RSS

Category Archives: Financial

On Stock Market Timing

14 Nov
On Stock Market Timing

Note: A few months after writing this post, I realized that I made a mistake: I was Fooled by Randomness. The analysis below is still interesting and parts of it are still correct, but the main conclusion was wrong.

Tracking the Market

There is widespread consensus that investing money in the stock market, despite its ever-present risks of losses, over the long run beats many other forms of investments like bonds or treasuries. To mitigate the risk of one or a few individual stocks falling, the general advice is to diversify your portfolio and spread investment across many different companies and sectors. The quintessential diversification is to invest in index funds which track a large basket of securities such as the S&P 500 or the Dow Jones Industrial Average (30 stocks). Investing in index funds is a form of tracking the market.

Historical data shows annual returns in the order of 11.8% for the S&P 500 over the last 60 years. However, there are big swings as can be seen in the following chart (Source article on Investopedia):

Moreover, there seems to be a consensus that one of the best strategies is simply to “Buy & Hold”, i.e. not to attempt any forms of timing the market. It certainly is easy to implement and there are plenty of data sources to track historical performance. If you have a long-term time horizon (i.e. many years or even decades), it can be a very good approach to just set & forget. But from the historical data, can one derive strategies which systematically do better than Buy & Hold? Let’s first look at the conventional wisdom (which says you can’t time the market), then let’s look at the data (which suggests that you can).

Timing the Market

Nobody can predict the market, certainly not on a daily basis. Since the general trend is upwards, the main argument against timing is the risk of missing out on the best days (which can not be predicted and can happen at any time). For example, there was a recent article in VisualCapitalist with this line of reasoning, expressing visually how much one would fall short over time when missing the best 10, 20, or more days.

This strikes me as a form of cherry picking: “If you missed out on the best days, you would fall behind.” The inverse of this argument is also true: “If you missed out on the worst days, you would surge ahead.”

Let’s look at some data. Specifically, I downloaded the publicly available historical data of the Dow Jones Industrial Average (DJIA) with daily open close levels from 1980-2023. Then I did a similar analysis for the hypothetical returns of the DJIA relative to “missing out” on the best 10 vs. the worst 10 days:

One observation is that the extremes are clustered around just a few events. 14 (of 20) extremes happened in the days around just two events: The Oct-Nov 2008 recession (Lehman Brothers bankrupcty) and the onset of the Covid-19 pandemic (Mar-2020). These two periods mark very high volatility, where you have both some of the worst and some of the best days in quick succession.

We can now look at the cumulative returns of the both scenarios of missing the 10 best and worst days during that 44 year period 1980 – 2023 (ending 1-Nov-2023):

(This analysis ignores transaction cost of buying and selling, as well as tax impact based on holding period. It is not meant to be investment advice, just an analysis of the oft-cited consensus that “Buy & Hold is the best strategy” and “market timing is impossible”.)

While it’s true that Buy & Hold is better than the hypothetical “Best 10 days missed” approach (with a 2.1x higher return), it is also true that Buy & Hold is worse than the “Worst 10 days missed” approach (with a 0.42x return). In both cases, there is more than a factor of 2 difference to the Buy & Hold reference strategy! This is a stark demonstration of the Pareto principle that very few events disproportionately affect the outcome. Here the missing of the 10 best (worst) trading days in a period of more than 11,000 trading days (44 years) changes the outcome by more than a factor of two! In fact, just sitting out the single-worst day of 10/19/1987 (the infamous “Black Monday” crash of 1987, single day loss of 22.6%) would result in a 29.2% better return (1 / (1 – 0.226) = 1.292)! A single one in 11,000+ events accounts for a near 30% difference in outcome!

Of course, one cannot predict the best or worst days, so the above hypothetical analysis cannot be converted into an actionable strategy. The general trend is upwards, and there are more up than down days. As a result, you generally want to be in the market more than out of the market. Hence it is intuitive that Buy & Hold is a good strategy. And when there are continuously incoming funds, buying quickly is far better than waiting on the side lines – as this good market timing study by Schwab comparing five different strategies is showing in detail. But all this certainly does not imply that Buy & Hold is the best strategy over the long run.

Stock market performance by Month

There have been many analyses of how the stock market is doing, by day of week, by month, quarter, year, presidential election or incumbent party, interest rates etc. While nobody can predict the up or down movement on a daily basis, one gets the sense that there are cyclical factors influencing the markets. One of the obvious cycles is the time of year. It affects not only external factors like the weather and with it consumer behavior, but also the planning and budgeting cycles of most companies in some form or another. Not every year will be the same, of course, but certain patterns may emerge over the long run (decades).

A good granularity into this is provided by looking at the stock market performance by month. One article on stockanalysis.com describes the average monthly stock return based on the S&P 500 from 1980- 2018:

Based on the DJIA data since 1980 I did a similar analysis for the monthly returns (1980-2023) as follows:

The details vary slightly, but the results are quite similar. A few things stand out:

  • Most months yield positive average returns.
  • April and November stand out as the two months with highest returns.
  • August and September are the only ones with negative returns.

Note these are averages over ~40 years, and there are some counter-examples such as years with bad April or November returns as well as years with good August or September returns. (Note also that the Compound Aggregate Growth Rate CAGR is a bit different over long series than the arithmetic average, but this distinction is barely relevant for the modeling here.)

Just like one can’t predict the market day by day, one can’t predict what the return of the next month in any given year is going to be. But this raises the question as to whether one might be able to exploit seasonal changes for a higher long-term return. An analogy might be the weather: While you can’t guarantee that any given day in summer is warm and sunny, there is a higher probability of that than in the other seasons. If you were to bet on it with your annual vacation planning, over the long run you should have favorable results.

Investing with a Summer Break

While several articles clearly show the persistent differences in monthly returns, I was a bit surprised not to have seen that translated into some strategies to improve on Buy & Hold. There also seems to be some sloppy reasoning, for example from this article on monthly returns on MoneyChimp:

“From the results, it looks like some months really are significantly better than average. November through January is a particularly strong stretch; and September is the “danger” month, with an overall negative return. Surprisingly, October shows positive returns on average, although October 1987 and 2008 were pretty hard to forget.
Note that December has been better than January, which contradicts two popular myths: the December Selloff, and the January Effect.
But also notice that there are lots of exceptions to the pattern. There have been bad Januaries, and great Septembers. And of course the biggest trend of all is that the market goes up over time. So maybe the lesson here is the usual one, that long-term buy and hold is the winning strategy.”

The first sentence highlights a signal (some months are better than average, some are worse),
yet the last sentence all but ignores that signal (safe to stick with “the usual” lesson).

To me, the obvious strategy would be one of annual selling at the end of July, sitting out the market in August and September, then buying at the beginning of October and holding until next year. Let’s call this the “Summer Break” strategy. It would be actionable, easy to implement with only one buy/sell cycle per year and only calendar dates as deciding factors.

Based on the DJIA data I simulated the Summer Break strategy and plotted the returns of an initial $10,000 investment at the beginning of 1980 until 1-Nov-2023 for Summer Break (orange) and Buy & Hold (blue):

While the Buy & Hold strategy yields $396,288 the Summer Break strategy yields $741,185 which is 187% or nearly 2x of the reference (Buy & Hold).

Of course, there are some years where Buy & Hold performs better than Summer Break.

  • In fact, the first 10 years Buy & Hold is slightly better (orange line often below blue line).
  • Only in 1990 does Summer Break pull and stay ahead, with 1996 almost leveling both strategies.
  • A select few years contribute the most to the difference, such as 2001 and 2002, when Summer Break leaps from 1.2x to 1.6x of Buy & Hold (see data callouts below).

The ratio and changes therein can be visualized as follows:

Criticism and Caveats

Critics might say it would take a long time to see the impact of this strategy. Past performance is not a guarantee of future results. And timing matters, i.e. if you miss the few years where there is a large difference you may not see the gains of this strategy for a long time. For example, if you started the Summer Break strategy right after the strong gains in 2001 and 2002, say on 1/1/2003, you would still be slightly behind the reference Buy & Hold strategy 19 years later. (Any 20-year or longer window, however, results in a higher return than Buy & Hold.)

These criticisims are all valid. However, they apply equally to the Buy & Hold strategy itself as well. For instance, historically, if you had bad timing on Buy & Hold, you could still have nominal losses after many years – for example it took 25 years to recover the previous high after the 1929 recession in 1954, or 18 years from 1964 to 1982. This is why above all investing in the stock market is considered to be more attractive the longer the time horizon, best measured in decades. (And why investing purely in stocks close to retirement may be too risky…)

There are of course constraints such as one not being able to achieve the exact sell price of the closing market, the overhead of trading cost, dividend payments or reinvestment, the tax impact of capital gains (especially when held for < 1 year), the inflation, etc. These factors are all ignored for this analysis. While they don’t change the fundamental conclusions here, they would certainly impact the specific returns achieved when applied in real markets.

Closing note: I thought this relatively straightforward analysis would be a good candidate for the new Advanced Data Analytics (ADA) plug-in of ChatGPT, which has received a lot of attention of late. So I started uploading the original .csv DJIA historical data file. ADA interpreted and described the data fine. However, when I aksed it to implement the Summer Break strategy (or variants thereof), it generated some Python code and produced charts, but they were incorrect. It frequently got thrown off by boundary conditions (like error due to not finding the last trading day before 1/1/1980 in the data). It did not properly aggregate the returns, and repeatedly set back to the original value at 100%. When I pointed this out, it said it would fix the code, but before long it just generated emtpy charts. I got the distinct impression of talking to an entity which understands some of the topic, but does not have a fully formed semantic model, and hence starts hallucinating some code together. I plan to dive deeper into this as a separate post, but found this a bit anti-climactic given all the hype out there.

 
1 Comment

Posted by on November 14, 2023 in Financial

 

Sankey diagrams for Income Statements

27 Oct
Sankey diagrams for Income Statements

We have posted about Sankey Diagrams some 11 years ago on this Blog. It is a remarkably useful visualization for the flow of continuous quantities like money, electricity, raw materials through systems. There have been other blogs dedicated entirely to Sankey diagrams, such as https://www.sankey-diagrams.com where the author wrote 660 posts during 2007 – 2021.

Yet, sadly, this type of diagram has still not made it into Excel or other mainstream chart libraries.

One interesting use of it has been popularized by AppEconomyInsight for Income Statements to visualize how companies make money. Here is a recent example for Meta (FaceBook):

It shows the size and diversity (or lack thereof) of a business revenue and profitability. It is so much more intuitive than the pages of numbers in the public income statements! One of the best examples of Visualign’s motto: Visualize Data. Generate Insight!

The website now has many dozens of charts depicting large publicly traded companies and the information from their quarterly income statements. The Sankey diagram has even become the website logo

We have also looked at four large tech companies, starting with this post back in 2012 on Side by Side: Apple, Microsoft, Google, Amazon. We had created a composite back then for these four companies to show just the sources of revenue (akin to the left side of the Sankey diagrams for the inflow to the central revenue bar):

Here is a similar composite of the Sankey diagrams from AppEconomyInsight for those four companies:

(Source: appeconomyinsight.com; Note: All recent but different quarters as some are not available or behind paywall.)

Such a side-by-side comparison reveals many interesting insights:

  • All companies have massive revenues in the order of $50-100B per quarter, with biggest by Amazon.
  • Software company Microsoft has the largest operating margin (40%) and net profit margin (36%).
  • Retail company Amazon has by far the smallest net profit margin (5%).
    • Its AWS division accounts for 16% of revenue, but 70% of operating profit!
  • Advertising companies Meta and Google are trying to diversify sources of revenue.
    • Google has been able to diversify via its Google Play and Cloud segments contributing nearly 22% of revenue and showing much stronger growth.
    • Meta’s Reality Labs (RL) revenues are paltry (1% of overall revenue), and the RL losses reduced the other operating profits by more than a quarter (28%).
  • Hardware company Apple is still dependent on its dominant iPhone for nearly half of all revenue (49%), but its fast growing Services segment already generates more than a quarter (26%) of that.

From a profitability perspective, these diagrams also nicely show what business schools will tell you:

  • Making money from retail is hard.
  • Making money from software and services is easier.
  • Making money from hardware can be very profitable – if there is killer demand. One great example of that is the Apple iPhone demand over the last 15 years. Another great recent example is the demand for Nvidia GPU chips for data centers due to the generative AI breakthroughs of ChatGPT and similar models. See the recent breakdown of Nvidia with a staggering 46% net profit margin:

The above comes from an article aptly called the “Nvidia: The iPhone moment of AI”. Coincidentally, in the last article on the four biggest tech companies back in 2018, I speculated which would be the next tech company to reach the trillion dollar valuation. It was not Meta, but first Tesla and then Nvidia.

Tesla’s income statement Sankey diagram is also interesting to see:

It would be great to have a generalized template to convert traditional income statements into such charts. Even better would be an interactive version of the Sankey diagram where you could:

  • Highlight or Filter one of the revenue branches to see its profitability
  • Highlight or Filter one of the expense or profit branches to see its contributors

Of course such interactivity would require a more detailed underlying model of accounting flows, which may or may not be disclosed by the company.

 
Leave a comment

Posted by on October 27, 2023 in Financial, Industrial

 

6 year growth: Apple, Microsoft, Google, Amazon

03 Jun
6 year growth: Apple, Microsoft, Google, Amazon

Back in 2012 we did a side-by-side comparison of the four largest technology companies and their quarterly growth and other financial metrics. A year later in 2013 the four companies were again compared using Wolfram Alpha to generate lots of charts and tables by simply typing in “Google vs. Amazon vs. Apple vs. Microsoft” in the search bar.

Today, six years later, this same exercise reveals very strong growth. The big companies are getting (much) bigger. Here are some comparisons:

QuaerterlyRevenue20122018

With the underlying numbers:

NumberComparison

(Market cap as of market close on 2/3/2012 and 6/1/2018; sources the respective 10-Q filings; scales are the same for left and right charts. Google refers to its parent Alphabet, Inc.)

When taken together, over the last six years the four companies have grown as follows:

  • Revenue more than doubled (+112%, 13.3% annualized)
  • Income grew only moderately (+31%, 4.6% annualized)
  • Market cap tripled (+202%, 20.2% annualized)
  • Employees almost quadrupled (+276%, 24.7% annualized)

Of course, the $ numbers need to be inflation-adjusted, but US inflation rates were around 2% or less between 2012-2018, which amounts to about 10% over that period of time. Hence inflation is not qualitatively influencing this analysis or comparison.

Amazon grew the most, with its market cap growing more than 9 fold (+833%) and its employees more than 8-fold (+763%) to more than half a million people. Back in 2012, all four combined just exceeded $1 trillion in market cap; this has swollen to $3.3 trillion.

These are the biggest nominal market cap values in history. When comparing them to the GDP of countries, they would each rank in the Top-20. According to 2018 GDP projections by the International Monetary Fund, Apple would rank 18 behind the Netherlands (17th, $945,327 million), the other three companies would rank 19 behind Turkey (18th, $909,855 million). The market cap of these four companies combined would rank 5th behind Germany (4th, $4,211,635 million). In other words, only the top four countries by GDP (United States, China, Japan and Germany) are bigger than the market cap of Apple, Microsoft, Google and Amazon combined.

These corporations are transnational entities with a global customer base. Arguably, their size and economic power has grown so rapidly that the legal, tax and trade frameworks governing their operations can’t always keep up. Similarly, when companies get so large and rich, they can buy startups and entice talent to join them at a rate newer entrants or even governments cannot match. Apple’s cash position at the end of Q3’2017 was roughly $270 billion (source Asymco). It is not obvious that consumers always benefit from companies growing that large (see monopoly and anti-trust laws). Thankfully, the current technology oligopoly leads to healthy competition.

As before, there remain significant differences in the revenue segmentation across these four companies:

RevenueBySegment

Arguably, Microsoft has the broadest diversification and hence the most stability against disruptive innovation. Its three segments are not only roughly equal in size, but in turn contain a variety of different sub-segments. Productivity and Business Processes includes Office, Exchange, Skype, LinkedIn, Dynamics; Intelligent cloud includes Windows Server, SQL Server, Azure and Consulting Services; Personal Computing includes Windows, Devices, XBox and Search/Advertising. Microsoft’s Azure cloud services have closed the gap to Amazon’s AWS business and recently overtaken it by quarterly revenue.

If consumers were to search somewhere else than using Google, shop somewhere else than Amazon or buy no more iPhones, these companies would all shrink by an order of magnitude. Microsoft stands well positioned by comparison.

The following radar plot shows the above table numbers in a different perspective:

RadarPlot20122018

For each metric, 100% corresponds to the maximum of the four companies. Amazon has the most employees, Apple is the largest in quarterly revenue, profit and market cap. Some comments on the 2012 – 2018 changes:

  • Employees: Microsoft only added +35% of employees; Apple and Google more than doubled at about +160%; Amazon exploded by adding +763% to an almost 9-fold increase from 65,600 to 566,000.
    While Microsoft had more than twice as many employees as Apple in 2012, they are the same size now (~123,000).
  • Profits: While the green line (market cap) in the radar plot almost looks like an even-sized  rectangle, the red line (profit) is much tilted towards Apple and leaves comparatively little for Amazon.
  • Revenue per employee: Apple still takes the price in this rank (~$2million/year), with Google ($1.47million/year) and Microsoft ($0.87million/year). Amazon “only” earns $0.36million/year. In that metric, Amazon slipped from rank 2 to the bottom and Apple’s lead is not as strong as it was in 2012.

Much has been speculated about the future of the biggest technology companies and the nature of the next disruptions such as cloud, augmented reality (AR) and artificial intelligence (AI). Perhaps the biggest disruptor for this elite club of technology companies is Facebook, which only went public six years ago. FB currently has a $562 billion market cap. It is now bigger than these four were back in 2012, and about 70% of the size they are now. My own skepticism at the time of the Facebook IPO was proven wrong by its continued and strong growth. Their base of about 2 billion free accounts is by far the largest of any company ever. That said, I personally still have no Facebook account, while I’m using the products and services of each of the top four companies nearly every day! It will be interesting to see which one first breaks the $1 trillion market cap threshold.

Addendum 11/19/2018:

A lot has happened over the last 6 months. First, the above mentioned run-up continued and produced AAPL in early Aug-2018 as the first company to be publicly traded company worth $1 trillion. AMZN followed suit soon thereafter in early Sep-2018, but only stayed at that lofty valuation for a day or so. Here is a snapshot of the valuations as of Aug-31, 2018:

IMG_3399

Later in the fall the tides turned, and four of the above five stocks are now in correction territory. Here is the above snapshot for today, Nov-19, 2018:

IMG_3704

Here are the changes for all five companies summarized:

Screen Shot 2018-11-19 at 9.20.11 PM

Microsoft appears to have weathered the recent turbulence much better than the other companies. MSFT is down only 6.8% over the last 10 weeks; AAPL and GOOG each lost 16-20%, which at these valuations amounts to $217B and $140B, respectively! And AMZN and FB each lost about 25% of their market cap.

The combined total market value loss of the five companies is near $788B or $157 each on average. It is amazing to see how volatile the tech market has become in recent months. [Note on 11/21: Coincidentally, the New York Times ran a headline story the next day 11/20 titled The Tech Stock Fall Lost These 5 Companies $800 Billion in Market Value; the only difference was they excluded Microsoft and included Netflix.]

The earlier post pointed out that Microsoft was very well positioned, strongly diversified in its business, under fresh leadership of its CEO Satya Nadella since 2015, investing in new technologies (cloud, AI, AR) and much more conservative personnel expansion during the good times. They are now number #2 and maybe on track to pass AAPL again on their way to becoming the most valuable company in the world.

 

 
Leave a comment

Posted by on June 3, 2018 in Financial, Industrial

 

Apple, Amazon, Google, Microsoft

28 Apr

Last year we looked at the four companies and compared their business model over two quarters: Apple (hardware), Amazon (retail), Google (advertising), Microsoft (software). It struck me how far the integrated Wolfram Alpha technology has come in the last two years. It combines the symbolic computing capabilities of the Mathematica platform with curated data (for example financial data) and some pretty impressive linguistic analysis capabilities for free-form text input.

For example, in Wolfram Alpha, just type in the following query: “Googe vs. Amazon vs. Apple vs. Microsoft” The results are shown as a series of three screen-shots below:

ComparisonWolframAlpha1

ComparisonWolframAlpha2

ComparisonWolframAlpha3

Not only do you get the various data such as the fundamentals or the analysis of a mean-variance optimal portfolio displayed, but you get all the code needed to programmatically load such data. For example, if you want to get the breakdown of the analyst ratings, the system will expand it as follows:

AnalystRatings

So far we haven’t done any coding or bothered with integrating any data source. This amount of integration and automation is pretty impressive. I am often surprised how few companies are taking advantage of such advanced technology platforms.

 
1 Comment

Posted by on April 28, 2013 in Financial

 

Tags: , , ,

Faceplant with Facebook?

16 May

With the Facebook IPO coming up this Friday there is a lot of attention around its business model and financials. I’m not an expert in this area, but my hunch is that a lot of people will lose a lot of money by chasing after Facebook shares. Why?

I think there are two types of answers. One from reasoning and one from intuition.

For reasoning one needs to look at a more technical assessment of the business model and financials. Some have written extensively about the comparative lack of innovation in Facebook’s business model and core product. Some have compared Facebook’s performance in advertising to Google – the estimates are that Google’s ad performance is 100x better than that of Facebook. Some have pointed out that many of Facebook’s core metrics such as visits/person, pages/visit or Click-Through-Rates have been declining for two years and go as far as calling this the Facebook ad scam. One can question the wisdom of the Instagram acquisition, buying a company with 12 employees and zero revenues for $1B. One can question the notion that the 28 year old founder will have 57% of the voting rights of the public company. One could look at stories about companies discontinuing their ad Facebook efforts such as the Forbes article about GM pulling a $10m account because they found it ineffective. The list goes on.

Here is a more positive leaning infographic from an article looking at “Facebook: Business Model, Hardware Patents and IPO“:

Analysis Infographic of pre-IPO Facebook (source: Gina Smith, anewdomain.net)

To value a startup at 100x last year’s income seems just extremely high – but then Amazon’s valuation is in similarly lofty territory. As for reasoning and predicting the financial success of Facebook’s IPO, people can cite numbers to justify their beliefs both ways. At the end of the day, it’s unpredictable and nobody can know for sure.

The other answer to why I am not buying into the hype is more intuitive and comes from my personal experience. Here is a little thought experiment as to how valuable a company is for your personal life: Imagine for a moment if the company with all its products and services would disappear overnight. How much of an impact would it have for you as an individual? If I think about companies like Apple, Google, Microsoft, or Amazon the impact for me would be huge. I use their products and services every day. Think about it:

No Apple = no iPhone, no iPad, no iTunes music on the iPod or via AppleTV on our home stereo. That would be a dramatic setback.

No Google = no Google search, no GMail, no YouTube, no Google maps, no Google Earth. Again, very significant impact for me personally. Not to mention the exciting research at Google in very different areas such as self-driving vehicles.

No Facebook = no problem (at least for me). I deactivated my own Facebook account months ago simply because it cost me a lot of time and I got very little value out of it. In fact, I got annoyed with compulsively looking at updates from mere acquaintances about mundane details of their lives. Why would I care? I finally got around to actually deleting my account, although Facebook makes that somewhat cumbersome (which probably inflates the account numbers somewhat).

I’m not saying Facebook isn’t valuable to some people. Having nearly 1B user accounts is very impressive. Hosting by far the largest photo collection on the planet is extraordinary. Facebook exploded because it satisfied our basic need of sharing, just like Google did with search, Amazon did with shopping or eBay did with selling. But the entry barrier to sharing is small (see LinkedIn, Twitter or Pinterest) and Facebook doesn’t seem to be particularly well positioned for mobile.

I strongly suspect that Facebook’s valuation is both initially inflated – the $50 per account estimate of early social networks doesn’t scale up with the demographics of the massive user base – as well as lately hyped up by greedy investors who sense an opportunity to make a quick buck. My hunch is that FB will trade below its IPO price within the first year, possibly well below. But then again, I have been surprised before…

I’m not buying the hype. What am I missing? Let me know what you think!

UPDATE 8/16/2012: Well, here we are after one quarter, and Facebook’s stock valuation hasn’t done so well. Look at the first 3 month chart of FB:

First 3 month of Facebook stock price (Screenshot of StockTouch on iPad)

What started as a $100b market valuation is now at $43b. One has to hand it to Mark Zuckerberg, he really extracted maximum value out of those shares. It turns out sitting on the sidelines was the right move for investors in this case.

 
2 Comments

Posted by on May 16, 2012 in Financial, Socioeconomic

 

Tags: , , , , , , ,

Sankey Diagrams

14 May
Sankey Diagrams

Whenever you want to show the flow of a quantity (such as energy or money) through a network of nodes you can use Sankey diagrams:

“A Sankey diagram is a directional flow chart where the width of the streams is proportional to the quantity of flow, and where the flows can be combined, split and traced through a series of events or stages.”
(source: CHEMICAL ENGINEERING Blog)

One area where this can be applied very well is that of costing. By modeling the flow of cost through a company one can analyze the aggregated cost and thus determine the profitability of individual products, customers or channels. Using the principles of activity-based costing one can create a cost-assignment network linking cost pools or accounts (as tracked in the General Ledger) via the employees and their activities to the products and customers. Such a Cost Flow can then be visualized using a Sankey diagram:

Cost Flow from Accounts via Expenses and Activities to Products

The direction of flow (here from left to right) is indicated by the color assignment from nodes to its outflowing streams. Note also the intuitive notion of zero-loss assignment: For each node the sum of the in- and outflowing streams (= height of that node) remains the same. Hence all the cost is accounted for, nothing is lost. If you stacked all nodes on top of one another they would rise to the same height. (Random data for illustration purposes only.)

The above diagram was created in Mathematica using modified source code originally from Sam Calisch who had posted it in 2011 here. Sam also included a “SankeyNotes.pdf” document explaining the details of the algorithms encoded in the source, such as how to arrange the node lists and how to draw the streams.

I find these a perfect example of how a manual drawing can go a long ways to illustrate the ideas behind an algorithm, which makes it a lot easier to understand and reuse the source code. Thanks to Sam for this code and documentation. Sam by the way used the code to illustrate the efficiency of energy use (vs. waste) in Australia:

Energy Flow comparison between New South Wales and Australia (Sam Calisch)

Note the sub-flows within each stream to compare a part (New South Wales) against the whole (Australia).

Another interesting use of Sankey Diagrams has been published a few weeks ago on ProPublica about campaign finance flow. This is particularly useful as it is interactive (click on image to get to interactive version).

Tangled Web of Campaign Finance Flow

Note the campaigns in green and the Super-PACs in brown color. The data is sourced from FEC and the New York Times Campaign Finance API. Note that in the interactive version you can click on any source on the left or any destination on the right to see the outgoing and incoming streams.

Finance Flow From Obama-For-America

Finance Flow to American Express

Here are some more examples. Sankey diagrams are also used in Google Flow Analytics (called Event Flow, Goal Flow, Visitor Flow). I wouldn’t be surprised to see Sankey Diagrams make their way into modern data visualization tools such as Tableau or QlikView, perhaps even into Excel some day… Here are some Visio shapes and links to other resources.

 
4 Comments

Posted by on May 14, 2012 in Financial, Industrial

 

Tags: , , ,

Quarterly Comparison: Apple, Microsoft, Google, Amazon

02 May
Quarterly Comparison: Apple, Microsoft, Google, Amazon

Last quarter we looked at the financials and underlying product & service portfolios of four of the biggest technology companies in the post “Side by Side: Apple, Microsoft, Google, Amazon“. With the recent reporting of results for Q1 2012 it is a good time to revisit this subject.

Comparison of Financials Q4 2011 and Q1 2012 for Apple, Microsoft, Google, and Amazon.

Market cap has grown roughly by 25% for both Apple and Amazon, whereas Microsoft and Google only added 5% or less. A sequential quarter comparison can be misleading due to seasonal changes, which impact different industries and business models in a different way. For example, Google’s ad revenue is somewhat less impacted by seasonal shopping than the other companies.

Sequential quarter comparison of financials

Apple and Microsoft seem to be impacted in a similar way by seasonal changes. For Amazon, which already has by far the lowest margin of all four companies, operating income decreased by 40% while it increased its headcount by 17%. This leads to much lower income per employee and with increased stock price to a doubling of its already very high P/E ratio. I’m not a stock market analyst, but Amazon’s P/E ratio of now near 200 seems extraordinarily high. By comparison, the other companies look downright cheap: Apple 8.8, Microsoft 10.5, Google 14.5

Horace Dediu from asymco.com has also revisited this topic in his post “Which is best: hardware, software, or services?“. What’s striking is that all three companies (except Amazon) now have operating margins between 30-40% – very high for such large businesses – with Apple taking the top near 40%. Over the last 5 years, Apple has doubled it’s margin (20% to 40%), whereas Microsoft (35-40%) and Google (30-35%) remained near their levels.

(Source: Asymco.com)

Long term the most important aspect of a business is not how big it has become, but how profitable it is. In that regard Amazon is the odd one out. Their operating income last quarter was about 1% of revenue. Amazon needs to move $100 worth of goods to earn $1. They employ 65,000 people and had revenue of $13.2b last quarter, yet only earned $130m during that time! Apple earns more money just with their iPad covers! Amazon’s strategy is to subsidize the initial Kindle Fire sale and hoping to make money on the additional purchases over the lifetime of the product. In light of these numbers, do you think Amazon has a future with it’s Kindle Fire tablet against the iPad?

But what really struck me about the extreme differences in profitability is this comparison of Apple and Microsoft product lines (source: @asymco twitter):

(source: @asymco twitter)

This shows what an impressive and sustained success the iPhone has been. And the iPad is on track to grow even faster. Horace Dediu guesses that Apple’s iPad will be a bigger profit generator than Windows in one quarter, and a bigger profit generator than Google (yes, all of Google) in three quarters. We will check on those predictions when the time comes…

 
3 Comments

Posted by on May 2, 2012 in Financial, Industrial

 

Tags: , , , , , ,

Probabilistic Project Management at NASA with Joint Confidence Level (JCL-PC)

25 Feb
Probabilistic Project Management at NASA with Joint Confidence Level (JCL-PC)

On the Strategic Project and Portfolio Management Blog by Simon Moore one can find many fascinating stories about project failures as well as a related collection of project management case studies. One entry there links to a project management method NASA is mandating internally since 2009 to estimate costs and schedule of their various aerospace projects. The method is called Joint Confidence Level – Probabilistic Calculator (JCL-PC). It’s a sophisticated method using historical data and insight into estimation psychology (like optimism bias) to arrive at corrective multipliers for project estimates based on project completion percentages with required confidence level. It’s also using Monte Carlo simulations to determine outcomes, leading to scatterplots of the simulated project runs on a Cost-vs.-Schedule plane. From there one can determine estimates with for example 70% confidence levels for what the cost and schedule overruns will likely be.

If you’re either already familiar with the method or if you are very good at abstract thinking the above paragraph will have meant something to you. If it didn’t, bear with me. In this post I make a brief attempt to explain what I understood about the method using the data visualizations from two sources (a 100+ page report and a 12 page FAQ). The report is fascinating on many levels, as it deals with the history of high-profile project overruns (Apollo program, Space Shuttle, Space Station) and the pervasive culture of under-estimation (optimism bias) through not accounting for project risks that are unknown, but historically evident.

JCL starts with historical observations of similar projects with regards to cost and schedule overruns. For example, the above cited report contains best fit histogram distributions for robotic missions.

Overrun Distributions of Cost and Schedule for Robotic Missions (Soucre: NASA)

The idea is to use a set of such distributions for probabilistic estimates of cost and schedule. The set of distributions needs to account for the fact that in the early stages of a project there are more unknowns and as such higher risk of overruns. From the report:

The JCL-PC estimating method is based on the hypotheses that in the beginning phases of a project there are many unknown risks – and over time the project will have a high probability of exceeding estimated costs and scheduled duration. … Work as it was initially planned will inevitably change. Quantifiable risks become clearer and NASA’s S-Curves will tend to lay down as the work goes forward. Keep in mind that it’s not the project that is becoming inherently riskier. It’s a matter of participants fully identifying the real work that was “out there” all along. Even though the scope of the work wasn’t fully perceived “back when” – progress has continued to identify the risks and quantify the corrective actions. History is written in real time and that history differs to a greater or lesser degree from what was anticipated. The JCL-PC helps us better plan for and manage that difference.

The JCL-PC method strikes a needed balance between subjectivity and anticipated risk variability leaving only one remaining probability influence factor to deal with. – namely, assigning the percentage complete of the subject project. This % complete factor includes both subjective and objective elements.

One of the key elements is the notion of a multiplier which implements this reduced-uncertainty-over-time as well as a so called optimism corrector and other project risk in line with historical aerospace project overruns. The multiplier is plotted below as a function of the project % complete parameter for different confidence levels:

Multiplier as function of project % complete for various confidence levels

The concept is illustrated via two charts of a fictitious $1m project (applied here to cost overruns, but equally applicable to schedule overruns): The first shows a point estimate and it’s S-curves (confidence bands) per project % complete.

The second shows the S-Curves after applying “the optimism corrector and some minor project risk, through a more typical project life cycle with project scope creep … As the project evolves the S-Curve moves slightly to the right and becomes more and more vertical.”

It would be great to have an interactive graphic where the S-Cruves are plotted in response to sliding the project % complete between 0% and 100%. The report lists the above multipliers in a numerical table spanning project % complete (in 1% increments) and four confidence levels (50%, 60%, 70%, 80%). Rather than copying the entire table I filtered this down to just 10% increments in project % complete. This table tells NASA officials at various confidence levels, how much money they will have to spend for a $1m project as a function of project % complete:

Cost Estimate Table with project % complete and confidence levels

The data point highlighted in yellow is described as follows:

When the project is 50% complete, you’ll notice that a 50% confidence level suggests that the project can be completed for the anticipated $1,000,000. However, if we adhere to the NASA standard of a 70% confidence level, we see that another $400,000+ will likely be needed to complete the project. No matter how well a project is managed, it rarely compensates for ultra- optimistic budget estimates that sooner or later return with a vengeance and overcome the most skillful leaders.

As a final illustration the FAQ document includes this scatterplot as JCL-PC output:

Scatter Plot of Monte Carlo simulation with JCL-PC

A Frontier Curve represents all possible combinations of cost and schedule that will give you a percent JCL. The plot shows the Frontier Curve for a 70% JCL in yellow. The green dots are simulated runs with outcomes below the selected cost and schedule (blue cross-hair, yellow labels). White dots have either cost or schedule overruns, red dots have both.

The report makes bold claims about the potential of JCL-PC, but also about the challenges inherent in attempting to change an entire management culture. I am not qualified to comment on these claims, but my impression is that such probabilistic project management methods will raise the bar in the field and should lead to more accurate estimates.

The more I think about such abstract concepts, the more I’m convinced that mental models are inherently visual. We remember some key visualizations or charts and anchor our understanding of the concept around those visual images. We also use them to communicate or teach the concepts to each other – hence the value of the whiteboard or even the napkin drawing. As such, the increasing computational ability to produce such visual images and ideally even interactive graphics is an important element of academic and scientific endeavors.

 
Leave a comment

Posted by on February 25, 2012 in Financial, Industrial, Scientific

 

Tags: , , ,

Side by Side: Apple, Microsoft, Google, Amazon

06 Feb
Side by Side: Apple, Microsoft, Google, Amazon

Ed Bott at ZDNet.com wrote a post with the title: Microsoft, Apple, and Google: where does the money come from? He looked at the quarterly reports of these companies (links to sources in the article) and displayed a pie-chart of the revenue mix for each of them. Inspired by that, I added a fourth company – Amazon (source: 10-K for 2011) – and aggregated those pie-charts into one graphic.

Revenue Mix for Apple, Microsoft, Google, and Amazon

All four are large consumer-oriented technology companies; like millions of customers, I use many of their products and services every day. They each operate with different businesses models:

  • Microsoft: Software
  • Apple: Hardware
  • Google: Advertising
  • Amazon: Retail

Yet as a consumer I rarely think about these differences. All of them use state-of-the-art technologies like cloud-computing and mobile devices to achieve integrated end-to-end experiences geared to increase revenues in personal computing (Microsoft), smart mobile devices (Apple), online search (Google) or shopping (Amazon). And arguably all of them derive major competitive advantage from their software, such as Apple’s iOS which introduced the touch interface.

Perhaps most surprising is Google’s almost singular reliance on advertising, which makes it a very different business model. They offer all their technology for free – from search to mapping to operating systems and social media – to grow and retain online attention as enabling condition for advertising revenue. For a business this big the near complete dependence on one source of revenue is unusual; perhaps its time for Google’s leadership to seriously consider a diversification strategy? Without it Google is arguably more prone to disruption (such as from Facebook) than the other companies. Speaking of disruption: Apple derives almost 3/4 of its revenue (73%) from iPhone and iPad, neither of which existed 5 years ago. As Ed Bott points out, those two products now drive an astonishing $33.5b revenue per quarter!

To compare the companies by their absolute numbers, here is a bar chart of market capitalization, revenue and profit: (all in billions of Dollars and for Q4 2011, market cap as of 2/3/12)

Market Cap, Revenue and Profit for Apple, Microsoft, Google, and Amazon

Market cap of these four companies combined is approaching $ 1 trillion. Much has been written about the differences in market valuations relative to revenue and most importantly profit. The markets undervalue Apple and overvalue Google and Amazon. Let’s compare these dimensions (and number of employees) in the following radar plot:

Relative business performance for Apple, Microsoft, Google, and Amazon

The plot shows the relative performance of all with the highest in each dimension normalized to 100%. Amazon shows by far the smallest profit in the last quarter. Given it’s retail nature, it’s profit margins have always been smaller; and CEO Jeff Bezos has long emphasized the strategy of investing in future growth at the expense of present profits. Microsoft continues to enjoy very solid profit margins in a large, well diversified business. Google has incredible talent and for now is the undisputed king of online advertising. But Apple leads in all three factors, and it achieves 2x Microsoft’s results with less than half the number of employees! Apple’s profit is 1.5 times that of the other three combined! And it makes more than 60% of the profit with less than 20% of the employees. In fact, Apple’s market capitalization is now higher than $10m per employee! It must feel pretty special to be one of them these days…

Postscript: On Feb-13 analyst Horace Dediu at Asymco.com published an article with time-series data for the above companies (except Amazon) over the last 18 quarters (since 2007). It shows the evolution over time as depicted in this chart:

Apple Microsoft Google - Revenue and Operating Income 2007-2011

The article is called “The World’s Biggest Startup“. It’s main point is this: Microsoft and Google both grew their businesses steadily, but did not change their type of business. Apple did some of that in its established business segments, but more importantly and disruptively it added new categories (iPhone and iPad) for dramatic growth. That’s what startups do. Just so happens that Apple – whose stock today for the first time hit $500 – is also the most highly capitalized company in the world (around $460B). If Apple is a startup now, what will they look like when they are fully established?

 
7 Comments

Posted by on February 6, 2012 in Financial, Industrial

 

Tags: , , , , , ,

TreeMap of the Market

29 Oct
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“.

 
4 Comments

Posted by on October 29, 2011 in Financial, Industrial

 

Tags: , ,

← Older posts