It doesn’t take a Neural Net model to tell you that the Apple’s iPod is running laps around Microsoft’s Zune. I even found a blogger, Ms. Danielle, is giving away her Zune after she won it on another blog contest!
Remember, an Apple Stock a day keeps the financial blues away! :)
[tags]Apple, iPod, Microsoft, Zune, MP3[/tags]
It looks like my Euro Currency Model is being vindicated for the long term trend. It wasn’t sensitive enough to pick up this 1-1/2 month intra-trend correction in the EURUSD pair. Luckily I hung in there during the downturn and I’m starting to show a little profit again.
I’m going to have to rethink my Forex trading plan one more time, this “no stop” theory really stinks and made me deal with a lot of pain. I used to get stopped out a lot so this time I’ll use a percent volatility type of stop.
[tags]NeuralNets, AI, Forex, Currency, Trend, Model, Euro, Dollar[/tags]
I bought a Monte Carlo Simulation for Excel this week and started to play around with it for my S&P500 Volatility Timing Model. Boy am I having fun! I ran the probability of a Financial Asteroid (FA) event happening in a Gaussian distribution world. Based on the simulation, we have a 0.04% chance of a major correction happening and we can expect 4 such events to occur over 10,000 observations.
This means that roughly 1 FA event will occur every 9.9 trading years (a trading year has about 252 trading days in it). Now, I wonder what happens if I use a Power Law distributive function instead. :)
The Nasdaq 100 Trust shares (QQQQ) has been doing pretty well since July of last year but the question is, will this trend continue? That’s an interesting question because I really don’t know. From the candlestick chart it looks like it might if it can clear the $47.92 level.
Either way, the QQQQ’s is a very volatile ETF as seen on my daily volatility chart. I drew in the one, two, and three standard deviations for it and the last 3 STDEV move was this past February (2/27/2007). If I applied my S&P500 timing rules to the QQQQ’s then then 2/27/2007 was a prime 
buying day.
Still though, the QQQQ’s usually see 2 STDEV volatility moves more often than not. If I were a daytrader, I’d be trading this market!
[tags]QQQQ, Volatility, Options, Nasdaq, Trading, Investing[/tags]
I’m still back-testing my S&P500 Volatility Timing Model and have come across some interesting results that I’d like to share. I back tested the model to the beginning of 2000 and let the model generate “buy” signals. I tabulated them in the first table below and then calculated a % return for each position based on Monday’s closing price for the S&P500.
You can see that the first “buy” signal on 8/28/2000 still has a negative return but gains increased as the model bought into the S&P500 by riding the market down between 2000 and 2002.
It’s pretty easy to get euphoric over that 81.61% gain since the low on 9/23/2002 but its all a matter of perspective. Imagine your losses if you looked at this model on that day. Your return table would’ve looked something like this below.
Now that’s a pretty painful ride to bottom! It’s one thing to say “buy them when they ain’t” but its another to actually do it. Implementing this timing strategy can be one of the toughest things you can do financially. After all, things can’t get any worse after you buy, or can they? :)
Blogging and posting will probably be light to non-existent next week as I’ll be traveling for work and running into several schedule conflicts.
Have a great weekend all!
[tags]Blogging, Taleb, BlackSwan, Asteroids, Trends, NeuralNets, Classification, Adsense, Traffic[/tags]
This week’s trends for the major currency pairs based on last Friday’s closing data. Notable changes, AUD flipped to RANGE.
[tags]FOREX, USD, Euro, Pound, Currencies, Trends, NeuralNets[/tags]
I’ve been reading Gauging Corporate Financial Results for a while now and really enjoy the site. I mentioned them in my Random Thoughts post on Friday as a further inspiration for my Fundamental Data Neural Net Model. Well inspiration did hit me last week when I remembered that when I data mined fundamental stock data, certain key components (Forward PE, Capitialization, etc) would have greater influences on price appreciation.
Still, the fundamental data makeup of a stock can be quite large and sifting through all that data can be tiresome, but there’s a way to solve that. I can either create a Cluster Neural Net model to see which components are grouped together or I can create a Genetic Algorithm model to help choose the best fundamental components to analyze. Decisions, decisions!
[tags]NeuralNet, Cluster, GeneticAlgorithm, GA, Genetic, Stock, Fundamental, Data, Analysis[/tags]
Good Morning! I’m really glad it’s Friday and I’m looking forward to a good weekend cleaning out my garage and working in the garden. To close out this week, I’m writing a linkfest!
Have a good weekend!
[tags]Blogs, Links, Linkfest, Smallcap, Stocks, Screener, Neuroplasticity, BlackSwan, Taleb[/tags]
Amgen Inc (AMGN) is probably one of those “Black Swan Postive” type of companies that Nassim Taleb speaks about and supposedly writes about in his Black Swan book (I haven’t read it yet). He says in his interviews that you should invest in companies that have the ability to change the world by the discovery of a single compound or medicine, and the biotech industry is rife with those possibilities. He forgets to mention the the biotech industry is rife with volatility too.
If you really want to understand my point of volatility clustering and how they can be good buying opportunities, take a look at this volatility cluster chart vs AMGN’s price for the past 10 years. What you’ll notice is that during the bubble years, the volatility was really high and then equally as high as AMGN sold off.
If you look real closely, you’ll see a huge volatility spike right after the bottom of AMGN’s share price in 2002. That was a great time to buy. Personally, if I were buying AMGN back then, I would’ve been averaging in at select points on the downtrend. Just so you know, my investment horizon can be several years into the future so I can stomach short to intermediate downturns.
How you time your purchases, in this case, is to look for high volatility clusters during selloffs. High volatility clusters at price highs usually signal a high and the possibility of a sell off.
Just for your reference, the last volatility cluster BUY signal was on May 7, 2007.
Update: kjeldahl pointed out a date error in my post. The chart above was built using weekly data points and was a bit misleading. When you drill down to the daily data, the last volatility cluster BUY signal was actually on May 10, 2007. See the revised chart next to this update.
[tags]AMGN, Amgen, Biotechnology, Biotech, drugs, chemicals, biology, stocks, volatility, investing, 401k, strategy[/tags]
If you look at the monthly chart of the iShares MSCI Brazil Index (EWZ), you’ll see a strong trend up starting around late 2002. A lot of other foreign ETF’s, funds, and stocks seemed to have started their trends UP around that time. When did the US start saber rattling about Iraq? Coincidence?
[tags]Brazil, EWZ, ETF, Foreign, International, Iraq[/tags]
Mandelbrot had it right when he said, “large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.” Don’t believe him? Well its true! To illustrate this point, I’m posting three graphs of clustered volatility for General Electric (GE), Intel (INTC), and Cisco Systems (CSCO).
You might wonder, why this fascination with volatility clustering? Why should you care? The answer is quite simple. If you understand that stocks and the markets go through cycles of high and low volatility, you can position yourself appropriately.
Being able to predict volatility clusters or identify a genuine buying opportunity (this is the basis for my S&P500 Volatilty Timing Model (PDF)) from just noise can make you rich. Still not convinced? Just graph in Excel the absolute (or squared) % returns for any asset of your
choosing over a long time period. You can use daily, monthly, even 5 minute time periods. You’ll see the same thing over and over again, periods of low volatility are clumped together and periods of high volatility are clumped together.
[tags]GE, INTC, CSCO, Volatility, Clusters, Investing, 401k, Timing[/tags]
