Preview | 2008.06.11

Naturally I will be indulging my curiosity as to the effectiveness of a good stock market prediction network. It would be a shame not to put money where my mouth is.

I think the greatest opportunity to improve the algorithm is to provide as many reasonable prediction inputs as possible. The network will be good at analyzing historical data, but it will be great at merging this with informed opinions. After all, share value is as dependent on the present and future as it is on the past.

Finding sources for predictive information is no easy task. Most estimates are quarterly, far too low in granularity.

One concept that has gained widespread public attention is the prediction market. The idea is that the informed opinion will prevail, and it's being applied most notably to the upcoming elections.

Prediction markets use currency, conveniently so does the stock exchange. Prediction markets apply economics and Darwinism to solve complex problems, neural network weight resolution is not much different.

So it follows that I could enlist a number of investors (using simulated money, at first) and query each for a market prediction. These values would be used as inputs to the neural network and, with many other factors, determine the predicted share values. Each day the network would retrain itself and add weight to the more accurate voices.

The three components of the algorithm: the network with investors as inputs, the short and long term stock value mapped to buy/hold/sell transactions, and the investor attributes.
Each investor would be driven to produce accurate estimates as the collective account balance would hinge on their prediction. Moreover, if the quarterly gains were distributed based on each investor's prediction performance, the highly dedicated would be rewarded.

This model should easily overcome the best case scenario of a single informed investor. Not only does it weigh and balance more factors than a human can individually process, but it sorts out bias toward or against particular stocks.


Artificial life concepts

I'm simulating a collective of independent agents. Each is controlled by a set of neural networks that determine the agent's action. The networks train themselves based on positive and negative action outcomes. Currently they eat, swarm, and reproduc...

Neural networks

I was chatting with Jon about the application of neural networks to stock trading, which is basically a perfect example for explaining the science. It went something like a'this:


A GPT replicant is added to our group chat.