Event Study

A NPM package for conducting event studies.

Installation

Run npm install event-study --save.

Features

• Market Model
  • normalReturn
  • abnormalReturn
  • statisticalTest
  • significantTest
  • CARS
  • newsType (Good, Bad, neutral)
  • unmatchedTradingDayStrategy
• Constant Mean Model
  • normalReturn
  • abnormalReturn
  • statisticalTest
  • significantTest
  • CARS
  • newsType (Good, Bad, neutral)
  • unmatchedTradingDayStrategy

Terminology

calendar: array of event dates.

stock: stock prices.

market: market prices.

timeline: event study time line that includes: Estimation period (T0T1), Pre-announcement window (T1E), Post-announcement window (ET2), Post-event period (T2T3).

dateColumn: which field in stock or market prices has date format.

operationColumn: which field in stock or market price you want to perform arithmetic operations (default is Close).

unmatchedTradingDayStrategy: if the event date does not exist we can select next trading day or previous trading day. available options are ( NEXT_TRADING_DAY , PREV_TRADING_DAY, SKIP). skip is the default.

How to use

Market Model

Option one: provide information for each date separately.

import { MarketModel } from 'event-study'

const data = {
    calendar: [{
            date: '2016-12-01',
            stock: [{ Date: '2016-12-01', Close: 200 }],
            market: [{ Date: '2016-12-01', Close: 10 }],
            timeline: {
                T0T1: 2,
                T1E: 2,
                ET2: 2,
                T2T3: 2
            },
            dateColumn: 'Date',
            operationColumn: 'Close',
            unmatchedTradingDayStrategy: 'NEXT_TRADING_DAY'
    }]
}

marketModel(data)

Option two: provide information globally.

In this method, every date will use same global information.

import { MarketModel } from 'event-study'

const data = {
    calendar: [
        { date: '2016-12-01' }
    ],
    stock: [{ Date: '2016-12-01', Close: 200 }],
    market: [{ Date: '2016-12-01', Close: 10 }],
    timeline: {
        T0T1: 2,
        T1E: 2,
        ET2: 2,
        T2T3: 2
    },
    dateColumn: 'Date',
    operationColumn: 'Close',
    unmatchedTradingDayStrategy: 'PREV_TRADING_DAY'
}

marketModel(data)

Option three: combine information.

In this method for date '2016-12-01' the global information(stock) will be ignored.

import { MarketModel } from 'event-study'

const data = {
    calendar: [
        { date: '2016-12-01', stock: [{ Date: '2016-12-01', Close: 30 }] }
    ],
    stock: [{ Date: '2016-12-01', Close: 200 }],
    market: [{ Date: '2016-12-01', Close: 10 }],
    timeline: {
        T0T1: 2,
        T1E: 2,
        ET2: 2,
        T2T3: 2
    },
    dateColumn: 'Date',
    operationColumn: 'Close',
    unmatchedTradingDayStrategy: 'SKIP'
}

marketModel(data)

Constant Mean Model

import { MeanModel } from 'event-study'

const data = {
    calendar: [
        { date: '2016-12-01', stock: [{ Date: '2016-12-01', Close: 30 }] }
    ],
    timeline: {
        T0T1: 2,
        T1E: 2,
        ET2: 2,
        T2T3: 2
    },
    dateColumn: 'Date',
    operationColumn: 'Close'
}

marketModel(data)

Analyse results

Results will be in below format:

Result contains information just for EVENT PERIOD (T1E + ET2), for example in above scenario the abnormal return will be an array with 4 item because T1E is 2 and ET2 is 2.

[{
    date, // event date
    normalReturn,
    abnormalReturn,
    statisticalTest, 
    significantTest,
    CARS,
    newsType // 1 means good news, -1 bad news, 0 neutral
}]

Contributing

If you saw any issue or have any recommendation you have 2 option to follow and I will be grateful:

Option one: Fill an issue in github account.

Option two: Send an Email to: [email protected]

Tests

run yarn test