This is a collection of some of the more interesting economic phenomenon observed recently, and forms a key part of my research work on markets and efforts to develop AI-based Augmenter tools.
picture at right, is of the Probability Calculator - here shown running on Samsung Tab-A tablet. It tells you - for a given level of capital, and a chosen level of acceptable risk, how big a bet should be, and what the expected outcome is. Everyone has this
type of tool now, and there are few asymetric opportunities for arbitrage. But they do sometimes still come up.
[June 25, 2017] - The Xerion-based Neural-Network experiment, using the boolean jump-delta table is producing interesting
results. The approach looks as if it might be useful. The information it provides is clear and actionable. Of course, one needs a lot more data to say anything with any legitimate scientific validity at this point. I will need to evaluate the results in conjunction
with a methodology for interpretation, that can be tested against various runs of randomness. I will need to see the network produce consistantly better-than-random results, before I can assert that it is providing value. And of course, the human world is
also changing, so I may find that I am trying to hit a moving target, perhaps unsuccessfully.
The NN-AI results, provided by the boolean-table driven neural-network, have to be looked upon with suspicion. The AI might just be getting lucky.
having pointed out this important caveat, the results do look interesting, and are explicitly keeping me in a trade that I probably would have exited. Here is the data-screen from the new AI-box, (with the nice wide-screen, which gives me more visual real-estate.).
The target security goes ex-dividend on Monday (June 26, 2017), so of course, it will show a negative delta of 1.27. Despite the actual security price moving up, the AI has shifted its view down below the zero-line, on the most recent data, and I find this
curious and a little surprising. This is what it is, of course, supposed to do, but I rather suspected that it would not do this, and would be "surprised" by the down-turn on Monday, which I know will show up in the data (I have not tried to use prices that
were "adjusted" for dividends). The raw price data is current to June 23, 2017, and the most recent price-data built into the boolean jump-delta table by MAKECASEBOOL program, is to close of June 22, 2017. And the NN-AI has flipped to negative, with the MarketNet
network generating a -0.533 value for the June 22, 2017 target. (See both the "compareMarketNet" output, and the GNUplot "plotValues" graphic.)
If you were using the NN-AI as the "old, wise fellow" it is conceptually designed to be, you would have sold
your position in the target-security on Friday, somewhere in the upper 107 level (say, 107.70). Mkt close of 107.38 on June 23rd (Friday), means that this strategy looks already to have been successful, as the target will open near 106.11 on Monday morning,
all else being equal.
My objective now is to produce an evaluation methodology that lets me assess both NN-AI effectiveness, and expected profitability. The idea is to see if there really is an *edge* being created here, or am I just curve-fitting randomness.
We will try not to be fooled here.
[June 22, 2017] - Update on the Neural-Network results. Four photo's below. Using photos is just about always the best way to explain a process, or describe an event.
These results are *very* preliminary - but remember, life is short, and it is also what usually happens while you are making plans. We need to move fast now, no? So, here are some quick thoughts on how the AI is doing...