My own highly risky private venture investments in the past earned an average 28% IRR over ten years, which was considered extraordinary at the time, and only 3 of the companies accounted for most of that gain, while the other 7 became “living-dead” drags on superior performance. This involved considerably more risk than standard equity markets, with considerably greater rewards, possible but by no means assured.

And while my VC team and I chose from at least 3000 Business Plans over time, even the best venture investments were highly illiquid and mysterious. Once you got in, it was exceedingly hard to get out.

Hedging was achieved by pitting high-risk companies against even higher-risk companies, a creatively human portfolio strategy. Rational? Irrational? Both? 

In public equity portfolio investment performance, a central attraction of any previous simulated and tested Fund will always be its ability to hedge the losses as markets and companies tank, while still outperforming other methods of portfolio management when markets gain. This must look at many more actual investments, and much more trading.  

Computers need to assist, or more correctly stated, humans must assist the computers processing the enormous data continually generated just to trade equities or futures. How even to measure risk in such an environment? How then to manage the risk-gain volatility? The best of both worlds, of risk-taking and hedge analytics, is called for at every instant. Human-designed computers must work seamlessly in a human-caused market environment. 

Our upcoming $1 million S&P500 Fund is expected to debut this month. It will be our first true big-money implementation, and that size of Fund in simulation over the past 18 months has been nothing short of amazing: Averaging 6% gains each of the 18 months has achieved 18-month 122% ROI and annualized 75% ROI so far, with very few downcycles, beating the S&P500 110% to 5%.  

As they say (and as we must say), past results do not prove or provide future results. We don’t know if the newest Fund will do nearly as well as the models, but there is no reason it cannot repeat its modeled algorithmic “Protective Growth” characteristics of the past: hedging combined with aggressive growth objectives. 

So how does IntualityAI hedge? The AI’s comprehensive analysis of ALL portfolio choices each and every day and every micro-second cannot be replicated by any human-alone, nor especially the picking mechanism based on all the observed trend biases, nor the individual-trade avoidance or change-of-holding of specific trades.  

Every Human has a personal risk-reward profile and comfort zone. In my own present “simplified” personal portfolio strategy favoring Tesla, for example, I have been dismayed but not surprised nor disappointed to note that IntualityAI has generally avoided even getting close to taking a Tesla position as I personally manage, and yet has outperformed me with much less promising equity choices. This is because my personal risk profile looks much farther into the future than most market participants, who get perturbed by near-term events that create enormous volatility, which I only see as buying opportunities to get myself even deeper into this risk. The last thing I want personally is risk-avoidance, though I have “interim” losses on quite a few of my Tesla purchases. I would have done better skipping those losses. 

IntualityAI’s “interim thinking” can be tracked and used for prediction and ideas, individually and in portfolios. We can know when IntualityAI is getting close to making buy or sell decisions on any S&P500 stock the program currently tracks historically and in real time. And Intuality’s real computer-enhanced genius applies especially well to managing a complete portfolio of stocks or futures, for risk, hedging, and maximum reward. 

by Michael Hentschel, Yale and Kellogg anthropology, economics, venture capital