I am currently writing my thesis about PE-backed IPOs and therefore am calculating the BHARs. I calculated the BHARs already in a way that works for me. Nonetheless, i have weekly data and want to convert it into monthly data. I tried a lot of things and checked older statlist topics (e.g. https://www.statalist.org/forums/for...-weekly-prices), but this did not got me to monthly Dates+corresponding returns. It would be nice if the new data variable would correspond with the stock return most nearby the turn of the month. Below you can find my data, hope you can help me with this.
Greetings,
Sander
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int Date double PriceCO byte Companyid int week float(rawreturn return1) double(SP600 SP500VALUE SP500EQUALWEIGHTED bharCO bharsp600 bharsp500value bharsp500equal returnproduct) 18611 9.9 1 0 . . 408.34000000000003 575.65 1874.65 1 1 1 1 1.6202015914789347 18618 9.89 1 1 -.001010101 .9989899 420.34000000000003 589.64 1920.03 .998989898989899 1.0293872753097908 1.0243029618691915 1.0242071853412635 1.6202015914789347 18625 10 1 2 .011122346 1.0111223 420.6 590.78 1918.9 1.0101010101010102 1.0300239996081697 1.0262833318856943 1.0236044061558158 1.6202015914789347 18632 10.34 1 3 .034 1.034 420.11 601.76 1937.3600000000001 1.0444444444444443 1.0288240191996865 1.045357422044645 1.033451577627824 1.6202015914789347 18639 10.21 1 4 -.012572533 .9874275 421.96000000000004 606.48 1955.42 1.0313131313131314 1.0333545574766125 1.0535568487796405 1.0430853759368415 1.6202015914789347 18646 10.57 1 5 .03525955 1.0352596 416.21000000000004 602.92 1947.07 1.0676767676767676 1.0192731547240046 1.0473725353947712 1.0386312111594163 1.6202015914789347 18653 11.27 1 6 .06622516 1.0662252 422 612.12 1969.24 1.1383838383838383 1.0334525150609786 1.0633544688612873 1.0504574187181608 1.6202015914789347 18660 12.35 1 7 .09582964 1.0958296 422.73 617.95 1981.72 1.2474747474747474 1.0352402409756576 1.0734821506123513 1.0571146614034619 1.6202015914789347 18667 12 1 8 -.02834008 .9716599 428.90000000000003 624.49 2016.19 1.2121212121212122 1.0503501983641084 1.0848432207070269 1.0755020937241617 1.6202015914789347 18674 12.51 1 9 .0425 1.0425 437.53000000000003 631.63 2049.8 1.2636363636363637 1.0714845471910663 1.0972465908103883 1.0934307737444324 1.6202015914789347 18681 12.24 1 10 -.021582734 .9784173 422.01 621.38 1994.95 1.2363636363636363 1.03347700445707 1.079440632328672 1.0641719787693702 1.6202015914789347 18688 12.59 1 11 .02859477 1.0285947 427.75 619.42 2002.73 1.2717171717171716 1.0475339178135867 1.0760357856336316 1.068322086789534 1.6202015914789347 18695 12.49 1 12 -.007942812 .9920572 436.17 627.02 2020.16 1.2616161616161616 1.0681539893226233 1.0892382524103188 1.0776198223668418 1.6202015914789347 18702 11.86 1 13 -.05044035 .9495596 415.25 596.63 1936.64 1.197979797979798 1.0169221726992212 1.0364457569703813 1.0330675059344412 1.6202015914789347 18709 12.55 1 14 .05817875 1.0581788 429.76 615.8100000000001 1995.64 1.2676767676767677 1.0524562864279765 1.0697646139147052 1.0645400474755287 1.6202015914789347 18716 12.05 1 15 -.03984064 .9601594 444.26 629.07 2048.45 1.2171717171717171 1.0879659107606405 1.0927994441066622 1.0927106393193395 1.6202015914789347 18723 11.71 1 16 -.02821577 .9717842 452.42 633.94 2066.9900000000002 1.1828282828282828 1.1079492579712984 1.101259445843829 1.102600485423946 1.6202015914789347 18730 11.61 1 17 -.008539709 .9914603 435.59000000000003 621.46 2031.68 1.1727272727272726 1.0667336043493167 1.0795796056631635 1.0837649694609661 1.6202015914789347 18737 11.33 1 18 -.02411714 .9758829 444.29 626.87 2063.64 1.1444444444444444 1.0880393789489151 1.0889776774081474 1.1008134851839009 1.6202015914789347 18744 11.88 1 19 .04854369 1.0485437 455.95 638.02 2099.9 1.2 1.1165940147915951 1.1083470859028923 1.120155762408983 1.6202015914789347 18751 11.41 1 20 -.03956229 .9604377 443.95 633.38 2079.2 1.1525252525252525 1.0872067394818044 1.1002866325023886 1.1091137012242285 1.6202015914789347 18758 11.82 1 21 .03593339 1.0359334 449.06 628.87 2087.73 1.1939393939393939 1.0997208208845568 1.0924520107704334 1.1136638839249993 1.6202015914789347 18765 12.92 1 22 .0930626 1.0930626 445.86 628.16 2090.23 1.305050505050505 1.0918842141352794 1.0912186224268219 1.1149974661936894 1.6202015914789347 18772 13.01 1 23 .006965944 1.006966 438.76 617.62 2054.95 1.314141414141414 1.0744967429103198 1.0729088856075741 1.0961779532179339 1.6202015914789347 18779 13.07 1 24 .004611837 1.0046118 439.88 613.44 2043.99 1.3202020202020202 1.0772395552725669 1.0656475288803962 1.0903315285519963 1.6202015914789347 18786 13.03 1 25 -.003060444 .9969395 422.79 596.63 1980.8500000000001 1.316161616161616 1.0353871773522065 1.0364457569703813 1.0566505747739579 1.6202015914789347 18793 12.1 1 26 -.07137375 .9286262 419.65000000000003 590.96 1959.7 1.222222222222222 1.0276975069794778 1.0265960218883003 1.045368468780839 1.6202015914789347 18800 13.14 1 27 .08595041 1.0859504 429.96000000000004 602.09 2000.3700000000001 1.3272727272727274 1.0529460743498065 1.0459306870494225 1.0670631851278904 1.6202015914789347 18807 13.35 1 28 .015981736 1.0159818 439.67 608.77 2034.07 1.3484848484848484 1.0767252779546457 1.057534960479458 1.0850398741098337 1.6202015914789347 18814 13.07 1 29 -.020973783 .9790262 454.71000000000004 619.4300000000001 2081.28 1.3202020202020202 1.1135573296762502 1.076053157300443 1.1102232416717788 1.6202015914789347 end format %tdnn/dd/CCYY Date
0 Response to How to convert weekly stock prices into monthly
Post a Comment