Globally, there has been a considerable rise in AI-powered tools. You can now find AI technology in the healthcare industry, logistics, retail, and more. Chances are, you interact with AI technology every day.
In fact, it’s estimated that the global AI market will be worth $1,394 billion by 2029. Sustained growth like this, given the current state of the economic market, is something to be celebrated. The technology industry has faced considerable challenges post-Covid, due to the rapid uptake of technology throughout industries during the pandemic. AI’s resilience to the shifting climate demonstrates just how important it is to the industries of the future.
However, while there has been a solid increase in AI adoption in the financial sector, investments have yet to see the real benefit. The primary reason for this is that not all AI solutions are equal, and the financial sector has some very clear criteria for AI and ML tools.
Despite this, AI tools are necessary for the future of equity forecasting and investing. Here, we will look at how these technologies are impacting the sector, and why they are a key component in the future of investments and equity.
The rise of big data can be seen in industries across the world. As digitization increases, so does the amount of availabledata. The financial sector is no exception. There has been considerable growthin data from both stocks and third-party sources.
Data, or rather the volume of available data, is a problem for people. Investors simply cannot process the amount of data required in the current market.
However, data is an advantage for AI and ML technologies. Data is the fuel that they run on, so these technologies are the natural progression for the industry. This isn’t to say that AI should replace investors. In fact, investors are critical to the success of AI solutions. The future of investments is the augmentation of investors with AI and ML.
As mentioned, AI needs and thrives on large volumes of data. The more data, the better performance for AI and ML technologies. Statistically speaking, data improves accuracy overall.
However, AI thrives best on structured data. Ultimately, what goes in must come out. Using messy, unorganized data, presents a challenge for AI.
Thankfully, the financial sector provides data that is structured, as the market is driven by this data. This makes AI and ML technology uniquely suited to the investment space.
Investors and advisors need to act fast. Speed is paramount. Inefficient investors arefar less likely to make returns, regardless of how accurate their predictionsor forecasts are. Needless to say, the more data, the slower an investor moves.
This means that the currently available data is a challenge for investors. They need to be able to make the most of the information available to them, while also moving as quickly as possible. This isn’t a scalable approach for the future, where data is likely to continue increasing in volume.
AI is not only capable of processing vast amounts of data, but it can also do it at speed. AI is the tool that investors need to manage the current financial landscape, and translate it into actionable information.
AI is not only capable of processing data provided by stocks. Valuable information can come from a variety of sources.
Part of the reason that data continues to increase in volume on the market is that third parties and external sources can contribute valuable information. As mentioned, the more data, the more challenging it is for investors to make accurate predictions.
AI and ML solutions are able to process data from any relevant sources. This allows investors to harness the most valuable information for their investment decisions.
Factor investing remains a popular investment strategy. It aims to allow investors to make the most of available data by breaking it down and analysing it based on specific relevant factors. However, the majority of investors are currently managing around five factors.
The truth is simple, the more factors, the deeper the understanding of the available data.
A deep factor framework allows you to analyse information on a deeper, empirical level. The more individual elements are understood about a single stock, the more accurate forecasts can be.
At 3AI, we have generated a deep factor framework that analyses based on over 326 factors for each of the 20,000 stocks on our database. This technology can identify what factors contributed to each AI-powered decision or forecast. Investors can evaluate each factor and each decision, and provide their own context before making investment decisions.
AI and ML technology has driven factor investing into a new age, and has the potential to change the way investors perform, allowing them to obtain alpha.
In the current market, investment managers arehandling more portfolios than ever before. The consistent shifts and evolutionsin the market mean that it can be difficult to maintain control and continuedriving returns.
Adding more and more funds to a manager’s portfolio just isn’t a scalable or sustainable approach.
AI and ML technologies aim to help investment managers to outperform at scale. There’s no limit to the amount of data that AI solutions can leverage in order to improve outcomes. Let AI technology do the heavy lifting, and allow investors do what they do best with 3AI.
Our primary goal is to understand how markets work. We believe that big data and deep factor framework analysis are the best way to reach that goal, and AI is the tool to help us get there.
We aren’t looking to replace investors, but to empower them to achieve the best results possible. That’s why we make our solutions as transparent as possible, as investors are the key to unlocking alpha.
Interested in learning more? Get in touch with us to discuss our AI and ML solutions today!