Improve your Investment Quotient
All intelligent investing is value investing. Cognitive Quant brings together a passion for investing, psychology, and technology to help augment the glut of quantitative metrics to improve systematic and value investing approaches.
We are distilling and encoding the collective wisdom of eminent investors together with insights gleaned from behavioral science, machine learning, backtesting and personal experience to highlight common pitfalls and improve investment outcomes.
Note: For most individuals a low cost passive ETF would be the best approach
Cognitive Quant platform is focused on enabling rational investment decisions rather than just delivering a surfeit of quantitative information which could lead to cognitive overload and overlooked risks. Our intent is to help nudge investors towards a more deliberative investment analysis and decision making process (i.e. System 2 Thinking).
Behavioral Science-driven UX Design
to help overcome cognitive constraints & enable more rational decisions
AI and NLP-enabled Stock Screener
to screen based on unique risk insights as well as current & historical quantitative metrics
Distinctive Investment Checklists
incorporating both quantitative and qualitative factors
Unique Risk Insights leveraging Natural Language Processing & AI
such as legal proceedings outcomes, concentration risks, etc. to enable more efficient & risk-aware decisions
to surface negative sentiments and qualitative risks buried in lengthy SEC filings
Refined Quantitative Metrics
to better assess true leverage, interest coverage, return on capital etc
Enhanced Valuation Tools*
to help assess intrinsic value taking into account the business cycle (*Except for firms in highly leveraged sectors)
~15 Year Financials, Recent News, SEC Filings, and more ...
to help improve your investment process and make more rational risk-aware decisions
“Attention is the most essential mental resource for any organism.”
the Organized Mind, Daniel Levitin
“Time is the friend of the wonderful business, the enemy of the mediocre.”
Invest in high-quality companies with resilient balance sheets and robust business models when they become available at prices closer to their more definite sources of intrinsic value.
“People calculate too much and think too little.”
We are in an age of information and sensory overload characterized by a glut of quantitative metrics, incessant social chatter, and addictive gamification. All of this exacerbates the greed, fear, uncertainty, and doubt prevalent in investment decision-making. And therein lies the genesis of Cognitive Quant as we set out to develop an investment due diligence platform focused on cutting out noise, overcoming cognitive biases, and enabling thoughtful evaluation of investment opportunities.
The past few years have not been very kind to the (systematic) value investing strategy - part of the reason, of course, is because no approach works all the time. It is also, in part, because machines/algorithms have become better at assessing qualitative risks and so cheap stocks are often (but not always) cheap for a reason.
We saw some recurring patterns from our analysis of the past 15 years among reasonably high-quality value stocks that suffered steep declines. These included excessive hidden leverage, business model weaknesses (e.g., customer concentration risks), and qualitative risks (such as legal proceedings), to name a few, besides previously well-documented quantitative risk factors.
In short, identifying temporarily out-of-favor ‘wonderful’ businesses requires consideration of not just quantitative metrics but also qualitative risk factors buried deep within SEC filings. Uncovering these risks can be very time-exhausting for many investment advisors and most individual investors (including ourselves) who do not have an army of analysts at their disposal.
To help address the above, we brought together the progress in Business Reporting Standards (such as XBRL) along with advances in Artificial Intelligence, Natural Language Processing, and other text analytics capabilities to deliver unique qualitative insights: concentration risks, risk factor summaries, legal proceedings & likely outcomes, fundamental sentiments, to name a few.
We have embedded these NLP and AI-powered capabilities into our screeners, checklists, and other platform features to help investors efficiently turn over more rocks and make more informed and risk-aware decisions.
As Dr. Kahneman noted in Thinking Fast and Slow, we can become satisfied with initial answers surprisingly soon. Recency bias tends to be a common culprit - it often manifests itself as expecting a promising financial report to continue for an extended period or conversely that an adverse financial result will continue indefinitely.
We designed our platform's user experience to emphasize the realities of the business cycle, highlight both quantitative and qualitative risks, and help investors overcome cognitive biases. For example, the valuation capabilities in our platform provide historical context around various value drivers (sustainable revenue, growth rate, operating margin, tax rate, etc.) across the business cycle so that users can overcome recency bias and get a sense of the range of possible outcomes before making an investment decision. The platform handles the calculation mechanics so users can invest their time in considering a firm's historical record and, more importantly, its likely prospects and value drivers.
In summary, we built the Cognitive Quant platform to help enable investors to:
We would love to hear from you! Please reach out with your questions/comments/inputs using the contact form on our website.
Founder & CEO
Teji Abraham is the founder of Cognitive Quant - and the platform draws on his background in management consulting and cognitive analytics (AI, NLP, ML) and his interests in Behavioral Science and UX Design. He was a founding member of the Big Data & Analytics consulting practice at Cisco. On secondment from Cisco, he also served as COO of Planetary Skin Institute (a scientific R&D institute co-founded by NASA and Cisco) and led the development of big data analytics and decision support platforms for enhancing risk management in collaboration with national governments, NASA, and other research institutions. Prior to this, Teji was a management consultant with Diamond Consultants (now part of PwC). Teji holds an MBA from Duke University's Fuqua School of Business and a Bachelor's degree in Computer Science & Engg. from Kerala University, India.
Andrew Oskoui, CFA, is the portfolio manager and principal of Blue Tower Asset Management and as Strategic Advisor at Cognitive Quant will help shape the future development and adoption of its platform. Andrew previously worked in investment research for YCG and managed an equity strategy for Allometric Research & Management. Andrew has a Bachelor of Science in Biomedical Engineering from Washington University in St. Louis, and a Master's Degree in Chemistry from the University of Wisconsin-Madison. Andrew passed all three levels of the CFA exam on his first attempt. Before beginning his career in finance, Andrew led a materials science research group for Halcyon Molecular (a Founder's Fund portfolio company) and researched nanoparticle drug delivery for cancer therapies as an engineer for Covidien.
Note: Cognitive Quant and Blue Tower are independent unaffiliated companies.