Background

One of the most frequently cited market anomalies relates to a price drift after earnings announcements (also known as post-earnings announcement drift (PEAD)). Ball and Brown (1968) were the first to note that the cumulative abnormal returns of firms which report good (bad) news on their earnings are inclined to drift upwards (downwards) following their earnings announcements. Lending credence to this notion, Foster, Olsen and Shevlin (1984) consider a period of 60 days after earnings announcements and document robust empirical evidence regarding the existence of post-earnings announcement drifts.

As an explanation to the phenomenon of post-earnings announcement drifts,Joy, Litzenberger and McEnally (1977) suggest that market participants initially underreact to newly released information related to earnings and will gradually impound the correction of their previous beliefs into future stock prices. An alternative explanation suggests that a post-earnings announcement drift is a result of the misspecified capital asset pricing model (CAPM) employed to calculate abnormal returns (Ball, 1978). If this is the case, such abnormal returns are nothing more than fair compensation for risk that is priced but not captured by the CAPM. In light of the limitations of CAPM-related abnormal returns, Watts (1978) implements methods to mitigate the shortcomings of the CAPM and still document significant abnormal returns after earnings announcements. Providing support to the delayed response to information argument, Bernand and Thomas (1989, 1990) show that asset prices fail to capture information implied by the current earnings about future earnings.

This assignment is designed to let you investigate a post-earnings announcement drift using market data of Fortescue Metals Group Ltd (ASX Code: FMG).

Data

On the Canvas page for this course, there are two datasets you will need to use for this assignment. Their details are as follows:

1. FMG_QEarnings: This dataset contains data on quarterly earnings of Fortescue Metals Group Ltd over 20 quarters starting from June 30, 2014 to March 31, 2019.

1. ASX_DailyReturns: This dataset contains data on S&P/ASX200 daily returns over the period from April 1, 2019 to August 30, 2019.

Please note that you will need to collect additional data from FACTSET to complete this assignment. Please contact your course coordinator if you dont have access to FACTSET by the end of week 6.

Due to copyright issue, please make sure that two datasets mentioned above are not distributed outside the University of Newcastle.

Questions to Answer

1. Does thephenomenon of post-earnings announcement drifts contradict with the efficient market hypothesis ? Why ? (10 marks)

2. Use the datasetFMG_QEarnings and perform the following regression:

where denotes quarterly earnings ofFortescue Metals Group Ltd in quarter . is the constant term (i.e., the intercept) and is the error term. The regression should be performed using 20 quarters of earnings data.

Report your regression estimates in the table below: (4 marks)

3.

a. Based on your regression estimates, compute the expected quarterly earnings for the quarter ending March 31, 2019. (2 marks)

b. Compute the forecast error by applying the following model:

where is the forecast error regarding the quarterly earnings ofFortescue Metals Group Ltd for quarter (i.e., the quarter ending March 31, 2019). denotes the expected quarterly earnings ofFortescue Metals Group Ltd for the quarterending March 31, 2019.

(2 marks)

c. Looking at the value of , what is your expectation regarding the direction of the short-term movement in the stock price of the company ? (2 marks)

4.

1. Identify the release date of Fortescue Metals Groups production report for the quarter ending March 31, 2019. You can find such information from this page:https://www.marketindex.com.au/

	Release date
Fortescue Metals Groups production report for the quarter ending March 31, 2019.

(2 marks)

1. Collect data onFortescue Metals Groups daily closing stock price from one day before the release of the companys production report for the March quarter to sixty trading days after the report release date (i.e., to , where denotes the release date of the March quarter production report)

(2 marks)

1. Calculate daily stock returns ofFortescue Metals Group over the period from therelease date of the March quarter production report to sixty trading days after the report release date (i.e., to , where denotes the release date of the March quarter production report). Daily stock returns can be calculated using the following formula:

(2 marks)

1. Calculate daily abnormal returns ofFortescue Metals Group over the period from therelease date of the March quarter production report to sixty trading days after the report release date (i.e., to , where denotes the release date of the March quarter production report). Daily abnormal returns () can be calculated using the following formula:

where isS&P/ASX200 daily return on day . Please refer to the datasetASX_DailyReturns for data on S&P/ASX200 daily returns over the period from April 1, 2019 to July 31, 2019. (2 marks)

1. A cumulative daily abnormal return (CAR) is the sum ofdaily abnormal returns over a certain time period. For example, 5-day cumulative daily abnormal turn ( is the sum of daily abnormal returns over the 5-day period (i.e., ). Calculatecumulative daily abnormal return over the following periods:

=

=

=

=

=

=

=

=

=

=

=

=

(6 marks)

5. Looking at the pattern of cumulative daily abnormal returns in Question 4.e, is there any evidence of a post-earnings announcement drift ? Why ?

Also, does it support the efficient market hypothesis ? Why ?

(6 marks)

6. Discuss 3 behavioural biases that potentially drive the post-earnings announcement drift.

(10 marks)

CARt+1,t+30 CARt+1,t+5 d+60 FEt=QEtQEtE(QEt) CARt+1,t+45 FEt CARt+1,t+5=ARt+1+ARt+2+ARt+3+ARt+4+ARt+5 CARt+1,t+25 CARt+1,t+15 t Daily_Return \( _{t}=\frac{\text { Closing_Price }_{t}-\text { Closing_Price }_{t-1}}{\text { Closing_Price }_{t-1}} \times 100 \) t QEtQEt4=+(QEt1QEt5)+t d CARt+1,t+35 QEt d+60 d+60 ARt = ARt= Daily_Return tASX200_Return t CARt+1,t+50 CARt+1,t+60 d ASX200_Return CARt+1,t+5) CARt+1,t+20 CARt+1,t+55 CARt+1,t+10 d t E(QEt) d1 = CARt+1,t+40 d FEt CARt+1,t+30 CARt+1,t+5 d+60 FEt=QEtQEtE(QEt) CARt+1,t+45 FEt CARt+1,t+5=ARt+1+ARt+2+ARt+3+ARt+4+ARt+5 CARt+1,t+25 CARt+1,t+15 t Daily_Return \( _{t}=\frac{\text { Closing_Price }_{t}-\text { Closing_Price }_{t-1}}{\text { Closing_Price }_{t-1}} \times 100 \) t QEtQEt4=+(QEt1QEt5)+t d CARt+1,t+35 QEt d+60 d+60 ARt = ARt= Daily_Return tASX200_Return t CARt+1,t+50 CARt+1,t+60 d ASX200_Return CARt+1,t+5) CARt+1,t+20 CARt+1,t+55 CARt+1,t+10 d t E(QEt) d1 = CARt+1,t+40 d FEt