H1 > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/test R last one.xlsx", + rownames=FALSE, header=TRUE, na="", sheet="industry key ratios2", + stringsAsFactors=TRUE) > library(abind, pos=14) > library(e1071, pos=15) > numSummary(Dataset[,"Market.cap...Mean"], statistics=c("mean", "sd", "IQR", "quantiles"), quantiles=c(0,.25, + .5,.75,1)) mean sd IQR 0% 25% 50% 75% 100% n NA 24.48282 11.5792 10.26512 11.12441 20.8969 30.66939 31.16203 31.65466 3 3 > with(Dataset, (t.test(Market.cap...Mean, alternative='two.sided', mu=0.0, conf.level=.95))) One Sample t-test data: Market.cap...Mean t = 3.6622, df = 2, p-value = 0.06714 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -4.281518 53.247162 sample estimates: mean of x 24.48282 > with(Dataset, (t.test(P.E...mean, alternative='two.sided', mu=0.0, conf.level=.95))) One Sample t-test data: P.E...mean t = 2.1272, df = 2, p-value = 0.1672 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -6.731432 19.895102 sample estimates: mean of x 6.581835 " > with(Dataset, (t.test(LD.to.equity...mean, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: LD.to.equity...mean t = -0.51713, df = 2, p-value = 0.6566 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -226.5213 177.9129 sample estimates: mean of x -24.3042 > with(Dataset, (t.test(Net.PM...mean, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Net.PM...mean t = -2.0877, df = 2, p-value = 0.1721 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -29.93483 10.37577 sample estimates: mean of x -9.779529 > with(Dataset, (t.test(Price.to.book...mean, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Price.to.book...mean t = -1.3332, df = 2, p-value = 0.314 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -2.690056 1.417367 sample estimates: mean of x -0.6363447 > with(Dataset, (t.test(R.D.to.sales...mean, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: R.D.to.sales...mean t = -1.0197, df = 2, p-value = 0.4152 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -5181.847 3196.343 sample estimates: mean of x -992.7518 H2 PCA Analysis and correlation matrix > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/test R last one.xlsx", + rownames=TRUE, header=TRUE, na="", sheet="overall data sample", + stringsAsFactors=TRUE) > Dataset <- Dataset[!(rownames(Dataset) %in% c("CIEN","SYNA")),] > Dataset.PCA<-Dataset[, c("D.E.", "WACC", "P.E", "Asset.turnover", + "Market.cap..B.", "Cash...total.assets", "X.Capex...depreciation", + "PX_TO_BOOK_RATIO")] > res<-PCA(Dataset.PCA , scale.unit=TRUE, ncp=3, graph = FALSE) > plot.PCA(res, axes=c(1, 2), choix="ind", habillage="none", col.ind="black", + col.ind.sup="blue", col.quali="magenta", label=c("ind", "ind.sup", "quali"), + new.plot=TRUE) > plot.PCA(res, axes=c(1, 2), choix="var", new.plot=TRUE, col.var="black", + col.quanti.sup="blue", label=c("var", "quanti.sup"), lim.cos2.var=0) > res$eig eigenvalue percentage of variance cumulative percentage of variance comp 1 2.6251193 32.8139909 32.81399 comp 2 1.8606203 23.2577538 56.07174 comp 3 1.0872046 13.5900569 69.66180 comp 4 0.9595951 11.9949387 81.65674 comp 5 0.6340454 7.9255671 89.58231 comp 6 0.4756337 5.9454218 95.52773 comp 7 0.3240692 4.0508645 99.57859 comp 8 0.0337125 0.4214062 100.00000 > res$var $coord Dim.1 Dim.2 Dim.3 D.E. 0.7358043 -0.43480262 -0.219517805 WACC -0.1047434 0.67478838 0.560385723 P.E 0.9278724 0.06786715 -0.067640518 Asset.turnover 0.2026171 0.81895128 -0.051445332 Market.cap..B. -0.1212200 0.61063282 -0.558602607 Cash...total.assets 0.5117499 0.07018950 0.544464681 X.Capex...depreciation -0.1663508 -0.37603281 0.330573659 PX_TO_BOOK_RATIO 0.9308514 0.14744760 0.002204341 $cor Dim.1 Dim.2 Dim.3 D.E. 0.7358043 -0.43480262 -0.219517805 WACC -0.1047434 0.67478838 0.560385723 P.E 0.9278724 0.06786715 -0.067640518 Asset.turnover 0.2026171 0.81895128 -0.051445332 Market.cap..B. -0.1212200 0.61063282 -0.558602607 Cash...total.assets 0.5117499 0.07018950 0.544464681 X.Capex...depreciation -0.1663508 -0.37603281 0.330573659 PX_TO_BOOK_RATIO 0.9308514 0.14744760 0.002204341 $cos2 Dim.1 Dim.2 Dim.3 D.E. 0.54140795 0.189053321 4.818807e-02 WACC 0.01097118 0.455339352 3.140322e-01 P.E 0.86094726 0.004605951 4.575240e-03 Asset.turnover 0.04105367 0.670681203 2.646622e-03 Market.cap..B. 0.01469430 0.372872439 3.120369e-01 Cash...total.assets 0.26188796 0.004926566 2.964418e-01 X.Capex...depreciation 0.02767257 0.141400676 1.092789e-01 PX_TO_BOOK_RATIO 0.86648439 0.021740795 4.859117e-06 $contrib Dim.1 Dim.2 Dim.3 D.E. 20.6241275 10.1607685 4.432291e+00 WACC 0.4179306 24.4724489 2.888437e+01 P.E 32.7965007 0.2475492 4.208260e-01 Asset.turnover 1.5638784 36.0461079 2.434337e-01 Market.cap..B. 0.5597574 20.0402220 2.870084e+01 Cash...total.assets 9.9762307 0.2647808 2.726642e+01 X.Capex...depreciation 1.0541453 7.5996524 1.005137e+01 PX_TO_BOOK_RATIO 33.0074293 1.1684703 4.469368e-04 > res$ind $coord Dim.1 Dim.2 Dim.3 EMC -0.721372614 1.52514036 0.24739398 INTC -0.827067341 2.73206391 -1.08268912 SNDK -0.733053698 1.39880681 0.91908932 XLNX -0.512411598 0.74204284 0.67586431 EQIX 0.003959154 -1.07235527 -0.55592123 NTAP -0.533415754 1.44519472 0.97221243 VRSN 0.713792631 -0.27687900 1.62204907 YHOO -1.076410361 -0.05777276 0.29276016 NVDA 0.293662295 1.54493884 2.12010378 LNKD -0.492055062 0.73834563 0.02903967 MCHP -0.242545274 0.18920267 0.67771420 MU -0.517453752 1.38674273 0.53388912 PYPL -1.024341386 0.75330613 0.01097697 LRCX -0.326702536 0.18981122 0.62021386 CSRA 0.115082534 -0.81527871 -0.35069448 MSFT -1.051438398 3.08962387 -2.47031209 ACT -0.464745815 -1.10048775 -0.29652977 TEVA -0.407391995 0.45715994 -0.57277066 GILD 0.149058778 2.18119979 -1.46076603 HOLX 0.267740540 -1.04961444 -0.75405257 NEPT -0.104588084 -0.57692970 0.06085530 NVLS 0.246400814 -1.80618080 -1.31283498 INCY 9.702401971 1.10003042 -0.44793574 BMRN -0.504488066 -0.20405665 0.11837675 ISIS 1.800707295 -1.07161963 1.59186757 FTR 1.064975401 -2.12852567 -1.39562468 TSRO 1.760341671 -1.66901697 1.78483638 VRTX 0.611894430 0.97143606 0.69111357 REGN -0.140969853 2.20252210 -0.08519683 LLY -0.412728316 0.96340029 -1.36642680 NEM -0.628354450 -1.26232543 -0.68953918 WLL -0.576265681 -1.41646748 -0.36771921 KMI -0.494767674 -1.54882138 -1.22097489 NEE -0.343142107 -1.20554784 -1.54672592 PDCE -1.262609130 -2.04182078 1.28290177 SWC -0.605052599 -0.09382508 -0.01282723 CXO -1.246627860 -1.91803476 1.48404964 D 0.209841530 -1.76150741 -1.74580762 FSLR -0.839501425 1.24326320 1.19879659 SPWR -0.237498109 0.31019823 1.07961357 CLR -0.293224737 -1.43643529 -0.47326927 JONE -0.094280926 -0.84503276 -0.22932963 BCEI -0.347648508 -0.34046477 0.35655373 JASO 0.122294066 0.53457057 0.06767617 $cos2 Dim.1 Dim.2 Dim.3 EMC 1.713473e-01 0.7659097097 2.015284e-02 INTC 6.860031e-02 0.7485590194 1.175579e-01 SNDK 1.231407e-01 0.4483793602 1.935734e-01 XLNX 1.491709e-01 0.3128269488 2.595166e-01 EQIX 6.857021e-06 0.5030463806 1.351939e-01 NTAP 6.936829e-02 0.5091925687 2.304367e-01 VRSN 7.780304e-02 0.0117066466 4.017727e-01 YHOO 2.159809e-01 0.0006221654 1.597657e-02 NVDA 9.383519e-03 0.2597119580 4.890842e-01 LNKD 1.885002e-01 0.4244279026 6.565501e-04 MCHP 6.394791e-02 0.0389130485 4.992682e-01 MU 6.959757e-02 0.4998535466 7.408890e-02 PYPL 3.932066e-01 0.2126543959 4.515399e-05 LRCX 1.229270e-01 0.0414940619 4.430218e-01 CSRA 8.011680e-03 0.4020841567 7.439823e-02 MSFT 4.951081e-02 0.4275073267 2.732977e-01 ACT 6.814674e-02 0.3821065753 2.774282e-02 TEVA 1.322675e-01 0.1665576158 2.614506e-01 GILD 2.128848e-03 0.4558483141 2.044517e-01 HOLX 2.700745e-02 0.4150638441 2.142193e-01 NEPT 1.403093e-02 0.4269410617 4.750278e-03 NVLS 6.153681e-03 0.3306535233 1.746912e-01 INCY 9.436005e-01 0.0121293917 2.011227e-03 BMRN 2.064649e-01 0.0337789342 1.136782e-02 ISIS 2.376442e-01 0.0841632174 1.857184e-01 FTR 1.102708e-01 0.4404930750 1.893732e-01 TSRO 1.764687e-01 0.1586336640 1.814139e-01 VRTX 1.009682e-01 0.2544837414 1.288043e-01 REGN 3.116391e-03 0.7607465969 1.138273e-03 LLY 4.873906e-02 0.2655597171 5.342215e-01 NEM 1.019596e-01 0.4114920979 1.227826e-01 WLL 1.098633e-01 0.6637743467 4.473422e-02 KMI 5.725966e-02 0.5611110171 3.487061e-01 NEE 2.820906e-02 0.3481844894 5.731486e-01 PDCE 7.267349e-02 0.1900524246 7.502828e-02 SWC 2.036686e-01 0.0048975143 9.153846e-05 CXO 6.756890e-02 0.1599507011 9.575690e-02 D 6.236247e-03 0.4394498717 4.316514e-01 FSLR 1.482248e-01 0.3250904558 3.022519e-01 SPWR 1.602806e-02 0.0273425609 3.312053e-01 CLR 2.529508e-02 0.6070256305 6.589486e-02 JONE 5.391945e-03 0.4331565249 3.190201e-02 BCEI 1.881435e-01 0.1804483617 1.979058e-01 JASO 3.341273e-03 0.0638427029 1.023229e-03 $contrib Dim.1 Dim.2 Dim.3 EMC 4.505236e-01 2.841248340 1.279423e-01 INTC 5.922159e-01 9.117405618 2.450437e+00 SNDK 4.652323e-01 2.390039325 1.765841e+00 XLNX 2.273192e-01 0.672585545 9.548938e-01 EQIX 1.357073e-05 1.404646209 6.460447e-01 NTAP 2.463372e-01 2.551187029 1.975870e+00 VRSN 4.411054e-01 0.093641771 5.500017e+00 YHOO 1.003123e+00 0.004076954 1.791678e-01 NVDA 7.466115e-02 2.915493985 9.396158e+00 LNKD 2.096166e-01 0.665899961 1.762867e-03 MCHP 5.093120e-02 0.043726438 9.601282e-01 MU 2.318149e-01 2.348991043 5.958517e-01 PYPL 9.084222e-01 0.693158522 2.518841e-04 LRCX 9.240666e-02 0.044008171 8.041165e-01 CSRA 1.146614e-02 0.811898554 2.570952e-01 MSFT 9.571190e-01 11.660060191 1.275674e+01 ACT 1.869947e-01 1.479312736 1.838115e-01 TEVA 1.436889e-01 0.255285355 6.858002e-01 GILD 1.923594e-02 5.811395896 4.460642e+00 HOLX 6.206211e-02 1.345702818 1.188610e+00 NEPT 9.470277e-03 0.406570032 7.741639e-03 NVLS 5.256329e-02 3.984850305 3.602934e+00 INCY 8.149985e+01 1.478083463 4.194377e-01 BMRN 2.203434e-01 0.050861725 2.929334e-02 ISIS 2.807272e+00 1.402719664 5.297243e+00 FTR 9.819230e-01 5.534104465 4.071677e+00 TSRO 2.682824e+00 3.402601357 6.659366e+00 VRTX 3.241539e-01 1.152704559 9.984697e-01 REGN 1.720485e-02 5.925569816 1.517340e-02 LLY 1.474779e-01 1.133712954 3.903092e+00 NEM 3.418280e-01 1.946400592 9.939258e-01 WLL 2.875039e-01 2.450771289 2.826628e-01 KMI 2.119341e-01 2.930166085 3.116374e+00 NEE 1.019404e-01 1.775245683 5.001068e+00 PDCE 1.380181e+00 5.092426959 3.440510e+00 SWC 3.169455e-01 0.010752945 3.439552e-04 CXO 1.345464e+00 4.493683855 4.603975e+00 D 3.812248e-02 3.790168480 6.371311e+00 FSLR 6.101564e-01 1.888059725 3.004188e+00 SPWR 4.883358e-02 0.117535264 2.436535e+00 CLR 7.443882e-02 2.520354898 4.682229e-01 JONE 7.695662e-03 0.872241329 1.099402e-01 BCEI 1.046355e-01 0.141590436 2.657578e-01 JASO 1.294819e-02 0.349059658 9.574314e-03 $dist EMC INTC SNDK XLNX EQIX NTAP VRSN YHOO 1.7426936 3.1577506 2.0889831 1.3267127 1.5119404 2.0252812 2.5590205 2.3161687 NVDA LNKD MCHP MU PYPL LRCX CSRA MSFT 3.0315541 1.1333336 0.9591348 1.9614377 1.6335571 0.9318133 1.2857236 4.7253484 ACT TEVA GILD HOLX NEPT NVLS INCY BMRN 1.7802990 1.1201751 3.2306155 1.6291923 0.8829557 3.1410485 9.9881529 1.1102679 ISIS FTR TSRO VRTX REGN LLY NEM WLL 3.6938520 3.2070767 4.1904733 1.9256804 2.5252261 1.8695009 1.9678436 1.7385868 KMI NEE PDCE SWC CXO D FSLR SPWR 2.0676496 2.0430535 4.6836120 1.3406984 4.7958258 2.6572340 2.1805245 1.8759433 CLR JONE BCEI JASO 1.8436673 1.2839589 0.8014856 2.1156772 > dimdesc(res, axes=1:3) $Dim.1 $Dim.1$quanti correlation p.value PX_TO_BOOK_RATIO 0.9308514 5.669109e-20 P.E 0.9278724 1.334849e-19 D.E. 0.7358043 1.267409e-08 Cash...total.assets 0.5117499 3.843817e-04 $Dim.2 $Dim.2$quanti correlation p.value Asset.turnover 0.8189513 1.095709e-11 WACC 0.6747884 5.082570e-07 Market.cap..B. 0.6106328 1.074280e-05 X.Capex...depreciation -0.3760328 1.188314e-02 D.E. -0.4348026 3.184008e-03 $Dim.3 $Dim.3$quanti correlation p.value WACC 0.5603857 7.623632e-05 Cash...total.assets 0.5444647 1.330702e-04 X.Capex...depreciation 0.3305737 2.840279e-02 Market.cap..B. -0.5586026 8.125885e-05 > remove(Dataset.PCA) > library(lattice, pos=16) > library(survival, pos=16) > library(Formula, pos=16) > library(ggplot2, pos=16) > library(Hmisc, pos=16) > rcorr.adjust(Dataset[,c("Asset.turnover","Cash...total.assets","D.E.", + "Market.cap..B.","P.E","PX_TO_BOOK_RATIO","WACC","X.Capex...depreciation")], + type="pearson", use="complete") Pearson correlations: Asset.turnover Cash...total.assets D.E. Asset.turnover 1.0000 0.6538 -0.0961 Cash...total.assets 0.6538 1.0000 0.3848 D.E. -0.0961 0.3848 1.0000 Market.cap..B. 0.3656 -0.0464 -0.2307 P.E 0.2686 0.7321 0.6610 PX_TO_BOOK_RATIO 0.3683 0.7771 0.6066 WACC 0.2964 0.2132 -0.5983 X.Capex...depreciation -0.3241 -0.2829 -0.1326 Market.cap..B. P.E PX_TO_BOOK_RATIO WACC Asset.turnover 0.3656 0.2686 0.3683 0.2964 Cash...total.assets -0.0464 0.7321 0.7771 0.2132 D.E. -0.2307 0.6610 0.6066 -0.5983 Market.cap..B. 1.0000 -0.1184 -0.0474 -0.0214 P.E -0.1184 1.0000 0.9844 -0.0818 PX_TO_BOOK_RATIO -0.0474 0.9844 1.0000 -0.0534 WACC -0.0214 -0.0818 -0.0534 1.0000 X.Capex...depreciation -0.2030 -0.0571 -0.0852 -0.1105 X.Capex...depreciation Asset.turnover -0.3241 Cash...total.assets -0.2829 D.E. -0.1326 Market.cap..B. -0.2030 P.E -0.0571 PX_TO_BOOK_RATIO -0.0852 WACC -0.1105 X.Capex...depreciation 1.0000 Number of observations: 22 Pairwise two-sided p-values: Asset.turnover Cash...total.assets D.E. Market.cap..B. Asset.turnover 0.0010 0.6707 0.0943 Cash...total.assets 0.0010 0.0770 0.8375 D.E. 0.6707 0.0770 0.3015 Market.cap..B. 0.0943 0.8375 0.3015 P.E 0.2268 0.0001 0.0008 0.5996 PX_TO_BOOK_RATIO 0.0917 <.0001 0.0028 0.8341 WACC 0.1805 0.3407 0.0033 0.9246 X.Capex...depreciation 0.1412 0.2020 0.5565 0.3650 P.E PX_TO_BOOK_RATIO WACC X.Capex...depreciation Asset.turnover 0.2268 0.0917 0.1805 0.1412 Cash...total.assets 0.0001 <.0001 0.3407 0.2020 D.E. 0.0008 0.0028 0.0033 0.5565 Market.cap..B. 0.5996 0.8341 0.9246 0.3650 P.E <.0001 0.7176 0.8008 PX_TO_BOOK_RATIO <.0001 0.8134 0.7060 WACC 0.7176 0.8134 0.6245 X.Capex...depreciation 0.8008 0.7060 0.6245 Adjusted p-values (Holm's method) Asset.turnover Cash...total.assets D.E. Market.cap..B. Asset.turnover 0.0232 1.0000 1.0000 Cash...total.assets 0.0232 1.0000 1.0000 D.E. 1.0000 1.0000 1.0000 Market.cap..B. 1.0000 1.0000 1.0000 P.E 1.0000 0.0028 0.0203 1.0000 PX_TO_BOOK_RATIO 1.0000 0.0006 0.0635 1.0000 WACC 1.0000 1.0000 0.0719 1.0000 X.Capex...depreciation 1.0000 1.0000 1.0000 1.0000 P.E PX_TO_BOOK_RATIO WACC X.Capex...depreciation Asset.turnover 1.0000 1.0000 1.0000 1.0000 Cash...total.assets 0.0028 0.0006 1.0000 1.0000 D.E. 0.0203 0.0635 0.0719 1.0000 Market.cap..B. 1.0000 1.0000 1.0000 1.0000 P.E <.0001 1.0000 1.0000 PX_TO_BOOK_RATIO <.0001 1.0000 1.0000 WACC 1.0000 1.0000 1.0000 X.Capex...depreciation 1.0000 1.0000 1.0000 > cor(Dataset[,c("Asset.turnover","Cash...total.assets","D.E.","Market.cap..B.","P.E","PX_TO_BOOK_RATIO","WACC","X.Capex...depreciation")], use="complete") Asset.turnover Cash...total.assets D.E. Market.cap..B. P.E PX_TO_BOOK_RATIO WACC X.Capex...depreciation Asset.turnover 1.00000000 0.6538176 -0.09606196 0.36561896 0.26860582 0.36829841 0.29636687 -0.32407830 Cash...total.assets 0.65381763 1.0000000 0.38483390 -0.04642230 0.73209020 0.77708048 0.21323882 -0.28294354 D.E. -0.09606196 0.3848339 1.00000000 -0.23074034 0.66096664 0.60664569 -0.59828083 -0.13256368 Market.cap..B. 0.36561896 -0.0464223 -0.23074034 1.00000000 -0.11844229 -0.04739793 -0.02143094 -0.20295484 P.E 0.26860582 0.7320902 0.66096664 -0.11844229 1.00000000 0.98440486 -0.08175064 -0.05708409 PX_TO_BOOK_RATIO 0.36829841 0.7770805 0.60664569 -0.04739793 0.98440486 1.00000000 -0.05339300 -0.08524743 WACC 0.29636687 0.2132388 -0.59828083 -0.02143094 -0.08175064 -0.05339300 1.00000000 -0.11048911 X.Capex...depreciation -0.32407830 -0.2829435 -0.13256368 -0.20295484 -0.05708409 -0.08524743 -0.11048911 1.00000000 H3 two days return regression all issuers > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", + rownames=FALSE, header=TRUE, na="", sheet="Regression two days returns", + stringsAsFactors=TRUE) > Dataset <- within(Dataset, { + X <- NULL + X.1 <- NULL + X.2 <- NULL + X.3 <- NULL + X.4 <- NULL + X.5 <- NULL + X.6 <- NULL + X.7 <- NULL + X.8 <- NULL + X.9 <- NULL + X.10 <- NULL + X.11 <- NULL + X.12 <- NULL + X.13 <- NULL + }) > Dataset <- Dataset[!(rownames(Dataset) %in% c("31","32","33","34","35","36", + "37")),] > Dataset <- within(Dataset, { + repurchase <- factor(repurchase, labels=c('no repurchase','repurchase')) + }) > Dataset$MTBtimedummychange <- with(Dataset, Dummy.change.in.asset* + market.to.book) > LinearModel.1 <- lm(Two.days.return ~ market.to.book + MTBtimedummychange + + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + + Preissue.runup.market, data=Dataset) > summary(LinearModel.1) Call: lm(formula = Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + Preissue.runup.market, data = Dataset) Residuals: Min 1Q Median 3Q Max -0.088218 -0.024705 -0.000312 0.009736 0.116424 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.561e-02 5.596e-02 -0.458 0.6542 market.to.book 9.951e-03 4.198e-03 2.370 0.0327 * MTBtimedummychange -1.148e-02 6.705e-03 -1.712 0.1089 Net.income.total.assets 1.801e-01 6.595e-02 2.730 0.0163 * Change.in.asset -7.901e-02 4.033e-02 -1.959 0.0703 . Long.term.debt.to.equity 4.926e-04 4.779e-04 1.031 0.3201 Market.cap -1.806e-07 2.686e-07 -0.672 0.5124 Financial.slack 2.418e-03 5.921e-03 0.408 0.6892 Volatility -1.346e-01 7.083e-02 -1.900 0.0782 . Preissue.runup.stock.price -3.470e-02 5.022e-02 -0.691 0.5008 Preissue.runup.market -8.130e-02 2.399e-01 -0.339 0.7398 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0522 on 14 degrees of freedom (5 observations deleted due to missingness) Multiple R-squared: 0.5765, Adjusted R-squared: 0.274 F-statistic: 1.906 on 10 and 14 DF, p-value: 0.1311 > BIC(LinearModel.1) [1] -52.55409 > LinearModel.2 <- lm(Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + + Preissue.runup.market + repurchase, data=Dataset) > summary(LinearModel.2) Call: lm(formula = Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + Preissue.runup.market + repurchase, data = Dataset) Residuals: Min 1Q Median 3Q Max -0.087145 -0.023499 -0.000111 0.010316 0.116377 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.629e-02 5.838e-02 -0.450 0.6598 market.to.book 9.851e-03 4.452e-03 2.213 0.0454 * MTBtimedummychange -1.147e-02 6.956e-03 -1.649 0.1231 Net.income.total.assets 1.804e-01 6.848e-02 2.634 0.0206 * Change.in.asset -7.839e-02 4.223e-02 -1.856 0.0862 . Long.term.debt.to.equity 4.479e-04 6.445e-04 0.695 0.4993 Market.cap -1.965e-07 3.147e-07 -0.624 0.5433 Financial.slack 2.439e-03 6.145e-03 0.397 0.6979 Volatility -1.353e-01 7.379e-02 -1.834 0.0896 . Preissue.runup.stock.price -3.880e-02 6.428e-02 -0.604 0.5565 Preissue.runup.market -6.923e-02 2.725e-01 -0.254 0.8034 repurchase[T.repurchase] -4.163e-03 3.830e-02 -0.109 0.9151 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.05415 on 13 degrees of freedom (5 observations deleted due to missingness) Multiple R-squared: 0.5769, Adjusted R-squared: 0.2188 F-statistic: 1.611 on 11 and 13 DF, p-value: 0.2049 > BIC(LinearModel.2) [1] -49.35792 H3 two days return regression equity-like sample > Dataset <- readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", rownames=FALSE, header=TRUE, na="", sheet="Regression equity like", stringsAsFactors=TRUE) > Dataset$MTBtimedummychange <- with(Dataset, Dummy.change.in.asset* market.to.book) > Dataset <- within(Dataset, { + repurchase <- factor(repurchase, labels=c('no repurchase','repurchase')) + }) > Dataset <- readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", rownames=FALSE, header=TRUE, na="", sheet="Regression equity like", stringsAsFactors=TRUE) > Dataset$MTBtimedummychange <- with(Dataset, Dummy.change.in.asset* market.to.book) > Dataset <- within(Dataset, { + repurchase <- factor(repurchase, labels=c('no repurchase','repurchase')) + }) > LinearModel.23 <- lm(Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset +Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + + Preissue.runup.market, data=Dataset) > summary(LinearModel.23) Call: lm(formula = Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + Preissue.runup.market, data = Dataset) Residuals: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 -0.0211341 0.0034193 -0.0339840 0.0268478 0.0101257 0.0386194 -0.0046079 -0.0428491 -0.0003793 -0.0159860 -0.0213584 0.0105729 -0.0092569 0.0414111 0.0185594 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.240e-01 8.313e-02 -2.694 0.0544 . market.to.book 1.828e-02 5.860e-03 3.120 0.0355 * MTBtimedummychange -2.854e-02 1.547e-02 -1.844 0.1389 Net.income.total.assets 2.491e-03 1.020e-01 0.024 0.9817 Change.in.asset -6.563e-02 5.182e-02 -1.267 0.2740 Long.term.debt.to.equity -3.450e-04 6.685e-04 -0.516 0.6331 Market.cap -3.034e-07 1.620e-06 -0.187 0.8606 Financial.slack -7.372e-03 1.032e-02 -0.715 0.5144 Volatility 3.937e-01 1.680e-01 2.343 0.0791 . Preissue.runup.stock.price -1.765e-01 1.342e-01 -1.315 0.2589 Preissue.runup.market 1.366e+00 5.633e-01 2.426 0.0723 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.04678 on 4 degrees of freedom Multiple R-squared: 0.7843, Adjusted R-squared: 0.2452 F-statistic: 1.455 on 10 and 4 DF, p-value: 0.3831 > BIC(LinearModel.23) [1] -36.62965 > LinearModel.24 <- lm(Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + + Preissue.runup.market + repurchase, data=Dataset) > summary(LinearModel.24) Call: lm(formula = Two.days.return ~ market.to.book + MTBtimedummychange + Net.income.total.assets + Change.in.asset + Long.term.debt.to.equity + Market.cap + Financial.slack + Volatility + Preissue.runup.stock.price + Preissue.runup.market + repurchase, data = Dataset) Residuals: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 -0.025440 0.015836 -0.028101 0.027529 0.001550 0.038212 -0.002106 -0.035480 0.008626 -0.011636 -0.015640 -0.001579 -0.024415 0.037831 0.014813 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.678e-01 1.184e-01 -2.262 0.1088 market.to.book 2.190e-02 8.956e-03 2.445 0.0921 . MTBtimedummychange -3.983e-02 2.585e-02 -1.541 0.2210 Net.income.total.assets 3.065e-02 1.219e-01 0.251 0.8177 Change.in.asset -7.618e-02 5.960e-02 -1.278 0.2911 Long.term.debt.to.equity -9.009e-04 1.208e-03 -0.746 0.5099 Market.cap -4.330e-07 1.788e-06 -0.242 0.8243 Financial.slack -5.826e-03 1.161e-02 -0.502 0.6504 Volatility 4.324e-01 1.958e-01 2.209 0.1143 Preissue.runup.stock.price -1.650e-01 1.484e-01 -1.112 0.3473 Preissue.runup.market 1.602e+00 7.396e-01 2.167 0.1188 repurchase[T.repurchase] -5.820e-02 1.006e-01 -0.579 0.6034 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.05123 on 3 degrees of freedom Multiple R-squared: 0.806, Adjusted R-squared: 0.09464 F-statistic: 1.133 on 11 and 3 DF, p-value: 0.5201 > BIC(LinearModel.24) [1] -35.50898 H4 test abnormal return with and without repurchase > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", + rownames=TRUE, header=TRUE, na="", sheet="Abnormal return with repurchase", + stringsAsFactors=TRUE) > with(Dataset, (t.test(Two.days.return, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = 1.1705, df = 8, p-value = 0.2755 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.008417298 0.025770005 sample estimates: mean of x 0.008676354 > with(Dataset, (t.test(Two.days.return, alternative='less', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = 1.1705, df = 8, p-value = 0.8623 alternative hypothesis: true mean is less than 0 95 percent confidence interval: -Inf 0.02246057 sample estimates: mean of x 0.008676354 > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", + rownames=TRUE, header=TRUE, na="", sheet="abnormal return without repurch", + stringsAsFactors=TRUE) > with(Dataset, (t.test(Two.days.return, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = -1.0978, df = 20, p-value = 0.2853 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.04790560 0.01486851 sample estimates: mean of x -0.01651855 > with(Dataset, (t.test(Two.days.return, alternative='less', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = -1.0978, df = 20, p-value = 0.1427 alternative hypothesis: true mean is less than 0 95 percent confidence interval: -Inf 0.009432927 sample estimates: mean of x -0.01651855 H5 logit regression all issuers > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", + rownames=TRUE, header=TRUE, na="", sheet="Abnormal return with repurchase", + stringsAsFactors=TRUE) > with(Dataset, (t.test(Two.days.return, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = 1.1705, df = 8, p-value = 0.2755 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.008417298 0.025770005 sample estimates: mean of x 0.008676354 > with(Dataset, (t.test(Two.days.return, alternative='less', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = 1.1705, df = 8, p-value = 0.8623 alternative hypothesis: true mean is less than 0 95 percent confidence interval: -Inf 0.02246057 sample estimates: mean of x 0.008676354 > Dataset <- + readXL("C:/Users/Arnaud/Documents/inge22/Calcul mémoire/ratio list date corrigé.xlsx", + rownames=TRUE, header=TRUE, na="", sheet="abnormal return without repurch", + stringsAsFactors=TRUE) > with(Dataset, (t.test(Two.days.return, alternative='two.sided', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = -1.0978, df = 20, p-value = 0.2853 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.04790560 0.01486851 sample estimates: mean of x -0.01651855 > with(Dataset, (t.test(Two.days.return, alternative='less', mu=0.0, + conf.level=.95))) One Sample t-test data: Two.days.return t = -1.0978, df = 20, p-value = 0.1427 alternative hypothesis: true mean is less than 0 95 percent confidence interval: -Inf 0.009432927 sample estimates: mean of x -0.01651855