Hello,
I am currently trying to measure investment efficiency based on Biddle, Hilary, and Verdi in 2009 (How does financial reporting quality relate to investment efficiency?).
Here is sample of my data:
I have measured the investment as the sum of capital expenditures, R&D expenditures, and acquisitions minus sales of PPE, scaled by lagged total assets.
Then, I have to do the next step:
“We first rank firms into deciles based on their cash balance and their leverage (we multiply leverage by minus one before ranking so that, as for cash, it is increasing with the likelihood of over investment) and re-scale them to range between zero and one. We then create a composite score measure, OverFirm, which is computed as the average of ranked values of the two partitions variables.”
another explanation :
"we rank firms into deciles based on the firms’ levels of cash balance (ranging from 0.1 at the lowest to 1.0 at the highest) and on leverage (ranging from 1 at the highest to 0.1 at the lowest). Thereafter, we obtain the averages of both deciles for each firm-year observation. Firms with less than the median decile value are likely to under-invest, whereas firms with more than the median decile value are likely to overinvest "
I have a doubt about this conclusion. I hope someone can help me on this.
Thank you in advance
Regards,
I am currently trying to measure investment efficiency based on Biddle, Hilary, and Verdi in 2009 (How does financial reporting quality relate to investment efficiency?).
Here is sample of my data:
Code:
gvkey fyear sic sic_2 invest capx cash_w lev_w lev_1_w xrd aqc sppe at_lg 1004 2013 5080 50 1.115483 6.214822 0.0417427 0.2640741 -0.2640741 178 15.3 0 2136.9 1004 2014 5080 50 5.967641 11.20252 0.0248693 0.0386451 -0.0386451 114 1 40.3 2199.5 1004 2015 5080 50 3.524695 29.9661 0.0205941 0.089835 -0.089835 110 0 0 1515 1045 2013 4512 45 2.797297 23.23534 0.4375159 0.6530412 -0.6530412 100 -206 128 23510 1045 2014 4512 45 3.974397 36.91624 0.191045 0.3830834 -0.3830834 5.072 0 33 42278 1045 2015 4512 45 10.76539 42.75499 0.1587581 0.4187704 -0.4187704 5.802 0 35 43771 1045 2016 4512 45 12.98633 39.83562 0.1445213 0.4645048 -0.4645048 6.945 0 123 48415 1050 2013 3564 35 8.432193 28.18833 0.2408081 0.841197 -0.841197 52.8 104.432 0.215 94.104 1050 2014 3564 35 8.475721 5.312716 0.0555524 0.297074 -0.297074 76.7 44.399 7.738 348.536 1050 2015 3564 35 26.17039 4.024049 0.095358 0.3809057 -0.3809057 90.1 37.481 0 414.365 1050 2016 3564 35 7.833648 2.392121 0.0790256 0.2043472 -0.2043472 90.2 0 0 598.819 1062 2013 6799 67 6.881711 4.925184 0.0064763 0 0 611.1 0 0 468.013 1062 2014 6799 67 6.287024 4.375733 0.000591 0 0 725.2 0 0 252.142 1062 2015 6799 67 2.479092 4.682548 0.0123179 0 0 790 0 0 223.333 1062 2016 6799 67 2.911833 4.270411 0.0263566 0 0 654.3 0 0 162.35 1072 2013 3670 36 2.897789 10.37891 0.3360072 0 0 718 1.6 0.795 2601.995 1072 2014 3670 36 8.609598 11.28391 0.353673 0 0 595.4 0 0.088 2384.988 1072 2015 3670 36 2.468549 24.07052 0.3858464 0 0 603.7 0 1.084 2459.015 1075 2013 4911 49 9.360869 9.918662 0.000712 0.2090094 -0.2090094 96 0 0 13379.62 1075 2014 4911 49 12.93368 8.504842 0.0005629 0.2243901 -0.2243901 102.5 0 0 13508.69 1075 2015 4911 49 14.10227 9.758029 0.0027588 0.2418963 -0.2418963 91.9 0 0 14313.53 1075 2016 4911 49 6.248275 10.97001 0.000591 0.2676148 -0.2676148 89.7 0 0 15028.26 1076 2013 7359 73 19.03485 4.925184 0.1579439 0.0635298 -0.0635298 67.2 0 6.841 1812.929 1076 2014 7359 73 26.28182 4.375733 0.0136057 0.2700807 -0.2700807 62.5 0 6.032 1827.176 1076 2015 7359 73 15.7515 4.682548 0.0151284 0.1844936 -0.1844936 61.9 0 7.515 2456.844 1076 2016 7359 73 8.873837 4.270411 0.1237666 0.1321288 -0.1321288 52.1 0 19.393 2658.875 1078 2013 2834 28 4.725221 14.20059 0.1204433 0.0503905 -0.0503905 150.823 580 0 67234.95 1078 2014 2834 28 13.36112 18.23878 0.1038344 0.0793425 -0.0793425 115.277 3317 0 42953 1078 2015 2834 28 6.662629 18.70261 0.1483949 0.1422411 -0.1422411 88.476 235 0 41275 1078 2016 2834 28 9.360869 19.5637 0.4551846 0.501394 -0.501394 65.386 80 0 41247 1084 2013 7370 73 7.659079 0 0.092827 0 0 0 0 0 0.237 1084 2014 7370 73 23.1202 0 0.0853659 0 0 0 0 0 0.328 1084 2015 7370 73 3.079134 0 0.9285714 0 0 0 0 0 0.028 1084 2016 7370 73 11.70583 0 3.562657 0 0 0 0 0 0.026 1094 2013 5160 51 12.3351 8.731312 0.1182003 0.0680132 -0.0680132 0 0 0 299.28 1094 2014 5160 51 12.90598 10.03506 0.134938 0.3003989 -0.3003989 0 86.14 0 323.43 1094 2015 5160 51 12.71292 5.331375 0.0799942 0.213597 -0.213597 0 0 0 467.984 1094 2016 5160 51 13.98514 10.78806 0.1382454 0.2421362 -0.2421362 0 0 0 489.774 1096 2013 6500 65 7.460923 13.70049 0.0294928 0.4042114 -0.4042114 0 0 0.797 4386.182 1096 2014 6500 65 9.360869 2.491499 0.0225836 0.6061386 -0.6061386 0 -12.612 15.634 5452.995 1096 2015 6500 65 12.67648 2.758143 0.0116972 0.4573994 -0.4573994 0 7.574 20.751 7993.684 1096 2016 6500 65 11.81735 2.328146 0.028077 0.4328883 -0.4328883 0 2.522 41.201 8602.132
I have measured the investment as the sum of capital expenditures, R&D expenditures, and acquisitions minus sales of PPE, scaled by lagged total assets.
Code:
gen invest = ((xrd + capx + aqc - sppe) *100) / at_lg
Then, I have to do the next step:
“We first rank firms into deciles based on their cash balance and their leverage (we multiply leverage by minus one before ranking so that, as for cash, it is increasing with the likelihood of over investment) and re-scale them to range between zero and one. We then create a composite score measure, OverFirm, which is computed as the average of ranked values of the two partitions variables.”
another explanation :
"we rank firms into deciles based on the firms’ levels of cash balance (ranging from 0.1 at the lowest to 1.0 at the highest) and on leverage (ranging from 1 at the highest to 0.1 at the lowest). Thereafter, we obtain the averages of both deciles for each firm-year observation. Firms with less than the median decile value are likely to under-invest, whereas firms with more than the median decile value are likely to overinvest "
Code:
gen lev_1 = lev_w * -1 winsor lev_1 , gen (lev_w_1) p(.01) egen lev1rank = group (lev_1_w) egen cashrank = group (cash_w) egen maxlev1=max(lev1rank) egen maxcash=max(cashrank) gen lev1rankmax = lev1rank / maxlev1 gen cashrankmax = cashrank / maxcash egen avelev1rankmax = mean(lev1rank) egen avecashrankmax = mean(cashrank) gen overfirm = 0 replace overfirm = 1 if lev1rank > avelev1rankmax replace overfirm = 1 if cashrank > avecashrankmax
Thank you in advance
Regards,
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