1. Collect daily data for market (MKT), size (SMB), value (HML FF) and risk-free rate (RF) for the whole sample for the Australian equity market from the AQR database:https://www.aqr.com/Insights/Datasets/Betting-Against-Beta-Equity-Factors-Daily

1. Ensure that non-trading days (if any) are excluded from the sample. You may identify non-trading days from http://www.asx.com.au/about/calendars.htm.

Required

The assignment should be provided in report format. This should include the following sections:

1. Title page (Your assignment cover sheet)
2. Abstract – around 100 words (5 marks)
3. Introduction – around 500 words (10 marks)

Provide some background information about the assignment topic and a concise review of the relevant literature. State the objectives of your study as stated at the start of this assignment document.

4. Methodology – around 500 words (20 marks)

A description of hypotheses, methods and data employed in the study.

First, explain the methodology relating to estimating returns and measures of risk.

Second, describe estimation procedure of single index model and multifactor model for each of the companies in the sample.

Third, explain the even study approach. The event window covers the event date (the declaration relating to COVID-19 outbreak and announcement of stimulus package), and 7 business days before and 7 business days after the event date (-7, -6, …, -1, 0, +1, … +6, +7).  The event date (day 0) refers to the relevant announcement date. The estimation period should cover 250 business days ending 50 days prior to the first event date. For example, if first event date is 30 January 2020, then estimation period should be between 21 November 2018 to 15 November 2019.

Consider the estimation period as the normal market state while time period between January 30 to May 30 as an extreme event period.

5. Empirical results – around 1300 words

5.1 Present descriptive statistics of returns for the normal market state and extreme period for individual companies and for weighted average industry portfolio. Provide a comparison. (10 marks)

5.2 Present the regression results for the single index model and Fama-French 3 factor model for individual companies and for weighted average industry portfolio with a concise interpretation. (15 marks)

[Hints: These models will provide you estimates of normal returns.]

5.3 Stock price reaction to COVID-19 and government’s policy response announcements (30 marks)

This section should present the following:

The average ARs over the event window for the selected firms’ stocks and statistical significance (at 5% level) of the average ARs. Interpretations of the results. Comment on the average ARs obtained from the Fama-French 3 factor model.

The average CARs over the event window for the selected firms’ stocks. (see example pp.355 of Bodie et al, 2017). Comment on the average CARs obtained from the Fama-French 3 factor model.

Interpretation of the above, paying separate attention to the period preceding the announcements (outbreak and policy responses), the time of the announcements and the period after the announcements.

A discussion of the implications of your results for market efficiency.

In each of the above cases, provide a comparison between the two industry sectors selected.

6. Limitations of the study – around 100 words (5 marks)
7. References – Not included in the word count (5 marks)

Solution

Introduction

In this report, we analyzed the two industries in which we transportation industry has been taken as that sector which had been adversely affected by the COVID 19 pandemic. While health care and services are the sectors which has been resilient in this pandemic. The best indicators to analyze these sectors is the trend of share prices of the companies which are lying in both sectors.

The ongoing COVID 19 pandemic is still visible globally and in Australia and it is significantly affecting number of industries. While there are some industries which are resilient prospering. Supply chains have been broken, the unemployment levels have increased, and in many nations the public health crisis has worsened. Despite unprecedented government stimulus packages being implemented, and interest rates falling to near zero, the pandemic still affects many industries.

Figure 1: Industries which is most impacted by COVID 19 (Kumar & Haydon, 2020)

According to the chart, the most impacted industry is the Airline sector. The Y-axis in the chart shows the probability of default which uses both the risk or price movement in the stock prices and asset volatility (Kumar & Haydon, 2020). The reasons for the decline in the airline industry are the closure of borders by the Australian government along with lack of demand for travelling during COVID 19 pandemic.

Figure 2: Chart also showing industries which are least impacted by COVID 19 (Kumar & Haydon, 2020)

From the above chart, it is shown that health care or services and industries were the least impacted sector during the COVID 19 pandemic. The probability of default was almost 0.4% for the health care and services which tells it has been more resilient.

The objective of this study is to analyze the returns and risks of the stock prices of the most impacted (Transportation) and least impacted (health care and services) industry in Australia. The two periods had been selected one which is pre-COVID 19 periods (21 November 2018 to 15 November 2019) and second is the extreme period of COVOD 19 (20 January 2020 to 30 May 2020). During this extreme period, there had been two events where the stock market had been impacted one is when the World health organization announced COVID 19 as a health emergency on 30th January 2020, and second is when World health organization announced COVID 19 as the pandemic and this event crashed the stock market badly (Anon., 2020).

From 21st  March 2020, the stock market shows the positive signs of improvements where the prices of most of the company were showing the increasing trend because it was the time when Australian government announced the AUD66.4 billion stimulus package and on 30th March 2020 the government also announced the AUD130 billion job keeper package which covers the 6 months’ salary of the workers (Anon., 2020).

Methodology

 The five companies which our group has selected from the most impacted industry (Transportation) are Qantas Airways (QAN), Virgin Australia (VAH), A2B Australia (A2B), Air New Zealand (AIZ) and Alliance aviation service limited (AQZ). For these companies, we calculated returns and risks for the two-period. Moreover, two regression models have calculated individual firms and as well as for the weighted portfolio of these five companies. The five companies which our group has selected for the least impacted industry during COVID 19 period are Nanosonics Limited (NAN), Sonic healthcare limited (SHL), Ansell Limited (ANN), Resmed Inc (RMD) and EBOS group limited (EBO). For the least impacted industry, we took an extreme period under consideration which is the only indicator to tell us whether the industry is resilient or has overall positive average returns.

For both industries, we calculated daily returns for each of the company for two different periods and then we calculated the average return for two particular periods. We also calculated the average portfolio return for the combined 5 companies in the most impacted sector.

Furthermore, the risk is also calculated for each of the firms in both the sector by calculating the standard deviation of the stock prices for each company in both sectors.

For the single index and fama-french 3 factor model we first collect the data of the market return which is in excess of risk-free return, we also collected data of the self-financing equity returns of small minus big and high minus low factors. We also collected the data of risk-free return which is the US treasury bill rate. Moreover, we also calculate how individual daily stock return is in excess of the risk-free rate for example Qantas airways daily return minus risk-free rate.  The single index model is the single regression model so in this model the dependent variable y is the (Rj-Rf) which is the individual stock return in excess of the risk-free rate and the independent variable is (Rm-Rf) which is the market return in excess of the risk-free rate. Fama French 3 factor model is the multiple regression model, so we added 2 other independent variables of Small minus Big (SMB) and High minus Low (HML) along with Rm-Rf which was included in the single-index model.

In this report we estimated the main events where the stock market has both positive and negative impact during the extreme period in both the industry. The stock market has the adverse impact when WHO announced COVID 19 as the pandemic on 12^th March 2020 as the following table which shows stock prices suggest that the transportation industry have the worse impact.