From IBM Watson to HSBC, AI-powered equity indices are becoming more and more popular.
It's no surprise considering that the Artificial Intelligence (AI) technology market remains strong. In fact, it’s expected to maintain a 20% CAGR over the next 7 years.
AI-powered equity indices appear to be one of the top trends over the coming years. This latest trend demonstrates a keenness to modernize and innovate in the financial sector. AI has seen a solid uptake in a variety of industries, including healthcare and logistics.
AI has the ability to change the way that investors and financial services work. Data volume continues to increase, and AI is the solution capable of harnessing that data.
However, it’s important to note that not all AI systems are made equal. The same can be said for AI-powered equity indices.
So, how do you find an equity index that’s worth your time as an investment manager? What key criteria do you need to look for?
Naturally, talking about AI technology can be challenging in the financial industry. As with many other sectors, it can be concerning to hand over your working responsibilities to a piece of technology. If nothing else, Hollywood has taught us to be fearful of the future of tech.
However, the best AI solutions don’t seek to replace investors. They augment investors. What does that mean? Investors are good at their jobs. In fact, on the global market, human contextualization is key to making smart investment decisions.
But, it’s also important to note that there’s so much accessible data, and it continues to grow in volume. Investors cannot process it all, without having a data scientist on staff 24/7. This is where AI technology can pick up the slack.
AI is an incredibly powerful tool for data management and analysis. That data is there to be leveraged by investors. You simply need a tool that unlocks that potential. In the tech-driven world, AI and ML algorithms are those tools.
In the past, passive investment strategies have been a popular trend. Risk remains low and there is a belief that the market provides the best achievable performance. However, this also means that investors are increasingly unlikely to achieve any real alpha results.
AI solutions that augment investors can help to unlock an improved performance. That’s why a change of strategy is critical for those looking to drive forwards out of the current challenging market.
This all sounds great, so why haven’t all investors adopted the latest AI technology? The truth is that there have been challenges with widespread AI adoption in the sector. Not all AI systems are the same, and the solutions that have been available in the past haven’t been perfect.
One of the primary challenges that AI has faced in the industry is that many AI solutions are what we refer to as “Black Box AI”. These systems tell investors what to do, but not why they should do them. Simply, the data goes in and a conclusion comes out. The system doesn’t make its reasoning accessible.
Experienced investors, like most people, don’t like to be told what to do without context behind it. It’s difficult to trust an entirely new solution without any proof or evidence of success. Generally, people like to understand the ‘why’ behind every decision.
This is why we believe that Black Box AI has hindered the uptake of AI technology in the finance industry. Its lack of transparency is a contributing factor to the failure of so many AI projects.
Explainable AI is the future of AI, especially in the world of investments. It lets investors understand the reasoning process behind every conclusion it draws. XAI provides access to the data or factors used to reach a decision, so that the user can evaluate this process.
Not only does this mean that the user can provide context that the data may not possess, but it increases the level of comfort with this innovative technology. The more comfortable we are with AI solutions, the more widely adopted they will be.
Explainable AI is a great move forward for the AI sector, but it is not a solution on its own. Data is the fuel for AI and ML technologies. They need enough of it to reach really meaningful conclusions.
There are quite a few examples of how AI has reached strange conclusions due to a lack of data. One popular example of this is the prediction of presidential election outcomes based on the performance of the NFL Washington Commanders. It was found that around 90% of the time, if they won their last home game, the current party in power won the presidency. This forecast held true until 2004.
This strange prediction mechanism was caused by a lack of data. Ultimately, the NFL had only existed since the 1920s, and the football team had only been around since the 1930s. In the roughly 70 years between the founding of the football team and the 2004 election, there were less than 20 presidential elections.
In simple terms, this was just a coincidence, not a data-based forecast.
It’s quite easy for ML systems to fall into a trap like this when data sets are limited. It can only draw the conclusions found in what’s been provided.
That’s why our systems at 3AI are trained on over 1 million years of cumulative stock data, analysing over 20,000 stocks. The more data, the more accuracy.
As mentioned, AI and ML aren’t really able to contextualize and understand the difference between causation and correlation. This is a human-only trait, which is why explainable AI is so critical.
One example of interesting causation and correlation between the consumption of margarine, and the divorce rate in Maine (USA). Data would suggest that the less margarine consumed, the lower the divorce rate. While an interesting correlation, the human mind is able to accept this as a simple coincidence, not a serious connection.
The problem raised here is that this data isn’t curated. You can compare any random data sets and find correlations. If you provide ML with that data, it will find those same correlations as if they are causation.
At 3AI, we address this challenge by implementing our deep factor framework. This ensures that all data provided to our technology is organized and structured. Our framework analyses based on over 326 factors for each stock, out of a database of 20,000.
The majority of AI systems don’t follow this approach. The more factors analysed, the more clear and transparent the data.
At 3AI, our experienced investors aim to understand the current market. We understand that, in the world of big data, we cannot do this alone. After searching for solutions, we believe that AI is the tool that investors need to make sense of readily accessible market data.
We built an AI solution that empowers and augments investors, not replaces them. Our explainable AI technology allows you to process hordes of data in moments, with a deep factor framework that explains how it reached its conclusions.
We want to drive investors to achieve Alpha performance. Want to learn more? Get in touch with us today!