Short Squeeze

Background

We wanted to demonstrate the dynamics of the Battle of GameStop© so we put together a simulation of the sharp escalation of price that can occur when an irresistible force (legally needing to cover your shorts) meets an immovable object (the combined will of thousands of redditors).

The simulation is made up of a mix of different agents – shorts that need to cover their positions before expiration, shareholders who are long the stock in question and will sell at normally-distributed prices, and diamond-hand-never-fold traders who won’t sell until the squeeze is done (at/or near an expiry data for a high price).

In the first scenario the shorts are buying from a relatively liquid market. They can offer low starting prices and gradually increase/decrease their price to match the desired price of shareholders, who slightly adjust their prices based on market conditions. Most of those shareholders don’t want to miss out on an opportunity to sell, so once the a certain portion of the shorts are covered, they will try lowering their price to ensure a trade. Due to this lowering of prices, the shorts can easily collect the shares they need.

However, if there is a large enough percentage of agents who won’t sell, and the market, losing liquidity, forces the shorts to bid the price up, you can see the price skyrocketing to the desired reserve price of the traders, successfully squeezing the shorts. In the next scenario, we’ve designated a certain portion of agents, determined by the hold_out_ratio parameter, that won’t ever lower their sell limit. When run the price initially drops as low priced shares are purchased, but then eventually go parabolic when faced with a large number of traders that don’t sell.

Raising and lowering that parameter can manipulate the likelihood and intensity of a squeeze. You can use Experiments to understand these effects. There are already two experiments defined in this model which can demonstrate the squeeze phenomena we've described.

Experimenting with different compositions of agents can create dramatically different outcomes. Below you can see all the other parameters that you can modify and sweep.

Parameters

• hold_out_ratio: the ratio of shareholders who will not lower prices as the short interest decreases
• total_sellers: the total number of selling shareholders
• short_interest_pct: the short interest percent determines the initial number of uncovered shorts
• trader_sell_distribution: the parameters for a triangular distribution defining the sell orders for traders
• holdout_sell_distribution: the parameters for a triangular distribution defining the sell orders for the hold-out traders
• panic_short_amount: the amount of uncovered shares at which traders will start to panic and drop their prices,
• steps: the total steps the model runs for,
• time_to_cover: the total steps before the shorts need to be fully covered,
• share_uniform_distribution: the parameters for the uniform distribution of initial shares