From: wendt@CS.ColoState.EDU (alan l wendt)
Subject: Cyclical patterns in DJIA 1952-1980
Date: Mon, 25 Jan 1993 05:26:13 GMT
Organization: Colorado State University, Computer Science Department


This is the output of a program that I wrote to evaluate the average
return of the DJIA as related to various calendar periods.  It's based
on the data from 1952-1980.  It does not include dividends.  It assumes
that you bought the average at the closing price the day before the
period began and sold at the closing price the day the period ended
and paid no commisions.  The performance figure is the amount that
you would have if you started in 1952 with  $1.00


This series evaluates the average performance of holding the DJIA for 
the 1st, 2nd, ... nth calendar day of the month.  Count is the number
of such days found in the sample.

calendar	count	accum

1:	224	1.19235
2:	238	1.45446
3:	240	1.45747
4:	218	1.04724
5:	238	1.30494
6:	242	1.14917
7:	241	1.07780
8:	244	0.92470
9:	246	0.92587
10:	248	0.97530
11:	244	1.20549
12:	242	0.96008
13:	245	0.96567
14:	247	1.04665
15:	248	1.24348
16:	244	1.04381
17:	248	1.12659
18:	246	0.85489
19:	246	0.75692
20:	243	0.99507
21:	243	0.81220
22:	233	0.94793
23:	241	0.89302
24:	238	1.01081
25:	222	0.83884
26:	238	1.08942
27:	238	0.96891
28:	240	1.00483
29:	231	1.12879
30:	212	1.17040
31:	137	1.11142

This series evaluates by trading day of the month.

trade	count	accum

1:	348	1.16042
2:	348	1.80073
3:	348	1.70531
4:	348	1.13599
5:	348	1.04787
6:	348	0.83337
7:	348	1.07688
8:	348	0.99815
9:	348	1.08499
10:	348	1.09453
11:	348	0.99816
12:	348	0.96616
13:	348	0.96422
14:	348	0.85569
15:	348	0.75091
16:	348	0.82782
17:	348	1.03175
18:	343	1.06486
19:	334	0.93842
20:	301	0.95730
21:	238	1.43164
22:	125	1.08935
23:	38	1.04980
24:	4	0.99928
25:	4	0.98958
26:	2	1.00164
27:	0	1.00000
28:	0	1.00000
29:	0	1.00000
30:	0	1.00000
31:	0	1.00000

This series evaluates from the end of the month; 1 is the last trading
day of the month.

rev trade	count	accum

1:	348	1.61256
2:	348	1.07479
3:	348	1.02221
4:	348	1.04643
5:	348	0.69214
6:	348	0.85441
7:	348	1.05073
8:	348	0.84988
9:	348	0.71611
10:	348	1.02889
11:	348	1.30045
12:	348	1.01645
13:	348	0.85775
14:	348	1.11543
15:	348	1.18330
16:	348	0.90494
17:	343	1.12452
18:	334	1.11604
19:	301	1.38701
20:	238	1.30121
21:	125	1.20519
22:	38	1.11292
23:	4	1.00480
24:	4	1.03107
25:	2	1.00120
26:	0	1.00000
27:	0	1.00000
28:	0	1.00000
29:	0	1.00000
30:	0	1.00000
31:	0	1.00000

This series evaluates by day of the week.  I'm not sure where
the single Sunday came from.

Weekday	count	accum

Sun:	1	1.00656
Mon:	1419	0.11337
Tue:	1474	1.19088
Wed:	1473	3.48056
Thu:	1462	1.55986
Fri:	1453	4.64808
Sat:	23	1.04164

The following two series evaluate 9 and 10 day cycles.  They
are intended to provide a check on the reliability of the other numbers.
I don't know how to do statistical tests for significant differences
between geometric means.
What they seem to say is that you can ignore any numbers in the above
that are within the range .8 to 1.5.

9-day	count	accum

0:	809	0.80870
1:	804	1.08661
2:	816	1.35301
3:	818	1.07179
4:	811	1.34463
5:	809	1.00667
6:	806	0.91113
7:	814	1.54170
8:	818	1.47433

10-day	count	accum

0:	730	1.45981
1:	732	1.39608
2:	729	1.02235
3:	734	0.95291
4:	735	1.03713
5:	727	1.17612
6:	724	1.21502
7:	731	1.13369
8:	734	1.02182
9:	729	1.04795

The following data goes by Month.

month	count	accum

Jan:	621	1.30909
Feb:	558	0.86641
Mar:	636	1.30702
Apr:	605	1.52877
May:	620	0.75466
Jun:	618	0.96515
Jul:	608	1.37844
Aug:	637	0.88661
Sep:	592	0.78991
Oct:	636	1.01058
Nov:	566	1.42991
Dec:	608	1.55128

This is intended to measure the presidential election cycle.
Administrations pour on the coal during the last two years of the
administration to try and get re-elected.

4-year	count	accum

0:	2013	1.85952
1:	1759	0.64670
2:	1767	1.02515
3:	1766	2.89765

This series looks at the 4-year cycle from a quarterly standpoint.

quarters	count	accum

0:	513	1.09138
1:	512	1.21681
2:	500	1.02930
3:	488	1.36038
4:	432	0.86109
5:	442	0.87490
6:	445	0.89678
7:	440	0.95721
8:	436	0.90965
9:	444	0.78345
10:	446	0.98384
11:	441	1.46211
12:	434	1.73411
13:	445	1.33505
14:	446	1.06304
15:	441	1.17740



The biggest effects are the Monday blues and the presidential
carrot and stick.   The numbers by themselves are not big enough
to make money on if you are paying commissions, but what they
say is that if you have decided to buy IBM for other reasons,
Tuesday morning would probably be a good time.  And Friday afternoon
is a good time to sell.

Alan Wendt