In early 2014, the first AIR Summit was held in Fort Lauderdale, Florida. It was somewhat of an experiment since the amount of interest by large active investment managers for emerging technologies was unknown. What we learned is that there was significant demand to understand some of the potential alpha generative technologies, platforms, or data sets that could give portfolio managers, traders, and analysts an “edge.”
At the time, exchange-traded funds (ETFs) were just under $2 trillion in total net assets, but they were starting to garner significantly more attention by those who hadn’t really thought that passive investment vehicles could challenge the active mutual fund industry.
My view was that ETFs were going to continue to grow at the expense of higher-cost products, unless active managers could create “new alpha” and begin to outperform their indices with more consistency.
That event helped bring innovation to the forefront at many large investment managers. Since then, investment managers have been making corporate investments in “alphatech” companies, set-up quantamental teams, created innovation labs, and began to hire and integrate more quantitative talent across their investment and operational organizations.
A transformation is occurring across investment management where best-of-breed products in a given strategy will dominate, and innovation is the main driver.
Moving Beyond Sentiment Analysis 1.0
At that first AIR Summit, one of the technologies that gained considerable attention was a subfield of artificial intelligence (AI) that is known as natural language processing (NLP). Several firms, such as iSentium, Social Market Analytics, and Kensho hyped their capabilities in NLP to drive insights and alpha generation in the investment process.
For the most part, this was through sentiment analysis where the NLP technology could parse through text and systemically assign a score based upon the negativity or positivity of the source authors. Some may consider this “sentiment analysis 1.0.” Eventually, this scoring methodology was used to predict future KPIs and ultimately share price performance.
While NLP has been around for decades, these use cases in investment management were just starting to take off. Today, the technology continues to evolve and 25% of all companies that apply to present at the AIR Summit self-identify as having an NLP component to their technology offering.
Some of the recent presenting companies that have significant NLP offerings are Glia Ecosystems, Coegil, and Agolo. NLP continues to improve and there is a vast amount of academic research and literature that drives the advancement of the technology.
Causality Link – The Research Assistant
At the AIR Summit 4.0 in 2018, a two-year old start-up named Causality Link wowed the audience in New York City with their research platform that collected and measured crowd wisdom using a more precise and less emotional signal than sentiment by linking it with details and context. It does this by auto-generating a model and leveraging the knowledge shared through causal links or explanations.
These causal links are explicit cause-and-effect statements that are extracted from scans of documents (such as news articles, company analyses, earnings call transcripts, government filings) and other text-based sources that can then create unique intelligence about companies, industries, or macroeconomics. In all, they are scouring over 87 million texts in 22 languages and can create causal links on 40,000 companies.
As an example, Causality Link’s NLP can scan for various mentions of Key Performance Indicators (KPIs) for a specific company, mentions of events tied to that company, then understand sentiment changes or trends related to those KPIs. This is important because an analyst can then identify the key trends that could impact the future performance of that company and all the links to those KPIs that are being impacted.
Being able to track all these causal linkages that will tie the data points together is the beauty of the Causality Link platform and technology. As they call it, the platform acts as a “research assistant” to help identify investment opportunities and risks to individual companies, industries, and economies.
Additionally, I am seeing an increased focus on environmental, social, and governance (ESG) metrics, data, indices, and dashboards across many of the alphatech companies in our universe. Causality Link has just started to socialize their next-generation ESG dashboard which will allow investors to decouple ESG factors and drill down into the nuances of each factor. Understanding the causal links between a company’s decisions on ESG and their profits is really the type of understanding that will add value, rather than just tracking a company relative to a standard ESG index.
While I am continually impressed by new innovations in AI and in the various sub-categories, what encourages me the most is that this technology, such as Causality Link’s, isn’t meant to replace humans or human decision making; it is meant to make them smarter. With the explosion of data and information, it is impossible for anyone to be able to aggregate or review all the information to make the right decision at the right time without such technology.
Active Management will Prosper
With ETF assets now well beyond $6 trillion, I continue to think it will be a popular low-cost wrapper for investment strategies, but I also believe that active management will evolve, grow, and prosper as new innovations and technologies are adopted. There will be consolidation, as we have seen, but those who can best combine human and machine decision making will ultimately outperform in the long-term, compared to strategies solely using one or the other.