# How causal AI could have predicted the outcome of the GameStop incident
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2021-03-05
Category: BUSINESS
Category URL: https://financedigest.com/category/business
Meta Title: Causal AI Unlocks Value in Financial Data
Meta Description: Discover how businesses are using causal AI to unlock value in data, predict market trends, and simulate future events. Stay ahead of the curve with precise
URL: https://financedigest.com/how-causal-ai-could-have-predicted-the-outcome-of-the-gamestop-incidenthtml

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_By **Darko Matovski,** CEO and Co-Founder of_ [_causaLens_](https://www.causalens.com/)

The recent [GameStop/Robinhood incident](https://www.independent.co.uk/news/business/analysis-and-features/gamestop-what-happened-reddit-short-selling-meaning-b1794936.html) 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](https://www.causalens.com/white-paper/gamestop-short-interest-analysis/), 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](https://www.financedigest.com/siemens-ceo-watching-the-share-price-of-siemens-energy-ceo.html "Siemens CEO watching the share price of Siemens Energy – CEO") causing investors to scramble for an explanation. Ultimately, though, the story [revolved around](https://www.financedigest.com/the-medication-dispenser-market-revolve-around-digitally-driven-innovation.html "The Medication Dispenser Market revolve around digitally driven innovation") 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](https://www.financedigest.com/mldw-technology-market-size-share-top-leading-companies-consumption-drivers-trends-forces-analysis-revenue-challenges-and-global-forecast-2028.html "MLDW Technology Market: Size, Share, Top Leading Companies, Consumption, Drivers, Trends, Forces Analysis, Revenue, Challenges And Global Forecast 2028"), 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](https://www.financedigest.com/eex-markets-to-remain-open-as-power-prices-soar-statement.html "EEX markets to remain open as power prices soar – statement") relatively stable, at around $20. There was a dramatic fall in this volatility in December, immediately [followed by a huge](https://www.financedigest.com/uk-watchdog-contacts-banks-following-huge-withdrawal-of-mortgage-deals-ft.html "UK watchdog contacts banks following huge withdrawal of mortgage deals -FT") 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](https://www.financedigest.com/austria-grants-credit-line-to-vienna-utility-squeezed-by-power-price-surge.html "Austria grants credit line to Vienna utility squeezed by power price surge").

![Darko Matovski](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/darko-headshot-updated-450x455-1736838545153-compressed.png)

Darko Matovski

**Cause and effect**

Humans tend to think of things in [terms of cause and effect](https://www.financedigest.com/ifrs-15-moving-away-from-short-term-fixes-is-vital-for-effective-compliance.html "IFRS 15: Moving away from short-term fixes is vital for effective compliance"). By ascertaining why something happened, we’re able to [change our behaviour and change the outcome of similar situations in the future](https://www.financedigest.com/the-future-of-energy-tech-and-data-are-driving-change-fast.html "The Future of Energy: Tech and Data are Driving Change, Fast"). Using causal inference enables AI algorithms to reason in a similar way, addressing one of the biggest challenges around [machine learning](https://www.financedigest.com/transforming-insurance-through-artificial-intelligence-and-machine-learning.html "Transforming Insurance Through Artificial Intelligence and 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](https://www.financedigest.com/acetylated-distarch-phosphate-market-comprehensive-shares-historical-trends-and-forecast-by-2027.html "Acetylated Distarch Phosphate Market Comprehensive Shares, Historical Trends and Forecast By 2027") 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](https://www.financedigest.com/how-brands-can-adapt-marketing-tactics-to-better-suit-customers-changing-shopping-habits.html "How brands can adapt marketing tactics to better suit customers’ changing shopping habits"). A good example of this is the [COVID-19 pandemic](https://www.financedigest.com/uk-sees-biggest-rise-in-foreign-workers-since-covid-19-pandemic.html "UK sees biggest rise in foreign workers since 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](https://www.financedigest.com/covid-19-impact-on-attitude-and-heading-reference-systems-market-volume-analysis-future-prediction-industry-overview-and-forecast-2028.html "Covid-19 Impact On Attitude and Heading Reference Systems Market | Volume, Analysis, Future Prediction, Industry Overview And Forecast 2028") observations. These [businesses are effectively driving](https://www.financedigest.com/the-collective-power-of-partnerships-integrating-payment-solutions-to-drive-business-success.html "The collective power of partnerships: integrating payment solutions to drive business success") 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](https://www.financedigest.com/sensor-based-glucose-measuring-systems-market-by-2028-with-technological-advancements-growth-of-industry-overview-and-dynamics-with-affecting-factors-2.html "Sensor Based Glucose Measuring Systems Market by 2028 With Technological Advancements, Growth Of Industry, Overview and Dynamics with Affecting Factors") where it’s possible to see cause and effect in action.

[**Unlocking additional value**](https://www.financedigest.com/how-innovations-in-edge-computing-can-unlock-value-for-global-organisations.html "How innovations in edge computing can unlock value for global organisations")

By combining the simultaneous [analysis of thousands of datasets with causal models and truly explainable](https://www.financedigest.com/geological-analysis-explains-durability-of-stonehenge-megaliths.html "Geological analysis explains durability of Stonehenge megaliths") insights, a causal AI machine learning platform can unlock significant additional value in data.

For example, Foreign Exchange [technology provider](https://www.financedigest.com/is-your-technology-provider-holding-you-back.html "Is your technology provider holding you back?") 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](https://www.financedigest.com/automotive-air-vent-assembly-market-size-key-players-growth-factors-regions-and-applications-industry-forecast-by-2027.html "Automotive Air Vent Assembly Market Size, Key Players, Growth Factors, Regions and Applications, Industry Forecast by 2027") 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](https://www.financedigest.com/what-makes-a-good-long-term-investment-strategy.html "What makes a good long-term investment strategy?") 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](https://www.financedigest.com/amino-acids-market-trends-size-share-growth-forecast-report-2025.html "Amino Acids Market Trends, Size, Share, Growth Forecast Report 2025"). 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](https://www.financedigest.com/the-evolving-world-of-work-what-next-for-employers.html "The Evolving World Of Work – What Next For Employers?").

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](https://www.financedigest.com/gary-mcgaghey-explains-how-cfos-can-strengthen-decision-making-processes-and-reimagine-the-finance-model.html "Gary McGaghey Explains How CFOs Can Strengthen Decision-Making Processes and Reimagine the Finance 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.


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