Searching for investment opportunities

TL;DR:

  • New screener to help investors discover (and assess) high-quality companies with strong balance sheets and robust business models

  • Screener incorporates both quantitative metrics and qualitative risk factors

  • Enables screening based not just on current quantitative snapshot but also historical record across the business cycle

  • NLP, AI, and text analytics to enable screening based on unique risk insights

Availability bias is a pernicious bias in investing arising out of several interconnected reasons (primarily retrievability, categorization, narrow range of experience, and resonance). This cognitive processing bias is understandable given the huge number of stocks that are available for investing.


Screeners provide a mechanism to sift through the large set of stocks down to a manageable set for further analysis as potential candidates for (systematic) investing. As our platform is oriented towards supporting a long-term value investing strategy, a core tenet for us is to enable investors to minimize the idiosyncratic (or firm-specific) risks by providing capabilities for discovering (and assessing) high-quality companies with strong balance sheets and robust business models.


Much of the screening capabilities currently available are quantitatively-focused and enable screening based on the current quantitative snapshot. Cognitive Quant platform builds on the quantitative value approaches articulated by Joel Greenblatt and Wes Gray. Further, in addition to current quantitative metrics, we also enable investors to screen based on various historical quality metrics across the business cycle.


Not everything that counts can be counted, and not everything that can be counted counts. — Albert Einstein

The past few years have not been very kind to the (systematic) value investing strategy - part of the reason, of course, is because no strategy works all the time. In addition, we think that, in part, it is also because machines/algos have become better at assessing qualitative risks (besides improvements in quantitative risk assessment). As such, investors will have to find ways to winnow out stocks that are cheaper for a reason from those that are cheaper because of investor overreaction.


At Cognitive Quant, given our proclivity towards supporting a long-term investment horizon, we looked for meaningful fundamental factors (and not for e.g. factors like correlation of Bangladeshi butter with S&P 500 😉). From our analysis, we saw some recurring patterns among reasonably high-quality* value stocks that had deep declines. In addition to the well-documented risks in value stocks with low Piotroski F-Score, inflated accrual earnings**, and high bankruptcy & fraud risks (based on Z-Score & M-Score respectively), these firms tended to have:

  • Excessive hidden leverage in terms of off-balance sheet operating leases: The retail apocalypse since 2017 is a salient example of this. What is precarious about this hidden leverage is that it also inflates the ROA and ROIC metrics thereby giving them a higher perceived quality than warranted.

  • Concentration risks in their business models: Firms with concentration risk of any kind (especially customer and product concentration risks) could become subject to sudden misfortunes. If an investor cannot appropriately handicap concentration risks for a given company it would be prudent to not include the corresponding stock in a systematic investment strategy (though there is a possibility it could turn out to be quite profitable in some cases).

  • Legal proceedings outcome risks: Legal proceedings (arising from intellectual property, labor, shareholder, environmental and other issues) could potentially have very negative outcomes. However, many investors, especially those following a purely quant strategy often end up ignoring/being unaware of legal outcome risks. Cognitive Quant utilizes NLP, AI, and other text analytics capabilities to provide an initial risk assessment of a firm's legal proceedings to help flag and nudge investors to pay closer attention to these risks.

  • Inflated Earnings Yield: Commodity and cyclical firms often tend to have inflated earnings yields at the outset of a contraction after a long business cycle expansion. Now, cyclical firms can from time to time, present one with very promising opportunities - but relative valuation will often not protect an investor from overpaying and it is important to consider the firm's valuation taking into account the business cycle. In addition, one needs to consider both one's ability to understand and also the firm's ability to withstand the cyclical downturn in order to benefit from the recovery. We flag cyclical firms in our investment checklist to help remind investors of this risk.


We have incorporated these and other capabilities into both the screener as well as the investment checklist on our platform to help enable more rational and informed decisions. You can read more about our platform & philosophy here.


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.


Find below an overview video - the screener is accessible to everyone for the next 7 days.

* stocks that are above average in terms of a composite quality score based on historical performance across business cycle of the firm's ROA, ROIC, CFOA, and Piotroski Score among other metrics largely based on the Quantitative Value approach


** based on Scaled Total Accruals (STA) and Scaled Net Operating Assets (SNOA) metrics

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