AlphaIQ’s Quantitative Linguistics Extract Language Nuances To Super-Charge Your Investment Research

What is Quantitative Linguistics? It empowers investors with never-before-seen insights pulled from the quantification of millions of qualitative data points to support your investment process.

Leveraging bespoke AI models trained for deep investment expertise, we transform thousands of pages of intricate regulatory and financial documents into an easy-to-understand company risk scoring system, that can benefit your investment-decision process and help you to get better outcomes.

What is the SPINDEX? The SPINDEX Risk Scoring System is derived from the quantification of the company-generated language such as 10-K, 10-Q, 8-K & earnings call transcripts documents.

This language describes the current and future trajectory of organization through the lens of management’s outlook on the future.

The SPINDEX Overall Risk Score is the average of nine (9) individual factors of SPINDEX risk that each shine light on the types of language being used by leaders. A high SPINDEX Risk Score indicates higher risk (bad), whereas a low SPINDEX Risk Score indicates lower risk (good). The factors capture the following language:

Evasive: doubt, obfuscation, lack of resolution, overall untrustworthy
Uncertain: uncertainties, risks, approximations, etc.
Speculative: guesses, assumptions, and other speculative statements
Constrained: constraints, restrictions, or other constraints on the company
Insecure: something in flux, not nailed down, not confident
Economic Risk: economic circumstances, events, or actions
Financial Risk: financial circumstances, events, or actions
Earnings Risk: earnings circumstances, events, or actions
Operational Risk: risk indicators such as product defects, audit findings, etc.

The Takeaway? Our results have been extremely strong – we help you avoid underperforming companies.

Companies that have had notable increases in their SPINDEX Overall Risk Score (bad) have tended to underperform on average than those companies who saw similar decreases in their Overall Score (good), over a variety of time periods.