do it yourself to appreciate the difference it makes.

Lastly, a good article on some of the pitfalls of tree map design with lots of links to good/bad examples comes from the folks at Juice Analytics in their Blog post titled “10 lessons in Treemap Design“.

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Posted by on October 29, 2011 in Financial, Industrial

 

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Share and Inequality of Mobile Phone Revenues and Volumes

Share and Inequality of Mobile Phone Revenues and Volumes

The analyst website Asymco.com visualizes various financial indicators of mobile phone companies in this interactive vendor bubble chart (follow link, select “Vendor Charts”). It covers the following 8 companies: Apple, HTC, LG, Motorola, Nokia, RIM, Samsung, Sony Ericsson. From the “vendor data” tab I downloaded the data and looked at the revenue and volume distributions for the last 4 years.

Revenue Share of Mobile Phones and corresponding Gini Index

Note the sharp reduction in inequality of revenue distribution in the 9/1/08 quarter, when Apple achieved nearly 10x in revenue (and volume) compared to the year before. While the iPhone 1 was introduced a year earlier in 2007, in commercial terms the iPhone 3G started to have strong market impact when introduced in the second half of 2008.

Volume Share of Mobile Phones and Gini Index

Volume inequality is considerably higher (average Gini = 0.61) than Revenue inequality (0.43) due to two dominant shippers (Nokia and Samsung), which continue to lead the peer group in volume. Only recently has the inequality been reduced, i.e. the volumes are distributed more evenly. Apple’s growth in volume share has come at the expense of other players (mainly Motorola and Sony Ericsson).

Volume share is a lagging indicator regarding a company’s innovation and success. It can be dominated for a long time by players who are past their prime and in financial distress (like Nokia). Revenue is more useful to predict a company’s future growth and success. But the real story is told when comparing Profit. Apple’s (Smart Phone) Profit dwarfs that of the other 7 competitors:

Profit Comparison between 8 Mobile Phone Vendors (Source: Asymco.com)

Click on the image to go to Asymco’s interactive chart (requires Flash). The bubble chart display over time is very revealing regarding Apple’s meteoric rise.

 
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Posted by on October 22, 2011 in Financial, Industrial

 

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Market Capitalization Inequality in the Steve Jobs era

The excellent analyst website asymco.com recently published a post titled Visualizing the Steve Jobs era. In it they display an area chart of the relative size of market capitalization of about 15 companies they have tracked for the last 15 years.

Since I had looked at the Gini index of a similar set of companies in an earlier post on Visualizing Inequality I contacted the author Dirk Schmidt. Thankfully he shared the underlying data. From that I calculated the Gini index for every quarter and overlaid a line chart with their area chart.

Share of Market Capitalization Area Chart overlaid with Gini Index

Dirk elaborated in his post and identified three distinct periods in his post:

  • Restructuring of Apple 1997-2000 – Gini remains very high near 0.85 due to MSFT dominance
  • iTunes era 2001-2006 – Gini decreases to ~ 0.55 due to AAPL increase and taking share from other established players
  • Mobile devices era 2007-2011 – Gini increases again to 0.65 due to increasing dominance of AAPL and irrelevance of smaller players

Regardless of the absolute value of the Gini index – note the caveat from the earlier post that it is very sensitive to the number of contributors – the trend in the Gini can be an interesting signal. One company dwarfing every other like a monopoly corresponds to high Gini (here 0.85 due to MSFT dominance). A return to lower Gini values (here down to ~0.5) signals stronger competition with multiple entrants. The recent reversal of the Gini trend (up to 0.65 due to AAPL dominance) is a sign that investors see less choices when it comes to buying shares in those tech companies. Whether that’s a leading indicator for consumers seeing less choices in the marketplace is another question…

 
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Posted by on September 29, 2011 in Financial, Industrial

 

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Inequality, Lorenz-Curves and Gini-Index

In a previous post we looked at inequality of profits and the useful abstraction of the Whale-Curve to analyze Customer Profitability. Here I want to focus on inequality and its measurement and visualization in a broader sense.

A fundamental graphical representation of the form of a distribution is given by the Lorenz-Curve. It plots the cumulative contribution to a quantity over a contributing population. It is often used in economics to depict the inequality of wealth or income distribution in a population.

Lorenz Curve (Source: Wikipedia)

The Lorenz-Curve shows the y% contribution of the bottom x% of the population. The x-axis has the population sorted by increasing contributions; (i.e. the poorest on the left and the richest on the right). Hence the Lorenz-Curve is always at or below the diagonal line, which represents perfect equality. (By contrast, the x-axis of the Whale-Curve sorts by decreasing profit contributions.)

The Gini-Index is defined as G =  A / (A + B) , G = 2A  or G = 1 – 2B

Since each axis is normalized to 100%, A + B = 1/2 and all of the above are equivalent. Perfect equality means G = 0. Maximum inequality G = 1 is achieved if one member of the population contributes everything and everybody else contributes nothing.

An interesting interactive graph demonstrating Lorenz-Curves and corresponding Gini-Index values can be found here at the Wolfram Demonstration project.

The GINI Index is often used to indicate the income or wealth inequality of countries. The corresponding values of the GINI index are typically between 0.25 and 0.35 for modern, developed countries and higher in developing countries such as 0.45 – 0.55 in Latin America and up to 0.70 in some African countries with extreme income inequality.

GINI index of world countries in 2009 (Source: Wikipedia)

Graphically, many different shapes of the Lorenz-Curve can lead to the same areas A and B, and hence many different distributions of inequality can lead to the same GINI index. How can one determine the GINI index? If one has all the data, one can numerically determine the value from all the differences for each member of the population. An example of that is shown here to determine the inequality of market share for 10 trucking companies.
Another approach is to model the actual distribution using a formal statistical distribution with known properties such as Pareto, Log-Normal or Weibull. With a given formal distribution one can often calculate the GINI index analytically. See for example the paper by Michel Lubrano on “The Econometrics of Inequality and Poverty“. In another example, Eric Kemp-Benedict shows in this paper on “Income Distribution and Poverty” how well various statistical distributions match the actually measured data. It is commonly held that at the high end of the income the Pareto distribution is a good model (with its inherent Power law characteristic), while overall the Log-Normal is the best approximation.

After studying several of these papers I started to ask myself: If x% of the population contribute y% to the total, what’s the corresponding GINI index? For example, for the famous “80-20 rule” with 20% of the population contributing 80% of the result, what’s the GINI index for the 80-20 rule?

To answer this question I created a simple model of inequality based on a Pareto distribution. Its shape parameter controls the curvature of the distribution, which in turn determines the GINI index. The latter is visualized as color-coded bands using a 2D contour plot in the following graphic:

GINI index contour plot based on Pareto distribution model

The sample data point “A” corresponds to the 80-20 rule, which leads to a GINI index of about 0.75 (strongly unequal distribution). Data point “B” is an example of an extremely unequal distribution, namely US political donations (data from 2010 according to a statistic from the Center of Responsive Politics recently cited by CNNMoney):

“…a relatively small number of Americans do wield an outsized influence when it comes to political donations. Only 0.04% of Americans give in excess of $200 to candidates, parties or political action committees — and those donations account for 64.8% of all contributions”

0.04% contribute 64.8% of the total! Here is another way of describing this: If you had 2500 donors, the top donor gives twice as much as the other 2499 combined. This extreme amount of inequality corresponds to a GINI index of 0.89 (needless to say that this does not seem like a very democratic process…)

As for US income I created a separate graphic with data points from the high end of the income spectrum (where the underlying Pareto distribution model is a good fit): The top 1% (who earn 18% of all income), top 0.1% (8%), and top 0.01% (3.5%).

GINI Index Contour Plot with high end US Income distribution data points

These 3 data points are taken from Timothy Noah’s “The United States of Inequality“, a 10-part article series on Slate, which in turn is based on data and research from 2008 by Emmanuel Saez and visualizations by Catherine Mulbrandon of VisualizingEconomics.com. This shows the 2008 US income inequality has a GINI Index of approximately 0.46, which is unusually high for a developed country. Income inequality has grown in the US since around 1970, and the above article series analyzes potential factors contributing to that – but that’s a topic for another post. In the spirit of visualizing data to create insight, I’ll just leave you with this link to the corresponding 10-part visual guide to inequality:

Postscript: In April 2012 I came across a nice interactive visualization on the DataBlick website created by Anya A’Hearn using Tableau. It shows the trends of US income inequality over the last 90 years with 7 different categories (Top x% shares) and makes a good showcase for the illustrative power of interactive graphics.

 
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Posted by on September 2, 2011 in Financial, Industrial, Scientific, Socioeconomic

 

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Customer Profitability

Inequality is often at the root of structure and contrasts. Exposing inequality can often lead to insight. For example, take the well-known Pareto principle, which states that roughly 80% of the effects come from 20% of the causes (hence also referred to as the 80-20 rule).

From the above Wikipedia page on the Pareto principle, chapter on business:

The distribution shows up in several different aspects relevant to entrepreneurs and business managers. For example:
80% of your profits come from 20% of your customers
80% of your complaints come from 20% of your customers
80% of your profits come from 20% of the time you spend
80% of your sales come from 20% of your products
80% of your sales are made by 20% of your sales staff
Therefore, many businesses have an easy access to dramatic improvements in profitability by focusing on the most effective areas and eliminating, ignoring, automating, delegating or re-training the rest, as appropriate.

Visualization can be a powerful instrument for such analysis. For customer profitability, a graphical representation of this inequality is often used as a starting point for analysis. A commonly used visualization is the so-called Whale-Curve. I created a short, 4 min video recording of a dynamic Whale-Curve Demonstration:

In case you’re curious, the above demonstration uses an underlying model I created in Mathematica. You can dynamically interact with it yourself using the free CDF (Computable Document Format) Player:

I have provided it as a contribution to the Wolfram Demonstration project, so you can download it, and even look at the source code if you are a Mathematica user.

If you are interested in applying customer profitability analysis to your business, you may want to consider the company RapidBusinessModeling, which has an elaborate analysis approach starting with such Whale-Curves.

The underlying notion of Inequality is a fundamental concept. We will look at it in other contexts in a later post.

 
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Posted by on August 18, 2011 in Financial, Industrial, Scientific

 

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StockTouch – interactive stock monitoring tool

Financial markets have always been an area of rapid innovation, with the evolution of graphical stock information being no exception. It looks like the famous stock-ticker could be replaced with the stock-toucher. A new iPad application by Visible Market Inc. provides an excellent example of the use of highly aggregated color graphics and touch-interaction. Here is the main UI showing 9 sectors and the 100 largest stocks (by market capitalization) in each sector:

Market Overview by Sector, 100 largest market cap companies per sector, color-coded heat-map of volume changes.

You can zoom in (expand- or tap-gesture), zoom out (pinch-gesture) to navigate between levels (market, sector, company) or use the auto-complete search-box for a list of company names matching the search string.

The 10*10 items can be organized either alphabetically or by market cap. Display is of Price or Volume changes between current values compared to a variable time-period (time-frame slider with values {1D, 1W, 1M, 3M, 6M, 1Y, 5Y}) at the company level and averages at the sector level.

From their website:

“Our vision for StockTouch is that it represents the first of a new genre of apps that look at the financial markets in new, powerful and useful ways. It is our belief that the act of touching and diving into data will change the way users engage with this data, and consequently translate it into information and knowledge.”

Price changes of 100 largest market cap companies by sector, Green-Red color-coded heat-map. Note market trends for three timeframes: Last month (green = advance), last week (mixed), last day (red = retreat).

The use of colors is particularly useful for Price changes: There is a heat map from light green (strong positive change) via darker tones (gray = neutral, no change) to light reds (strong negative change). This shows at a glance how the entire sector or market is doing. In the above example the last month saw a broad advance (majority of companies across all sectors in green); the last week more of a mixed bag, and the last day a broad retreat across the entire market (almost all red). Think about how much information is aggregated into this dashboard! 900 companies, grouped by sector, sorted by market cap, color-coded for price/volume change. No wonder they post a quote on their website:

“StockTouch tells you more in five seconds than you would learn reading financial news all day.”

 
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Posted by on July 11, 2011 in Financial

 

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Branded Data Visualizations: LUMAscapes

In this article on the Spotfire Blog Amanda Brandon recently posed the question: Can Data Visualizations Change the Business Decision Game? The article recounts the creation of data visualizations by Terrence Kawaja to show the complex online advertising space with over 1200 companies involved in a $10b annual business. The graphics show the flow of information and involved service providers from advertiser to consumer. It is said that the original chart published in 2009 became a “go-to tool for advertising executives”.

Advertising Technology Landscape by Terrence Kawaja (2009)

Kawaja of investment firm LUMA Partners refined this approach and created six such landscapes called LUMAscapes for display, video, search, mobile, commerce, and social online advertising.

Search online advertising technology landscape (Source: Lumascapes from lumapartners.com)

The Spotfire Blog conlcudes with four takeaways for business analysts from the approach to use such visualizations:

Data visualizations are the ultimate content marketing. The simplification of complex data in a visually appealing format can take your information and brand viral. Giving away data on the major players and how they work together to drive an industry set the stage for authority and respect. …

Data visualizations can become an industry standard. Simply look at how Kawaja was able to help ad executives navigate the digital ad space.

Data visualizations can become a game-changer. Kawaja is branding these tools and using the graphics as a tool in generating business for his investment firm.

Data visualizations can be central to business decision-making. According to the WSJ, these new visualizations could enhance discussions at the Digital Media Summit, a meeting of top execs from the investment and Internet advertising space.

A picture can be worth more than a thousand words…

 
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Posted by on June 23, 2011 in Financial, Industrial

 

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