TL;DR:
Investment checklists can help investors more methodically sift potential investment candidates, efficiently assess risks, and enable better investment outcomes
Checklists aid memory recall, establish an explicit set of checks and associated minimum acceptable thresholds, and help prioritize due-diligence aspects
Cognitive Quant checklist incorporates both quantitative metrics & qualitative risks to help investors assess if investment candidates are high-quality companies with strong balance sheets and robust business models
Our quantitative checklist items are based not just on a firm's current quantitative snapshot but also on its historical record across the business cycle
In addition, we have integrated several differentiated qualitative risk factors by leveraging progress in Business Reporting Standards along with advances in NLP, AI, and other text analytics capabilities
In his book The Checklist Manifesto, Atul Gawande, discusses the problem of extreme complexity and the two main difficulties we face in complex situations:
The fallibility of human memory and attention, where we may overlook relevant inputs especially under the stress of pressing events.
Our tendency to be lulled into skipping steps, given that not every step/check may be useful in every circumstance
A related issue, as Daniel Levitin writes in the Organized Mind, is that while our brains can process the deluge of information we encounter, we can have trouble separating the trivial from the important and all this information processing makes us tired.
Checklists are a great way to improve outcomes involving complex decision processes, such as in investing, as it:
aids memory recall and helps overcome our brain's inability to, on average, (concurrently) hold more than seven chunks of information
establishes an explicit set of steps/checks and associated minimum thresholds for relevant parameters
can help prioritize and focus attention on key due-diligence areas
Most of the currently available checklists are almost entirely quantitative in nature and primarily focused only on the current value of a metric. Our investment checklists incorporate not just the current value but also the historical record of relevant quantitative metrics. In addition, we have integrated several differentiated qualitative risk factors by leveraging progress in Business Reporting Standards along with advances in NLP, AI, and other text analytics capabilities.
Given our platform's primary aim of enabling investors to discover (and assess) high-quality companies with strong balance sheets and robust business models, our checklist components are constructed to help assess companies from this perspective.
The checklist below for Microsoft Corp, shows how the various checklist components are organized within our platform.
The checklist items are elaborated within their respective sections below:
Please note that algorithmic assessments of legal outcomes, concentration, or other risks are *not* substitutes for human judgment — the algorithms mostly operate within the narrow context of what has been disclosed/filed by the company. These disclosures could be incomplete, erroneous, misleading, and yes in some cases even fraudulent.
Also, very few firms will likely pass all checklist items, but checklists will help you more methodically and efficiently assess risks to enable better and timely investment decisions.
Additional resources:
The checklist items discussed above have been informed by a number of investment books as well as recurring patterns we observed from our backtesting and (humbling) personal investing experiences. The following books in particular have had a substantial influence on our investment approach:
Quantitative Value by Wesley R. Gray, Tobias E. Carlisle
Value Investing from Graham to Buffet and Beyond by Bruce Greenwald et al.
Quality Investing: Owning the best companies for the long term by Torkell Eide et al.
The Investment Checklist by Michael Shearn
The Five Rules for Successful Stock Investing by Pat Dorsey
It's Earnings that Count by Hewitt Heiserman
Quality of Earnings by Thornton L. O'glove
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