TRADING PERFORMANCE RESULTS OF THE INTUALITYAI SYSTEM HAVE MANY INHERENT LIMITATIONS. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN REPORTED PERFORMANCE RESULTS AND RESULTS SUBSEQUENTLY ACHIEVED BY THE SYSTEM OR PORTFOLIO. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF THE SYSTEM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF REPORTED PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.
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When considering risks, Sherlock first evaluates the downside
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“Is it worth going to interview that woman about the Baskerville murders? I suspect she knows very little if anything of relevance,” Watson asked.
“Not sure. I’m still weighing it in my mind,” said Sherlock. “Maybe tomorrow could be better spent revisiting the scene of the crime. But who knows? Perhaps she has the one piece of information that is critical to the investigation.”
“You have said on many occasions that timing is critical. I’m just concerned we would be wasting valuable time we can’t get back,” said Watson. “It just feels wrong.”
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“There’s no information that can guide us here,” said Holmes. “We have to take a risk, whatever we choose.”
“Well, is there any way we can mitigate the risk, Holmes?” asked Watson.
“Good point. We could hedge our bets, so to speak. First thing, tomorrow, Watson, you head down to meet with the lady, and I’ll go straight to the scene of the crime. Then you can meet me there when you’re done.”
“I appreciate your trust in me Sherlock. May I ask you in which school did you learn such wisdom?”
“Elementary, my dear Watson.”
When considering risks, we typically first evaluate the downside. What’s the worst that could happen? If that cost is bearable, then how should we proceed? This is the initial fast and frugal thinking analysis, which might then be influenced by sensations, arising from the gut, the subconscious, or anywhere else within our mind-body.
The sensations we feel influence the thoughts we have. How much and in what way they do this are the core issues around which IntualityAI operates. The evaluation of risk can be so circumstantial and individualized but nevertheless there is decent empirical support for the notion that, as a general rule, a 2.5:1 ratio is the tipping point. If the probability of a reward is less than 2.5:1 i.e., for a ten dollar wager you wouldn’t make any more than $25, most people are not interested. As the possible reward exceeds this ratio, the risk seems to be warranted by the possibility of a bigger pay out.
Specifically, we can get a sense of anxiety, as we face any decision. That anxiety could be anything from a mild sense of uneasiness to a full-blown panic attack. That feeling of tension is influenced by many variables, such as time of day, the social environment, and even how much you had to drink the night before. However, hedging allows us to modify the risks and payoffs. In such ways are decisions made about future possibilities.
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by Howard Rankin PhD, Science Director, psychology and cognitive neuroscience
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Our computers found the optimum ratio between risk and gain
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Research shows that given a chance to win $100 or avoid losing $100, most people choose not to lose money rather than speculate. However, as the benefits of the potential gain rise, choices change. More people would choose to risk losing $100 if the prize was $1000. Many more would risk losing $100 if the prize was $10,000. And each week many people are prepared to risk around $20 for a chance to win billions in the lottery, even though the chance of winning is minute but hey, someone’s got to win, right?
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Actually, no, there is no guarantee that the major prize is won each week, and in any event, the talk is about the $1.6 billion that someone could win, not the $20 you are almost certain to lose.
The results of extensive research to determine a mathematical relationship between risk and gain were surprising! Heavy computer number crunching showed that the relationship was non-linear between the two extremes, like the real-world examples, above. Not only that, and regardless of the application, the mathematical risk/gain relationship converged on a consistent value within a simple formula.
The value was 2.5:1, the general value of perceived risk over gain, basically confirming how we behave. We are a risk averse specie. When calculating the probabilities of future events as being on either side of 50:50, our humanized AI uses an adjusted probability of 72:28 in favor of risk. The variables were found to be direct functions of the size of the potential risk versus gain relationships, independent of the application sectors.
When confronted with potential risk versus gain, human behavior and resulting decisions were showing high degrees of consistency for all applications, regardless of their apparent disconnectedness. And our computers found this to be the optimum ratio regardless of the application: like bets on football games, trading in the markets, predicting winning politicians and heart arrythmias, and even the direction of random number series.
Hedging is an integral function in decision making and game playing. It's the potential offset to failure. It changes the 72/28 ratio towards 50/50, increasing the prospect for gain. And, it can also attempt to eliminate all risk and gain. Chicago could have punted! The S&P 500 Futures Index is typically used to protect stock equity portfolios. But, hedging is never perfect. Some gain or loss always leaks through. Humanized AI knows this and can help by simulating us out of an unhappy future.
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by Grant Renier, engineering, mathematics, behavioral science, economics
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This content is not for publication
©Intuality Inc 2022-2024 ALL RIGHTS RESERVED
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