Wednesday, November 21, 2012

A Blogful of Thanks - 2012

This is the 2nd year that Treasury Café has been in existence at Thanksgiving time. While regular readers know that while I might mention a personal experience here and there, for the most part I focus on topic and try hard to deliver useful, informative, and empowering content .
However, in keeping with the tradition started last year, for today we make an exception with respect to topic, though our content objectives remain the same.
For Those Who May Not Know
In the US, the fourth Thursday in November is called Thanksgiving, and it is one of the national holidays. Wikipedia can explain its history better than I, but essentially its roots are in harvest festivals that many cultures hold.
Nowadays it is a holiday where families get together, often traveling in order to do so (in many US airports the busiest travel day is today), eat a large meal, watch football, and in general just hang out together.
I like Thanksgiving because of the low-key nature of it. Unlike other holidays, stores do not have rows and rows of merchandise to sell, and there is not the same advertising blitz that occurs. You might find that grocery stores play it up a bit, stocking up on turkeys, sweet potatoes, cranberry sauce, and pumpkin pie along with other things, but that’s about it. It’s all about the people.
A Time For Gratitude
The primary emotion of Thanksgiving is gratitude. During the hustle and bustle of everyday life, we often get caught up in the pressures of too-long to-do lists, the various frustrations that arise and need to be dealt with, etc.
However, after taking a deep breath or two, and focusing our attention on higher order matters, we often realize that despite all that stuff going on we actually have a lot to be grateful for. It is important to remember this, as it keeps us humble and open to what the world has to offer.
This is no less true with respect to Treasury Café. Please allow me to tell you all about it.
Inside the Mind of a Blogger
A new blogger’s primary fear is that we will write it and no one will come. Writing that first post takes a lot of time, effort and thought, for several reasons. First, we haven’t done it before, so it is more conscious. Second, we don’t entirely know what we are doing, so it involves a lot of concentration (think babies walking for the first time). Third, there is the technology aspect, which is all new as well.
So once the “Publish” button is hit, we kick back for a second and go “ahhh, I did it”. That gratifying feeling lasts all of about two seconds, and is then swiftly replaced by a nagging question – “Is anyone going to read this?”
All of a sudden we are no longer in control. Some of us might call friends, or post on LinkedIn, Facebook, Twitter, etc. But the ball has been passed, it is in the potential reader’s power at that point. We are at the mercy of the audience.
One of the most comforting things to a blogger, then, is to find out real people in real places are reading what we have posted. There’s someone out there!
Comments Hall of Fame
The most noticeable way this occurs is when someone leaves a comment on your blog. This not only lets you know someone has read it, you also get to take the blog topic to a new level because there is another perspective now in the mix!
For this reason, my sincere appreciation goes out to the following:
Mandy Kilinskis‏ (@ImAmandaJulius, and I got to know each other via comments on someone else’s blog. Ironically, it was after that we realized that we were both in the Chicago Most Valuable Blogger contest, which her Quality Logo site won. I don’t feel too bad about that, since they have something like 10 zillion people who write posts for that, and when they are not writing posts all 10 zillion comment on the other’s posts. Given that I am only 1, that’s the breaks of being an independent blog. Being a good sport about it, she has stopped by on several occasions to put in her 2 cents.
Samuel Dergel (@DergelCFO, and I connected after I relentlessly commented on his each and every blog post for a month or more, and he decided that if you can’t beat ‘em, join ‘em and came over to Treasury Café to give me a dose of my own medicine. While he hasn’t found that great CFO job for me, he certainly is successful in matching the right talent with the right company when it comes to the executive suite.
Samantha Gluck (@texascopywriter, and I first met each other on Twitter, and after trading re-tweets and mentions I made my way to her blog and she made her way to mine for a little comment exchange. This is the classic maneuver that all those social media advisors keep telling you to do – build blog traffic and relationships by commenting on other people’s blogs. In this case it worked (I will tell you more often than not it does not work). However, in our exchange I got the better end of the deal, because Samantha is a real live actual writer, and so my comments section benefits from her excellent craftsmanship.
Scott Pezza (@scottpezza, came to Treasury Café during the enormously popular Cash Conversion Cycle and Working Capital Series of posts. As his focus at the time was payments and the order to cash cycle, this was no accident, but rather a result of his resourcefulness and expertise in the ways of social media and the internet in general. He discussed his own research findings in the comments, thus making that post particularly enlightening.
Rachel Kaberon (@rkaberon, is someone I met at a monthly discussion group sponsored by my school’s alumni association. She has got to be one of the most intellectually curious people I have ever met and is someone who is continuously learning more and more, soaking it all up like a sponge. Hopefully her head does not explode from all the knowledge contained within! She is a great source for interesting perspectives and has a knack for finding the perfect reading material to incite a passionate discussion.
Mark McCarthy ( is a real, live, actual Treasury person (from the bank side) and also someone I first met in person and only thereafter engaged over the social media platforms. He is an innovative thinker and is interested in driving change within his and his client’s organizations. This is not the easiest thing to do from the banking side of things, so we know that Mark does not shy away from a challenge.
Sudhir Saran Singh (@sss_stratandops, and I met via a Twitter discussion I was having with someone else and from there started following each other. I then happened to write the “In Search of the Talent Equation”, which is a topic right up his alley, as he has expertise in the HR world. In fact, in his comment he shared his own firm’s version of the talent equation, adding valuable additional information to that topic.
Denisha Lacey ( and I met through her Treasury Café comments, and from there we have connected on other networks. She is an up and comer with a lot of fire in her belly and a desire to continue learning, improving and growing. For these reasons, there are probably no limits to what her future holds and the leadership roles she will fulfill in that future. Good luck on the CTP!
Brett Bauer (@DoubleB72) is someone I have known for over a decade, and whether or not that qualifies as pre-dating the social media age it certainly pre-dates my active involvement. He knows a ton of things about sports and tons more about beer, wine and spirits, as that is his profession. A true relationship sales and marketing pro! I would not be surprised to find a blog by Brett sometime in the future.
Faye Oney’s (@FayeinCbus, ) main line of work is social media management, and as you all know mine is finance and strategy, so there is something “social-media symbolic” to the fact that we met via the geocaching hash tag of all things! Since then we have expanded the engagement, which obviously includes the occasional blog comment from time to time. Her blog is a great resource for pointers on using the various Social Media channels (Linked In, Twitter, etc.) as well as interesting perspectives on other business and life-in—general topics.
Barbara Swafford (@BSwafford, has a blog site that is a shining example of community creation within the blog space. Her blog is in my RSS Reader and within hours it will have 20+ comments on it – simply amazing. Unfortunately, simply reading her blog does not make the “secret sauce” to getting that kind of engagement, but us mere mortals can only keep trying!
Also thanks to:
Errin (@PuzzledTweeter),
Mike Hewitt and
Stephanie Connolly.
By this time next year perhaps we will know each other better and I can write a paragraph about you too!
Finally, the first blog comment after last Thanksgiving was from maddie, who was my first real social media friend. Here is what I said about her last year, when talking about my “firsts”:
“My first E-mail dialogue was with maddie. However, to say that she was my first blog-generated e-mail correspondence really does her a disservice, because no matter what happens - in all the rest of my life - I will always have a special place in my heart for maddie.
I ran into maddie by way of another blog. Through that blog, she reached out to me with the wisdom and advice of someone who has “been there, done that” and really took me “under her wing”. maddie is really the first one, and to some extent the only one, who provided the “somebody believes in me” feeling with respect to Treasury Café. And I tell you what, during the first couple months of blogging, you need that feeling! Thank you, maddie!”
Unfortunately for me, maddie dropped out of social media right after her post-Thanksgiving comment on Treasury Café. Her blog site won’t even pull up anymore (yet I cannot bear to take it off my blogroll!), there are no tweets since late November, no emails… I miss her.
Shout Out Hall of Fame
The other big way for a blogger to know that others are reading is when people mention your blog in their blog, or in other social media settings. This has the added touch of attracting new readers to your site, who might come back in the future and/or leave comments.
For this reason, my sincere appreciation goes out to the following:
Wally Bock (@WallyBock, , ) is someone who connected with me after I commented on his blog. He has had a longtime focus on leadership which I covered here on Treasury Café in “A Life’s Worth of Leadership Lessons”. Since a lot of the posts we cover here are about case studies of statistical distributions and financial strategies, replete with formulas, spreadsheet images and R output, mentioning Treasury Café would not seem to be a particular fit for his topic areas. However, on occasion we do cover issues with respect to managing the team, or being a valued partner, or working within the organization, and these have often been included in his “Midweek Look at the Independent Business Blogs” posts every Wednesday. Always willing to accept a challenge, he also found a way to work the Working Capital series into his Zero Draft blog about writing.
Rene Michau (@renemichau, ) , from ANZ Bank, found me very early on in the life of Treasury Café. He was the first blogsite “member” and the first to include Treasury Café on a blogroll (which is yet another way of saying, “I read this and you should too”). He will often include Treasury Café posts in his newspaper ( ). You may have noticed that most of the connections I have made were through blog comments and Twitter, but early on Rene found me “out of the blue” somehow and thought I was worth staying in touch with.
Stephen Gill (@sjgill, ) picked up the “5 Reasons NOT to use the Olympics for Business Lessons” Treasury Café post as a perfect segue into a post he was writing which covered the reasons and motivations underlying why people look to sports for analogies.
And Finally
Thank you for reading this blog! One of the things that I have been most proud is the fact that Treasury Café has been read on every continent except Antartica (if you know someone there, please get them to visit!). It is amazing to me that we can reach out anywhere in the world, form connections and work together like we can these days.
I am very grateful for your visit, and the fact that you come back. I am very lucky to have you and to be living in these times.
Thank you!
·         What are you grateful for today?
Add to the discussion with your thoughts, comments, questions and feedback! Please share Treasury Café with others. Thank you!

Saturday, November 17, 2012

Was Apple’s Earnings Announcement Really Important?

Many public companies go through the quarterly ritual of announcing their earnings results to the investment community through press releases, conference calls, website slide shows, etc.
There is a lot of disagreement about whether this activity is relevant. On one side are folks who say that without these announcements the company’s share price and valuation will suffer in the investor market place. On the other side are those that believe investors who drive market prices pay attention to long-term cash flow, not quarterly earnings.
Often, share price changes following these announcements are used by management as a gauge as to how the market has responded to the news. We find folks walking around with exaggerated swagger because the stock is up 2% since the conference call, or holed up behind closed doors because it is down 2%.
However, these changes need to be taken with a grain of salt, and in the context of the market in general. Perhaps our stock has gone up 2%, but that can be interpreted quite differently if we also know that the market was up 10% that day, or down 5%.
Fortunately for us, simple statistical tools can tell us something about the relevance of earnings announcements (and other things) to the marketplace.
A Brief Trip Back to Algebra Class
Back in algebra, we learned that there were two equations for a line on a graph, the “point-slope” form and the “slope-intercept” form (see here for more details if you are interested).
A line with the slope-intercept form is depicted by the following equation:
y = mx + b 
Figure A
In this equation, b is called the intercept because when x is 0, the first term of the equation (m times x) is also 0, and thus b is the only number remaining. It is therefore the value of y when the line intersects the y axis.
The m term is called the slope, because for any value of x, the line will move up or down along the graph incrementally according to that number. The greater the value of m, the more steeply the line will move along the graph.
Figure A shows 2 lines, one with the values m=0.5 and b=3 (the orange line) and the other m=1.5 and b=1 (the brown line). Notice that these lines intersect the y axis at the values of b in the equation, and the lines increase by the value of m for each increase of 1 in the value of x.
In terms of mathematical functions, the slope-intercept form has two variables, the x and the y. Since the value of y depends on what value x holds (remember the line intersects the axis when x is 0, and increases by the amount of change in x), y is known as the dependent variable and x is the independent variable.
Figure B
An average is a measure of the “central tendency” in a set of data. It is obtained by adding all the elements of the data set together and then dividing by the number of items in the data set. The equation for an average is shown in Figure B.
Let’s say we have two sets of data: Set A and Set B. Using the R statistical program, Figure C shows the data set, the two elements of the average formula (sum of items and number of items) and the resulting calculation itself.
Figure C
We use two data sets here to illustrate the dangers of averaging. As noted two paragraphs above, an average is supposed to provide an indication of “central tendency” in the data set. We use it often in place of a real figure, or we use its result to anticipate, project, or forecast what future numbers might be - “What is the average temperature in such and such?” “How many sales do we usually have in that region?”
In Figure D we plot the frequency of occurrence of values in each data set and use the dashed lines to represent the resulting average. For Set A, we can see clearly the “central tendency” component – the dashed brown line is in the middle of the “hump of data”.
However, for Set B there are two “humps”, one down near 1 and 2 and the other in the upper teens. The average for Set B is nowhere near any of its data elements! Which implies that the 9.9 average does not at all accurately represent the underlying data!
Figure D
So while an average may communicate a realistic assessment of the data, it also may not, and actually serve to mask other dynamics or obscure important information.
The Linear Regression
Linear Regression is a statistical technique that assesses the relationship between two variables. Figure E shows the linear regression equations, with the one at the top of the list the final “product” of the analysis.
Figure E
The form of that equation should look suspiciously like the slope-intercept form of a line equation, because that is exactly what it is. Given a scatter plot of points, with one variable on the x-axis and the other on the y-axis, a regression will essentially add a line to the plot (we’ll see this a bit later).
The other thing to note is that the build-up of the regression equations begins with averages (the X bar and Y bar equations), so the cautions we mentioned in the last section apply to this technique.
When I first encountered regression, I thought it was some mystical, rarified technique used by Nobel prize winning scientists. Once I learned more, it became clear that regression is just a fancy way of averaging.
Since I always find equations with subscripts, bars, and Greek letters a bit confusing when first encountered, it helps me to figure out how they work by using real numbers. Figure F shows all of the equations above at work in Excel (I used an example from the Statistical Methods textbook by Snedecor and Cochran). I added arrows to show the flow from one calculation to the next.
Figure F
The final item of note is that the regression equation yields a predicted value for Y, which is the reason for the “hat” symbol on top of the letter in Figure E (oh those mathematicians!). Given actual data, the difference between the actual Y value and the Y hat value is known as the residual.
Using the data in Figure F, the 2nd X value is 4, so the regression equation is:
            b0 + b1*X = 8.41 + 0.09375 * 4 = 8.785
This predicted value (the “Y hat” value) is compared to the actual 2nd Y value of 8.89, and the difference between the two is 0.105. (8.89 – 8.785), so this is the residual for this data point.
The Apple Analysis
In order to assess Apple’s earnings announcement impact, we will use the “Market Model” of security prices. The Market Model assumes that for a given change in a stock index, our individual company stock will rise or fall with it to some extent. Thus, the market index is the independent variable, and our individual stock return is the dependent variable.
Figure G
The Market Model equation for our regression is shown in Figure G. This is nothing more than the equation of a line in slope-intercept form. The only “tweak” we have made to this equation is that instead of using the predicted value of Y (i.e. the “Y hat” value) on the other side of the equals sign we use the actual value of Y. Since we saw in the last section that there is a difference between the predicted and actual values of Y, we account for this difference in the Market Model by use of the ϵ (the “error term”).
Because this model is in slope-intercept form, we can use Regression Analysis to explore the significance of Apple’s earnings announcements.
On July 24, Apple made their earnings announcements for the 2nd Quarter. They did this after the market closed for the day, so the first chance for investors to act on the information was on July 25.
In order to perform our analysis, we divide time into two sections: pre-event and event. In order to avoid “contaminating” the factors we will derive using the regression, we will add a buffer of 5-days between the earnings announcement and the data we use in our regression.
Figure H
So in this case since the announcement was made on July 24, we begin excluding data for the regression from July 17th and later.

Figure H shows the regression results of Apple’s daily returns against the NASDAQ (which we use in this example as the stand-in for the “Market Index”) from May 3rd to July 16th.
As we discussed in the prior section, we need to be careful when it comes to averages since the underlying data might not be representative of the averages calculated. One way to assess this visually for regression analysis is to plot the underlying data and then superimpose the regression line. Figure I shows the results of this. This does not look extremely abnormal (except for one data point – do you see it?), which allows us to have a little higher confidence in our calculations.
Figure I
Now that we have our regression parameters (i.e. the α and β) and have verified that the averaging process has not obscured any major obstacles, we apply these factors to our event time period (July 17th through  August 1st, 5-days after July 25th). From the market return, we calculate using our regression parameters the “predicted” Apple return, and then calculate the residual values (Apple’s actual return vs. the calculated one).
There are several ways we can analyze the results of this. In this post we will focus on the most visual methods, which is simply to examine the market return, apply 2 standard deviations to the predicted Apple return, and then examine whether the residual return is within these parameters or is not. We use 2 standard deviations because that approximates a 95% confidence level in the results.
Figure J
The result of this approach is shown in Figure J. The earnings announcement date is represented by the vertical gray line, and we can see that on the day after the residual value was over 2 standard deviations lower than predicted. This would indicate that the earnings announcement was a significant event.
Given that all investors do not immediately respond to announcements, one way to account for this within our regression framework is to look at the cumulative residuals. Figure K shows this approach. On the date of announcement, the residuals were right about where the market returns were, indicating our regression model was almost a perfect predictor by that day.
Figure K
Again, on the day after the residuals indicated a significant occurrence, but interestingly enough by the 5th day after the cumulative residuals were again within the 2 standard deviation band. So maybe it was not significant after all!  
What Can You Do?
There are two things you can do when presented with a regression analysis that will allow you to improve your decision making results. These are:
1. Be a Data Inquisitor – when presented with an average or summary information, inquire into information related to the underlying data structure. Are there histograms available to verify that there is only “one hump”? Are there enough data points to warrant a conclusion? What are the regression related statistics that support any assumed relationships (look at Figure I: R-squared, t-ratios, p-values)?
2. Examine the data in multiple ways – in the Apple analysis we discovered that from one perspective the results were significant, whereas in the other approach they were significant for a few days but by 5 days time they were not. This suggests that an “overlay of judgment” needs to be applied when deciding what conclusions should be drawn. Beware those who are unable or unwilling to show you different looks at the results.
Key Takeaways
Regression can be valuable analytical tool to use in a number of settings within your organization. However, it is not always the appropriate approach, due to issues with the underlying data or results which are not clearly definitive. For these reasons, business leaders need to dig into these areas prior to making decisions.  
What are your experiences where regression analysis let to inaccurate conclusions and/or decisions?
Add to the discussion with your thoughts, comments, questions and feedback! Please share Treasury Café with others. Thank you!