We realize that even a strategy with an 80% chance of beating the S&P 500 in any year has only about a 51% chance of, in fact, winning three years in a row. This journey helped us develop, test, and refine our HQ portfolio, but subsequently also the Bayesian portfolio, and recently allowed us to launch the Reinforced Value portfolio - a hedged strategy where we seek to hedge away the market’s idiosyncratic risks. This and much more content, when stitched together with the learning process described above, helped us build a multi-lens system***. Each of these provides a clear view of how we think - our most valuable asset. We were fearful - yes - literally - not to miss incorporating macro events - the impact of covid, inflation, correlation, and cross-correlations (post-covid, and post-inflation shock trajectory), along with valuation ( Buy or Fear), and technicals. In the same breath, we thought aloud about counterintuitive ideas. On our way to a 3-year outperformance, we ran many 1:1 peer comparisons - we think about peers broadly - same industry, similar size, or even profit margin peers, it quickly becomes quite rich computationally. We’ve, in fact, documented much of it through published analyses over many years - including on these pages, but here’s a quick summary. OK, so where are the secrets of the process and the journey? Teams that cultivate this mindset of “humans & machine” are likely to see outsized rewards - way, way more than a culture that focuses on machines alone - or competing/comparing with machine performance. We call it Understandable-AI**.Īs the use and influence of AI grows, we believe everyone can - and should focus on learning from the machines. One that helps us understand an investment, take it apart, and then put it back together - much like you'd do for a puzzle if you wanted to master it. We wanted to build an understandable decision-making system - an auditable decision system. While some of us have experience with analytics and neural networks, fuzzy logic, and all else that gets bundled into AI today - that wasn't enough for us. One that never stops learning, growing, and improving. We wanted to learn from the machine's analytic powers - and, in turn, wanted to teach the engine. What has been distinctive, is our team’s ability to learn from the machine, and being able to feed that learning back into the thinkHub platform. While thinkHub uses modern analytical techniques, the machine alone has not been the most distinctive attribute of our process. We started building our 1-million-analyst platform - thinkHub, almost five years ago in early 2019. There is something simpler, yet more powerful than the machine They’re happy to share more about how they’ve put into practice HQ and other strategies we’ve produced.īefore we get into more specifics of our approach, our process, and the practice - however, let’s take a step back and reflect on a bigger, more important learning. Interested in learning more, or want to evaluate if you could invest? Feel free to schedule a chat with Empirical, a local Boston-based firm. The practical side of the trade is equally important.
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