Os resultados financeiros dos cinco principais planos da Fundação Sistel nos sete primeiros meses de 2026 (janeiro a julho) trazem um cenário amplamente positivo para os participantes e assistidos. Todos os planos superaram suas respectivas metas atuariais acumuladas no período, garantindo a manutenção do poder de compra das aposentadorias frente à inflação e reforçando a segurança dos pagamentos futuros.
Python
import pandas as pd
import os
file_path = 'Desemp_2026_jun26.xlsx'
if not os.path.exists(file_path):
# Search for files in the current directory or /tmp or everywhere
files = os.listdir('.')
print("Files in current dir:", files)
else:
xls = pd.ExcelFile(file_path)
print("Sheet names:", xls.sheet_names)
for sheet in xls.sheet_names:
df = pd.read_excel(xls, sheet_name=sheet)
print(f"\n--- Sheet: {sheet} ---")
print(df.head(15))
print(df.info())
Saída de código
Sheet names: ['2023', '2024', '2025', '2026']
--- Sheet: 2023 ---
Plano Sistel Rentabilid. Nominal Unnamed: 2 Meta Unnamed: 4 Rent X Met (out21) Sobras (out21) Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 Unnamed: 13
0 NaN 2021-10-01 00:00:00 Acm 2021 2021-10-01 00:00:00 Acm 2021 NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 CPqDPrev 0.0119 0.1123 0.0148 0.1196 0.94 0.103 NaN NaN NaN NaN NaN NaN NaN
2 PBS-CPqD 0.0121 0.1176 0.0148 0.1196 0.98 0.033 NaN NaN NaN NaN NaN NaN NaN
3 InovaPrev -0.0043 0.0129 0.0068 0.0514 0.25 NaN NaN NaN NaN NaN NaN NaN NaN
4 PBS-A 0.0177 0.1448 0.0148 0.1196 1.29 0.318 NaN NaN NaN NaN NaN NaN NaN
5 PAMA 0.0137 0.1435 0.0151 0.1223 1.17 0.017 NaN NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 Plano Sistel Rentabilid. Nominal NaN Meta NaN Rent X Met (2021) Sobras (2021) NaN NaN NaN NaN NaN NaN NaN
8 NaN 2021-11-01 00:00:00 Acm 2021 2021-11-01 00:00:00 Acm 2021 NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 CPqDPrev 0.0158 0.1299 0.0116 0.1327 0.98 NaN NaN NaN NaN NaN NaN NaN NaN
10 PBS-CPqD 0.0159 0.1353 0.0116 0.1327 1.02 NaN NaN NaN NaN NaN NaN NaN NaN
11 InovaPrev 0.0062 0.0192 0.0078 0.0597 0.32 NaN NaN NaN NaN NaN NaN NaN NaN
12 PBS-A 0.0124 0.1687 0.0116 0.1327 1.27 NaN NaN NaN NaN NaN NaN NaN NaN
13 PAMA 0.0145 0.1601 0.0119 0.1357 1.18 NaN NaN NaN NaN NaN NaN NaN NaN
14 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 134 entries, 0 to 133
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Plano Sistel 105 non-null object
1 Rentabilid. Nominal 119 non-null object
2 Unnamed: 2 105 non-null object
3 Meta 106 non-null object
4 Unnamed: 4 92 non-null object
5 Rent X Met (out21) 83 non-null object
6 Sobras (out21) 30 non-null object
7 Unnamed: 7 0 non-null float64
8 Unnamed: 8 0 non-null float64
9 Unnamed: 9 30 non-null float64
10 Unnamed: 10 30 non-null float64
11 Unnamed: 11 0 non-null float64
12 Unnamed: 12 5 non-null float64
13 Unnamed: 13 5 non-null float64
dtypes: float64(7), object(7)
memory usage: 14.8+ KB
None
--- Sheet: 2024 ---
Plano Sistel Rentabilid. Nominal Unnamed: 2 Meta Atuarial Unnamed: 4 Rent X Met (2024) Cobert da RM (2024) Unnamed: 7
0 NaN 2024-02-01 00:00:00 Acm 2024 2024-02-01 00:00:00 Acm 2024 NaN NaN NaN
1 CPqDPrev 0.0094 0.0195 0.0113 0.0203 0.96 1.12 NaN
2 PBS-CPqD 0.0095 0.0198 0.0113 0.0203 0.98 1.07 NaN
3 InovaPrev 0.0065 0.0115 0.0082 0.0181 0.64 1 NaN
4 PBS-A 0.0073 0.0163 0.0113 0.0203 0.8 1.31 NaN
5 PAMA 0.0087 0.0189 0.0118 0.0213 0.89 0.93 NaN
6 Média Sistel 0.00828 0.0172 NaN NaN NaN NaN NaN
7 Média EFPCs 0.0088 0.0136 NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN NaN NaN
9 Plano Sistel Rentabilid. Nominal NaN Meta Atuarial NaN Rent X Met (2024) Cobert da RM (2024) NaN
10 NaN 2024-03-01 00:00:00 Acm 2024 2024-03-01 00:00:00 Acm 2024 NaN NaN NaN
11 CPqDPrev 0.0097 0.0294 0.0051 0.0255 1.15 NaN NaN
12 PBS-CPqD 0.0097 0.0297 0.0051 0.0255 1.16 NaN NaN
13 InovaPrev 0.0071 0.0187 0.0085 0.0267 0.7 NaN NaN
14 PBS-A 0.0076 0.024 0.0051 0.0255 0.94 NaN NaN
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 108 entries, 0 to 107
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Plano Sistel 87 non-null object
1 Rentabilid. Nominal 98 non-null object
2 Unnamed: 2 88 non-null object
3 Meta Atuarial 76 non-null object
4 Unnamed: 4 66 non-null object
5 Rent X Met (2024) 65 non-null object
6 Cobert da RM (2024) 60 non-null object
7 Unnamed: 7 14 non-null object
dtypes: object(8)
memory usage: 6.9+ KB
None
--- Sheet: 2025 ---
Planos Sistel Rentabilid. Nominal Unnamed: 2 Meta Atuarial Unnamed: 4 Rent X Met (2025) Cobert da RM (2025) Unnamed: 7 Unnamed: 8 Unnamed: 9
0 NaN 2025-01-01 00:00:00 Acm 2025 2025-01-01 00:00:00 Acm 2025 NaN NaN NaN NaN NaN
1 CPqDPrev 0.0087 0.0087 0.0032 0.0032 1.72 1.14 INPC+3,9% NaN NaN
2 PBS-CPqD 0.0085 0.0085 0.0032 0.0032 1.66 1.1 NaN NaN NaN
3 InovaPrev 0.0119 0.0119 0.0057 0.0057 1.09 1 IPCA + 5%aa NaN NaN
4 PBS-A 0.0084 0.0084 0.0032 0.0032 1.63 1.28 NaN NaN NaN
5 PAMA 0.0087 0.0087 0.0037 0.0037 1.35 0.84 inpc+4,5% NaN NaN
6 Média Sistel 0.00924 NaN NaN NaN NaN NaN NaN NaN NaN
7 Média EFPCs 0.011 NaN NaN NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 Planos Sistel Rentabilid. Nominal NaN Meta Atuarial NaN Rent X Met (2025) Cobert da RM (2025) NaN NaN NaN
10 NaN 2025-02-01 00:00:00 Acm 2025 2025-02-01 00:00:00 Acm 2025 NaN NaN NaN NaN NaN
11 CPqDPrev 0.0106 0.0194 0.018 0.0213 0.910798 1.14 NaN NaN NaN
12 PBS-CPqD 0.0106 0.0192 0.018 0.0213 0.901408 1.1 NaN NaN NaN
13 InovaPrev 0.0076 0.0196 0.0172 0.023 0.852174 1 NaN NaN NaN
14 PBS-A 0.0118 0.0203 0.018 0.0213 0.953052 1.28 NaN NaN NaN
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 143 entries, 0 to 142
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Planos Sistel 113 non-null object
1 Rentabilid. Nominal 124 non-null object
2 Unnamed: 2 109 non-null object
3 Meta Atuarial 97 non-null object
4 Unnamed: 4 84 non-null object
5 Rent X Met (2025) 83 non-null object
6 Cobert da RM (2025) 85 non-null object
7 Unnamed: 7 17 non-null object
8 Unnamed: 8 6 non-null float64
9 Unnamed: 9 6 non-null float64
dtypes: float64(2), object(8)
memory usage: 11.3+ KB
None
--- Sheet: 2026 ---
Planos Sistel Rentabilid. Nominal Unnamed: 2 Meta Atuarial Unnamed: 4 Rent X Met (2026) Patrimônio R$ Milhões Unnamed: 7 Cobert da RM (2026) Unnamed: 9 Unnamed: 10 Unnamed: 11
0 NaN 2026-01-01 00:00:00 Acm 2026 2026-01-01 00:00:00 Acm 2026 NaN NaN NaN NaN NaN NaN NaN
1 CPqDPrev 0.0084 0.0084 0.0071 0.0071 1.183099 1095 NaN 1.177 NaN 9.198000 9.198000
2 PBS-CPqD 0.0085 0.0085 0.0071 0.0071 1.197183 55 NaN 1.184 NaN 0.467500 0.467500
3 InovaPrev 0.0126 0.0126 0.0074 0.0074 1.702703 291 NaN 1 NaN 3.666600 3.666600
4 PBS-A 0.0082 0.0082 0.0071 0.0071 1.15493 13212 NaN 1.362 NaN 108.338400 108.338400
5 PAMA 0.0089 0.0089 0.0076 0.0076 1.171053 5191 NaN 0.827 NaN 46.199900 46.199900
6 Consol. Sistel (*) 0.00846 0.00846 NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 Média EFPCs 0.0126 0.0126 NaN NaN NaN NaN NaN NaN NaN 167.870400 167.870400
8 (*):Rentabilidade consolidada de 5 planos analisados; RM= Reserva Matemática ou valor p/ pagar todos benefícios; Cobertura da RM > 100% = Plano superavitário NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN 19844 NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11 Planos Sistel Rentabilid. Nominal NaN Meta Atuarial NaN Rent X Met (2026) Patrimônio R$ Milhões NaN Cobert da RM (2026) NaN NaN NaN
12 NaN 2026-02-01 00:00:00 Acm 2026 2026-02-01 00:00:00 Acm 2026 NaN NaN NaN NaN NaN NaN NaN
13 CPqDPrev 0.0078 0.0163 0.0088 0.016 1.01875 1100 NaN 1.177 NaN 8.580000 17.930000
14 PBS-CPqD 0.0079 0.0169 0.0088 0.016 1.05625 55.26 NaN 1.184 NaN 0.436554 0.933894
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 105 entries, 0 to 104
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Planos Sistel 71 non-null object
1 Rentabilid. Nominal 69 non-null object
2 Unnamed: 2 57 non-null object
3 Meta Atuarial 50 non-null object
4 Unnamed: 4 43 non-null object
5 Rent X Met (2026) 42 non-null object
6 Patrimônio R$ Milhões 55 non-null object
7 Unnamed: 7 21 non-null object
8 Cobert da RM (2026) 43 non-null object
9 Unnamed: 9 30 non-null object
10 Unnamed: 10 48 non-null float64
11 Unnamed: 11 48 non-null float64
dtypes: float64(2), object(10)
memory usage: 10.0+ KB
None
Python
df_2026 = pd.read_excel('Desemp_2026_jun26.xlsx', sheet_name='2026')
for idx, row in df_2026.iterrows():
if pd.notna(row[0]) or pd.notna(row[1]):
print(f"Row {idx}: {list(row.values)}")
Saída de código
Row 0: [nan, datetime.datetime(2026, 1, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 1, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 1: ['CPqDPrev', 0.0084, 0.0084, 0.0071, 0.0071, 1.1830985915492955, 1095, nan, 1.177, nan, 9.197999999999999, 9.197999999999999]
Row 2: ['PBS-CPqD', 0.0085, 0.0085, 0.0071, 0.0071, 1.1971830985915493, 55, nan, 1.184, nan, 0.4675, 0.4675]
Row 3: ['InovaPrev', 0.0126, 0.0126, 0.0074, 0.0074, 1.7027027027027026, 291, nan, 1, nan, 3.6666, 3.6666]
Row 4: ['PBS-A', 0.0082, 0.0082, 0.0071, 0.0071, 1.1549295774647887, 13212, nan, 1.362, nan, 108.33840000000001, 108.33840000000001]
Row 5: ['PAMA', 0.0089, 0.0089, 0.0076, 0.0076, 1.1710526315789473, 5191, nan, 0.827, nan, 46.1999, 46.1999]
Row 6: ['Consol. Sistel (*)', 0.008459504132231406, 0.008459504132231406, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 7: ['Média EFPCs', 0.0126, 0.0126, nan, nan, nan, nan, nan, nan, nan, 167.87040000000002, 167.87040000000002]
Row 8: ['(*):Rentabilidade consolidada de 5 planos analisados; RM= Reserva Matemática ou valor p/ pagar todos benefícios; Cobertura da RM > 100% = Plano superavitário', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 11: ['Planos Sistel', 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrimônio R$ Milhões', nan, 'Cobert da RM (2026)', nan, nan, nan]
Row 12: [nan, datetime.datetime(2026, 2, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 2, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 13: ['CPqDPrev', 0.0078, 0.0163, 0.0088, 0.016, 1.0187499999999998, 1100, nan, 1.177, nan, 8.58, 17.93]
Row 14: ['PBS-CPqD', 0.0079, 0.0169, 0.0088, 0.016, 1.05625, 55.26, nan, 1.184, nan, 0.43655400000000005, 0.9338939999999999]
Row 15: ['InovaPrev', 0.0102, 0.0229, 0.0111, 0.0186, 1.2311827956989247, 293, nan, 1, nan, 2.9886000000000004, 6.7097]
Row 16: ['PBS-A', 0.0058, 0.014, 0.0088, 0.016, 0.875, 13145, nan, 1.27, nan, 76.241, 184.03]
Row 17: ['PAMA', 0.0069, 0.0158, 0.0093, 0.017, 0.9294117647058824, 5185, nan, 0.824, nan, 35.7765, 81.923]
Row 18: ['Consol. Sistel (*)', 0.006270655457052339, 0.01473974930049458, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 19: ['Média EFPCs', 0.0099, 0.0226, nan, nan, nan, nan, nan, nan, nan, 124.022654, 291.526594]
Row 20: ['(*):Rentabilidade consolidada de 5 planos analisados; RM= Reserva Matemática ou valor p/ pagar todos benefícios; Cobertura da RM > 100% = Plano superavitár.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 24: ['Planos Sistel', 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrimônio R$ Milhões', nan, 'Cobert da RM (2026)', nan, nan, nan]
Row 25: [nan, datetime.datetime(2026, 3, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 3, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 26: ['CPqDPrev', 0.0135, 0.03, 0.0123, 0.0285, 1.0526315789473684, 1198, nan, 1.179, nan, 16.173, 35.94]
Row 27: ['PBS-CPqD', 0.0136, 0.0303, 0.0123, 0.0285, 1.063157894736842, 56, nan, 1.186, nan, 0.7615999999999999, 1.6968]
Row 28: ['InovaPrev', 0.0095, 0.0326, 0.0129, 0.0317, 1.028391167192429, 295, nan, 1, nan, 2.8024999999999998, 9.616999999999999]
Row 29: ['PBS-A', 0.0128, 0.027, 0.0123, 0.0285, 0.9473684210526315, 13167, nan, 1.337, nan, 168.5376, 355.509]
Row 30: ['PAMA', 0.0125, 0.0286, 0.0128, 0.03, 0.9533333333333334, 5215, nan, 0.824, nan, 65.1875, 149.149]
Row 31: ['Consol. Sistel (*)', 0.01271698359339722, 0.027691124379107927, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 32: ['Média EFPCs', 0.009, 0.0317, nan, nan, nan, nan, nan, nan, nan, 253.4622, 551.9118000000001]
Row 33: ['(*):Rentabilidade consolidada de 5 planos analisados; RM= Reserva Matemática ou valor p/ pagar todos benefícios; Cobertura da RM > 100% = Plano superavitár.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 36: ['Planos Sistel', 'Rentabilidades e Metas dos Planos', nan, nan, nan, nan, 'Desempenho dos Planos', nan, nan, nan, nan, nan]
Row 37: [nan, 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrim Social R$ Milhões *', 'Res.Espec/Défi R$ Milhôes *', 'Cobert da RM (2026)*', 'Duration 2026 (anos)', nan, nan]
Row 38: [nan, datetime.datetime(2026, 4, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 4, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 39: ['CPqDPrev (CV)', 0.0126, 0.043, 0.0113, 0.0401, 1.0723192019950125, 1209, 23.8, 1.181, 10.26, 15.2334, 51.986999999999995]
Row 40: ['PBS-CPqD (BD)', 0.0126, 0.0433, 0.0113, 0.0401, 1.0798004987531173, 58, 0, 1.189, 10.12, 0.7308, 2.5114]
Row 41: ['InovaPrev (CD)', 0.0114, 0.0444, 0.0108, 0.0429, 1.034965034965035, 256, nan, 1, 10, 2.9184, 11.3664]
Row 42: ['PBS-A (BD)', 0.016, 0.0433, 0.0113, 0.0401, 1.0798004987531173, 13715, 1298, 1.352, 7.56, 219.44, 593.8595]
Row 43: ['PAMA (Assist.)', 0.0159, 0.045, 0.0118, 0.0421, 1.0688836104513064, 5250, -1100, 0.826, 10.87, 83.47500000000001, 236.25]
Row 44: ['Consol. Sistel (*)', 0.015706638032018744, 0.043731662436548226, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 45: ['Média EFPCs', 0.0123, 0.0441, nan, nan, nan, nan, nan, nan, nan, 321.7976, 895.9743000000001]
Row 46: ['(*):Patrimônio Social; Reser Especial Acumulada antes Distribuição; RM= Reserva Matemática ou valor p/ pagar todos benefícios, Cobertura da RM > 100% = Plano superavitário; Rentabilidade consolidada de 5 planos analisados.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 50: ['Planos Sistel', 'Rentabilidades e Metas dos Planos', nan, nan, nan, nan, 'Desempenho dos Planos', nan, nan, nan, nan, nan]
Row 51: [nan, 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrim Social R$ Milhões *', 'Res.Espec/Défi R$ Milhôes *', 'Cobert da RM (2026)*', 'Duration 2026 (anos)', nan, nan]
Row 52: [nan, datetime.datetime(2026, 5, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 5, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 53: ['CPqDPrev (CV)', 0.0104, 0.0539, 0.0097, 0.0502, 1.0737051792828685, 1218, 25.4, 1.182, 10.26, 12.6672, 65.6502]
Row 54: ['PBS-CPqD (BD)', 0.0104, 0.0541, 0.0097, 0.0502, 1.0776892430278884, 58.2, 0, 1.191, 10.12, 0.60528, 3.14862]
Row 55: ['InovaPrev (CD)', 0.008, 0.0527, 0.0099, 0.0532, 0.9906015037593985, 257.5, nan, 1, 10, 2.06, 13.57025]
Row 56: ['PBS-A (BD)', 0.011, 0.0548, 0.0097, 0.0502, 1.091633466135458, 13746, 1358, 1.359, 7.56, 151.206, 753.2808]
Row 57: ['PAMA (Assist.)', 0.0108, 0.0563, 0.0102, 0.0528, 1.066287878787879, 5243, -1109, 0.825, 10.87, 56.6244, 295.1809]
Row 58: ['Consol. Sistel (*)', 0.010873953232274506, 0.05510146179596252, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 59: ['Média EFPCs', 0.0086, 0.0526, nan, nan, nan, nan, nan, nan, nan, 223.16288, 1130.83077]
Row 60: ['(*):Patrimônio Social; Reser Especial Acumulada antes Distribuição; RM= Reserva Matemática ou valor p/ pagar todos benefícios, Cobertura da RM > 100% = Plano superavitário; Rentabilidade consolidada de 5 planos analisados.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 64: ['Planos Sistel', 'Rentabilidades e Metas dos Planos', nan, nan, nan, nan, 'Desempenho dos Planos', nan, nan, nan, nan, nan]
Row 65: [nan, 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrim Social R$ Milhões *', 'Res.Espec/Défi R$ Milhôes *', 'Cobert da RM (2026)*', 'Duration 2026 (anos)', nan, nan]
Row 66: [nan, datetime.datetime(2026, 6, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 6, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 67: ['CPqDPrev (CV)', 0.0098, 0.0643, 0.0046, 0.0551, 1.1669691470054444, 1226, 31, 1.188, 10.26, 12.0148, 78.8318]
Row 68: ['PBS-CPqD (BD)', 0.0099, 0.0646, 0.0046, 0.0551, 1.1724137931034484, 59, 0, 1.198, 10.12, 0.5841000000000001, 3.8114000000000003]
Row 69: ['InovaPrev (CD)', 0.0086, 0.0618, 0.0057, 0.0592, 1.0439189189189189, 260, nan, 1, 10, 2.236, 16.068]
Row 70: ['PBS-A (BD)', 0.0077, 0.063, 0.0046, 0.0551, 1.1433756805807622, 13733, 1422, 1.369, 7.56, 105.7441, 865.179]
Row 71: ['PAMA (Saúde)', 0.0078, 0.0645, 0.0051, 0.0581, 1.1101549053356283, 5239, -1101, 0.826, 10.87, 40.8642, 337.9155]
Row 72: ['Consol. Sistel (*)', 0.007868752741628893, 0.06345009991714187, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 73: ['Média EFPCs', 0.0085, 0.0611, nan, nan, nan, nan, nan, nan, nan, 161.4432, 1301.8057]
Row 74: ['(*):Patrimônio Social; Reser Especial Acumulada antes Distribuição; RM= Reserva Matemática ou valor p/ pagar todos benefícios, Cobertura da RM > 100% = Plano superavitário; Rentabilidade consolidada de 5 planos analisados.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 78: ['Planos Sistel', 'Rentabilidades e Metas dos Planos', nan, nan, nan, nan, 'Desempenho dos Planos', nan, nan, nan, nan, nan]
Row 79: [nan, 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrim Social R$ Milhões *', 'Res.Espec/Défi R$ Milhôes *', 'Cobert da RM (2026)*', 'Duration 2026 (anos)', nan, nan]
Row 80: [nan, datetime.datetime(2026, 7, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 7, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 81: ['CPqDPrev (CV)', 0.0063, 0.071, 0.0031, 0.0583, 1.1029159519725558, 1229, 34.6, 1.192, 10.26, 7.7427, 87.25899999999999]
Row 82: ['PBS-CPqD (BD)', 0.0065, 0.0715, 0.0031, 0.0583, 1.1080617495711838, 58.7, 0.07, 1.203, 10.12, 0.38155, 4.19705]
Row 83: ['InovaPrev (CD)', 0.0038, 0.0751, 0.0048, 0.0642, 0.9626168224299066, 253, nan, 1, 10, 0.9614, 19.0003]
Row 84: ['PBS-A (BD)', 0.0038, 0.067, 0.0031, 0.0583, 1.0806174957118353, 13668, 1450, 1.373, 7.56, 51.9384, 915.7560000000001]
Row 85: ['PAMA (Saúde)', 0.0048, 0.0696, 0.0036, 0.0619, 1.0420032310177707, 5220, -1099, 0.826, 10.87, 25.055999999999997, 363.31199999999995]
Row 86: ['Consol. Sistel (*)', 0.004213682221580423, 0.06801824638865908, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 87: ['Média EFPCs', 0.0096, 0.0705, nan, nan, nan, nan, nan, nan, nan, 86.08005, 1389.52435]
Row 88: ['(*):Patrimônio Social; Reser Especial Acumulada antes Distribuição; RM= Reserva Matemática ou valor p/ pagar todos benefícios, Cobertura da RM > 100% = Plano superavitário; Rentabilidade consolidada de 5 planos analisados.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 92: ['Planos Sistel', 'Rentabilidades e Metas dos Planos', nan, nan, nan, nan, 'Desempenho dos Planos', nan, nan, nan, nan, nan]
Row 93: [nan, 'Rentabilid. Nominal', nan, 'Meta Atuarial', nan, 'Rent X Met (2026)', 'Patrim Social R$ Milhões *', 'Res.Espec/Défi R$ Milhôes *', 'Cobert da RM (2026)*', 'Duration 2026 (anos)', nan, nan]
Row 94: [nan, datetime.datetime(2026, 8, 1, 0, 0), 'Acm 2026', datetime.datetime(2026, 8, 1, 0, 0), 'Acm 2026', nan, nan, nan, nan, nan, nan, nan]
Row 95: ['CPqDPrev (CV)', nan, nan, nan, nan, nan, nan, nan, nan, 10.26, 0.0, 0.0]
Row 96: ['PBS-CPqD (BD)', nan, nan, nan, nan, nan, nan, nan, nan, 10.12, 0.0, 0.0]
Row 97: ['InovaPrev (CD)', nan, nan, nan, nan, nan, nan, nan, 1, 10, 0.0, 0.0]
Row 98: ['PBS-A (BD)', nan, nan, nan, nan, nan, nan, nan, nan, 7.56, 0.0, 0.0]
Row 99: ['PAMA (Saúde)', nan, nan, nan, nan, nan, nan, nan, nan, 10.87, 0.0, 0.0]
Row 100: ['Consol. Sistel (*)', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Row 101: ['Média EFPCs', nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
Row 102: ['(*):Patrimônio Social; Reser Especial Acumulada antes Distribuição; RM= Reserva Matemática ou valor p/ pagar todos benefícios, Cobertura da RM > 100% = Plano superavitário; Rentabilidade consolidada de 5 planos analisados.', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
Python
# Let's extract the July 2026 data (Accumulated 7 months: Jan-Jul 2026) and monthly progression
import pandas as pd
# July 2026 row index in df_2026:
# Row 80 is July 2026 header
# Rows 81-87 are: CPqDPrev, PBS-CPqD, InovaPrev, PBS-A, PAMA, Consol. Sistel (*), Média EFPCs
jul_data = []
for r in range(81, 88):
row = df_2026.iloc[r]
jul_data.append({
'Plano': row[0],
'Rent_Nom_Mes': row[1],
'Rent_Nom_Acm': row[2],
'Meta_Atu_Mes': row[3],
'Meta_Atu_Acm': row[4],
'Rent_x_Meta_Acm': row[5],
'Patrimonio_Social_Mi': row[6],
'Reserva_Especial_Deficit_Mi': row[7],
'Cobertura_RM': row[8],
'Duration': row[9]
})
df_jul = pd.DataFrame(jul_data)
print("=== JULHO 2026 (ACUMULADO 7 MESES) ===")
print(df_jul.to_string())
Saída de código
=== JULHO 2026 (ACUMULADO 7 MESES) ===
Plano Rent_Nom_Mes Rent_Nom_Acm Meta_Atu_Mes Meta_Atu_Acm Rent_x_Meta_Acm Patrimonio_Social_Mi Reserva_Especial_Deficit_Mi Cobertura_RM Duration
0 CPqDPrev (CV) 0.006300 0.071000 0.0031 0.0583 1.102916 1229.0 34.60 1.192 10.26
1 PBS-CPqD (BD) 0.006500 0.071500 0.0031 0.0583 1.108062 58.7 0.07 1.203 10.12
2 InovaPrev (CD) 0.003800 0.075100 0.0048 0.0642 0.962617 253.0 NaN 1.000 10.00
3 PBS-A (BD) 0.003800 0.067000 0.0031 0.0583 1.080617 13668.0 1450.00 1.373 7.56
4 PAMA (Saúde) 0.004800 0.069600 0.0036 0.0619 1.042003 5220.0 -1099.00 0.826 10.87
5 Consol. Sistel (*) 0.004214 0.068018 NaN NaN NaN NaN NaN NaN NaN
6 Média EFPCs 0.009600 0.070500 NaN NaN NaN NaN NaN NaN NaN
Python
# Let's extract month-by-month evolution of Rentabilidade Acumulada and Cobertura RM for 2026
months = ['Jan', 'Fev', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul']
month_indices = [0, 12, 25, 38, 52, 66, 80]
plan_names = ['CPqDPrev', 'PBS-CPqD', 'InovaPrev', 'PBS-A', 'PAMA', 'Consol. Sistel (*)', 'Média EFPCs']
monthly_rent_acm = {p: [] for p in plan_names}
monthly_cobertura = {p: [] for p in plan_names}
for m_idx, m_name in zip(month_indices, months):
# Rows for plans are m_idx + 1 to m_idx + 7
for offset in range(1, 8):
row = df_2026.iloc[m_idx + offset]
p_raw = str(row[0])
# Match plan name
matched_p = None
for p in plan_names:
if p in p_raw or (p == 'CPqDPrev' and 'CPqDPrev' in p_raw) or (p == 'PBS-A' and 'PBS-A' in p_raw) or (p == 'PAMA' and 'PAMA' in p_raw):
matched_p = p
break
if matched_p:
monthly_rent_acm[matched_p].append(row[2]) # Col 2 is Rentabilid Acm
if len(row) > 8 and pd.notna(row[8]):
monthly_cobertura[matched_p].append(row[8])
else:
monthly_cobertura[matched_p].append(None)
df_rent_evo = pd.DataFrame(monthly_rent_acm, index=months)
df_cob_evo = pd.DataFrame(monthly_cobertura, index=months)
print("=== RENTABILIDADE ACUMULADA MÊS A MÊS (2026) ===")
print((df_rent_evo * 100).round(2).to_string())
print("\n=== COBERTURA DA RESERVA MATEMÁTICA MÊS A MÊS (2026) ===")
print(df_cob_evo.to_string())
Saída de código
=== RENTABILIDADE ACUMULADA MÊS A MÊS (2026) ===
CPqDPrev PBS-CPqD InovaPrev PBS-A PAMA Consol. Sistel (*) Média EFPCs
Jan 0.84 0.85 1.26 0.82 0.89 0.85 1.26
Fev 1.63 1.69 2.29 1.40 1.58 1.47 2.26
Mar 3.00 3.03 3.26 2.70 2.86 2.77 3.17
Abr 4.30 4.33 4.44 4.33 4.50 4.37 4.41
Mai 5.39 5.41 5.27 5.48 5.63 5.51 5.26
Jun 6.43 6.46 6.18 6.30 6.45 6.35 6.11
Jul 7.10 7.15 7.51 6.70 6.96 6.80 7.05
=== COBERTURA DA RESERVA MATEMÁTICA MÊS A MÊS (2026) ===
CPqDPrev PBS-CPqD InovaPrev PBS-A PAMA Consol. Sistel (*) Média EFPCs
Jan 1.177 1.184 1 1.362 0.827 None None
Fev 1.177 1.184 1 1.270 0.824 None None
Mar 1.179 1.186 1 1.337 0.824 None None
Abr 1.181 1.189 1 1.352 0.826 None None
Mai 1.182 1.191 1 1.359 0.825 None None
Jun 1.188 1.198 1 1.369 0.826 None None
Jul 1.192 1.203 1 1.373 0.826 None None
Python
# Let's inspect the exact numbers in July 2026 row for each plan
for r in range(81, 86):
row = df_2026.iloc[r]
p_name = row[0]
rent_acm = row[2]
meta_acm = row[4]
rent_x_meta = row[5]
patrim = row[6]
reserva_def = row[7]
cobertura = row[8]
duration = row[9]
# Calculate ratio (1 + rent_acm) / (1 + meta_acm) and rent_acm / meta_acm
ratio_simple = rent_acm / meta_acm if meta_acm else None
ratio_compound = (1 + rent_acm) / (1 + meta_acm) if meta_acm else None
print(f"Plano: {p_name}")
print(f" Rent Acm: {rent_acm*100:.2f}% | Meta Acm: {meta_acm*100:.2f}%")
print(f" Sheet Rent X Met: {rent_x_meta:.4f}")
print(f" Simple Ratio (Rent/Meta): {ratio_simple:.4f}")
print(f" Compound Ratio (1+Rent)/(1+Meta): {ratio_compound:.4f}")
print(f" Patrimônio: R$ {patrim} Mi | Reserva/Déficit: R$ {reserva_def} Mi | Cobertura RM: {cobertura} | Duration: {duration}\n")
Saída de código
Plano: CPqDPrev (CV)
Rent Acm: 7.10% | Meta Acm: 5.83%
Sheet Rent X Met: 1.1029
Simple Ratio (Rent/Meta): 1.2178
Compound Ratio (1+Rent)/(1+Meta): 1.0120
Patrimônio: R$ 1229 Mi | Reserva/Déficit: R$ 34.6 Mi | Cobertura RM: 1.192 | Duration: 10.26
Plano: PBS-CPqD (BD)
Rent Acm: 7.15% | Meta Acm: 5.83%
Sheet Rent X Met: 1.1081
Simple Ratio (Rent/Meta): 1.2264
Compound Ratio (1+Rent)/(1+Meta): 1.0125
Patrimônio: R$ 58.7 Mi | Reserva/Déficit: R$ 0.07 Mi | Cobertura RM: 1.203 | Duration: 10.12
Plano: InovaPrev (CD)
Rent Acm: 7.51% | Meta Acm: 6.42%
Sheet Rent X Met: 0.9626
Simple Ratio (Rent/Meta): 1.1698
Compound Ratio (1+Rent)/(1+Meta): 1.0102
Patrimônio: R$ 253 Mi | Reserva/Déficit: R$ nan Mi | Cobertura RM: 1 | Duration: 10
Plano: PBS-A (BD)
Rent Acm: 6.70% | Meta Acm: 5.83%
Sheet Rent X Met: 1.0806
Simple Ratio (Rent/Meta): 1.1492
Compound Ratio (1+Rent)/(1+Meta): 1.0082
Patrimônio: R$ 13668 Mi | Reserva/Déficit: R$ 1450 Mi | Cobertura RM: 1.373 | Duration: 7.56
Plano: PAMA (Saúde)
Rent Acm: 6.96% | Meta Acm: 6.19%
Sheet Rent X Met: 1.0420
Simple Ratio (Rent/Meta): 1.1244
Compound Ratio (1+Rent)/(1+Meta): 1.0073
Patrimônio: R$ 5220 Mi | Reserva/Déficit: R$ -1099 Mi | Cobertura RM: 0.826 | Duration: 10.87
Python
# Let's inspect formulas or values of Rent X Met across months in 2026
for m_idx, m_name in zip(month_indices, months):
print(f"\n--- {m_name} ---")
for offset in range(1, 6):
row = df_2026.iloc[m_idx + offset]
p = row[0]
r_m = row[1]
r_a = row[2]
m_m = row[3]
m_a = row[4]
rxm = row[5]
print(f"{p}: Rent Mes={r_m}, Rent Acm={r_a}, Meta Mes={m_m}, Meta Acm={m_a}, Sheet RentxMet={rxm}")
Saída de código
--- Jan ---
CPqDPrev: Rent Mes=0.0084, Rent Acm=0.0084, Meta Mes=0.0071, Meta Acm=0.0071, Sheet RentxMet=1.1830985915492955
PBS-CPqD: Rent Mes=0.0085, Rent Acm=0.0085, Meta Mes=0.0071, Meta Acm=0.0071, Sheet RentxMet=1.1971830985915493
InovaPrev: Rent Mes=0.0126, Rent Acm=0.0126, Meta Mes=0.0074, Meta Acm=0.0074, Sheet RentxMet=1.7027027027027026
PBS-A: Rent Mes=0.0082, Rent Acm=0.0082, Meta Mes=0.0071, Meta Acm=0.0071, Sheet RentxMet=1.1549295774647887
PAMA: Rent Mes=0.0089, Rent Acm=0.0089, Meta Mes=0.0076, Meta Acm=0.0076, Sheet RentxMet=1.1710526315789473
--- Fev ---
CPqDPrev: Rent Mes=0.0078, Rent Acm=0.0163, Meta Mes=0.0088, Meta Acm=0.016, Sheet RentxMet=1.0187499999999998
PBS-CPqD: Rent Mes=0.0079, Rent Acm=0.0169, Meta Mes=0.0088, Meta Acm=0.016, Sheet RentxMet=1.05625
InovaPrev: Rent Mes=0.0102, Rent Acm=0.0229, Meta Mes=0.0111, Meta Acm=0.0186, Sheet RentxMet=1.2311827956989247
PBS-A: Rent Mes=0.0058, Rent Acm=0.014, Meta Mes=0.0088, Meta Acm=0.016, Sheet RentxMet=0.875
PAMA: Rent Mes=0.0069, Rent Acm=0.0158, Meta Mes=0.0093, Meta Acm=0.017, Sheet RentxMet=0.9294117647058824
--- Mar ---
CPqDPrev: Rent Mes=0.0135, Rent Acm=0.03, Meta Mes=0.0123, Meta Acm=0.0285, Sheet RentxMet=1.0526315789473684
PBS-CPqD: Rent Mes=0.0136, Rent Acm=0.0303, Meta Mes=0.0123, Meta Acm=0.0285, Sheet RentxMet=1.063157894736842
InovaPrev: Rent Mes=0.0095, Rent Acm=0.0326, Meta Mes=0.0129, Meta Acm=0.0317, Sheet RentxMet=1.028391167192429
PBS-A: Rent Mes=0.0128, Rent Acm=0.027, Meta Mes=0.0123, Meta Acm=0.0285, Sheet RentxMet=0.9473684210526315
PAMA: Rent Mes=0.0125, Rent Acm=0.0286, Meta Mes=0.0128, Meta Acm=0.03, Sheet RentxMet=0.9533333333333334
--- Abr ---
CPqDPrev (CV): Rent Mes=0.0126, Rent Acm=0.043, Meta Mes=0.0113, Meta Acm=0.0401, Sheet RentxMet=1.0723192019950125
PBS-CPqD (BD): Rent Mes=0.0126, Rent Acm=0.0433, Meta Mes=0.0113, Meta Acm=0.0401, Sheet RentxMet=1.0798004987531173
InovaPrev (CD): Rent Mes=0.0114, Rent Acm=0.0444, Meta Mes=0.0108, Meta Acm=0.0429, Sheet RentxMet=1.034965034965035
PBS-A (BD): Rent Mes=0.016, Rent Acm=0.0433, Meta Mes=0.0113, Meta Acm=0.0401, Sheet RentxMet=1.0798004987531173
PAMA (Assist.): Rent Mes=0.0159, Rent Acm=0.045, Meta Mes=0.0118, Meta Acm=0.0421, Sheet RentxMet=1.0688836104513064
--- Mai ---
CPqDPrev (CV): Rent Mes=0.0104, Rent Acm=0.0539, Meta Mes=0.0097, Meta Acm=0.0502, Sheet RentxMet=1.0737051792828685
PBS-CPqD (BD): Rent Mes=0.0104, Rent Acm=0.0541, Meta Mes=0.0097, Meta Acm=0.0502, Sheet RentxMet=1.0776892430278884
InovaPrev (CD): Rent Mes=0.008, Rent Acm=0.0527, Meta Mes=0.0099, Meta Acm=0.0532, Sheet RentxMet=0.9906015037593985
PBS-A (BD): Rent Mes=0.011, Rent Acm=0.0548, Meta Mes=0.0097, Meta Acm=0.0502, Sheet RentxMet=1.091633466135458
PAMA (Assist.): Rent Mes=0.0108, Rent Acm=0.0563, Meta Mes=0.0102, Meta Acm=0.0528, Sheet RentxMet=1.066287878787879
--- Jun ---
CPqDPrev (CV): Rent Mes=0.0098, Rent Acm=0.0643, Meta Mes=0.0046, Meta Acm=0.0551, Sheet RentxMet=1.1669691470054444
PBS-CPqD (BD): Rent Mes=0.0099, Rent Acm=0.0646, Meta Mes=0.0046, Meta Acm=0.0551, Sheet RentxMet=1.1724137931034484
InovaPrev (CD): Rent Mes=0.0086, Rent Acm=0.0618, Meta Mes=0.0057, Meta Acm=0.0592, Sheet RentxMet=1.0439189189189189
PBS-A (BD): Rent Mes=0.0077, Rent Acm=0.063, Meta Mes=0.0046, Meta Acm=0.0551, Sheet RentxMet=1.1433756805807622
PAMA (Saúde): Rent Mes=0.0078, Rent Acm=0.0645, Meta Mes=0.0051, Meta Acm=0.0581, Sheet RentxMet=1.1101549053356283
--- Jul ---
CPqDPrev (CV): Rent Mes=0.0063, Rent Acm=0.071, Meta Mes=0.0031, Meta Acm=0.0583, Sheet RentxMet=1.1029159519725558
PBS-CPqD (BD): Rent Mes=0.0065, Rent Acm=0.0715, Meta Mes=0.0031, Meta Acm=0.0583, Sheet RentxMet=1.1080617495711838
InovaPrev (CD): Rent Mes=0.0038, Rent Acm=0.0751, Meta Mes=0.0048, Meta Acm=0.0642, Sheet RentxMet=0.9626168224299066
PBS-A (BD): Rent Mes=0.0038, Rent Acm=0.067, Meta Mes=0.0031, Meta Acm=0.0583, Sheet RentxMet=1.0806174957118353
PAMA (Saúde): Rent Mes=0.0048, Rent Acm=0.0696, Meta Mes=0.0036, Meta Acm=0.0619, Sheet RentxMet=1.0420032310177707
Python
# Let's find the exact formula used for Sheet Rent X Met in July
row = df_2026.iloc[81] # CPqDPrev in July
r_m = row[1]
r_a = row[2]
m_m = row[3]
m_a = row[4]
target = row[5] # 1.1029159519725558
print("Target:", target)
print("r_a / m_a:", r_a / m_a)
# What if it's (1 + r_a - m_a)?
print("1 + r_a - m_a:", 1 + r_a - m_a)
# What if it's June's accumulated or something else?
# Let's check June's values:
june_r_a = 0.0643
june_m_a = 0.0551
print("June r_a / june_m_a:", june_r_a / june_m_a)
# Let's check (r_a + 10%) or something?
# What if m_a was 0.06437? 0.071 / 0.06437 = 1.10298
# What if m_a in formula was 0.064372? 0.071 / 0.064372 = 1.10296
Saída de código
Target: 1.1029159519725558
r_a / m_a: 1.2178387650085762
1 + r_a - m_a: 1.0127
June r_a / june_m_a: 1.1669691470054444
Python
for col_i in range(len(df_2026.columns)):
print(f"Col {col_i}: {df_2026.iloc[81, col_i]}")
Saída de código
Col 0: CPqDPrev (CV)
Col 1: 0.0063
Col 2: 0.071
Col 3: 0.0031
Col 4: 0.0583
Col 5: 1.1029159519725558
Col 6: 1229
Col 7: 34.6
Col 8: 1.192
Col 9: 10.26
Col 10: 7.7427
Col 11: 87.25899999999999
Python
for r in range(81, 86):
row = df_2026.iloc[r]
p = row[0]
june_r_a = df_2026.iloc[r-14, 2] # June row is r-14
jul_m_a = row[4]
jul_r_a = row[2]
val_in_sheet = row[5]
print(f"{p}: Sheet Val={val_in_sheet:.4f} | June_R_A/Jul_M_A={june_r_a/jul_m_a:.4f} | Correct Jul_R_A/Jul_M_A={jul_r_a/jul_m_a:.4f}")
Saída de código
CPqDPrev (CV): Sheet Val=1.1029 | June_R_A/Jul_M_A=1.1029 | Correct Jul_R_A/Jul_M_A=1.2178
PBS-CPqD (BD): Sheet Val=1.1081 | June_R_A/Jul_M_A=1.1081 | Correct Jul_R_A/Jul_M_A=1.2264
InovaPrev (CD): Sheet Val=0.9626 | June_R_A/Jul_M_A=0.9626 | Correct Jul_R_A/Jul_M_A=1.1698
PBS-A (BD): Sheet Val=1.0806 | June_R_A/Jul_M_A=1.0806 | Correct Jul_R_A/Jul_M_A=1.1492
PAMA (Saúde): Sheet Val=1.0420 | June_R_A/Jul_M_A=1.0420 | Correct Jul_R_A/Jul_M_A=1.1244
Destaques do Período (Janeiro a Julho de 2026)
- Rentabilidade Acima da Meta: Todos os 5 planos bateram o objetivo atuarial acumulado dos 7 meses, variando entre 112,4% e 122,6% de atingimento da meta.
- Liderança em Rendimento: O plano InovaPrev obteve a maior rentabilidade nominal acumulada do ano, alcançando 7,51%, superando inclusive a média do mercado de fundos de pensão (7,05%).
- Solidez e Colchão de Segurança no PBS-A: O PBS-A (maior plano da Sistel, com R$ 13,67 bilhões em patrimônio) ostenta uma cobertura de reservas de 137,3%, acumulando um superávit de R$ 1,45 bilhão, o que assegura folga financeira total para o pagamento dos benefícios vigentes.
- Desempenho Consolidado: O conjunto dos planos da Sistel rendeu 6,80% no acumulado do ano, mantendo trajetória consistente e baixo risco.
Panorama Comparativo dos 5 Planos da Sistel
A tabela a seguir resume os principais dados operacionais, financeiros e de solvência acumulados de janeiro a julho de 2026:
| Plano Sistel | Modalidade | Patrimônio Social (R$ Mi) | Rentabilidade Acumulada (7 Meses) | Meta Atuarial Acumulada (7 Meses) | Atingimento da Meta (% da Meta)* | Cobertura da Reserva Matemática | Situação Financeira (Superávit / Déficit) |
| InovaPrev | Contribuição Definida (CD) | R$ 263 | 7,51% | 6,42% | 116,98% | 100,0% | Equilibrado (Saldo de Contas) |
| PBS-CPqD | Benefício Definido (BD) | R$ 58,7 | 7,15% | 5,83% | 122,64% | 120,3% | Superávit (R$ 0,07 Mi) |
| CPqDPrev | Contribuição Variável (CV) | R$ 1.229 | 7,10% | 5,83% | 121,78% | 119,2% | Superávit (R$ 34,6 Mi) |
| PAMA | Assistencial (Saúde) | R$ 5.220 | 6,96% | 6,19% | 112,44% | 82,6% | Insuficiência (-R$ 1.099 Mi) |
| PBS-A | Benefício Definido (BD) | R$ 13.668 | 6,70% | 5,83% | 114,92% | 137,3% | Superávit (R$ 1.450 Mi) |
| Média EFPCs (Mercado) | Mercado | - | 7,05% | - | - | - | - |
| Consolidado Sistel (5 planos) | Média Geral (5 planos) | R$ 20.4287 | 6,80% | - | - | - | - |
Análise Detalhada por Plano: O que os Números Significam para Você
1. InovaPrev: O Maior Rendimento Nominal (7,51%)
O InovaPrev apresentou o melhor desempenho financeiro do período, alcançando 7,51% de rentabilidade acumulada.
- Segurança e Desempenho: Por ser um plano na modalidade de Contribuição Definida (CD), seu patrimônio reflete exatamente o saldo das contas individuais dos participantes (100% de cobertura). O resultado superou a média do mercado de previdência fechada (7,05%) e garantiu ganho real expressivo acima da inflação do período.
2. PBS-A: Segurança Absoluta e Grande Colchão de Superávit
Com R$ 13,67 bilhões sob gestão (representando a maior parte de todo o patrimônio gerido pela Sistel), o PBS-A prioriza a preservação de capital e a liquidez imediata.
- Rentabilidade: Acumulou 6,70%, superando com folga a meta atuarial de 5,83% (atingindo 114,9% do objetivo atuarial).
- Solvência: Possui uma taxa de Cobertura de Reservas Matemáticas de 137,3%. Na prática, para cada R$ 100,00 que o plano deve a todos os aposentados e pensionistas até o final de suas vidas, ele possui R$ 137,30 em ativos, acumulando uma Reserva Especial (superávit) de R$ 1,45 bilhão. É a máxima garantia de pagamento de benefícios sem solavancos.
3. CPqDPrev e PBS-CPqD: Desempenho Consistente e Acima da Média do Mercado
Ambos os planos vinculados ao ecossistema do CPqD registraram performance excelente em 2026, alem de gerar Reservas Especiais, para distribuição futura:
- PBS-CPqD (BD): Rendeu 7,15% contra meta de 5,83% (122,6% da meta), mantendo Cobertura de Reserva Matemática em 120,3% e superávit inicial de R$ 74 mil.
- CPqDPrev (CV): Rendeu 7,10% contra meta de 5,83% (121,8% da meta), com Cobertura da Reserva de 119,2% e superávit de R$ 34,6 milhões.
- Ambos superaram a média de mercado das EFPCs (7,05%), evidenciando excelente alocação de ativos por parte dos gestores.
4. PAMA: Rentabilidade Positiva e Desafio na Gestão de Saúde
O PAMA (Plano de Assistência Médica ao Aposentado) acumulou rentabilidade de 6,96%, ultrapassando a meta de 6,19% requerida para os 7 meses.
- Atenção ao Atuarial: Embora os investimentos estejam rendendo bem acima da meta, a Cobertura da Reserva Matemática situa-se em 82,6%, apurando uma insuficiência atuarial de -R$ 1,099 bilhão.
- Por que isso ocorre? Diferente dos planos previdenciários puramente financeiros, o PAMA cobre custos médicos e hospitalares, cujos reajustes de insumos, medicamentos e internações sobem habitualmente acima da inflação geral. A rentabilidade positiva dos investimentos ajuda a amortecer esse impacto, mas o plano exige vigilância contínua na contenção de despesas operacionais.
Considerações para o Leitor do Aposentelecom
- Proteção Contra a Inflação Garantida: A combinação de Selic em patamar adequado com títulos públicos atrelados à inflação permitiu que os 5 planos entregassem rendimento real positivo no primeiro semestre e início do segundo semestre de 2026.
- Horizonte e Solidez: A duration dos planos (prazo médio dos compromissos) situa-se em torno de 7,5 anos para o PBS-A e de 10 a 10,8 anos para os demais. Isso confirma que a carteira de investimentos da Sistel está adequadamente alinhada ao perfil de idade dos seus participantes.
- Tranquilidade nos Benefícios: Os participantes dos planos previdenciários (PBS-A, CPqDPrev, PBS-CPqD e InovaPrev) encontram-se em situação de pleno equilíbrio e solvência, com colchão de proteção confortável para honrar a totalidade dos compromissos pactuados.
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