Executive Compensation API
The Executive Compensation API provides standardized compensation data for all key executives as reported in SEC filing DEF 14A. The API covers compensation data reported since 2005 and is updated in real-time. Three API endpoints provide access to the data. You can search compensation data by 13 parameters, such as company ticker, executive name & position, annual salary, option awards and more.
The executive compensation database includes the following data points:
- CIK of the reporting company, e.g. 789019
- Ticker of the reporting company, e.g. MSFT
- The year of the reporting period, e.g. 2020
- Executive name
- Position of the executive, e.g. Chief Financial Officer
- Salary of the executive for the reporting year
- Cash bonus
- Stock awards
- Option awards
- Non-equity incentive plan compensation
- Change in pension value and nonqualified deferred compensation earnings
- All other compensation
- Total compensation
Compensation data is searchable by any of the data points listed above. Complex boolean search queries are also supported.
Dataset size:
Compensation data extracted from all DEF 14A filings filed since 2005 to present.
Data update frequency:
Compensation data is extracted, indexed and searchable in less than 300 milliseconds on average after a new DEF 14A filing is published.
Survivorship bias free:
Yes. The API includes compensation data of companies that are no longer listed on stock exchanges.
API Endpoints
Three API endpoints are provided to search and access executive compensation data by ticker, CIK or search query. All APIs return executive data in the same JSON format.
| Endpoint | HTTP Method | Response Format | Description |
|---|---|---|---|
| https://api.sec-api.io/compensation/<CIK> | GET | JSON | Return all compensation data for a given ticker. |
| https://api.sec-api.io/compensation/<TICKER> | GET | JSON | Return all compensation data for a given CIK. |
| https://api.sec-api.io/compensation | POST | JSON | Perform simple and complex search queries. |
Supported HTTP methods: GET, POST
Response format: JSON
Authentication
The API supports two authentication strategies. Either set your API key as Authorization header or attach your key as token query parameter:
- Set as
Authorizationheader. Before making aGETrequest tohttps://api.sec-api.io/mapping, you need to set theAuthorizationheader toYOUR_API_KEY. - Set as
tokenquery parameter. Example:https://api.sec-api.io/compensation/1318605?token=YOUR_API_KEYIn this case, you makeGETrequests to the endpointhttps://api.sec-api.io/compensation/1318605?token=YOUR_API_KEYand not tohttps://api.sec-api.io/compensation/1318605.
Request & Response Parameters
Get Executive Compensation Data by Ticker
Send a GET HTTP request to retrieve all historical and current executive compensation data of a company by its ticker symbol (e.g. TSLA for Tesla Inc.) to the following API endpoint:
https://api.sec-api.io/compensation/<TICKER>
Supported HTTP methods: GET
Response format: JSON
Replace <TICKER> with the ticker symbol to receive all executive compensation data of the company.
Example
Request: GET https://api.sec-api.io/compensation/TSLA
Response:
JSON
1
[
2
{
3
"id": "0700aaecae7435258399867de91d3edc",
4
"cik": "1318605",
5
"ticker": "TSLA",
6
"name": "Zachary Kirkhorn",
7
"position": "Former Chief Financial Officer",
8
"year": 2023,
9
"salary": 280385,
10
"bonus": 0,
11
"stockAwards": 0,
12
"optionAwards": 0,
13
"nonEquityIncentiveCompensation": 0,
14
"changeInPensionValueAndDeferredEarnings": 0,
15
"otherCompensation": 3000,
16
"total": 283385
17
},
18
{
19
"id": "fff9d469f4bd846c15cae96d8f74a775",
20
"cik": "1318605",
21
"ticker": "TSLA",
22
"name": "Tom Zhu",
23
"position": "SVP, Automotive",
24
"year": 2023,
25
"salary": 381009,
26
"bonus": 0,
27
"stockAwards": 0,
28
"optionAwards": 31641961,
29
"nonEquityIncentiveCompensation": 0,
30
"changeInPensionValueAndDeferredEarnings": 0,
31
"otherCompensation": 545868,
32
"total": 32568838
33
},
34
{
35
"id": "5671a3ef72cf37c76e39d0122c218e41",
36
"cik": "1318605",
37
"ticker": "TSLA",
38
"name": "Andrew Baglino",
39
"position": "Former SVP, Powertrain and Energy Engineering",
40
"year": 2023,
41
"salary": 300000,
42
"bonus": 0,
43
"stockAwards": 0,
44
"optionAwards": 0,
45
"nonEquityIncentiveCompensation": 0,
46
"changeInPensionValueAndDeferredEarnings": 0,
47
"otherCompensation": 3000,
48
"total": 303000
49
},
50
{
51
"id": "6af2a0433019cd481713df6d8b616c94",
52
"cik": "1318605",
53
"ticker": "TSLA",
54
"name": "Elon Musk",
55
"position": "Technoking of Tesla and Chief Executive Officer",
56
"year": 2023,
57
"salary": 0,
58
"bonus": 0,
59
"stockAwards": 0,
60
"optionAwards": 0,
61
"nonEquityIncentiveCompensation": 0,
62
"changeInPensionValueAndDeferredEarnings": 0,
63
"otherCompensation": 0,
64
"total": 0
65
},
66
{
67
"id": "51a315d354aa4c29d9afd3703be230c3",
68
"cik": "1318605",
69
"ticker": "TSLA",
70
"name": "Vaibhav Taneja",
71
"position": "Chief Financial Officer",
72
"year": 2023,
73
"salary": 275000,
74
"bonus": 0,
75
"stockAwards": 0,
76
"optionAwards": 0,
77
"nonEquityIncentiveCompensation": 0,
78
"changeInPensionValueAndDeferredEarnings": 0,
79
"otherCompensation": 3000,
80
"total": 278000
81
},
82
{
83
"id": "ee9c18a958433e4bd6ac226e32f52cc0",
84
"cik": "1318605",
85
"ticker": "TSLA",
86
"name": "Elon Musk",
87
"position": "Technoking of Tesla and Chief Executive Officer",
88
"year": 2022,
89
"salary": 0,
90
"bonus": 0,
91
"stockAwards": 0,
92
"optionAwards": 0,
93
"nonEquityIncentiveCompensation": 0,
94
"changeInPensionValueAndDeferredEarnings": 0,
95
"otherCompensation": 0,
96
"total": 0
97
},
98
{
99
"id": "859da1540ec810c24b13e9c732767f55",
100
"cik": "1318605",
101
"ticker": "TSLA",
102
"name": "Andrew Baglino",
103
"position": "Former SVP, Powertrain and Energy Engineering",
104
"year": 2022,
105
"salary": 300000,
106
"bonus": 0,
107
"stockAwards": 0,
108
"optionAwards": 0,
109
"nonEquityIncentiveCompensation": 0,
110
"changeInPensionValueAndDeferredEarnings": 0,
111
"otherCompensation": 3000,
112
"total": 303000
113
},
114
{
115
"id": "ce2754bd74efb79b876700e838812699",
116
"cik": "1318605",
117
"ticker": "TSLA",
118
"name": "Zachary Kirkhorn",
119
"position": "Former Chief Financial Officer",
120
"year": 2022,
121
"salary": 300000,
122
"bonus": 0,
123
"stockAwards": 0,
124
"optionAwards": 0,
125
"nonEquityIncentiveCompensation": 0,
126
"changeInPensionValueAndDeferredEarnings": 0,
127
"otherCompensation": 3000,
128
"total": 303000
129
},
130
// ... more results
131
]
Get Executive Compensation Data by CIK
Send a GET HTTP request to retrieve all historical and current executive compensation data of a company by its Central Index Key (CIK) to the following API endpoint:
https://api.sec-api.io/compensation/<CIK>
Supported HTTP methods: GET
Response format: JSON
Replace <CIK> with a CIK to receive all executive compensation data of the company.
Example
Request: GET https://api.sec-api.io/compensation/1318605
Response:
JSON
1
[
2
{
3
"id": "0700aaecae7435258399867de91d3edc",
4
"cik": "1318605",
5
"ticker": "TSLA",
6
"name": "Zachary Kirkhorn",
7
"position": "Former Chief Financial Officer",
8
"year": 2023,
9
"salary": 280385,
10
"bonus": 0,
11
"stockAwards": 0,
12
"optionAwards": 0,
13
"nonEquityIncentiveCompensation": 0,
14
"changeInPensionValueAndDeferredEarnings": 0,
15
"otherCompensation": 3000,
16
"total": 283385
17
},
18
{
19
"id": "fff9d469f4bd846c15cae96d8f74a775",
20
"cik": "1318605",
21
"ticker": "TSLA",
22
"name": "Tom Zhu",
23
"position": "SVP, Automotive",
24
"year": 2023,
25
"salary": 381009,
26
"bonus": 0,
27
"stockAwards": 0,
28
"optionAwards": 31641961,
29
"nonEquityIncentiveCompensation": 0,
30
"changeInPensionValueAndDeferredEarnings": 0,
31
"otherCompensation": 545868,
32
"total": 32568838
33
},
34
{
35
"id": "5671a3ef72cf37c76e39d0122c218e41",
36
"cik": "1318605",
37
"ticker": "TSLA",
38
"name": "Andrew Baglino",
39
"position": "Former SVP, Powertrain and Energy Engineering",
40
"year": 2023,
41
"salary": 300000,
42
"bonus": 0,
43
"stockAwards": 0,
44
"optionAwards": 0,
45
"nonEquityIncentiveCompensation": 0,
46
"changeInPensionValueAndDeferredEarnings": 0,
47
"otherCompensation": 3000,
48
"total": 303000
49
},
50
{
51
"id": "6af2a0433019cd481713df6d8b616c94",
52
"cik": "1318605",
53
"ticker": "TSLA",
54
"name": "Elon Musk",
55
"position": "Technoking of Tesla and Chief Executive Officer",
56
"year": 2023,
57
"salary": 0,
58
"bonus": 0,
59
"stockAwards": 0,
60
"optionAwards": 0,
61
"nonEquityIncentiveCompensation": 0,
62
"changeInPensionValueAndDeferredEarnings": 0,
63
"otherCompensation": 0,
64
"total": 0
65
},
66
{
67
"id": "51a315d354aa4c29d9afd3703be230c3",
68
"cik": "1318605",
69
"ticker": "TSLA",
70
"name": "Vaibhav Taneja",
71
"position": "Chief Financial Officer",
72
"year": 2023,
73
"salary": 275000,
74
"bonus": 0,
75
"stockAwards": 0,
76
"optionAwards": 0,
77
"nonEquityIncentiveCompensation": 0,
78
"changeInPensionValueAndDeferredEarnings": 0,
79
"otherCompensation": 3000,
80
"total": 278000
81
},
82
{
83
"id": "ee9c18a958433e4bd6ac226e32f52cc0",
84
"cik": "1318605",
85
"ticker": "TSLA",
86
"name": "Elon Musk",
87
"position": "Technoking of Tesla and Chief Executive Officer",
88
"year": 2022,
89
"salary": 0,
90
"bonus": 0,
91
"stockAwards": 0,
92
"optionAwards": 0,
93
"nonEquityIncentiveCompensation": 0,
94
"changeInPensionValueAndDeferredEarnings": 0,
95
"otherCompensation": 0,
96
"total": 0
97
},
98
{
99
"id": "859da1540ec810c24b13e9c732767f55",
100
"cik": "1318605",
101
"ticker": "TSLA",
102
"name": "Andrew Baglino",
103
"position": "Former SVP, Powertrain and Energy Engineering",
104
"year": 2022,
105
"salary": 300000,
106
"bonus": 0,
107
"stockAwards": 0,
108
"optionAwards": 0,
109
"nonEquityIncentiveCompensation": 0,
110
"changeInPensionValueAndDeferredEarnings": 0,
111
"otherCompensation": 3000,
112
"total": 303000
113
},
114
{
115
"id": "ce2754bd74efb79b876700e838812699",
116
"cik": "1318605",
117
"ticker": "TSLA",
118
"name": "Zachary Kirkhorn",
119
"position": "Former Chief Financial Officer",
120
"year": 2022,
121
"salary": 300000,
122
"bonus": 0,
123
"stockAwards": 0,
124
"optionAwards": 0,
125
"nonEquityIncentiveCompensation": 0,
126
"changeInPensionValueAndDeferredEarnings": 0,
127
"otherCompensation": 3000,
128
"total": 303000
129
},
130
// ... more results
131
]
Query the Executive Compensation Data Corpus
Search the executive compensation database by ticker, CIK or name of the company the executives work for, name of the executive, position (e.g. "CEO"), reporting year, salary, bonus, stock awards, total compensation and more.
Send a POST HTTP request with a JSON payload including the search query, and parameters for pagination and sorting to the following API endpoint:
https://api.sec-api.io/compensation
Supported HTTP methods: POST
Request & response content type: JSON
Request Structure
The request body should include the following parameters in JSON format:
query(string) - The query string specifies the search terms and is written in Lucene syntax. UseANDandORoperators to create a boolean search expression. Parantheses,(and), are used to create filter groups. Finding compensation data matching a range of numbers (e.g. return all data between 2018 and 2020) is possible using brackets range expressions with square brackets[ ](e.g.year:[2018 TO 2022]). You can find more details in the example section below. More information about the Lucene syntax can be found in our tutorial here. The following represent the searchable parameters:- Ticker(s):
ticker:TICKER_OF_INTERESTorticker:(TICKER_1 OR TICKER_2 OR TICKER_3 ...)- Retrieve compensation data of all executives working for a specific company or a list of companies by ticker symbol(s). - CIK(s):
cik:CIK_OF_INTERESTorticker:(CIK_1 OR CIK_2 OR CIK_3 ...)- Retrieve compensation data for a specific company or a list of companies by CIK(s). - Executive Name:
name:NAME_OF_INTEREST- Search for compensation data of a specific executive by his/her name. - Position:
position:POSITION_OF_INTEREST- Find compensation data of executives with a specific position (e.g. CEO, CFO, etc.). The positions are not standardized and may vary between companies. For example, the CEO may be referred to as "Chief Executive Officer", "CEO", "Technoking of Tesla", etc. - Year:
year:YEAR_OF_INTERESToryear:[YEAR_START TO YEAR_END]- Retrieve compensation data for a specific year or a range of years. Use brackets range expressions with square brackets[ ]to find data within a range of years (e.g.year:[2018 TO 2022]). - Salary and other compensation parameters:
salary:[STARTING_SALARY TO ENDING_SALARY]orsalary:[* TO ENDING_SALARY]orsalary:[STARTING_SALARY TO *]- Find compensation data matching a range of salaries. Use brackets range expressions with square brackets[ ]to find data within a range of salaries (e.g.salary:[100000 TO 200000]). Use an asterisk*to search for all salaries above or below a certain value. For example,salary:[100000 TO *]returns all compensation data with a salary above $100,000 per annum. Alternatively,salary:[* TO 200000]returns all compensation data with a salary below $200,000 per annum. The same applies to other compensation parameters, such as bonus, stock awards, etc. For example,bonus:[100000 TO 500000]returns all compensation data with a bonus between $100,000 and $500,000.
- Ticker(s):
from(string) - Starting position of your search. Default:0. Max:10,000items, which is also the maximum number of items returned per searchquery. To retrieve all compensation data items in your search universe, incrementfromby the value of thesizeparameter (e.g., 50) until no more items are returned or the 10,000 limit is reached. For example, use 0, 50, 100, and so on. If yourquerylocates more than 10,000 compensation items, consider narrowing your search by refining your filter criteria, for example, using a date range filter to iterate over reporting years. One such approach would be to search for compensation data with a range filter applied to the reportingyear, e.g.year:[2021 TO 2022](all compensation items disclosed in 2021 and 2022), then paginate through the results by incrementingfrom, and once completed, repeat the process for the next years, and so on.size(string) - Number of compensation items to be returned per query. Maximum:50. Default:50sort(array) - Array including the sort definition. For example, sort the result bysalaryand start with the highest salary item is achieved with:[{ "salary": { "order": "desc" } }]. The sorting order can be changed to ascending by settingordertoasc. Default:"sort": [{ "year": { "order": "desc" } }]
Query Examples
Return all executive compensation items for 2020, 2019 and 2018 for all companies and sort the result by salary, start with the highest salary. The square brackets [ ] in year:[2018 TO 2020] serve as a range filter, including all years from 2018 to 2020.
JSON
1
{
2
"query": "year:[2018 TO 2020]",
3
"from": "0",
4
"size": "10",
5
"sort": [{ "salary": { "order": "desc" } }]
6
}
Return all executive compensation items for 2020 with a total salary exceeding $1,000,000, sort by total salary, highest first. The range query total:[1000000 TO *] is used to find all items with a total salary of at least $1,000,000 and upwards.
JSON
1
{
2
"query": "year:2020 AND total:[1000000 TO *]",
3
"from": "0",
4
"size": "10",
5
"sort": [{ "salary": { "order": "desc" } }]
6
}
Return all executive compensation items of MSFT, AAPL, TSLA and ADBE for 2020 and 2019.
JSON
1
{
2
"query": "year:[2019 TO 2020] AND ticker:(MSFT OR AAPL OR TSLA OR ADBE)",
3
"from": "0",
4
"size": "10",
5
"sort": [{ "salary": { "order": "desc" } }]
6
}
References
For more information about Form DEF14A filings visit the SEC websites here: