Replies to post #108060 on Zeev's Turnips Patch-No Politics (ZEEV)

[wstera2]

North American Semiconductor Equipment Industry Posts April 2003 Book-to-Bill Ratio of 0.86

SAN JOSE, Calif., MAY 15, 2003 -- The North American-based manufacturers of semiconductor equipment posted $823 million in orders in March 2003 (three-month average basis) and a book-to-bill ratio of 0.86, according to the April 2003 Express Report published today by Semiconductor Equipment and Materials International (SEMI). A book-to-bill of 0.86 means that $86 worth of new orders were received for every $100 of product billed for the month.

The three-month average of worldwide bookings in April 2003 was $737 million. The bookings figure is five percent below the revised March 2003 level of $777 million and 26 percent below the $996 million in orders posted in April 2002.

The three-month average of worldwide billings in April 2003 was $854 million. The billings figure is nominally below the revised March 2003 level of $857 million and five percent above the April 2002 billings level of $815 million.

"Despite hopeful indications in last months figures, orders for new semiconductor manufacturing equipment remain at relatively low levels," said Stanley Myers, president and CEO of SEMI. "The April data reflects continuing uncertainty in the broader markets in regards to recovery in consumer and commercial spending."

The SEMI book-to-bill is a ratio of three-month moving average bookings to three-month moving average billings for the North American semiconductor equipment industry. Billings and bookings figures are in millions of U.S. dollars.

 
  Billings 
 (Three-month avg.) Bookings 
 (Three-month avg.) Book-to-Bill  
 November 2002 976.4 776.7 0.80  
 December 2002  878.3 826.5 0.94  
 January 2003 784.4 739.0 0.94  
 February 2003  777.7 760.6 0.98  
 March 2003 (final) 857.1 777.3 0.91  
 April 2003 (prelim.) 853.8 737.2 0.86  
 

The data contained in this release was compiled by David Powell, Inc., an independent financial services firm, without audit, from data submitted directly by the participants. SEMI and David Powell, Inc. can assume no responsibility for the accuracy of the underlying data.

The data are contained in a monthly Express Report published by SEMI that tracks billings and orders worldwide of North American-based manufacturers of equipment used to manufacture semiconductor devices, not billings and orders of the chips themselves. The April 2003 Express Report is scheduled for publication on June 17, 2003 (subject to change).

Based in San Jose, Calif., SEMI is an international industry association serving more than 2,500 companies participating in the semiconductor and flat panel display equipment and materials markets. SEMI maintains offices in Austin, Beijing, Brussels, Hsinchu, Moscow, Seoul, Shanghai, Singapore, Tokyo and Washington, D.C. For more information, visit SEMI on the Internet at www.semi.org.

http://www.semi.org/web/wpress.nsf/33fa5c225257afa5882565e3006d9c77/2e59d8bca9f956ba88256d270075d8cc....

[Myst]

Zeev, I swing trade JCOM using X_DEV signals.

Ever hear of X_DEV yet? If not, c'mon over sometime:

http://www.investorshub.com/boards/board.asp?board_id=1074

[kevinb]

Zeev

(Or anybody else). I have a simple question relating to statistics which I hope I can get your help with.

I am studying a simple trading system based on a daily pivot. In an attempt to improve entries I am looking at several aspects to see if I can get a basis for a daily bias.

Simple things I can look at are whether yesterday was a long or a short day, and/or whether yesterday’s close was less than the pivot figure for today. In the back test sample I have 509 short days out of 1030 - Short days being those where the close was less than the pivot figure. On 300 of those days the previous day was also a short day. That says to me that if yesterday was short there is a 60% likelihood that today will also be short. Is that a correct interpretation?

The figures for where the previous close was less than the pivot figure were 525 (out of 1030 days). Of those 332 were short days. First question do I divide this 332 by 525 (# of pivot > prev close days) or 509 (# of short days in backtest) to arrive at a probability for predicting whether a day with a previous close less than the pivot will be a short day.

This gets even more complicated when I try to combine these stats. If I take the set of all days where yesterday was short, and the close was less than today’s pivot figure. I have a total of 375 days.

If I take the number of those days that were short days in the backtest, I get a total of 241 days.

Now my question is if I want to draw probability statistics from this data do I divide this 241 days by:

a) The total number of short days that were previously short (300 )
b) The total number of short days where the pivot was greater than the previous close. (332)
c) Or the total number of days where the previous day was a short day and the close was less than the pivot (375)

The last would seem the most logical but I can make a case for the others as well. Is there a rule to follow here that defines which one to use?

Also I notice that almost all the statistical relationships I have found so far are clustered around the 6n% area. This seems to be more than accidental but I don’t know what significance to attach to it. Does it suggest anything to you.

Thanks for all your help




Kevin