event-study

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: m_javidi@outlook.com

Tests

run yarn test`