x=1:17/12/2007.
y=1:Day;y=2:Week;y=3:Month;y=4:Year.
Green: projection correct; Red: projection incorrect.
Daily Prediction Rate for this Month: 21/30~70%
On 16/12/2007 the author devised a statistical data prediction algorithm. The results are updated each day and are shown below, with the algorithm applied to the author's web page page-counts and excluding visits of the author to his own web pages.
Results:
The experiment is over. The results are as follows:
| Average Daily Prediction Rate[1] | 221/367~60.21% |
| Average Weekly Prediction Rate[2] | 22/52~42.3% |
| Average Monthly Prediction Rate[3] | 9/12~75% |
The author's algorithm predicted the future trend of the author's web pages with the total success rates shown in the above table for the dates 17/12/2007-17/12/2008. A web page's statistics are essentially data which vary similarly to a stock's value. These web pages are the author's stock. Hence, if the author's algorithm is applied to the Stock Market, it is expected to deliver daily, weekly and monthly returns which can be calculated approximately based on the table, above. This table implies that when an investor plays on the Stock Market using the author's algorithm, on average, one can expect the following long term predictions:
| Daily Stock Value Prediction (+/-) | ~3/5 |
| Weekly Stock Value Prediction (+/-) | ~2/5 |
| Monthly Stock Value Prediction (+/-) | ~3/4 |
The last table means, that if an investor plays on the Stock Market every day, he/she can predict the variation of the stock's value with an approximate success rate of 3/5. If an investor plays every week, he/she can predict the variation of the stock's value with an approximate success rate of 2/5. If an investor plays every month, he/she can predict the variation of the stock's value with an approximate success rate of 3/4.
Just how does prediction rate affect an investor's decision? In the most obvious way. If the algorithm predicts that a stock moves up, the investor sells. If the algorithm predicts that a stock drops, the investor buys or waits.
Now, if you are a Stock Market investor, think why would you be interested in the author's algorithm.
Hint: The total confidence TC of such a predictive algorithm is:
TC(pd,pw,pm,py)=(365*pd+52*pw+12*pm+1*py)/430;
The author's algorithm above, then, has a total confidence rate of:
TC(221/367,22/52,9/12,0)~0.58324
Note that since TC > 1/2, the algorithm makes money!