Valuing My Paper: Education for my Comparable Group

FINALLY

My full paper is in its really rough draft form, and I couldn't be any happier. After so many months of work and research, I've finally put together something that at this stage at least looks like a real research paper! Anyways, as I've hinted a lot of time already, my paper isn't even close to done, so this blog post is just a heads up for what I'm feeling really iffy and worried about.

The first thing that I'm worried about is the length of my entire paper. It's a bit like 1000 words over the limit and I need to cut as much as possible from it, with it still making sense. I know I have a tendency to be redundant and use a lot of complex language complex language, so pointing out anything that can be cut or that can be simplified will be very useful.

Another worry that I have in terms of my paper is the definitions that I provide. Because my paper is highly technical, I want to make sure than any person can read and understand it. Therefore, if I have any unclear definitions (I most probably will), or don't explain something well enough, please bring it up in my comments. Most of definitions are in the literature review, but I also deal with statistics in my results section that I somewhat avoid full explaining. Insight into whether I need to change this or expand on it would be really helpful.

In additions to my overwhelming definitions, I'm also worried about the data in my results section coming off as too much. I have a lot of tables in the section, and although they have explanations right beneath them, I'm wondering if I should discard some of the tables altogether. I would greatly appreciate if anyone has any insight into the specific tables I should keep, get rid of, or re-explain.

Finally, and I think this is another big concern of mine, are the transitions within my paper, particularly the results and discussion section. Transitions are incredibly important, especially in a technical paper like mine, and I want to ensure that everything I say flows smoothly from one topic to another. Therefore, commenting on any missing or bad transitions I have would be, again, incredibly helpful. I have a lot of complex concepts, and if I don't explain how they connect to one another well, I might as well not have written the paper in the first place.

Anyways, that's all I've got right now. (427)

Weirdly not tired,
Akash

Real Talk

AHHHHH!!!!

At the end of this week I'm going to have a whole research paper! It's really hard to believe how long I've come since my very first post, but I'm happy for this entire class and project. It's been a really good time. So without any further delay, I'm going to launch into my post.

The general structure of discussion sections for Finance papers doesn't vary much from the standard way that discussions are written.

As in Nissim and Liu's study on IPO valuation, discussions for results begin with an explanation of the pricing errors for the multiples (ie. why specific multiples preformed in specific ways). Often, as studies from researcher Ashwath Damodaran and Erik Lie suggest, these explanations are rooted in properties of the industry being analyzed, or the types comparable firms obtained within the results. Because methods are integral to the way the results within the study, discussion sections also talk about the way in which the methods were limited and how these limitations contributed to the results obtained within the study. In doing so, finance papers are able to connect their results, not only to a larger context of analysis available within literature reviews, but also to those in methods sections.
This connection allows the future directions sections of finance papers to be especially well-developed, connecting areas of the methods and literature review to suggest future directions in a specific industry or case-study.

I plan on incorporating all of these aspects into my own discussion section. Here's my plan:
First, after reiterating the purpose of my study, I'm going to discuss the implications of my results in the context of my initial question. Then, I'm going to expand the implications of my results to begin explaining why they were a certain way. Essentially, because the firms I picked were not only representative of Snapchat, but most other Social Media IPOs, the results of my study indicate that there is no optimally accurate comparable firm multiple for valuing Social Media IPOs.

To explain the results of my study I am going to include two "levels". The first includes an explanation for how conventional comparable firm multiples fail for Social Media IPOs, and the second is an explanation of how the method I used to pick comparable firms may have been flawed for the Social Media Industry in particular. From then, I'm going to transition into a discussion of the limitations of my paper. The very first limitation rests on one of my explanations: the method for choosing comparable firms. The method I for choosing comparable firms was used in a study on Biotechnology firms, which have a substantial amount of intangible assets (like research and development), similar to Social Media firms. It turns out however, that social media firms have such high investor sentiment and intangible assets that my application of the method was flawed. This was something I could only have seen after my research study, and it draws me to a discussion of future directions.

Ultimately, the method I used within the study is the only one that has ever been used in finance research. Therefore, another means of sorting and finding comparable firm groups would be an interesting avenue for future direction. I also present two or three other avenues for future directions based on other limitations and explanations I came across in my study.

Finally, I'm going to end my paper with a description of the implications of my future directions. This is relatively simple for me: it provides institutional and individual investors a better means for pricing the social media industry as well as other largely growth-oriented industries, which could be incredibly useful in the future with the shift towards more technology based companies carrying lower barriers to entry, more competition, and strong emphasis on growth.

Anywho, that's all I have right now. (645)

Tired As Always,
Akash

Racing To The Results Section

WELL,

Today's been a really long day. I ran a crazy long race with my friends, and it was pretty actually really exhausting. The problem was that I only had like 5 glasses of water before the race, and I ended up cramping a bit farther than halfway through. AT LEAST I FINISHED THOUGH.

Anyways, I'm going to start my blog post and talk for a bit because I really want to keep my mind off of what's going to happen to my body tomorrow.

So far I've already finished an outline of my results section, and I'm working on writing my results section up currently.

For the most part, among all finance studies that handle comparable firm multiples, the results section is rather short. Usually this is because researchers (such as Guo, Nissim, and Kim) simply present the general accuracy of the multiples and then move on to the discussion section to talk about whichever multiple performed the best and the reasons for its performance.

Because I am writing to a more lay, and less technical audience, however, my results section will greatly expand upon the format of the results section for most finance papers.

First, I will present the findings for the comparable firms of each of the five firms I analyzed within my section. The information will be in a table and will have each column as a different firm and its comparable firms. Thus, the table allows someone looking at the firms to determine which are more alike and which are more different. For example, Facebook and LinkedIn have a common comparable firm.

Next, I will establish the threshold for an effective multiple. Although this will only take a sentence, it is essential for later parts of the results section, which will use this threshold to draw conclusions from the data.

After establishing a threshold, I will begin presenting all of the data I have collected for the five firms. This will consist of five tables each with the harmonic mean, mean, median, and average accuracy of each of the multiples.

After establishing the table, I will then begin to make the first of my conclusions from the data in my results section. To determine which of the three methods (harmonic mean, mean, or median) is the best, I will present a table of p-values for paired two-tailed significance tests for the estimates. I was not able to conduct independent, multi sample significance tests, as the methods were not entirely independent of one another and relied on a common set of data that varied between successive trials.

Because my table shows that there is not significant difference between any of the three methods, to determine which multiple is the best, I will present a two figures. The first figure is to determine whether any multiple is significantly better than the others. I used the average error for each multiple (because the three methods were essentially the same), and performed significance tests two determine if the errors were different than one another. This would indicate that the multiple that caused these errors was significantly different from the others. Since each firm was a different "trial", the different trials are not independent of one another. Thus, I had to perform a paired two-tailed t-test for these values. My results show that no multiple is significantly different from the others.

The final figure in my study shows a comparative bar chart of the overall error of each of the multiples (taken using an average of averages) against the maximum threshold for error (15% error). The chart clearly shows how no multiple is even close to being considered effective. Thus, the final two figures helps me reach my ultimate conclusion in my results section: that no multiple is more effective than the others, and no multiple is effective at valuing Social Media IPOs.

Anywhoozies, that's all I have for now. I'm going to go sleep. (657)

Night,
Akash

Time to ANALYZE

HEY EVERYONE!

Data collection is finally over, and even though I have a lot more stuff to do, it's really nice knowing that I have everything that I need for my ultimate conclusions. That being said, the numbers that I've obtained aren't the best for what my original question was. The percent errors for almost all of my multiple estimates are close to 100, and only a handful are below 30 percent.

As mentioned before, this likely indicative of a more general characteristic of social media firms, which I plan to develop for my discussion section.

For now, I'm planning on developing exactly what I plan to do to analyze the data that I have collected.

First, I will compute the average error of the multiples for each firm. I have already done this, and plan on including this in a table for my data section. Thus, my data section will have five different tables with four columns and five rows. Each column for a single row will be the estimation error for a specific multiple and a specific firm. This will allow me to most directly compare the effectiveness of multiples for a given firm.

Next, I will need to compare the accuracy of multiples across the five firms I am analyzing. To do this, I will have a table for each multiple with the average error of each method for every firm. This allows me to determine which multiples perform the best for which firms, allowing me to draw conclusions for why specific multiples work better for specific firms.

Finally, I will determine which multiple is best overall across all five firms. To do this I will draw five box plots for each multiple representing the different errors for all five firms. I can then analyze the spreads for the box plots, and their average accuracy to compare how accurate they are in general, and how much they differ in their accuracy. The box plots also give a visual representation of the pricing errors, which allows me readers to more quickly grasp the differences between the multiples. To further explain the box plots, I intend to include a brief description of the box plots below them, summarizing important features of the plot such as the mean error of the box plot, which represents the mean error of the multiples.

I may also do the same box plots for each of the three methods that I used so that I can provide better comparative analysis of the data.

I think what I have right now is a pretty good start, but it might be hard to graphically fit five box plots side by side to ultimately compare them. If anyone has suggestions on how I can more nicely compare the data for the methods, it would be really useful. Anyways, thats it! (476)

Signing off,
Akash

The Search for Multiples

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

Numbers, Numbers, and More Numbers...

HEY EVERYONE!

We just got out of school, and the Superbowl just happened! Honestly, I'm not really much of a sport fan at all, but the food is still pretty darn good, and it's fun to watch people get really overly passionate and argue with each other. My research is going great right now, and its really starting to develop into something that I like doing.

Right now, I've just finished all of my data collection, and am in the process of finding the specific comparable firms for each firm that I outlined within my question. As of now, I've only found comparable firms for Facebook (which are Google, Apple, and Microsoft), but I have all the data for the other firms, so I don't envision my work being too much in respect to that.  I can get that done by Thursday, and that gives me a week and a half to begin my multiple analysis, which should be ample time given that it only involves comparing only a few numbers (only the range of the 50's, rather than the hundreds I've had to deal with up to now.) The big problem that I now am seeing for my firms is in respect to the comparable firm group itself. This problem really only surfaces with Facebook, but it may possibly have a big impact on the results of my study.

Essentially, the problem is that there are incredibly few firms like Facebook that are wildly successful, or at the "mega-capitalization" size. This essentially means that the number of comparable firms that I have to choose for Facebook is dramatically decreased. In fact, Google, Apple, and Microsoft are the only eligible firms at Facebook's level. I could potentially consider firms with fewer assets than Facebook, but this difference starts getting big very quickly: in nearly the 200 Billion Dollar range. I think I have found the very first limitation of my paper: that companies which are wildly successful are very difficult to have prices that are easy to predict because of the few comparable firms that they have. Although none of my other firms have assets as large as Facebook, and have comparable groups in the hundreds, I fear that this data may not ultimately be applicable to Snapchat. Snapchat's IPO has been valued in the 25 billion dollar range, something which is already incredibly high. Ultimately, I think I'll have to wait and see how the comparable firms for he other companies turn out, but I'm not too hopeful for the future.

This result could point to something more intrinsic to the social media industry for the future, however. Perhaps, because of these large social media firms already attracting consumers, there is such a large barrier to entry to the industry that only uniquely special, large firms that are difficult to value can enter the industry.

Either way, it seems like I have a handful ahead of me when analyzing my results. (495)

Signing off,
Akash

Getting Down To Business

Hey Everyone!

We only have one week left of school!!!! I'm definitely looking forward to the freedom I'll finally have, and I'm really excited to get into the meat of my research. On the other hand, I'm also somewhat nervous for what the future has to hold. After this week, the next time I'm going to be in school is in college, and that's a really scary thought.

Anyways, since last week, I've caught up with a good deal of the data collection I outlined within my schedule. I've recorded the total assets and EBITDA values for Facebook, LinkedIn, Tencent Holdings, Yelp, and Twitter, and then began recording the same values for all firms in their industries. The main problem I had this week was finding that LinkedIn's data was no longer on the NASDAQ site. This is because, in December of 2016, it was acquired by Microsoft. I was able to find a quick solution this problem, however, as Google keeps historical data from companies trading on large exchanges like NASDAQ or the NYSE. Therefore, my project is still on track. Last week I mentioned that I would have to collect data for analyze over 1000 firms in order to obtain a proper comparable firm sample for the 5 firms I listed above. I'm happy to say I no longer have to do that anymore, thanks to a filtering method I'm taking. NASDAQ has companies ranked based on their market capitalization. By filtering based on this metric, I am essentially looking only at companies that have similar levels of market capitalization to one another, and therefore, have similar size and total assets. This allows me to collect data for far fewer firms than I had initially needed to. For the rest of the week, I anticipate finishing my data collection phase and moving on to finding comparable firms for each of the companies that I picked out. From there, I will perform my analyses on each of the comparable firm multiples that I chose for my study.

Other than this, I've been working to clean up my methods and literature reviews for the end of the trimester. My methods assignment definitely needs more explanation, and I need to articulate complex statistical calculations in ways that everyone can understand easily. Specifically, my biggest problem is making sure my derivation for the harmonic mean projection of a multiple is simple and easy to understand.

That's all for now! Stay tuned next time for musings on calculations! (413)

Akash