İlk Dönem Ocak 2008 ve Aralık 2009 arasındak borsa kapanış fiyatlarının logaritmik farklarının 100 ile çarpılması ile elde edilmiştir.

Transcription

1 periodical Kağan Karademir Tuesday, May 26, 2015 library(xts) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.date, as.date.numeric library(tseries) library(psych) library(pastecs) Loading required package: boot Attaching package: 'boot' The following object is masked from 'package:psych': logit Attaching package: 'pastecs' The following objects are masked from 'package:xts': first, last İlk Dönem Ocak 2008 ve Aralık 2009 arasındak borsa kapanış fiyatlarının logaritmik farklarının 100 ile çarpılması ile elde edilmiştir. first < read.delim("c:/users/kkarademir/desktop/baha/bist100_periods/1period/first.tx t", dec=",") getiri< first[,2]*100 head(getiri) [1] stat.desc(getiri)

2 nbr.val nbr.null nbr.na min max e e e e e+01 range sum median mean SE.mean e e e e e 01 CI.mean.0.95 var std.dev coef.var e e e e+03 describe(getiri) vars n mean sd median trimmed mad min max range skew kurtosis se getirixts< ts(getiri) plot(getirixts) plot(getiri)

3 gecikme< getiri[ 1] head(getiri) [1] head(gecikme) [1] acf(gecikme)

4 pacf(gecikme)

5 PP.test(gecikme) Phillips Perron Unit Root Test data: gecikme Dickey Fuller = , Truncation lag parameter = 5, p value = 0.01 kpss.test(gecikme) KPSS Test for Level Stationarity data: gecikme KPSS Level = , Truncation lag parameter = 5, p value = adf.test(gecikme) Warning in adf.test(gecikme): p value smaller than printed p value Augmented Dickey Fuller Test data: gecikme Dickey Fuller = , Lag order = 7, p value = 0.01 alternative hypothesis: stationary getirinew< getiri[ 500] regress< lm(gecikme~getirinew) summary(regress)

6 Call: lm(formula = gecikme ~ getirinew) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) getirinew * Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 497 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 1 and 497 DF, p value: d1< first[,3] d1< d1[ 1] head(d1) [1] d2< first[,4] d2< d2[ 1] head(d2) [1] d3< first[,5] d3< d3[ 1] head(d3) [1] d4< first[,6] d4< d4[ 1] head(d4) [1] d5< first[,7] d5< d5[ 1] head(d5)

7 [1] weekeffect< lm(gecikme~getirinew+d1+d2+d3+d4+d5) summary(weekeffect) Call: lm(formula = gecikme ~ getirinew + d1 + d2 + d3 + d4 + d5) Residuals: Min 1Q Median 3Q Max Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(> t ) (Intercept) getirinew * d d d d d5 NA NA NA NA Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 493 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 5 and 493 DF, p value: plot(weekeffect)

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10 2. Dönem Ocak 2010 Aralık 2011 tarihleri borsa kapanış fiyatlarının logaritmik farklarının 100 ile çarpılması ile elde edilmiştir.

11 second < read.delim("c:/users/kkarademir/desktop/baha/bist100_periods/2period/second.t xt", dec=",") getiri< second[,2]*100 head(getiri) [1] stat.desc(getiri) nbr.val nbr.null nbr.na min max e e e e e+00 range sum median mean SE.mean e e e e e 02 CI.mean.0.95 var std.dev coef.var e e e e+02 describe(getiri) vars n mean sd median trimmed mad min max range skew kurtosis se getirixts< ts(getiri) plot(getirixts)

12 plot(getiri)

13 gecikme< getiri[ 1] head(getiri) [1] head(gecikme) [1] acf(gecikme) pacf(gecikme)

14 PP.test(gecikme) Phillips Perron Unit Root Test data: gecikme Dickey Fuller = , Truncation lag parameter = 5, p value = 0.01 kpss.test(gecikme) Warning in kpss.test(gecikme): p value greater than printed p value KPSS Test for Level Stationarity data: gecikme KPSS Level = , Truncation lag parameter = 5, p value = 0.1 adf.test(gecikme) Warning in adf.test(gecikme): p value smaller than printed p value

15 Augmented Dickey Fuller Test data: gecikme Dickey Fuller = , Lag order = 7, p value = 0.01 alternative hypothesis: stationary getirinew< getiri[ 500] regress< lm(gecikme~getirinew) summary(regress) Call: lm(formula = gecikme ~ getirinew) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) getirinew Residual standard error: 1.58 on 500 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 1 and 500 DF, p value: d1< second[,3] d1< d1[ 1] head(d1) [1] d2< second[,4] d2< d2[ 1] head(d2) [1] d3< second[,5] d3< d3[ 1] head(d3) [1]

16 d4< second[,6] d4< d4[ 1] head(d4) [1] d5< second[,7] d5< d5[ 1] head(d5) [1] weekeffect< lm(gecikme~getirinew+d1+d2+d3+d4+d5) summary(weekeffect) Call: lm(formula = gecikme ~ getirinew + d1 + d2 + d3 + d4 + d5) Residuals: Min 1Q Median 3Q Max Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(> t ) (Intercept) getirinew d d d d d5 NA NA NA NA Residual standard error: on 496 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 5 and 496 DF, p value: plot(weekeffect)

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19 3.Dönem Ocak 2012 ve Aralık 2013 borsa kapanış fiyatlarının logaritmik farklarının 100 ile çarpılması ile elde edilmiştir

20 third < read.delim("c:/users/kkarademir/desktop/baha/bist100_periods/3period/third.tx t", dec=",") getiri< third[,2]*100 head(getiri) [1] stat.desc(getiri) nbr.val nbr.null nbr.na min max range sum median mean SE.mean CI.mean.0.95 var std.dev coef.var describe(getiri) vars n mean sd median trimmed mad min max range skew kurtosis se getirixts< ts(getiri) plot(getirixts)

21 plot(getiri)

22 gecikme< getiri[ 1] head(getiri) [1] head(gecikme) [1] acf(gecikme) pacf(gecikme)

23 PP.test(gecikme) Phillips Perron Unit Root Test data: gecikme Dickey Fuller = , Truncation lag parameter = 5, p value = 0.01 kpss.test(gecikme) Warning in kpss.test(gecikme): p value greater than printed p value KPSS Test for Level Stationarity data: gecikme KPSS Level = , Truncation lag parameter = 5, p value = 0.1 adf.test(gecikme) Warning in adf.test(gecikme): p value smaller than printed p value

24 Augmented Dickey Fuller Test data: gecikme Dickey Fuller = , Lag order = 7, p value = 0.01 alternative hypothesis: stationary getirinew< getiri[ 500] regress< lm(gecikme~getirinew) summary(regress) Call: lm(formula = gecikme ~ getirinew) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) getirinew Residual standard error: 1.55 on 500 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 1 and 500 DF, p value: d1< second[,3] d1< d1[ 1] head(d1) [1] d2< third[,4] d2< d2[ 1] head(d2) [1] d3< third[,5] d3< d3[ 1] head(d3) [1]

25 d4< third[,6] d4< d4[ 1] head(d4) [1] d5< third[,7] d5< d5[ 1] head(d5) [1] weekeffect< lm(gecikme~getirinew+d1+d2+d3+d4+d5) summary(weekeffect) Call: lm(formula = gecikme ~ getirinew + d1 + d2 + d3 + d4 + d5) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) getirinew d d d d d Residual standard error: on 495 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 6 and 495 DF, p value: plot(weekeffect)

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28 4.Dönem Ocak 2014 ve Nisan 2015 tarihleri arası borsa kapanış fiyatlarının logaritmik farklarının 100 ile çarpılması ile elde edilmiştir

29 fourth < read.delim("c:/users/kkarademir/desktop/baha/bist100_periods/4period/fourth.t xt", dec=",") getiri< fourth[,2]*100 head(getiri) [1] stat.desc(getiri) nbr.val nbr.null nbr.na min max range sum median mean SE.mean CI.mean.0.95 var std.dev coef.var describe(getiri) vars n mean sd median trimmed mad min max range skew kurtosis se getirixts< ts(getiri) plot(getirixts)

30 plot(getiri)

31 gecikme< getiri[ 1] head(getiri) [1] head(gecikme) [1] acf(gecikme) pacf(gecikme)

32 PP.test(gecikme) Phillips Perron Unit Root Test data: gecikme Dickey Fuller = , Truncation lag parameter = 5, p value = 0.01 kpss.test(gecikme) Warning in kpss.test(gecikme): p value greater than printed p value KPSS Test for Level Stationarity data: gecikme KPSS Level = , Truncation lag parameter = 4, p value = 0.1 adf.test(gecikme) Warning in adf.test(gecikme): p value smaller than printed p value

33 Augmented Dickey Fuller Test data: gecikme Dickey Fuller = , Lag order = 6, p value = 0.01 alternative hypothesis: stationary getirinew< getiri[ 327] regress< lm(gecikme~getirinew) summary(regress) Call: lm(formula = gecikme ~ getirinew) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) getirinew Residual standard error: on 324 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 1 and 324 DF, p value: d1< fourth[,3] d1< d1[ 1] head(d1) [1] d2< fourth[,4] d2< d2[ 1] head(d2) [1] d3< fourth[,5] d3< d3[ 1] head(d3) [1]

34 d4< fourth[,6] d4< d4[ 1] head(d4) [1] d5< fourth[,7] d5< d5[ 1] head(d5) [1] weekeffect< lm(gecikme~getirinew+d1+d2+d3+d4+d5) summary(weekeffect) Call: lm(formula = gecikme ~ getirinew + d1 + d2 + d3 + d4 + d5) Residuals: Min 1Q Median 3Q Max Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(> t ) (Intercept) getirinew d d d d d5 NA NA NA NA Residual standard error: on 320 degrees of freedom Multiple R squared: , Adjusted R squared: F statistic: on 5 and 320 DF, p value: plot(weekeffect)

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