Science of x100s in Stock Market IMKBa first order approach

function of Science

= prediction

Data Prediction (=problem) (=solution)

Science

Can we use the methods of science to make predictions on stock markets?...

No experimentation. Lack of observation. No/ lack of/ full (?) mathematization.

Very hot issue; more papers, more projects, more money...

d1, d2, d3, ...p1, p2, p3, ...D={di}P={pj}f(D) = P

DataProblem d1 p1 d2 p2 d3 p3 d4 p4 ... ...calculation=thinking

About 100 data and/or questions per day.Relevancy and weights of dj pk connections.

f(dj)=pk

For a specific stock

kmax=1 and p1= for each investor i.

-1 i +1 and

-1 i Si 0 SELL 0 Bi i +1 BUY,

where Si and Bi are personal tresholds, both can be set to 0 as default.

i = i(t) decision is made at t.

If mi(t) is the portfolio value of the investor i

mi(t)=i(t) + i(t) i(t)=i(t) + i(t),

where is the liquid, stock amount and is initial capital, net gain.

mi(t)i(t) defines the whole market at t.

mi(t)i(t) mi(t-)i(t-) market is SOLD in mi(t)i(t) mi(t-)i(t-) market is BOUGHT

i(t)i(t) i(t-)i(t-) market is (expected) to be BOUGHT in the next i(t)i(t) i(t-)i(t-) market is (expected) to be SOLD

All the resulting information becomes new data djs (for the next ).

Time constants All investors aim is to make profit. Buy cheap sell expensive Investors profile is nonuniform, so is i(t). (Investors profile displays the same distrubution as in society.)

AssumptionsInvestors are close followers, aware of all information and news. (tight-binding approach )Investors are reflexive, decide and behave immediately. (pseudo-potential approach ) Then i(t) equals to tendencies and actions at the same time.

Mathematisation

All the resulting information on the previous slide become new dj (for the next time period ).

Iterative methods

Optimization of i(t)i(t)s,where stands for any of , , , and .

Any more?

TOASO 22.11.2004

-%0,66

INCLUDEPICTURE "http://www.bigpara.com/graph/20041122180640280.gif" \* MERGEFORMATINET

ADANA-C 22.11.2004

+%9,68

THYAO 22.11.2004

%0.0

COMUN 22.11.2004

-%9,09

BLEK 22.11.2004

%-1,06

TOASO 22.11.2004

%0,00

INCLUDEPICTURE "http://www.bigpara.com/graph/20041123173737280.gif" \* MERGEFORMATINET

ADANA-C 22.11.2004

+%2,94

THYAO 22.11.2004

%0.0

COMUN 22.11.2004

+%4,17

BLEK 22.11.2004

+%2,36

ENDEKS HSSELER

Hisse Kodu

irket Ad

HAO%

Piyasa Deg.(Milyon $)

Tahmini Arlk(%)

Kmlatif Arlk(%)

1

ISCTR

T. Bankas A..

33

7,739.4

12.9

12.9

2

AKBNK

Akbank T.A..

28

7,127.9

10.1

22.9

3

TCELL

Turkcell letiim Hizmetleri A..

14

9,016.8

6.4

29.3

4

EREGL

Ereli D elik Fabrikalar T.A..

49

2,028.5

5.0

34.3

5

GARAN

T. Garanti Bankas A..

32

2,935.0

4.7

39.0

6

TUPRS

Tpra Trkiye Petrol Rafinerileri A..

34

2,537.4

4.3

43.4

7

YKBNK

Yap ve Kredi Bankas A..

42

2,008.4

4.3

47.6

8

KCHOL

Ko Holding A..

19

4,332.3

4.1

51.8

9

SAHOL

Sabanc Holding

19

3,790.4

3.6

55.4

10

AEFES

Anadolu Efes Biraclk ve Malt San.

36

1,845.8

3.3

58.8

11

MIGRS

Migros Trk T.A..

49

890.1

2.2

61.0

12

DOHOL

Doan irketler Grubu Holding A..

34

1,274.3

2.2

63.1

13

ARCLK

Arelik A..

20

2,152.1

2.2

65.3

14

SISE

T.ie ve Cam Fabrikalar A..

39

994.4

2.0

67.3

15

VESTL

Vestel Elektronik Sanayi ve Ticaret A..

63

555.9

1.8

69.0

16

FROTO

Ford Otomotiv Sanayi A..

17

2,043.5

1.8

70.8

17

DYHOL

Doan Yayn Holding A..

30

1,153.4

1.7

72.5

18

HURGZ

Hrriyet Gazetecilik ve Matbaaclk A..

40

809.6

1.6

74.1

19

ENKAI

Enka inaat ve Sanayi A..

12

2,585.6

1.6

75.7

20

ULKER

lker Gda San. A..

26

834.0

1.1

76.8

21

FINBN

Finansbank A..

31

667.9

1.0

77.8

22

TOASO

Tofa Trk Otomobil Fabrikas A..

24

849.1

1.0

78.9

23

AYGAZ

Aygaz A..

42

476.3

1.0

79.9

24

AKGRT

Aksigorta A..

38

487.5

0.9

80.8

25

TRKCM

Trakya Cam Sanayii A..

31

591.7

0.9

81.7

26

IHLAS

hlas Holding

61

300.6

0.9

82.6

27

KIPA

Tesco Kipa Kitle Paz. Tic. ve Gda San.

99

174.1

0.9

83.5

28

KRDMD

Kardemir

99

160.2

0.8

84.3

29

DISBA

T. D Ticaret Bankas A..

35

412.0

0.7

85.0

30

TNSAS

Tansa Parakende Maazaclk Tic. A..

43

334.7

0.7

85.8

_1162912147.bin

_1162912194.bin

Growth types

Random (arithmetical) growth:A = A0 Astate economies

Forced arithmetical growth:minimized Amaximized+A

YKBNK

Geometrical growth

A / A = r (r, constant)A1= A0 + rA0= (1+r) A0A2= A1 + rA1= (1+r)2A0A3= A2 + rA2= (1+r)3A0...An-1An+1=An2population

An= (1+r)nA0

Chart1

1.511

2.2522

3.37533

5.062544

7.5937555

11.39062566

multiple

#REF!

#REF!

term

multiples

50%'s

Chart2

11

1.51.3

2.251.69

3.3752.197

5.06252.8561

7.593753.71293

11.3906254.826809

17.08593756.2748517

25.628906258.15730721

38.44335937510.604499373

57.665039062513.7858491849

86.497558593817.9216039404

129.746337890623.2980851225

194.619506835930.2875106592

291.929260253939.373763857

437.893890380951.1858930141

656.840835571366.5416609183

985.261253356986.5041591938

1477.8918800354112.455406952

2216.8378200531146.1920290375

term

multy

,5's & ,3's

Sheet1

term0.50.3

011

11.51.3

22.251.69

33.3752.197

45.06252.8561

57.593753.71293

611.3906254.826809

717.08593756.2748517

825.628906258.15730721

938.44335937510.604499373

1057.665039062513.7858491849

1186.497558593817.9216039404

12129.746337890623.2980851225

13194.619506835930.2875106592

14291.929260253939.373763857

15437.893890380951.1858930141

16656.840835571366.5416609183

17985.261253356986.5041591938

181477.8918800354112.455406952

192216.8378200531146.1920290375

Sheet1

term

1/2 & 1/3

Exponential growth

A/A = nr

An=enrinformation, bacteria, garbage

Q: Can we manage extreme (geometrical or exponential) growths of portfolios in stock market (IMKB)?...

A: Yes!...

FMIZP, ADANA-c, ECZYT, ... too expensive

Q: New & cheap ones?

A: Yes!

Summary: Hamsi fishing or whale hunting...

Yours questions please...

Thanks a lot for yours kindly interest and listenning...

a bis

ANHYT

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