So the Meraki dashboard is awesome. In addition to doing things like manage wireless and wired networks, it can do several other things. I know of 3. Add more in the comments if you know them:
In the make-a-wish field at the bottom of the dashboard type:
“unicorn”
“make me a sandwich”
“make me pink”
John Chambers has given us a consistent vision over the past couple years – transforming Cisco into the #1 IT company worldwide.
Awesome!
During the breakouts I started reflecting on this vision in a little more depth. The first obvious observation is that if our goal is to be the #1 IT company worldwide then we can’t be #1 yet.
So if we are not #1 who is? And how far away are we from that position? How will we know when we get there?
Turns out there actually is a Wikipedia entry dedicated to this question. Their criterion is annual revenue, which is a good start, but I don’t think greatest revenue equates to #1 IT company. In my mind the #1 IT company would be a leader in annual revenue, market cap, and number of employees. Market cap in particular would be the most compelling singe statistic to me.
So how do we (Cisco) rank among our IT peers in market cap?
As of August 2014 Cisco is the #41 largest market cap corporation in the S&P. That is #41 of all companies, not just IT companies.
This begs the question “Of the 40 companies with a larger market cap, which companies are IT companies?” As we have articulated all companies are IT companies now (Ford is an IT company that sells cars, Coke is an IT company that sells soda) – and that is very true. There are no companies large or small that can survive this market without a strategic vision enabled by technology. However, for the purposes of this article let’s just agree that IBM is an IT company and Exxon is not an IT company, OK?
So pulling out the Exxons, Proctor&Gambles and Pfizers of the world I get that we are currently the IT #10 company by market cap. This is based off of S&P data – so only US companies considered here. Here is the list:
OK – so how long reasonably would it take us to get from #10 to #1? Well, let’s say the Internet of Things is that vision of every tree and cow is an internet connected tree and cow and the growth engine for us to get to say ~15% revenue growth a year for the next 5 years . On top of that say we can grow total earnings a year by 20% and say we even get P/E multiple expansion from where it is now at 15 to 20 – the math comes to
So that gets us to $431B market cap. Note that projection is waaaaaay optimistic. Everything would have to go right and then some to get to those kinds of numbers. Consider that 5 years ago in August 2009 we were at $130B market cap and $36B in sales – so in the last 5 years we have grown market cap by donuts and sales by a total of 25%. To think we will grow sales 100% in the next 5 years when we only did 25% in the last five is definitely stretch thinking.
Well, even at $431B five years from now, that still 30% smaller than Apple is today. Could Apple decline significantly? Sure. The iPhone is getting older and who know if the iPhone phablets will be a big bust? So sure they could decline over the next 5 years.
How about Google, currently in the #2 position right now? $431B would be bigger than Google right now, but it is real hard to see how Google would decline over the next 5 years. In fact, 5 years from now there will be more ad spending on digital media than TV media. Could they lose advertising dollars to Facebook? Sure, but the point is someone will win – weather it is Apple vs Microsoft or Google vs Facebook. So the short answer is no, even in a best case scenario we will not be the #1 IT company in 5 years.
These aren’t the droids you are looking for. If you look at the list closely, I’d argue that the top ones on the list are all consumer companies (Apple, Google, Facebook), leaving less enterprise challengers in our path (IBM, Oracle, Cisco). So, if you take the vision is to be the #1 Enterprise IT company in the world (which actually I think is the vision JC has) then things get more possible.
Here is the list again with the enterprise focused companies called out in green
I think IBM and Oracle are well within our sites. There is no reason we can not have an Advanced Services arm like IBM and I don’t see what propels names like IBM, Oracle and HP forward over the next 5 years. We have Internet of Everything and growth in mobility. They have— old technology.
So yes, even at a more reasonable 5% annual revenue growth and 10% annual earnings growth with no P/E expansion, that math gets us to
So yes, if IBM and Oracle stagnate and we execute, in 3-4 years we will be a bigger market cap player than either of those.
Microsoft however is a different story. We would need the optimistic scenario to play out to overtake them. If they grow at expectations and we grow at others’ expectations of us then we will not surpass Microsoft in the next ten years.
So here’s to double digit revenue growth and 20% earnings growth! I personally am looking forward to the journey of becoming #1!
If you have made it through the article to this point then tweet me back (@nevilleaga) I am curious to see who you think is the #1 IT company right now and who we are going to take down when we achieve our vision.
So I have decided to change my handle from @nevilleaga to @CiscoNeville. Why? I tend to push work related content on twitter and I tend to do personal content on Facebook. What can I say? Scott Hanson inspired me!
So I probably will not change my style — that is– still tweeting only about a couple times a week and trying to back up my tweets with more in-depth analysis on this blog.
Will I be tweeting about IT Security? Cisco Security? Probably just a little bit. After 6 years now in a generalist role I have lost a little of the pure play around security, so my tweets tend to be everything Cisco sells (which is quite a lot). But I’ll always have the Security first perspective things (it touches every point of the network) and that’s where my hat lies.
Why not @CiscoSecurityGeek? Too many characters. Twitter only allows 15 
So catch me at @CiscoNeville
This was so non-intutive to me that I deemed it worthy of a quick blog post. Take historical prices from yahoo finance (or another website) and look at the daily high and low quoted prices of the S&P 500 during any handful of days from 1972 and compare them to a handful of days from 2014.
For all 1972-1975 the lowest daily volatility was 1.05% (October 9, 1972). The highest was 6.47% (interestingly October 9 again, this time 1974) and the median was 1.85%. An average day during that period would be Feb 26, 1973 where the S&P500 high was 113.26 and the low was 111.15 for a daily volatility of ~1.86%.
Contrast that to 2014. Over the last 90 days the lowest volatility has been 0.31% (April 23) and the median has been ~ 0.7%. In fact, 80% of the trading days had volatility below 1%, where as pointed out in the paragraph above zero trading days from 1972 to 1975 were below 1%
Conclusion: Even with all the internet trading and cheap commissions, daily volatility has gone down significantly over the past 40 years. You would think the free-er flow of capital would increase volatility, but it has not.
The movie critic reviews Non-stop, and finds it lacking. Nonstop is the story of a flight from New York to London with a terrorist and federal air Marshall on board. The intent of the film makers is to draw the audience in by developing several characters among the passengers and have the audience try to figure out who the terrorist is. Is it the cop traveling to see his sister? The Arab? The flight attendant? The chatty passenger? The pilot? The air Marshall himself? The better question is who cares?
Last week Warren Buffet offered up a $1 Billion prize for anyone picking a perfect NCAA tournament bracket this March. Just exactly how difficult could it be to pick a perfect bracket? I set to find out that answer mathematically. After a couple days of analysis and Monte-Carlo simulations I have come up with an answer:
1 in 500,000,000,000 (1 in 500 billion)
For comparison, the odds of winning powerball are about 1 in 175,000,000. So for every person out there with a perfect NCAA bracket there would be 2,860 lottery winners. Daunting, but do-able.
How could I come up with such a number? Read on.
To answer this problem you have to first know how the NCAA brackets work. Not counting the first 4 teams in (which usually don’t factor into the bracket anyways) 64 teams are slotted into a 6 round, single elimination tournament. In order to get from 64 teams down to 1, precisely 63 teams must lose 1 game. So, if you assume that eachgame is a 50/50 crapshoot, then the math is simple- the odds are 1 in 2^63, or about 1 in 9.2 quintillion (9.2 * 10^18). Now that indeed would be an impossible task. To put that number in perspective, for each correct bracket with those odds there would be 52 billion powerball winners. Another way to wrap your mind around that number is to visualize that there are 8*10^17 square inches on the surface of earth (including all oceans, etc), so if you placed 11 brackets on every square inch of earth and they were all unique (no repeated brackets), you would have precisely one winner. Odds are that one square inch of real estate is not in your yard.
But each game is not a 50/50 crapshoot, and there lies the tantalizing probability of bringing those odds way, way down in your favor. For instance a #16 seed has never beaten a #1 seed. They have been playing this bracket format since I was a kid and it has not happened yet. Similarity a #2 seed rarely loses to a #15 seed. And if a #15 seed wins a game, it rarely wins a 2nd game. So we can use the fact that some teams are better than others to weight down the probabilities around the whole bracket.
To model this I used a Monte-Carlo simulation. Label each team A1-A16 (first region) through D1-D16 (last region). Assign “ping-pong balls” to each team based on rank. A #1 seed gets 99 ping pong balls (or their virtual equivalent). #2 gets 95, #3 gets 90 all they way down to #16 getting 1 ball. When 2 teams play, assign a random number between 0 to 1 for each team and then multiply that number by their ping-pong ball rating. The team with the higher final number wins.
Using this method, a #1 seed will beat a #16 seed about 99 times out of 100. Similarly, a #1 seed will beat a #3 seed about 99 times out of 99+90=189 (just over 50%). We can actually adjust these weights based on historical brackets. Here is the actual perl script I used to do this simulation.
Now, getting a perfect bracket is hard enough. If this year a bunch of #16-#13 seeds make runs deep into the tournament that scenario would be impossible to predict with the weights I have assigned to the teams. My weights make it much more likely that the top teams will make the deepest tournament runs, which models reality. To test the model I used the 2008 bracket as the model bracket I want the computer to come up with. Why 2008? That was a less-madness year, where all 4 #1 seeds made the final four, and there were few upsets throughout the brackets.
My script simulates a whole tournament, multiple times. I wanted to see how many times the computer would have to simulate the tournament to get all 63 games right (compared to the 2008 perfect bracket).
The first time through, the computer got 34 games right (out of a possible 63). The second and third tries yielded less than 34 correct games, but the 4th try improved on the result and correctly predicted 45 games. From there it takes a lot of iterations to get more games accurately predicted. In fact, here are the numbers I got.
Number of correct picks (compared to 2008 perfect bracket)
Iterations the computer needed to achieve this result
34 1
45 4
47 236
48 6,144
49 9,688
50 19,770
51 101,360
52 212,544
53 351,630
54 2,162,574
55 33,794,131
56 43,477,602
63 500,000,000,000 (est)
As you can see, the numbers go up very slowly past 50 correctly predicted games. My computer can crunch about 10,000 brackets a second, but that still takes a long long time to get to 500,000,000,000. So then, how do I arrive at 500,000,000,000? Graph the known data points on a logarithmic scale, and extrapolate where 63 lands. A decently straight line to me shows about 500,000,000,000.
So, tell me. What are you going to do with your billion?
The Tuesday morning after the BCS title game this showed up on my twitter account:
Gone in 79 seconds – Hey #Auburn you gotta read this and @AUFamily spread the word – this is special! http://t.co/64ecvOxIoI via @usatoday
— Peter Zanetti (@Peter_Zanetti) January 8, 2014
I have not clicked the link. I have not read the article. I do not intend to do so either.
In the aftermath of the Rose Bowl BCS title game everything was simply too painful for me. There was no way I could listen to the post-game radio call. There was no way I could look up the game stats on ESPN Scorecenter. There was no way I could read the post-game write up on USA today. Mind you, this was not a technology limitation- I did posses a smartphone and my hotel room gave me a perfectly good copy of USA today Tuesday morning. However, I still could not, can not, and likely never will be able to go back there.
What follows are my thoughts from the game
On the game itself
I don’t need a stat sheet to tell me that Auburn likely out gained FSU by 50-100 yards. I also remember Jameis Winston getting sacked and hit multiple times (I’d guess 4 sacks and 10 total hits). Auburn was clearly the more dominant team, but they had those 21 points in ~20 minutes of football, and then suddenly no more for a long time. That’s where the game was lost. As well as the defense was playing, you can’t go scoreless for ~30 minutes and not expect your defense to continue to play at a high level.
I will admit, the final Auburn touchdown had me pretty excited, but the long pass play by FSU on their final drive had me sitting down in my chair. I could not bring myself to watch. Fortunately there are others who know the pain, this lady in front of me did the same thing.
On asking the other team to take your picture
Check out this pair. The FSU fan on the left asked the Auburn fan to his right to take his pic at the stadium and gave the Auburn fan his phone. The Auburn fan obliged, snapped a pic and then, um, dropped the phone while returning it to him. The result was a ridiculously cracked screen you can see in the post.
The wall of champions outside the Rose Bowl
At the front entrance of the Rose Bowl are a series of plaques depicting the Rose Bowl champions back to 1910 or so. All of them have gold lettering except one- the 2002 Rose Bowl which OU won. That game was also the first non-sellout for the Rose Bowl ever and the first non title game here that did not involve a Big10-PAC10 matchup. Coincidence? I doubt it.
The Goodyear blimp
During the whole game the Goodyear blimp circled overhead. Strangely, the only message that it could say said “check your tire pressure monthly”. Really? The national championship game? Do people really want to care about tire pressure at that time?
Scoreboard cover photo
I so very desperately wanted to take a scoreboard pic and use it as my FB cover photo for the next 365 days. Alas, this is as close as I came
Sunrise in Pasadena
It was a beautiful day weather wise here. While Norman was 7° this morning with the wind chill of 0, we were warm and toasty in the mid-70s. The next morning we had our feet in the sand at the beach. Very hard to be in Norman Oklahoma when you know Pasadena is out there.
Happy 2014!
It is time for agafamily.com to have a new blog home.
Why?
I first started blogging on a laptop in my house in 2006, a bit before Facebook became the go-to app of everyone’s spouse. I got to learn some basics on HTML and how to control the message (my direct HTML editing) and the location of the message (a laptop in my house).
At that time I used blogger by Google to do some of the text editing. Well, somewhere about 2009 Blogger changed something and I lost the ability to quickly and easily edit my blog. So there were very few blog posts over the past 4 years.
I figure it is time to now move to WordPress and move to the cloud.
So, hello world! Here I am again!
If you trade stocks you have no doubt heard of the January effect. It goes something like this:
“The stock market for the year does the same that it does in January. If the month of January is up, then stocks will be up for the full year. If the month of January is down, then stocks will be down for the full year. Furthermore, the 1st trading day in January is a predictor for the month of January and thereby a predictor for the full year”
Well, I thought it would be a good exercise to start the year by testing this theory, As good as last year’s 30% gains were, I sure would not want to go through 30% losses this year
Specifically I wanted to test the 1st day theory and test it with the NASDAQ index. Most of my mutual fund holdings today are in QQQ or TQQQ – which is the NASDAQ 100 ETF.
I wanted to go back through at least my lifetime (from 1980 on), since I remember the NASDAQ even back then was talked about as the “tech index”. Here are the numbers. Two rows for each year, which show the NASDAQ close on 31 Dec and 2 Jan for each year:
year NASDAQ closing price Adj Close Percentage Gain for year Percentage Gain for 1st trading day of year Verdict on Swami…
31-Dec-80 202.34
2-Jan-81 203.55
1981 31-Dec-81 195.84 -4% 0.60% Incorrect Positive Prediction
4-Jan-82 195.53
1982 31-Dec-82 232.41 19% -0.16% Incorrect Negative Prediction
3-Jan-83 230.59
1983 30-Dec-83 278.6 21% -0.78% Incorrect Negative Prediction
3-Jan-84 277.63
1984 31-Dec-84 247.1 -11% -0.35% Correct Negative Prediction
2-Jan-85 245.9
1985 31-Dec-85 324.9 32% -0.49% Incorrect Negative Prediction
2-Jan-86 325
1986 31-Dec-86 348.8 7% 0.03% Correct Positive Prediction
2-Jan-87 353.2
1987 31-Dec-87 330.5 -6% 1.26% Incorrect Positive Prediction
4-Jan-88 338.5
1988 30-Dec-88 381.4 13% 2.42% Correct Positive Prediction
3-Jan-89 378.6
1989 29-Dec-89 454.8 20% -0.73% Incorrect Negative Prediction
2-Jan-90 459.3
1990 31-Dec-90 373.8 -19% 0.99% Incorrect Positive Prediction
2-Jan-91 372.2
1991 31-Dec-91 586.34 58% -0.43% Incorrect Negative Prediction
2-Jan-92 586.45
1992 31-Dec-92 676.95 15% 0.02% Correct Positive Prediction
4-Jan-93 671.8
1993 31-Dec-93 776.8 16% -0.76% Incorrect Negative Prediction
3-Jan-94 770.76
1994 30-Dec-94 751.96 -2% -0.78% Correct Negative Prediction
3-Jan-95 743.58
1995 29-Dec-95 1,052.13 41% -1.11% Incorrect Negative Prediction
2-Jan-96 1,058.65
1996 31-Dec-96 1,291.03 22% 0.62% Correct Positive Prediction
2-Jan-97 1,280.70
1997 31-Dec-97 1,570.35 23% -0.80% Incorrect Negative Prediction
2-Jan-98 1,581.53
1998 31-Dec-98 2,192.69 39% 0.71% Correct Positive Prediction
4-Jan-99 2,208.05
1999 31-Dec-99 4,069.31 84% 0.70% Correct Positive Prediction
3-Jan-00 4,131.15
2000 29-Dec-00 2,470.52 -40% 1.52% Incorrect Positive Prediction
2-Jan-01 2,291.86
2001 31-Dec-01 1,950.40 -15% -7.23% Correct Negative Prediction
2-Jan-02 1,979.25
2002 31-Dec-02 1,335.51 -33% 1.48% Incorrect Positive Prediction
2-Jan-03 1,384.85
2003 31-Dec-03 2,003.37 45% 3.69% Correct Positive Prediction
2-Jan-04 2,006.68
2004 31-Dec-04 2,175.44 8% 0.17% Correct Positive Prediction
3-Jan-05 2,152.15
2005 30-Dec-05 2,205.32 2% -1.07% Incorrect Negative Prediction
3-Jan-06 2,243.74
2006 29-Dec-06 2,415.29 8% 1.74% Correct Positive Prediction
3-Jan-07 2,423.16
2007 31-Dec-07 2,652.28 9% 0.33% Correct Positive Prediction
2-Jan-08 2,609.63
2008 31-Dec-08 1,577.03 -40% -1.61% Correct Negative Prediction
2-Jan-09 1,632.21
2009 31-Dec-09 2,269.15 39% 3.50% Correct Positive Prediction
4-Jan-10 2,308.42
2010 31-Dec-10 2,652.87 15% 1.73% Correct Positive Prediction
3-Jan-11 2,691.52
2011 30-Dec-11 2,605.15 -3% 1.46% Incorrect Positive Prediction
3-Jan-12 2,648.72
2012 31-Dec-12 3,019.51 14% 1.67% Correct Positive Prediction
2-Jan-13 3,112.26
2013 31-Dec-13 4,176.59 34% 3.07% Correct Positive Prediction
2-Jan-14 4,143.07
2014 ??? -0.80%
All total that’s 33 years.
23 up years and 10 down years (up 70% of the years)
14 correct positive predictions
4 correct negative predictions
6 Incorrect Positive Predictions
9 Incorrect Negative Predictions
In total, that 18 correct predictions and 15 incorrect predictions (55%)
Some interesting things fall out of this table. For instance, do you remember that Jan 2 2001 was down -7.23% for the 1st trading day of January! Whew!
At first glance you may say that something that predicts with 50+% accuracy is a good predictor. That is not correct. You have to remember the stock market is not a random coin flip (where 55% prediction accuracy would be great!) but something that tends to rise over time. So a predictor that says “Any year beginning with the digit 1 or 2 will be an up year” would be right 70% of the time. In that light, I’d need a January effect predictor that would need to be something much more accurate than 70% for me to be interested.
More interesting still, if trying to use the data to your trading advantage.
If you bought the NASDAQ on Jan 2, 1980 and held it to Dec 31, 2013- your gain would be 1952% (4176-203)/203. If instead you watched the 1st trading day of Jan and went 100% in the market in years where that 1st trading day was up and stayed out of the market for the whole year when the 1st day was down, you would have a 33 year total return of 1945%, virtually identical to buy and hold.
Therefore, in proper Mythbuster’s fashion, the 1st trading day of January theory is *BUSTED*