So my bride and I are both sick. That makes for an interesting time with our expanded family. With a teen and a double digit kid who are pretty self sufficient we could manage but with a 6 month old it can be pretty challenging. We don't want to get him sick because baby, but we can't just hand him off to his sisters either.

Fun Times :-)

On top of all of that there is also my February Declaration to consitently create. Today I am sharing a couple of items.

Life in the Clouds Episode 15 - Heavily Medicated

We fought our way through this one trying to finish before NyQuil madness set in for me. I don't handle medicine well or I should say it impacts me quickly. ;-) Enjoy

Graduation Invitation

I was trying to let my graduation go by with as little fanfare as possible. My wife and mother had other ideas. They both wanted me to walk the stage so I set out creating invitation but decided not to finish them. Here is the finished project.

Baby's 1st Dissertation

In a similar vein here is a funny want to tease your wife. We were having a disagreement about whether we should wake up the baby to feed him. My arguement is that you never wake a sleeping baby. My well reasoned arguement was met with disapproval and so off I went to feed the baby. I thought that a more scholarly approach, from a different presenter was needed.

I think I win this round.

Get out there and do something amazing!

Go Go GO!

TO: Senior Director Business Analysis
FROM: Carlton Matthews
DATE: 24 April 2016
SUBJECT: Using Python for Textual Analysis

Introduction

At our last information exchange meeting, you put to the team to determine ways to increase our analytical toolkit. As a financial services business much of what we do involves numbers and other hard data points. However, during our discussion, we wondered if there were ways to determine sound investments based on more than just hard financial data.

I recommended that we look at other sources of information related to companies we are investigating. Specifically, I wondered if we could look through the annual reports looking at the CEOs letter to identify any interesting patterns. Over the last week I have spent time developing a program using the Python language to look at a series of CEO letters from Berkshire Hathaway Incorporated a publicly traded company headed by Warren Buffett. Every year Mr. Buffett writes a letter to the shareholders as part of their annual report.

Processing the Raw Text

I pulled CEO letters in 10 year intervals starting in 1977 and ending in 2016. This gave me a total word count 53219. Using Python to extract the text was a simple exercise however turning that data into something meaningful was a challenge. First the more commonly used words in the English language should be excluded from our analysis. These words are known as stop words and there are collections that exist online for use for this application. Here is some of the list that I used for my analysis.

'beside', 'besides', 'between', 'beyond', 'bill', 'both', 'bottom', 'but', 'by', 'call', 'can', 'cannot', 'cant', 'during', 'each'

After processing the text files across the time periods I was give a list of over 200 words. Each of these words was used at least 10 times within that year’s letter. To further reduce the number of words I began to combine root words with their tenses. I combined business and businesses as an example. While this still left us with a list of 227 words now some interesting patterns were beginning to emerge.

Quantitative Textual Analysis

The above figure shows the top 50 words used within the CEO letters. I could have removed Berkshire as you would expect it to be used multiple times but it serves as a visual descriptor of the data. From here we can see the most used words are business, company and earnings. This makes sense as Berkshire Hathaway grows by buying portions of companies. From their “Owners Manual” one of their stated goals is, “… directly owning a diversified group of businesses that generate cash and consistently earn above-average returns on capital.” (Buffett, Berkshire Hathaway Inc. - An Owners Manual, 1999))

Another way to represent this data can be seen below. This chart shows the top 20 words stacked by year.

As with the first visualization the top words are business, company and earnings. Later on I will examine these three terms but I want to note that from 1977 to 1987 there was significant growth in both the company and the size of the CEO letter. The 1977 letter is a little more than 2300 words while the 1987 letter is just over 12000. During that time period Berkshire Hathaway also increased their stock value from $138 in 1977 to $2,950 in 1987, primarily through increasing their business holdings. (Pritchard, 2008)

Most Used Words

This leads us to the most important set of words in our dataset. These three words are used more times than any other words within the CEO letters. The words, which have already been discussed, are business, company and earnings. In the below chart we see one representation of how these words have been used over time. The dark blue area of each bar represent 1977, followed by 1987 in orange, ’97 in green, 2007 is read and the 2015 words in purple. Business includes both the singular and plural usage of the word. For a company that is in the business of holding stocks of other businesses you expect to see multiple instances. From 1977 to 1987, as mentioned previously, there were a lot of businesses added the the Berkshire Hathaway portfolio. That shows in the spike in the usage from 1977 to 1987.
In 1977 there was a lot of discussion of the company and the companies that make up their portfolio. As more companies were added which caused growth in Berkshire as a company the usage increased with a peak in the most recent letter. As expected, as the company grew the earnings also grew which is seen in the usage of the the word earnings from 1977-2015. Below we see the same data represented. This time however we are looking at each word and their word counts over time. It paints the same picture as described above. This style of graph best illustrates the spike in the usage of the word business.

Conclusion

After walking through this process. I think that we can use this type of analysis to supplement financial models to better understand our investments. Berkshire Hathaway was a good choice for this analysis because provides a long history and publically accessible data to glean from. If you think that this is a worthwhile endeavor, I can begin enhancing the program to ingest and process even larger volumes data. Berkshire Hathaway could be used as test case again as they have all their CEO letters publically available.

References

Buffett, W. E. (1978, March 14). 1977 Shareholder Letter from the CEO. Retrieved April 18, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/letters/1977.html

Buffett, W. E. (1988, February 29). 1987 Shareholder Letter From the CEO. Retrieved April 20, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/letters/1987.html

Buffett, W. E. (1998, February 27). 1997 Shareholder Letter From the CEO. Retrieved April 20, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/letters/1997.html

Buffett, W. E. (2008, February 27). 2007 Shareholder Letter From the CEO. Retrieved April 20, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/letters/2007ltr.pdf

Buffett, W. E. (2016, February 27). 2015 Shareholder Letter From the CEO . Retrieved April 20, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/letters/2015ltr.pdf

Buffett, W. E. (1999, January 30). Berkshire Hathaway Inc. - An Owners Manual. Retrieved April 20, 2016, from Berkshire Hathaway Inc.: http://www.berkshirehathaway.com/owners.html

Pritchard, J. (2008, April 02). A Look at Berkshire Hathaway’s Annual Market Returns From 1968 – 2007. Retrieved April 20, 2016, from All Financial Matters: http://allfinancialmatters.com/2008/04/02/a-look-at-berkshire-hathaways-annual-market-returns-from-1968-2007/

Appendix: Python Code

See project on Github

Here is the transcript:

Good Evening and welcome to my time series presentation. As you can see by the title I will be adding to what has been titled The Quantified Self movement. The tagline over on the website quantifiedself.com is self knowledge through numbers.

My name is Carlton Matthews and I will be you guide through some of my personal data.

So how did I become interested in this type of data tracking. For me it started after listening to a TED talk given by Dr. Talithia Williams entitled, Show Me the Data: Becoming an Expert in Yourself in Feb 2014. She gave this talk at Claremont College in 2014 and she explained how knowing your bodies data can help you make better decisions. It was a fascinating talk that pushed me to further my own data collecting habit.

In the summer 2014 I purchased my first fitbit flex and started tracking daily activity like the number of steps I was taking as well as the number of hours I slept and the number of calories I would eat. Even though I had been tracking this data for almost 2 years I have never exported it and looked for any trends.

I decided to extract a small slice from my data and look at the 1st 2 months of 2016 and see what i can predict going through the rest of the year. From 2 January 2016 through 2 March we will look primarily at the how the number of steps I take fluctuate from day to day. We will also look at hours slept and calories eaten and see if either of them impact the steps.

We will also be looking at the high temperature for each day to see if that has any influence on my daily activity.

I expect to see that warmer temperatures lead to more steps taken. I also think that days that I am ore active I eat less calories. So as Dr. Williams says in her video Show Me The Data.

In this slide we take a look at both number of steps and hours of sleep laid out together. The Blue line shows the number of steps take while the yellowish green line shows hours sleep. There are 2 reference lines that show the 2 goals I have with this data. My step goal for each day is 10000 steps and my sleep goal is 8 hours per night as you can see I consistently do not meet either goal.

The interesting thing that I see here is that periods of higher activity seemed to indicated a period of longer sleep. I guess this is to be expected but I did not think they lines would mirror each other as much as they do.

Here we see steps and calories shown similarly to the previous slide. This time however I do not see the number of calories impacting the number of steps as much. My assumption was that on days when I was more focused on what I ate I would likewise be conscious of the amount of activity I would participate in. Again we see that I have not been hitting my goals here denoted by the reference lines for my step and calorie goals.

When I decided to see if the daily temperature impacted the number of steps I thought that it would be an simple task to pull in the necessary data. That was not the case. I eventually found the Quality Controlled Local Climatological Data on the National Oceanic and Atmospheric Administration page. This allowed me to extract the data from the local weather station near the BWI Airport.

With all of that introduction we see the step counts plotted against the daily temperatures for my area. As I expected we see based on the trend line that there is an increase in the step count as the warmer weather hits. I am glad to see this confirmed.

This last chart is the most interesting to me. I decided to look at which days I was the most active and got the most sleep. Here is the breakdown. I am the most active on the weekends. This makes sense since I work in an office and am at my desk for most of the day. I also get the most sleep on the weekends. Again this make sense due to family, work and school commitments. I do the least amount of moving and get the least amount of sleep on Thursdays. I did not expect that.

Not the question is what can we predict for the data that we have just seen. Looking at the data I expect to see an increase in the number of steps taken. I also expect this to result in a greater amount of sleep.

Here are some additional observations that I made from this exercise. Having good data is key. This means that the data set should have consistency. I had to exclude a larger range of data because there were gaps in my logs. Even in this dataset there were days of missed food logging, step counts and sleep records.

Sadly I also observed that I am eating too much and not moving as much as I should. Going forward I need to correct that.

Thank you for joining me on this look at my quantified self. Start tracking your own data. Who knows what you might learn.