Call me an old cranky fart but I’m not buying the recent drop off in $VIX as a return to the happy days of market madness, its just too manipulated for me. Ben and the Fed have dumped trillions of dollars into the markets and that’s bound to smooth things over for a while, but ask yourself this: is it sustainable? With unemployment rising and the markets seemingly to have hit a brick wall lately, the answer is NO! ...

I've made some serious headway last week in analyzing NFL football data to model game point spreads.  I was able to determine with great accuracy (84%), using backfitting, what team would win a matchup. I did this by building a backpropogation model from 2007-2008 data with about 20 matchups as the prediction set.  The only bad thing I discovered was that the point spread prediction was way off. ...

In the early part of this year, I had a discussion with a coin dealer who was selling American Eagle Gold Proof coins. He quoted me a price with a large "over the spot" spread fee. I asked him why and he said that the US mint had suspended making them and are no longer in stock. Production of United States Mint American Eagle Gold Proof and Uncirculated Coins has been temporarily suspended because of unprecedented demand for American Eagle Gold Bullion Coins. [via US Mint] ...

The problem with developing a point spread betting system for football teams is that you can't initially use a neural net. This is because backfitting raw stat data could lead to poor forecasting, or handicapping. What you have to do is come up with a method where you rank teams first and then feed them into a neural net system to forecast the estimated handicap or point spread. Why? Teams change over the season; they may lose a key player to injury or evolve their strategies per game. Although neural nets are fantastic, they can't cope with these paradigm shifts easily. However, if they are fed a ranking system that gets updated weekly based on data from played games, those rankings can be fed into the model and compared to opposing teams in the next game. ...

If you had to build a neural net model to help you rank NFL teams, what bits of data would you want to include in your input data? Pass Attempts vs Completed? Yards Rushed? Score Differential? Time of Possession? What? I'm interested to hear your comments, if any.

I've come across some very interesting, albeit very initial, relationships in NFL data. The actual point spread between teams seem to have a strong relationship with the defense's pass interception ability, the attempted rushing by the defense, and sack yards by the defense. Way more interesting stuff that I'm not listing at the moment.

Sorry folks, been busy. Started working on a NFL point spread neural net for betting purposes.

I have $STEC front and center for entry this morning . I've been following this one for a while now and I think it might give me a BUY signal today. I'm also stalking $GOLD again but won't start salivating until it breaks to all time highs again. UPDATE: I went long STEC today at $23.67.

The following is a list of stocks that my neural net classification model ranked as most interesting from 6/15 to 7/15. $TTM $CTRP $BIDU $SID $WBD $IBN $SSRX It seems that China and India is a theme here. I may be long anyone of these at anytime, or none. Do your own due diligence!

Looks like I got stopped out of $WIT on Monday at $11.77. That's $1.07 gross profit on my $10.70 entry on 4/15/09. So far my neural market classification system is 2 for 2 with real money. My other trend following system is 1 for 1, let's see how long my "luck" lasts.