Data Source: FactSet, searching for all public company PE ratio, deleting row with missing observations, Could be significant bias introduced during deletion¶

In [1]:

import numpy as np
import pandas as pd
from pylab import mpl, plt
import statsmodels.formula.api as smf
import statsmodels.api as sm
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline

/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

In [2]:

import os 
os.getcwd()

Out[2]:

'/content'

In [10]:

data = pd.read_csv('https://raw.githubusercontent.com/cyrus723/my-first-binder/main/data/Factset_corp.csv', header=0)  
data

Out[10]:

	Identifier	Name	Revenue	Industry	Country	EV_EBITDA	PE	DY	ROE	D_EBITDA	SalesGrowth	EV_Sales	IntCoverage	Sales_Growth	NAICS
0	III-GB	3i Group plc	310.80	Investment Managers	United Kingdom	5.71	6.00	3.34	21.93	0.48	35.23	50.11	44.26	-15.75	Finance and Insurance
1	MMM-US	3M Company	35,355.00	Industrial Conglomerates	United States	12.36	17.56	3.33	42.42	1.85	9.85	3.33	16.47	2.57	Manufacturing
2	MAERSK.B-DK	A.P. Moller - Maersk A/S	61,802.00	Marine Shipping	Denmark	3.18	3.96	10.66	47.77	0.67	49.62	1.23	23.30	16.15	Transportation and Warehousing
3	SAGA.B-SE	AB Sagax	376.95	Real Estate Development	Sweden	61.96	13.80	0.70	36.64	10.53	9.81	48.63	5.68	11.86	Real Estate and Rental and Leasing
4	ABBN-CH	ABB Ltd.	28,949.00	Electrical Products	Switzerland	18.57	16.95	2.35	30.14	1.33	8.15	2.71	22.22	-0.83	Manufacturing
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1595	ZM-US	Zoom Video Communications, Inc.	4,099.86	Packaged Software	United States	34.88	34.31	0.00	28.53	0.00	54.63	10.21	NaN	131.49	Information
1596	ZI-US	ZoomInfo Technologies, Inc.	747.20	Internet Software/Services	United States	117.64	216.53	0.00	9.34	5.54	56.91	35.16	3.49	73.00	Information
1597	ZS-US	Zscaler, Inc.	673.10	Packaged Software	United States	NaN	NaN	0.00	-51.70	NaN	56.07	46.75	-3.89	52.40	Information
1598	2057-HK	ZTO Express (Cayman), Inc.	4,714.55	Air Freight/Couriers	China	20.29	30.96	0.00	9.75	0.54	29.34	4.58	37.26	20.68	Professional, Scientific, and Technical Services
1599	ZURN-CH	Zurich Insurance Group AG	69,591.00	Multi-Line Insurance	Switzerland	NaN	12.52	5.49	13.92	NaN	15.45	1.23	NaN	11.39	Finance and Insurance

1600 rows × 15 columns

In [11]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1600 entries, 0 to 1599
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Identifier    1600 non-null   object 
 1   Name          1600 non-null   object 
 2   Revenue       1600 non-null   object 
 3   Industry      1600 non-null   object 
 4   Country       1600 non-null   object 
 5   EV_EBITDA     1271 non-null   float64
 6   PE            1482 non-null   float64
 7   DY            1516 non-null   float64
 8   ROE           1559 non-null   float64
 9   D_EBITDA      1303 non-null   float64
 10  SalesGrowth   1594 non-null   float64
 11  EV_Sales      1539 non-null   float64
 12  IntCoverage   1328 non-null   float64
 13  Sales_Growth  1579 non-null   float64
 14  NAICS         1600 non-null   object 
dtypes: float64(9), object(6)
memory usage: 187.6+ KB

In [ ]:

In [12]:

count_nan = data.isnull().sum()
print(count_nan)

Identifier        0
Name              0
Revenue           0
Industry          0
Country           0
EV_EBITDA       329
PE              118
DY               84
ROE              41
D_EBITDA        297
SalesGrowth       6
EV_Sales         61
IntCoverage     272
Sales_Growth     21
NAICS             0
dtype: int64

In [13]:

data2 = data.dropna()
data2

Out[13]:

	Identifier	Name	Revenue	Industry	Country	EV_EBITDA	PE	DY	ROE	D_EBITDA	SalesGrowth	EV_Sales	IntCoverage	Sales_Growth	NAICS
0	III-GB	3i Group plc	310.80	Investment Managers	United Kingdom	5.71	6.00	3.34	21.93	0.48	35.23	50.11	44.26	-15.75	Finance and Insurance
1	MMM-US	3M Company	35,355.00	Industrial Conglomerates	United States	12.36	17.56	3.33	42.42	1.85	9.85	3.33	16.47	2.57	Manufacturing
2	MAERSK.B-DK	A.P. Moller - Maersk A/S	61,802.00	Marine Shipping	Denmark	3.18	3.96	10.66	47.77	0.67	49.62	1.23	23.30	16.15	Transportation and Warehousing
3	SAGA.B-SE	AB Sagax	376.95	Real Estate Development	Sweden	61.96	13.80	0.70	36.64	10.53	9.81	48.63	5.68	11.86	Real Estate and Rental and Leasing
4	ABBN-CH	ABB Ltd.	28,949.00	Electrical Products	Switzerland	18.57	16.95	2.35	30.14	1.33	8.15	2.71	22.22	-0.83	Manufacturing
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1591	2899-HK	Zijin Mining Group Co., Ltd.	34,366.65	Precious Metals	China	8.59	12.88	2.49	25.03	2.42	40.66	1.28	9.11	29.27	Mining, Quarrying, and Oil and Gas Extraction
1593	ZBH-US	Zimmer Biomet Holdings, Inc.	7,836.20	Medical Specialties	United States	14.70	66.55	0.76	3.23	3.12	11.56	4.29	5.77	-0.41	Manufacturing
1594	ZTS-US	Zoetis Inc.	7,776.00	Pharmaceuticals: Major	United States	36.74	57.11	0.44	49.01	2.03	16.49	15.38	10.95	10.11	Manufacturing
1596	ZI-US	ZoomInfo Technologies, Inc.	747.20	Internet Software/Services	United States	117.64	216.53	0.00	9.34	5.54	56.91	35.16	3.49	73.00	Information
1598	2057-HK	ZTO Express (Cayman), Inc.	4,714.55	Air Freight/Couriers	China	20.29	30.96	0.00	9.75	0.54	29.34	4.58	37.26	20.68	Professional, Scientific, and Technical Services

1106 rows × 15 columns

In [14]:

data2.info()
data2.shape

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1106 entries, 0 to 1598
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Identifier    1106 non-null   object 
 1   Name          1106 non-null   object 
 2   Revenue       1106 non-null   object 
 3   Industry      1106 non-null   object 
 4   Country       1106 non-null   object 
 5   EV_EBITDA     1106 non-null   float64
 6   PE            1106 non-null   float64
 7   DY            1106 non-null   float64
 8   ROE           1106 non-null   float64
 9   D_EBITDA      1106 non-null   float64
 10  SalesGrowth   1106 non-null   float64
 11  EV_Sales      1106 non-null   float64
 12  IntCoverage   1106 non-null   float64
 13  Sales_Growth  1106 non-null   float64
 14  NAICS         1106 non-null   object 
dtypes: float64(9), object(6)
memory usage: 138.2+ KB

Out[14]:

(1106, 15)

In [15]:

count_nan = data2.isnull().sum()
print(count_nan)

Identifier      0
Name            0
Revenue         0
Industry        0
Country         0
EV_EBITDA       0
PE              0
DY              0
ROE             0
D_EBITDA        0
SalesGrowth     0
EV_Sales        0
IntCoverage     0
Sales_Growth    0
NAICS           0
dtype: int64

In [16]:

formula = 'PE ~ EV_EBITDA + DY + ROE + D_EBITDA + SalesGrowth + EV_Sales + IntCoverage'
results = smf.ols(formula, data2).fit()
print(results.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     PE   R-squared:                       0.140
Model:                            OLS   Adj. R-squared:                  0.135
Method:                 Least Squares   F-statistic:                     25.61
Date:                Thu, 19 May 2022   Prob (F-statistic):           1.40e-32
Time:                        06:59:28   Log-Likelihood:                -6991.6
No. Observations:                1106   AIC:                         1.400e+04
Df Residuals:                    1098   BIC:                         1.404e+04
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Intercept      17.3022      7.352      2.353      0.019       2.876      31.729
EV_EBITDA       2.2773      0.191     11.910      0.000       1.902       2.652
DY              1.4979      1.589      0.943      0.346      -1.619       4.615
ROE            -0.0073      0.015     -0.484      0.629      -0.037       0.022
D_EBITDA       -3.5974      0.575     -6.259      0.000      -4.725      -2.470
SalesGrowth    -0.0539      0.044     -1.223      0.222      -0.140       0.033
EV_Sales       -1.7662      0.729     -2.423      0.016      -3.197      -0.336
IntCoverage    -0.0075      0.011     -0.670      0.503      -0.029       0.014
==============================================================================
Omnibus:                     2200.053   Durbin-Watson:                   2.028
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4265213.315
Skew:                          15.095   Prob(JB):                         0.00
Kurtosis:                     305.726   Cond. No.                         679.
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

In [17]:

data2.corr()

Out[17]:

	EV_EBITDA	PE	DY	ROE	D_EBITDA	SalesGrowth	EV_Sales	IntCoverage	Sales_Growth
EV_EBITDA	1.000000	0.328878	-0.260691	-0.022547	0.576225	0.076867	0.479353	0.017639	0.189467
PE	0.328878	1.000000	-0.079341	-0.019495	0.062110	-0.008015	0.149428	-0.010633	0.049153
DY	-0.260691	-0.079341	1.000000	-0.023072	-0.021939	-0.017622	-0.237462	-0.038418	-0.100704
ROE	-0.022547	-0.019495	-0.023072	1.000000	-0.019213	0.009836	-0.024447	-0.004273	-0.002051
D_EBITDA	0.576225	0.062110	-0.021939	-0.019213	1.000000	0.014906	0.005065	-0.041356	-0.016838
SalesGrowth	0.076867	-0.008015	-0.017622	0.009836	0.014906	1.000000	0.097530	0.040734	0.734134
EV_Sales	0.479353	0.149428	-0.237462	-0.024447	0.005065	0.097530	1.000000	0.095952	0.221300
IntCoverage	0.017639	-0.010633	-0.038418	-0.004273	-0.041356	0.040734	0.095952	1.000000	0.060372
Sales_Growth	0.189467	0.049153	-0.100704	-0.002051	-0.016838	0.734134	0.221300	0.060372	1.000000

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