Earnings event study (market-model CAR)

MacKinlay market-model event study around the company's quarterly earnings announcements. Announcement dates are sourced from SEC 8-K item 2.02 filings (the report/announcement date carried by the filing); period, year and quarter reflect that report/announcement (calendar) date, NOT a fiscal quarter-end. Per event: OLS market-model fit on the estimation window [-250, -11] vs market_proxy (default SPY), daily abnormal returns over [0, +20], and CAR over [0,+1], [0,+5], [0,+20]. Cross-event significance is a two-sided one-sample Student-t test (df = n-1, exact incomplete-beta - NOT a normal approximation) plus a percentile bootstrap CI. Returns are SIMPLE (close_t/close_{t-1} - 1) computed on stock + proxy close prices aligned on common trading days. Events with no calendar announcement date are excluded (skip_reason=missing_announcement_date). The retrospective filter (drop events whose announcement is >45 days after period) is effectively inactive here because the announcement date is taken directly from the filing, so period equals the announcement and there is no period-vs-announcement lag to screen. Daily only (interval=1d).

Request Code Samples

Shell

JavaScript

Java

Swift

PHP

Python

HTTP

Objective-C

Ruby

OCaml

Dart

Responses

🟢200OK

application/json

Successful Response

Bodyapplication/json

Example

{
    "data": {
        "symbol": "AAPL",
        "market_proxy": "SPY",
        "interval": "1d",
        "estimation_window": [
            -250,
            -11
        ],
        "min_estimation_days": 200,
        "event_window": [
            0,
            20
        ],
        "car_windows": [
            [
                0,
                1
            ],
            [
                0,
                5
            ],
            [
                0,
                20
            ]
        ],
        "include_daily_ar": false,
        "events": [
            {
                "period": "string",
                "announcement_date": "string",
                "event_day0_date": "string",
                "year": 0,
                "quarter": 0,
                "filing_window": "string",
                "event_timing_assumed": true,
                "actual": 0,
                "estimate": 0,
                "surprise_label": "string",
                "market_model": {
                    "alpha": 0.0003,
                    "beta": 1.12,
                    "r_squared": 0.61,
                    "n_obs": 240,
                    "model_degenerate": false
                },
                "car": {
                    "property1": 0,
                    "property2": 0
                },
                "daily_ar": [
                    0
                ],
                "skip_reason": "string"
            }
        ],
        "aggregate": {
            "property1": {
                "window": [
                    0,
                    1
                ],
                "n": 8,
                "mean_car": 0,
                "t_stat": 0,
                "p_value": 0,
                "p_value_method": "student_t",
                "ci_low": 0,
                "ci_high": 0,
                "confidence": 0.95
            },
            "property2": {
                "window": [
                    0,
                    1
                ],
                "n": 8,
                "mean_car": 0,
                "t_stat": 0,
                "p_value": 0,
                "p_value_method": "student_t",
                "ci_low": 0,
                "ci_high": 0,
                "confidence": 0.95
            }
        },
        "source": "sugra_finance"
    },
    "meta": {
        "endpoint": "/api/v1/fred/us/cpi",
        "data_time": "2026-04-16T14:30:00Z",
        "response_time": "2026-04-16T14:30:12Z",
        "provider": "Sugra API v1.0.1",
        "data_age_days": 0,
        "source": "string",
        "attribution": "string",
        "fallback_used": true,
        "fallback_chain": [
            "string"
        ],
        "cached": true,
        "stale": true,
        "period": "string",
        "notes": "string"
    }
}

🟠401Unauthorized

🟠422Parameter Error

🟠429

🔴503Service Unavailable