How causal AI could have predicted the outcome of the GameStop incident
By Darko Matovski, CEO and Co-Founder of causaLens
The recent GameStop/Robinhood incident highlights that trading signals are moving beyond traditional technical analysis. Hedge funds and traditional finance companies are now paying more attention to social media platforms to follow trends in retail sentiment and social movements. But with more data to monitor, it’s becoming increasingly difficult to sift through all the data to reduce risk and uncover trading opportunities.
However, Causal AI can unlock significant value in that data with an understanding of cause and effect and enable businesses to access more precise predictions than ever before. As analysis of the GameStop incident illustrates, such an understanding is a powerful tool for predicting market trends and simulating events that haven’t yet occurred.
Conditions were in place
The world of r/WallStreetBets and its fascination with GameStop hit the headlines in January 2021, with scarcely believable changes in share price causing investors to scramble for an explanation. Ultimately, though, the story revolved around the intricate cause and effect of short contracts on stock prices. Indeed, by using causal AI to analyse short interest data, it’s possible to understand the driving forces behind the GameStop phenomenon and how its effects could have been predicted early on.
One of the top causal drivers, for example, was the volatility in the number of units shorted. By examining the relationship between this variable and price, a clear story emerged. The initial unexpected spike in GameStop’s share price in September 2020 generated a period of high volatility in Q4 2020, during which the price remained relatively stable, at around $20. There was a dramatic fall in this volatility in December, immediately followed by a huge surge in the price throughout January.
It was clear from the data – as early as September 2020 – that all the necessary conditions were in place for a short squeeze. By contrast, mainstream media interest in the stock didn’t materialise until around January 25th, after prices had begun to surge.
Cause and effect
Humans tend to think of things in terms of cause and effect. By ascertaining why something happened, we’re able to change our behaviour and change the outcome of similar situations in the future. Using causal inference enables AI algorithms to reason in a similar way, addressing one of the biggest challenges around machine learning.
Businesses in the financial services sector tend to rely upon analysis generated by machine learning platforms which rely solely upon historical correlations to make predictions about the market trends and the wider economy. But focusing on predicting outcomes rather than understanding causality means these machine learning algorithms are unable to adapt to environmental changes. A good example of this is the COVID-19 pandemic, where non-causal machine learning platforms were unable to adapt sufficiently to the fluctuating market conditions. When making predictions, businesses will severely overfit to historical data, thereby failing to fit additional data or reliably predict future observations. These businesses are effectively driving forward by looking in the rear-view mirror.
But it needn’t be this way, especially in the world of finance – a truly dynamic system where it’s possible to see cause and effect in action.
Unlocking additional value
By combining the simultaneous analysis of thousands of datasets with causal models and truly explainable insights, a causal AI machine learning platform can unlock significant additional value in data.
For example, Foreign Exchange technology provider CLS recently implemented causal AI.
By helping the company understand the relationships between its own data and other datasets, the platform enabled it to identify significant and unexpected changes in key factors associated with the FX markets during the height of the pandemic. This, in turn, proved immensely valuable to its clients, allowing them to react quickly to changing market conditions, and enhance their investing strategies accordingly.
Many businesses use machine learning algorithms to solve complex, data-rich business problems, inform data-driven decisions and, in the case of the financial services industry, predict market trends. These traditional have severe limitations, however, often doing little more than fitting curves to historical data with no understanding of how the real world works.
Ideally, though, these algorithms should be able to adapt to new and previously unseen data. Understanding causality, and working out what would happen when multiple variables are introduced to an established model – just as they would be in the real world – will enable machine learning to make more accurate predictions in complex systems such as those in found in financial services.
As we saw, an understanding of cause and effect could have predicted how the GameStop scenario would have played out long before it became headline news.