Use of AI and NLP is not only not ubiquitous, but also not uniform across active investors, writes Evan Schnidman
Although active asset management has existed for centuries, the modern variation has grown increasingly complex. As a rough guide, it is useful to break active investors into broad categories of fundamental investors, quantitative (or systematic) investors and ‘quantamental’ investors.
Fundamental investors rely primarily on traditional subjective research scrutinizing both standard financial metrics and increasingly using factor models to determine how individual companies perform relative to their peers on specific KPIs that may be unique to the company, sub-sector or sector. These factor models help fundamental investors screen for investment opportunities and identify strengths/weaknesses among assets in their existing portfolio. Most fundamental investors do not rely solely on these factor models, but use them as an input into their established subjective process of evaluating sell-side research, corporate communications, regulatory filings and management team credibility.
Quantitative or systematic investors, commonly referred to as quants, have received a great deal of attention in recent years for their use of quantitative data as the primary or sole means for making investment decisions. Quants have benefited greatly from increased disclosure, proliferation of alternative data and rapidly expanding computer power allowing them to run increasingly complex models that rely on ever-expanding datasets. In the last couple of decades, quant investors have fostered a perception that their teams possess other-worldly mathematical prowess, but in many cases quant models are only slightly more sophisticated than factor investing. These relatively simple models, equipped with triggers for trading certain assets under specified conditions, have allowed for increased speed in quant investing and the emergence of quant funds serving as dominant market-makers so they have an inside track on valuable data.
Only a small subset of quant investors have truly embraced more sophisticated artificial intelligence (AI) models and they are typically used to augment a more standardized quant approach, much as factor models have augmented fundamental investing.
The final broad category of active investor is quantamental investors. This amalgam of the words quant and fundamental reflects the fastest-growing style of active investor. In fact, many asset managers believe pure fundamental investing is dying, only to be replaced by an increasingly quantamental approach. Like their fundamental counterparts, these quantamental investors rely on factor models as a filtering mechanism, but they also rely on workflow enhancement tools like natural language processing (NLP) to digest information that would otherwise have been subjectively analyzed. Just as with their quant counterparts, the sophistication of these tools varies widely, but the hallmark of quantamental investing is an effort to use data and technology tools to mitigate one’s own biases and streamline their workflow.
Types of tools
Active investors rely on five broad categories of technology/data tools:
- Sell-side research
- Factor models
- Alternative data
- NLP and automation
- AI models.
Of these tools, sell-side research is certainly the most well understood in the IR community. Analysts at investment banks or independent research providers analyze corporate disclosures for financial information, impressions of corporate strategy and competitive analysis to provide subjective written assessments of existing and likely future company performance.
Factor models have also become standard fare for most investors as they allow for streamlined analysis of KPIs. These models have grown increasingly complex in recent years as companies have become more granular in their KPI reporting, allowing investors to more accurately compare companies with one another, and across time horizons.
In the last five years, alternative data has emerged as uniquely valuable in not only enhancing factor models, but also allowing for more sophisticated models of everything from foot traffic to credit-card swipes and crop yields. Despite a great deal of press about the explosion of alternative data, relatively few datasets have been widely used by investors. But a huge number of niche datasets have been used by quant and quantamental investors to provide unique insight into a sector, sub-sector or even specific companies.
As we move into more sophisticated technologies, NLP has gained prominence in the investor workflow, mostly as a means to streamline the analysis process. Although numerous tools have been launched to provide sentiment and other specified investment signals based on NLP, the vast majority of investors that use these tools primarily rely on them to streamline their workflow. This means most NLP is used to summarize key topics in a particular document so that quantamental investors can make subjective judgements about a potential investment. Only a small minority of NLP tools generate quantitative signals indicating sentiment or likely price movement, and these tools are predominantly used by quantitative investors.
The final category of technology tools used by active investors is both the most sophisticated and the broadest: AI. Despite a great deal of marketing to the contrary, very few investors are actively using AI in their day-to-day workflow. Many investors use standard statistical models and even Bayesian modeling, but a relatively small number of asset managers use deep learning or neural network technology. That said, these technologies are growing in prevalence and, as high-quality data becomes increasingly available to train these sophisticated models, they are likely to continue gaining traction among quant investors.
Who is really using these technologies?
As alluded to throughout this article, the use of these technologies is not only not ubiquitous, but also not uniform across active investors. Most investors use a subset of these tools, but not all of them – yet.
Many active managers now claim they do not make new hires unless they know how to code. This is a bit of a badge of honor for these elite financial institutions, but it is also a signal that technology-oriented tools are an increasingly important part of the investor workflow. Given this elevated importance, it stands to reason that these tools are becoming increasingly widespread and thus increasingly impact the relationship between corporates and their shareholders.
It also stands to reason that IROs would benefit from understanding how these tools are being used to analyze their company. In many cases, the very same tools used by the buy side can even be repurposed to streamline the IR workflow and ensure companies are communicating in a way that will resonate with their investors.
This article is the second in a series intended to explain the shifting landscape of active and passive asset management by highlighting the tools and technology used by active investors. Click here to view the first article, which explores active and passive dynamics.
The third article will highlight how technology is used by passive managers and why that results in structural differences in asset allocation. The final article in the series will explain how IROs can keep pace with changes in the asset management industry by adopting new technologies to better understand investor behavior.
Evan Schnidman is founder of EAS Innovation Consulting
