Hey All,
The past week has been amazing! For the first time ever, I don't actually need to go to school, and now I'm spending so much more time doing things I want to do, including research. I've picked up a Machine Learning Class from Stanford, and I'm also taking a Real Analysis Class from MIT OpenCourseWare.
Aside from all of this, I've made considerable progress with my research itself. At this point, I've written a program in C++ to sort the firms into comparable groups for the five firms that I intend on analyzing, and I've run the program on these five firms. The program took a bit longer than I intended to take (about a day and a half), but i'm still right on track for the data collection for next Sunday. At this point, I'm beginning to analyze the multiples for each firm within my question. Currently, I am working on the price-earnings multiple, and am calculating the error percentages for the different firms. I expect to be done with this multiple by tomorrow and begin working on the Book Value multiple right after that.
At this rate, I will easily have all the data that I need for next Sunday. Because the data needs to be in an organized manner, however, I'm just going to spend the rest of my post describing how I intend to organize the data.
For first picking the comparable firms for Facebook, Twitter, LinkedIn, Yelp, and Facebook, I intend to display the average values of total assets and EBITDA over the nine groups that I calculated for each firm, highlighting the one group each of the five above firms fell into.
Next, for each firm, I will have a excel spreadsheet that shows the pricing errors for each prediction using the different multiples. The rows will be represented as a different multiple, and the columns will consist of the comparable firm group, and the different mean, median, and harmonic mean values for their multiples. Finally, the pricing error for each of the above methods will be listed, allowing for a direct comparison of them.
In general, I'm feeling really positive about the entire process, and I can't wait to comment about my whole results next week! I'm just sad that most of the data collection part is coming to a close.
Signing off,
Akash
The past week has been amazing! For the first time ever, I don't actually need to go to school, and now I'm spending so much more time doing things I want to do, including research. I've picked up a Machine Learning Class from Stanford, and I'm also taking a Real Analysis Class from MIT OpenCourseWare.
Aside from all of this, I've made considerable progress with my research itself. At this point, I've written a program in C++ to sort the firms into comparable groups for the five firms that I intend on analyzing, and I've run the program on these five firms. The program took a bit longer than I intended to take (about a day and a half), but i'm still right on track for the data collection for next Sunday. At this point, I'm beginning to analyze the multiples for each firm within my question. Currently, I am working on the price-earnings multiple, and am calculating the error percentages for the different firms. I expect to be done with this multiple by tomorrow and begin working on the Book Value multiple right after that.
At this rate, I will easily have all the data that I need for next Sunday. Because the data needs to be in an organized manner, however, I'm just going to spend the rest of my post describing how I intend to organize the data.
For first picking the comparable firms for Facebook, Twitter, LinkedIn, Yelp, and Facebook, I intend to display the average values of total assets and EBITDA over the nine groups that I calculated for each firm, highlighting the one group each of the five above firms fell into.
Next, for each firm, I will have a excel spreadsheet that shows the pricing errors for each prediction using the different multiples. The rows will be represented as a different multiple, and the columns will consist of the comparable firm group, and the different mean, median, and harmonic mean values for their multiples. Finally, the pricing error for each of the above methods will be listed, allowing for a direct comparison of them.
In general, I'm feeling really positive about the entire process, and I can't wait to comment about my whole results next week! I'm just sad that most of the data collection part is coming to a close.
Signing off,
Akash
Hey Akash!
ReplyDeleteThat Machine Learning Class sounds awesome! Let me know how that goes.
As for your research, I think its awesome and makes a lot of sense to use a program to sort the groups, as it will not only save a lot of time, but be much more efficient overall and maybe slightly more accurate than doing it by hand. I think that really lends to your credibility overall and makes your project a bit clearer. It's great that you are already moving on to your Book Value multiple, but I'd love to hear if you've encountered any new limitations and if you have found a clear way to address the problem that you brought up in last week's blog! Other than that, its great that you have a way to organize all your data once it has been collected, and I can't wait to see what results you have!
Hey Akash! Congrats on finishing your program. It is really impressive that you were able to build your own to analyze your data.
ReplyDeleteOne concern that I have is that you have a lot of variables and numbers to report in your data analysis. Even just from reading your blog I feel like it would be hard to focus on what exactly each number means, while not being overwhelmed by all of the numbers. Do you have an idea for how you will organize your data so it is not just a list of numbers? Can you make the firms interact in your data report?
Otherwise it sounds like you are going in a very strong direction. Keep up the good work!
Grace -- being a TA has really sharpened your skills at identifying key organizational problems. Akash, as we discussed today in our meeting, it's going to be key in figuring out how to have the findings from the firms converse with each other. Considering the amount of raw data you have, showing relationships and meaning is imperative to making a clear narrative from said data.
DeleteYikes Akash, as always, your intelligence and hacker skills amaze me. It's awesome that your data collection is going well, but I really didn't understand your explanation of how your organizing things. I think it may be because I don't understand your project that well, but definitely think about how to present the numbers in the most effective way possible, and take into account how readers may not be able to fully process the depth of your project without you guiding them a little in the figures. Your project seems v sick tho and i can't wait to see it culminate and SNAP into place lololol :) good luck !! you're a star
ReplyDeleteNope -- I think that your lack of understanding stems largely from it being hard to understand. Sorry Akash, we can't all be as seamlessly brilliant as you are. Explain things more!
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