Overview

Dataset statistics

Number of variables9
Number of observations184
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.1 KiB
Average record size in memory72.7 B

Variable types

Text1
Categorical2
Numeric5
Boolean1

Alerts

3-Year Return is highly overall correlated with 5-Year ReturnHigh correlation
5-Year Return is highly overall correlated with 3-Year ReturnHigh correlation
Expense Ratio is highly overall correlated with FeesHigh correlation
Fees is highly overall correlated with Expense RatioHigh correlation
Fund Number has unique valuesUnique
Return 2009 has 2 (1.1%) zerosZeros

Reproduction

Analysis started2026-04-14 18:25:50.487461
Analysis finished2026-04-14 18:25:52.310413
Duration1.82 second
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Fund Number
Text

Unique 

Distinct184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2026-04-14T11:25:52.454341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4130435
Min length4

Characters and Unicode

Total characters996
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)100.0%

Sample

1st rowFN-1
2nd rowFN-2
3rd rowFN-3
4th rowFN-4
5th rowFN-5
ValueCountFrequency (%)
fn-11
 
0.5%
fn-21
 
0.5%
fn-31
 
0.5%
fn-41
 
0.5%
fn-51
 
0.5%
fn-61
 
0.5%
fn-71
 
0.5%
fn-81
 
0.5%
fn-91
 
0.5%
fn-101
 
0.5%
Other values (174)174
94.6%
2026-04-14T11:25:52.694323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F184
18.5%
N184
18.5%
-184
18.5%
1124
12.4%
239
 
3.9%
339
 
3.9%
439
 
3.9%
538
 
3.8%
638
 
3.8%
738
 
3.8%
Other values (3)89
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F184
18.5%
N184
18.5%
-184
18.5%
1124
12.4%
239
 
3.9%
339
 
3.9%
439
 
3.9%
538
 
3.8%
638
 
3.8%
738
 
3.8%
Other values (3)89
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F184
18.5%
N184
18.5%
-184
18.5%
1124
12.4%
239
 
3.9%
339
 
3.9%
439
 
3.9%
538
 
3.8%
638
 
3.8%
738
 
3.8%
Other values (3)89
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F184
18.5%
N184
18.5%
-184
18.5%
1124
12.4%
239
 
3.9%
339
 
3.9%
439
 
3.9%
538
 
3.8%
638
 
3.8%
738
 
3.8%
Other values (3)89
8.9%

Type
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Short Term Corporate
97 
Intermediate Government
87 

Length

Max length23
Median length20
Mean length21.418478
Min length20

Characters and Unicode

Total characters3941
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermediate Government
2nd rowIntermediate Government
3rd rowIntermediate Government
4th rowIntermediate Government
5th rowIntermediate Government

Common Values

ValueCountFrequency (%)
Short Term Corporate97
52.7%
Intermediate Government87
47.3%

Length

2026-04-14T11:25:52.764175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-14T11:25:52.815067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
short97
20.9%
term97
20.9%
corporate97
20.9%
intermediate87
18.7%
government87
18.7%

Most occurring characters

ValueCountFrequency (%)
e629
16.0%
r562
14.3%
t455
11.5%
o378
9.6%
281
 
7.1%
m271
 
6.9%
n261
 
6.6%
a184
 
4.7%
h97
 
2.5%
S97
 
2.5%
Other values (8)726
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e629
16.0%
r562
14.3%
t455
11.5%
o378
9.6%
281
 
7.1%
m271
 
6.9%
n261
 
6.6%
a184
 
4.7%
h97
 
2.5%
S97
 
2.5%
Other values (8)726
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e629
16.0%
r562
14.3%
t455
11.5%
o378
9.6%
281
 
7.1%
m271
 
6.9%
n261
 
6.6%
a184
 
4.7%
h97
 
2.5%
S97
 
2.5%
Other values (8)726
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e629
16.0%
r562
14.3%
t455
11.5%
o378
9.6%
281
 
7.1%
m271
 
6.9%
n261
 
6.6%
a184
 
4.7%
h97
 
2.5%
S97
 
2.5%
Other values (8)726
18.4%

Assets
Real number (ℝ)

Distinct183
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean910.64837
Minimum12.4
Maximum18603.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2026-04-14T11:25:52.880829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.4
5-th percentile37.235
Q1113.725
median268.4
Q3621.95
95-th percentile4593.92
Maximum18603.5
Range18591.1
Interquartile range (IQR)508.225

Descriptive statistics

Standard deviation2253.2667
Coefficient of variation (CV)2.4743543
Kurtosis33.940084
Mean910.64837
Median Absolute Deviation (MAD)192.55
Skewness5.367329
Sum167559.3
Variance5077210.9
MonotonicityNot monotonic
2026-04-14T11:25:52.971162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
640.32
 
1.1%
7268.11
 
0.5%
475.11
 
0.5%
1931
 
0.5%
18603.51
 
0.5%
142.61
 
0.5%
1401.61
 
0.5%
985.61
 
0.5%
2188.81
 
0.5%
390.61
 
0.5%
Other values (173)173
94.0%
ValueCountFrequency (%)
12.41
0.5%
17.31
0.5%
17.41
0.5%
18.61
0.5%
24.21
0.5%
26.31
0.5%
331
0.5%
33.81
0.5%
36.51
0.5%
36.81
0.5%
ValueCountFrequency (%)
18603.51
0.5%
16297.11
0.5%
10744.61
0.5%
7268.11
0.5%
6981.51
0.5%
6332.51
0.5%
5701.61
0.5%
5282.91
0.5%
4772.91
0.5%
4615.41
0.5%

Fees
Boolean

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size316.0 B
False
130 
True
54 
ValueCountFrequency (%)
False130
70.7%
True54
29.3%
2026-04-14T11:25:53.028797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Expense Ratio
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71179348
Minimum0.12
Maximum1.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2026-04-14T11:25:53.089883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.326
Q10.5275
median0.7
Q30.9025
95-th percentile1.08
Maximum1.94
Range1.82
Interquartile range (IQR)0.375

Descriptive statistics

Standard deviation0.25633725
Coefficient of variation (CV)0.36012869
Kurtosis2.4063212
Mean0.71179348
Median Absolute Deviation (MAD)0.19
Skewness0.59658099
Sum130.97
Variance0.065708788
MonotonicityNot monotonic
2026-04-14T11:25:53.183398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67
 
3.8%
0.77
 
3.8%
0.456
 
3.3%
0.556
 
3.3%
0.516
 
3.3%
0.656
 
3.3%
0.55
 
2.7%
0.85
 
2.7%
15
 
2.7%
0.95
 
2.7%
Other values (66)126
68.5%
ValueCountFrequency (%)
0.121
0.5%
0.132
1.1%
0.141
0.5%
0.21
0.5%
0.221
0.5%
0.261
0.5%
0.271
0.5%
0.31
0.5%
0.321
0.5%
0.362
1.1%
ValueCountFrequency (%)
1.941
 
0.5%
1.581
 
0.5%
1.161
 
0.5%
1.151
 
0.5%
1.132
1.1%
1.12
1.1%
1.084
2.2%
1.061
 
0.5%
1.051
 
0.5%
1.031
 
0.5%

Return 2009
Real number (ℝ)

Zeros 

Distinct127
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1641304
Minimum-8.8
Maximum32
Zeros2
Zeros (%)1.1%
Negative14
Negative (%)7.6%
Memory size1.6 KiB
2026-04-14T11:25:53.275786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8.8
5-th percentile-1.355
Q13.475
median6.4
Q310.725
95-th percentile16.4
Maximum32
Range40.8
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation6.0908438
Coefficient of variation (CV)0.85018606
Kurtosis2.455985
Mean7.1641304
Median Absolute Deviation (MAD)3.5
Skewness0.9085474
Sum1318.2
Variance37.098378
MonotonicityNot monotonic
2026-04-14T11:25:53.370806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65
 
2.7%
7.34
 
2.2%
5.43
 
1.6%
5.73
 
1.6%
3.53
 
1.6%
53
 
1.6%
5.23
 
1.6%
123
 
1.6%
5.53
 
1.6%
6.83
 
1.6%
Other values (117)151
82.1%
ValueCountFrequency (%)
-8.81
0.5%
-4.81
0.5%
-3.82
1.1%
-3.62
1.1%
-3.31
0.5%
-2.91
0.5%
-1.61
0.5%
-1.41
0.5%
-1.11
0.5%
-0.71
0.5%
ValueCountFrequency (%)
321
0.5%
29.71
0.5%
28.61
0.5%
24.81
0.5%
22.31
0.5%
19.21
0.5%
17.41
0.5%
17.11
0.5%
16.61
0.5%
16.42
1.1%

3-Year Return
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6625
Minimum-13.8
Maximum9.4
Zeros1
Zeros (%)0.5%
Negative6
Negative (%)3.3%
Memory size1.6 KiB
2026-04-14T11:25:53.461784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-13.8
5-th percentile0.445
Q14.05
median5.1
Q36.1
95-th percentile7.1
Maximum9.4
Range23.2
Interquartile range (IQR)2.05

Descriptive statistics

Standard deviation2.5164065
Coefficient of variation (CV)0.53971186
Kurtosis16.193167
Mean4.6625
Median Absolute Deviation (MAD)1
Skewness-2.902127
Sum857.9
Variance6.3323019
MonotonicityNot monotonic
2026-04-14T11:25:53.548556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.512
 
6.5%
4.910
 
5.4%
68
 
4.3%
6.17
 
3.8%
6.46
 
3.3%
5.16
 
3.3%
6.25
 
2.7%
5.65
 
2.7%
5.95
 
2.7%
4.75
 
2.7%
Other values (60)115
62.5%
ValueCountFrequency (%)
-13.81
0.5%
-4.51
0.5%
-31
0.5%
-2.71
0.5%
-0.21
0.5%
-0.11
0.5%
01
0.5%
0.11
0.5%
0.21
0.5%
0.41
0.5%
ValueCountFrequency (%)
9.42
1.1%
8.91
 
0.5%
81
 
0.5%
7.81
 
0.5%
7.51
 
0.5%
7.32
1.1%
7.21
 
0.5%
7.13
1.6%
71
 
0.5%
6.91
 
0.5%

5-Year Return
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9858696
Minimum-7.3
Maximum6.8
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)1.6%
Memory size1.6 KiB
2026-04-14T11:25:53.629960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.3
5-th percentile1.7
Q13.6
median4.3
Q34.9
95-th percentile5.5
Maximum6.8
Range14.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.4852338
Coefficient of variation (CV)0.37262479
Kurtosis18.484955
Mean3.9858696
Median Absolute Deviation (MAD)0.6
Skewness-3.058312
Sum733.4
Variance2.2059195
MonotonicityNot monotonic
2026-04-14T11:25:53.717252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4.414
 
7.6%
411
 
6.0%
5.110
 
5.4%
4.910
 
5.4%
4.38
 
4.3%
4.68
 
4.3%
3.88
 
4.3%
4.77
 
3.8%
4.27
 
3.8%
4.17
 
3.8%
Other values (38)94
51.1%
ValueCountFrequency (%)
-7.31
 
0.5%
-1.51
 
0.5%
-0.71
 
0.5%
0.21
 
0.5%
1.22
1.1%
1.31
 
0.5%
1.51
 
0.5%
1.73
1.6%
1.82
1.1%
1.91
 
0.5%
ValueCountFrequency (%)
6.81
 
0.5%
6.42
 
1.1%
6.21
 
0.5%
6.11
 
0.5%
5.71
 
0.5%
5.62
 
1.1%
5.53
1.6%
5.42
 
1.1%
5.35
2.7%
5.26
3.3%

Risk
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Average
69 
Above average
59 
Below average
56 

Length

Max length13
Median length13
Mean length10.75
Min length7

Characters and Unicode

Total characters1978
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBelow average
2nd rowBelow average
3rd rowAverage
4th rowAverage
5th rowAverage

Common Values

ValueCountFrequency (%)
Average69
37.5%
Above average59
32.1%
Below average56
30.4%

Length

2026-04-14T11:25:53.799994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-14T11:25:53.846951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
average184
61.5%
above59
 
19.7%
below56
 
18.7%

Most occurring characters

ValueCountFrequency (%)
e483
24.4%
a299
15.1%
v243
12.3%
r184
 
9.3%
g184
 
9.3%
A128
 
6.5%
o115
 
5.8%
115
 
5.8%
b59
 
3.0%
B56
 
2.8%
Other values (2)112
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e483
24.4%
a299
15.1%
v243
12.3%
r184
 
9.3%
g184
 
9.3%
A128
 
6.5%
o115
 
5.8%
115
 
5.8%
b59
 
3.0%
B56
 
2.8%
Other values (2)112
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e483
24.4%
a299
15.1%
v243
12.3%
r184
 
9.3%
g184
 
9.3%
A128
 
6.5%
o115
 
5.8%
115
 
5.8%
b59
 
3.0%
B56
 
2.8%
Other values (2)112
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e483
24.4%
a299
15.1%
v243
12.3%
r184
 
9.3%
g184
 
9.3%
A128
 
6.5%
o115
 
5.8%
115
 
5.8%
b59
 
3.0%
B56
 
2.8%
Other values (2)112
 
5.7%

Interactions

2026-04-14T11:25:51.816407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.649628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.950686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.250870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.550830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.876864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.712905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.012604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.312917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.604828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.936516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.774370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.073095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.374496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.660686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.998201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.836212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.134938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.435643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.715264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:52.051367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:50.891320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.190870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.489937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-14T11:25:51.763788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-04-14T11:25:53.967604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
3-Year Return5-Year ReturnAssetsExpense RatioFeesReturn 2009RiskType
3-Year Return1.0000.9720.326-0.0310.000-0.0610.1970.348
5-Year Return0.9721.0000.374-0.0960.000-0.0080.0000.365
Assets0.3260.3741.000-0.1870.0000.1770.0000.000
Expense Ratio-0.031-0.096-0.1871.0000.636-0.0240.0280.165
Fees0.0000.0000.0000.6361.0000.0000.0900.176
Return 2009-0.061-0.0080.177-0.0240.0001.0000.3780.464
Risk0.1970.0000.0000.0280.0900.3781.0000.000
Type0.3480.3650.0000.1650.1760.4640.0001.000

Missing values

2026-04-14T11:25:52.202633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-04-14T11:25:52.269796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Fund NumberTypeAssetsFeesExpense RatioReturn 20093-Year Return5-Year ReturnRisk
0FN-1Intermediate Government7268.1No0.456.96.95.5Below average
1FN-2Intermediate Government475.1No0.509.87.56.1Below average
2FN-3Intermediate Government193.0No0.716.37.05.6Average
3FN-4Intermediate Government18603.5No0.135.46.65.5Average
4FN-5Intermediate Government142.6No0.605.96.75.4Average
5FN-6Intermediate Government1401.6No0.545.76.46.2Average
6FN-7Intermediate Government985.6No0.493.06.85.3Average
7FN-8Intermediate Government2188.8No0.557.46.45.2Below average
8FN-9Intermediate Government390.6No0.675.36.15.0Below average
9FN-10Intermediate Government544.1No0.635.76.25.1Below average
Fund NumberTypeAssetsFeesExpense RatioReturn 20093-Year Return5-Year ReturnRisk
174FN-175Short Term Corporate95.4Yes1.021.5-0.21.8Below average
175FN-176Short Term Corporate237.1No0.5014.50.71.7Above average
176FN-177Short Term Corporate983.0No0.6015.20.21.9Above average
177FN-178Short Term Corporate51.9No0.7013.40.11.2Above average
178FN-179Short Term Corporate249.7No0.552.40.41.5Average
179FN-180Short Term Corporate33.8No0.5316.40.71.8Above average
180FN-181Short Term Corporate249.8Yes0.436.7-4.5-1.5Above average
181FN-182Short Term Corporate52.9No0.875.2-3.0-0.7Above average
182FN-183Short Term Corporate39.7No0.51-8.8-13.8-7.3Above average
183FN-184Short Term Corporate182.3No0.5332.0-2.70.2Above average