In the Search of Value Stocks in Korean Market pt. 2
Data Analysis
To continue with the prior post, let us answer some of the questions that I have added at the end of the blog. I shifted the coding environment from Python to R for data manipulation convenience.* **1. What is the average PBR, PER and dividend yield rate in each market, KOSPI and KOSDAQ? Is the number ‘5’ the accurate standard?
yearPer yearPbr yearDivYield
Min. : 0.020 Min. : 0.000 Min. : 0.0000
1st Qu.: 7.902 1st Qu.: 0.650 1st Qu.: 0.1425
Median : 14.870 Median : 1.200 Median : 1.0300
Mean : 56.727 Mean : 1.934 Mean : 1.4392
3rd Qu.: 30.075 3rd Qu.: 2.220 3rd Qu.: 2.1575
Max. :6789.070 Max. :35.280 Max. :15.7600
yearPer yearPbr yearDivYield
Min. : 0.24 Min. : 0.160 Min. : 0.000
1st Qu.: 5.68 1st Qu.: 0.480 1st Qu.: 0.690
Median : 10.77 Median : 0.740 Median : 1.580
Mean : 49.12 Mean : 1.226 Mean : 1.995
3rd Qu.: 22.56 3rd Qu.: 1.420 3rd Qu.: 2.990
Max. :4064.73 Max. :26.310 Max. :15.760
yearPer yearPbr yearDivYield
Min. : 0.02 Min. : 0.000 Min. : 0.0000
1st Qu.: 10.46 1st Qu.: 0.990 1st Qu.: 0.0000
Median : 19.04 Median : 1.690 Median : 0.6200
Mean : 63.26 Mean : 2.542 Mean : 0.9622
3rd Qu.: 37.10 3rd Qu.: 2.880 3rd Qu.: 1.4500
Max. :6789.07 Max. :35.280 Max. :10.2000
As we can see the result above, 5 seems to be a safe number for overall. However, the number seems too harsh for KOSDAQ stocks. So, let us examine stocks for each market that are undervalued from the data above. I manipulated the stocks data into rows with top 10% lowest PBR, PER and top 10% highest dividend yield rate.
> low_value_kospi <- kospi[which(kospi$yearPer <= quantile(kospi$yearPer, 0.1) & kospi$yearPbr <= quantile(kospi$yearPbr, 0.1) & kospi$yearDivYield >= quantile(kospi$yearDivYield, 0.9)),]
> low_value_kosdaq <- kosdaq[which(kosdaq$yearPer <= quantile(kosdaq$yearPer, 0.1) & kosdaq$yearPbr <= quantile(kosdaq$yearPbr, 0.1) & kosdaq$yearDivYield >= quantile(kosdaq$yearDivYield, 0.9)),]
# low_value_kospi
code name engName market sector yearPer yearPbr yearDivYield
324 017940 E1 E1 KOSPI 가스유틸리티 2.09 0.21 4.70
500 016610 DB금융투자 DBFI KOSPI 증권 1.96 0.30 4.57
765 092230 KPX홀딩스 KPXHOLDINGS KOSPI 화학 1.86 0.30 4.98
1196 294870 HDC현대산업개발 HDC-OP KOSPI 건설 3.09 0.32 4.23
# low_value_kosdaq
code name engName market sector yearPer yearPbr yearDivYield
283 012700 리드코프 LEADCORP KOSDAQ 기타금융 5.17 0.48 10.20
490 078020 이베스트투자증권 eBEST IS KOSDAQ 증권 3.24 0.59 7.04
1028 017480 삼현철강 SAMSCO KOSDAQ 철강 3.85 0.51 2.68
1209 054930 유신 Yooshin KOSDAQ 건설 5.57 0.56 3.00
1212 013120 동원개발 Dongwon KOSDAQ 건설 4.11 0.49 3.65
1214 037350 성도이엔지 SUNGDO ENG KOSDAQ 건설 0.95 0.30 3.83
1221 036190 금화피에스시 Geumhwa PSC KOSDAQ 건설 5.68 0.64 4.36
1225 035890 서희건설 Seohee Construction KOSDAQ 건설 2.18 0.54 2.54
1226 011370 서한 Seohan KOSDAQ 건설 4.40 0.45 2.60
Now, as we all can see the sector is quite different from the first post which is very interesting. I would like to hold onto this result rather than the previous post because it seems more reasonable than just the number ‘5’.
2. What is the growth potential of each sector above? Then, how is the flow of each sector?