Financial Analysts Might Be Out of a Job Soon…
Do me a favor real quick. Go on Google, Reddit, ChatGPT, or any website you personally use to search for factual information. Find me the answer to this question:
what biotech stocks make more than $100 million in revenue, are profitable, and their profit margin increased in the past two years?
Having a tough time? Go ahead and pull out your favorite stock screener. Start a timer and let me know how long it takes to find this information.
Don’t worry. I’ll wait.
Or, you can do the alternative: ask Aurora.
In less than two minutes, I received the exact information I needed in a nicely formatted, plain-English explanation.
Okay. Maybe that was just an easy question. How about let’s try one more:
what stocks have the highest net income increase from covid until now
Go ahead. Search Google or Perplexity or whatever tool you use. Let me try finding the same information.
Boom! Done. In 60 seconds or less. This is incredible. But its not magic, and it has risks.
How it works
When you ask a question like that into ChatGPT, GPT is just gonna make up the answer. That’s how it works.
But with Aurora, the answers are backed by data.
Instead of asking ChatGPT to answer the question, we generate a query for the database, fetch all of the relevant data, and then ask GPT to summarize it.
The procedure is similar to advanced techniques like Retrieval Augmented Generation. Relevant data is fetched and input into the model, so that the model generates more accurate responses.
Want a more detailed explanation on how it all works? Check out the following article.
The Dangers of AI Insights
The biggest advantage of this feature is also its biggest drawback. The answers for the model come from a database. But if the data is wrong, how will the model perform?
Spoiler alert. Poorly.
In this case, the database was populated with incorrect values for the free cash flow, giving an inaccurate response to the user.
While this specific instance was raised to the vendor and fixed, this issue is widespread, and smaller, less well-known companies are more likely to have data issues.
But this will get better over time! As I continue to deploy monitors to detect unusual data, and as more users raise issues, there will be slowly fixed over time.
But for now, we must still be diligent. Investors need to evaluate (and potentially fact-check) the response that the model gives you.
Concluding Thoughts
The advent of AI-powered tools like Aurora is revolutionizing the way we conduct financial research, making it faster and more efficient than ever before. By leveraging large language models and data-driven insights, Aurora can provide precise and timely information that would otherwise take hours, if not days, to gather manually. This capability has the potential to significantly disrupt traditional roles in finance, such as financial analysts, by democratizing access to sophisticated data analysis.
However, this newfound efficiency comes with its own set of challenges. The accuracy of AI-generated insights is only as good as the data they are based on.
As demonstrated, incorrect or outdated data can lead to misleading conclusions, underscoring the importance of diligent verification. While AI tools can vastly improve the speed and scope of financial analysis, investors and analysts must continue to exercise critical thinking and due diligence when interpreting AI-generated insights.
AI tools like Aurora are poised to transform financial research. By combining the strengths of AI with human expertise, we can achieve a more efficient and accurate approach to financial analysis, paving the way for smarter investment decisions.
Thank you for reading! If you’re interested in algorithmic trading and AI subscribe to Aurora’s Insights! Want to try Aurora for yourself? Create an account on NexusTrade today!
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