<img src="https://github.com/iamcalebjones/WallStreetBots/blob/main/images/bots2.png"> <figcaption>Source www.ingridhusby.com</figcaption>

r/WallStreetBots

This is a natural language study of the subreddit r/WallStreetBets as it relates to the surge in the stock price of GameStop, and investigates whether or not bots contributed to the hype that helped drive up the stock's price in early 2021.

By Caleb Jones

LinkedIn | GitHub | Presentation Slides

Background

Reddit is a place on the internet. Wikipedia states it is "a social news aggregation, web content rating, and discussion website" which is grouped into pages called subreddits, or subs, which contain topic-specific content. Reddit users can join specific subs to participate in that community's discussion.

One particular subreddit called r/WallStreetBets, exists to discuss stock and options trading. For the last year or so, members there have been speculating about the value of GameStop (GME) stocks. GameStop is a chain of brick and mortar stores that sells consumer electronics and gaming merchandise. Over the last few years, the company has been in decline due to the popularity of games being bought and sold online, and combined with the pandemic and struggle that brought to physical stores, GME was in bad shape. Such bad shape that hedge fund managers took large short positions on the stock, betting the value would continue to decrease, which would make them money. Hedge fung managers were so commmitted to this strategy that the stock was over 100% shorted. Members of r/WallStreetBets noticed this high short interest ratio in early 2020, and GME became a frequent topic of discussion on the sub.

The plot below shows both the GME stock ticker price and the posts contributed to r/WallStreetBets on a daily basis, and the relationship between the two is strikingly similar.

Here are a few details about these two trends:

Between December 14th and January 12th, GME averaged $18.55 per share
Over the same time period, r/WallStreetBets averaged 275 posts per day
GME hit its peak on January 28th, at $483 per share
r/WallStreetBets also saw its peak daily posts contributed on January 28th, with 28119 individual posts

Focusing on January 28th for a moment, there was a particularly high occurence of emojis, The following emojis showed up the most on this day:

🚀 had 4476 uses
💎 had 200 uses
🦍 had 100 uses
🙌 had 73 uses

A typical post incorporating emojis looks something like "To the moon!!! 🚀🚀🚀🚀🚀🚀🚀🚀🚀 we have 💎💎💎🙌!" The 💎💎💎🙌 emoji combination is known as 'diamond hands' which users who were holding shares of GME said in reference to holding stocks that were suddenly so valuable. "To the moon" was said in reference to users wanting to see the stock continue to rise, and many believe $1,000 per share was possible.

Although the stock hasn't reached that price, several frequent contributors to the subreddit managed to make massive money on January 28th. Mike McCaskill held 750 $60 call options which he exited at a price of $347 per share and walked away with $25 million. Reddit user u/DeepF*ckingValue, who's YouTube name is Roaring Kitty and goes by Keith Gill when not on the internet, had been posting to r/WallStreetBets nearly daily for several weeks with screenshots of his brokerage account and the value that he had amassed. At his peak, he was worth $47 million, but he didn't sell, a true 💎💎💎🙌. In reality, most of his value was due to the long-term options positions he had opened, which expire on April 12th. He's going to wait as long as possible to exercise his options in the hope that the price is as high as possible before he exits.

Natural Language Processing

As far as data is concerned, at first I thought it would take a lot of web scraping or navigating the Reddit API to obtain the information, but as it turns out, this topic is suddenly very popular, and someone had already done the legwork and posted it to Kaggle. The provided data was broken into posts and comments, and the datasets were 915 mb and 3.39 gb, respectively. Because of the size of the datasets, I started off working with the posts data.

I generally followed the standard steps associated with Natural Language Processing, or NLP. My workflow consisted of removing line breaks, apostrophes, hyperlinks, special characters, and finally numbers. I chose to just remove apostrophes from the contractions instead of expanding them on my first pass through the data, and I felt that the quality of the processed data didn't suffer as a result of this decision.

A big task with this dataset was handling emojis, because the contributors to r/WallStreetBets frequently use them and they were everywhere in the dataset. What I came up with was a method to translate them to their word descriptions, and adding the word 'emoji-' to the beginning. For example, 🚀 is the emoji named 'rocket', but just translating this symbol to 'rocket' would not preserve information that it was originally an emoji, so in my dataset this became 'emojirocket'. This actually worked very well, and in my modeling it was very easy to identify the emojis later on.

Next I lemmatized words, and finally removed stop words. I'd planned on using n-grams to preserve some information about word usage and context, but the size of the dataset was prohibitive. The resulting dataset was too large, both in RAM space and in processing/modeling time, so I proceeded with just single words.

Modeling

After the data was processed, I first built a WordCloud for the sake of the visual. The WordCloud shows the payoff of the previously outlined emoji translation efforts, as it is very easy to see their presence here. Several standouts are the emojis for rocket, gemstone, eggplant, and raising hands (the character in 'diamond hands'). Looking a little closer, the bots have identified themselves aboard the rocket emoji, right between the 'i' and 'r', surely riding this stock to the moon.

<img src="https://github.com/iamcalebjones/WallStreetBots/blob/main/images/comments_wordcloud.png"> Next, I performed dimensionality reduction using TruncatedSVD, which was the only matrix factorization technique I could get to work given the size of the data and the limits of my machine. I decided to reduce the matrix all the way down to 2 components, for the sake of being able to visualize them graphically. I then ran the reduced data data through a clustering model. My theory was that if bots were regularly contributing to the sub and hyping GME to drive up the price, their posts would be evident in a clustering model, as they were likely constructed very similarly. I expected that the posts would cluster into 3 groups: humans, bots, and other (or 'unable to tell if human or bot'), so I ran a KMeans clustering model with it constrained to 3 clusters, and the results of that model are shown below. <img src="https://github.com/iamcalebjones/WallStreetBots/blob/main/images/clustering_plot.png" | width = 700> After looking at the top words in each of these three clusters, shown in the table below, my summary of each is as follows:

Cluster 1: I'd classify this cluster as hobby investors most likely. With words like gme, buy, hold, the rocket emoji, and even some colorful language rounding out the top 15, this sounds like the majority of normal people scrambling to get in on the action.

Cluster 2: The posts in this cluster appear to be generated by a more highly educated group of authors, or at least the more long-winded authors, with the top word being “tldr” or “too long don’t read," which is conventionally included at the end of long posts on Redded in a sort of summary, indicating there were quite a lot of lengthy posts present here.

Cluster 3: include several more financially technical terms with 'smallcaps' and 'largecap' standing out. These posts are likely written by financial professionals, or at least by Redditors with more financial system knowledge.

| Cluster 1 | Cluster 2 | Cluster 3 | | ----------- | --------- | --------- | | delete | tldr | oems | gme | short | entrant | buy | market | redefining | stock | price | revenue | emojirocket | investor | highperformance | just | retail | endpoint | let | people | ndp | amc | fund | grube | robinhood | make | smallcaps | hold | share | largecap | dont | company | shortcoming | make | year | iso | like | hedge | jolt | sell | position | spokesman | f*ck | money | keeper

Next Steps

With every project I take on, it never feels finished. That being the case, for this one I have a few things in mind to continue this study. Firstly, I plan to open an instance on AWS SageMaker with much more RAM than my local machine has so that I can incorporate n-grams and try out other matrix factorization techniques. I would like to see if PCA and SVD would produce anything different or interesting than the TruncatedSVD reduction that I used. I am also interested in seeing how reducing to a different number of components affects the outputs of the clustering model.

WallStreetBots

Install / Use

README