spy611.com serves stock market predictions to you.
Dan Bikle: email@example.com
What makes the stock market (S & P 500) predictable?
Over the long term (duration greater than one month), the market will go up because wealthy people will want power more than money.
The most obvious example of this are the people who own companies like FaceBook, Google, and Apple.
They have all the money they need to bring security to their families. The next obvious thing they could want would be control over corporations.
Additionally, once they gain enough power at executive and board levels, they can use corporate resources to borrow money to buy back stock.
Stock buy-backs bolster the value of stocks and reward wealthy people who use wealth to purchase corporate power. In short, the market will go up because the One-Percent wants it to go up.
To the Electrical Engineer in me, this behavior looks like a positive feedback loop. Instead of electricity, the loop is driven by power bought by money.
Also age-old wisdom says that stock ownership represents an ability share in profits via dividends. I know many people who buy stocks for the dividends.
In my mind it makes sense for me to hold a dividend paying stock valued by wealthy people more than keeping my money in a bond or savings account which pays very little interest.
To me, it seems easy to predict the stock market over any one month interval; the market will probably go up.
Over the short term (duration of one day), the market is predictable probably due to greed.
It is easy to verify using ordinary SQL that the market demonstrates mean-reversion after it has fallen more than say 1% in a day.
I suspect that many optimistic people watch the market closely and wait for opportunities to 'buy-on-the- dip'.
I see less evidence of mean reversion after sharp moves up by the market. Perhaps sharp moves up make people feel prosperous and less inclined to sell.
On most days the market is quieter than what would be predicted by a Gaussian distribution. On these quiet days I often wonder what will happen on the next day. The software and ideas presented in this site help me gauge the probabilities associated with these quiet days.