The following text is from a response from the EquBot leadership team to the media on AI Investing

Artificial intelligence (AI) continues to build its presence across global industries so its progression into the asset management sector is only a matter of time. Before we explore the development of investment industry disruption let’s level set on what AI actually is. For this piece we should think of AI as a field in computer science that builds intelligence into electronic systems. These AI systems are able to perceive data environments, learn, and take actions to achieve its objectives. This definition presents a compelling value proposition for AI versus traditional portfolio management systems in light of today’s growing data environment. The key technological advantage of AI investment platforms is their overall flexibility to appropriately process perpetually shifting market data and identify opportunities.

AI can help us better understand what to trade, and how to trade it. Important AI capabilities include: consuming, learning, and aggregating growing volumes of traditional and alternative data across mediums, continually adjusting portfolio risk based on observed market signals (removal of rigid factor-based criteria), and the ability to connect new market signals to derive an optimized portfolio free of human bias.

Investors must understand that not all AI is created equal. There is a myth that AI investment picks cannot be fully observed let alone explained. The truth is that AI investment platform design will vary significantly, and in turn influence performance. High performing systems should appropriately action structured and unstructured data in a timely manner as well as appropriately manage erroneous or blatantly fake financial news.

Many early stories of AI investment systems described the brute force design of jamming massive data sets into the conceptual “black boxes” and allowing the machine to produce a series of recommendations. Better solutions exist and are more suitable for the explosion of available investment data and the demand for system operational observability. The systems unable to deconstruct results may be prone to introducing unintended investment risks into a portfolio rather unintelligently. The more transparent and higher performing AI data processing systems will drive increased flows into the space. We anticipate an influx of AI investment products so investors should look to understand how traditional and alternative data is processed and what the AI investment process entails as they shift out of their (soon to be) legacy investment vehicles.

Even with the recent AI technological developments and improved commercial accessibility, many investors will struggle to understand the potential value in well-designed AI investment solutions (limited AI track records available). Early adopters of this emerging technology stand to be the largest beneficiaries. Platform users will benefit from growth in their investment knowledge base facilitated by machine learning algorithms. Investors will likely experience tailwinds from incremental investments as AI success stories continue to emerge. About 90% of electronic data was created in the past two years, and in two years from now we will likely be saying the same thing; AI will become a necessary tool for global investors.

AI is already disrupting the ETF industry through AI powered ETFs and it is having spillover effects into other investment vehicles. EquBot AI Powered ETFs are running on AI platforms using IBM Watson that work around the clock to process traditional and alternative data on over 15,000 publicly traded global companies. Millions of market signals, news articles, and social media posts are processed to produce thousands of hypothetical test portfolios which are further distilled down into daily trade recommendations. We observe an increase in the number of usually secretive and tight-lipped hedge fund managers admitting to using AI after these AI ETFs launched. Others will eventually catch on that more cost-efficient and effective asset management structures exist with the use of AI investing.

We do not feel AI investing will immediately replace human managers and analysts but will allow them to more efficiently manage the overwhelming amounts of market data. As AI investment products challenge broad benchmark market indices we anticipate the same type of shift we saw from taxis to ride share programs like Uber and Lyft. AI brings humility and potential economic upside to investors willing to admit they aren’t able to keep up with the growing snowball of information. It is very much an admission that even the most sophisticated investors don’t know what combinations of datasets are best suited to build target investment portfolios. We propose a start with AI investment solutions.