CHAPTER 1 INTRODUCTION
4.2 Method of Data Collection
The population in this research includes all companies that were listed in the Singapore Exchange and Bursa Malaysia from 2007 to 2016. Using a purposive sampling method, a total sample of 99 companies from Singapore Exchange (46) and Bursa Malaysia (53) will be used in this research (see Appendix A). The included companies belong to various industries namely energy, materials, industrials, consumer discretionary, consumer staples, healthcare, financials, information technology, communications services, utilities and real estate industry (See
Appendix A). The samples represent the top three (3) and bottom three (3) performing listed companies in SGX and KLSE per year. However, due to the insufficiency of data by some companies, these companies were replaced by the following top or bottom performing listed companies in SGX or KLSE per year. The performances of the companies were assessed using the percentage change of the IPO stock considering its first-day opening price and third-year closing price. This method was chosen by the researchers as this can represent the performance of the company and its progress in the stock exchange until its third year of listing.
The consistency in terms of obtaining the third year closing price makes the method a fair and uniform measurement. Furthermore, the researchers believe that this mode of sampling can represent both extremes of the population. Thus, underpricing in both extremes will be obtained to represent the population in this research.
Quantitative and minimal qualitative secondary data are used in this study to account for the variables that are taken into consideration. The selected IPOs must satisfy the following criteria:
1. All necessary information needed by the researchers must be available on the Singapore Exchange website (www.sgx.com), Bursa Malaysia website (www.bursamalaysia.com), Refinitiv Eikon, or in the company’s respective IPO prospectus.
2. All samples that will be utilized should have all the necessary information in order to maintain uniformity in this research.
Underpricing is considered as the dependent variable in the first model whereas it is the independent variable for the second model of this research. Underpricing will be measured using the total return (raw return) for stock “ at the end of the first trading day see Equation 1:
,1
=
,0
− ,1 ,0
(1) Where:
total first-day return (raw return) for stock “ ”
,1=
the price of stock “ ” at the close of the first trading day
,1=
offering price of stock “ ”
,0=
Furthermore, the level of underpricing is adjusted based on the market index using the market-adjusted abnormal return model (MAAR). This methodology is consistent with a study done by Sohail and Nasr (2007) to measure underpricing in Pakistan. This can be calculated as seen in Equation 2:
00 ( )
,1 = 1 1+ 1+ ,1
,1 − 1
(2) Where:
market-adjusted abnormal return for stock “ ” on the first trading day
,1=
,1
=
,0
− ,1 ,0
(3) Where:
equivalent market return on the first trading day
,1=
market index value at the close of the first trading day
,1=
market index value on the offer day of the appropriate stock
,0=
Meanwhile, the dependent variable in the second model in the long-run performance of IPOs. The researchers will use the weekly cumulative abnormal returns (CAR) for each IPO stock and generate its average CAR in order to regress it against the level of underpricing. CAR will be measured weekly in order to capture the volatility of the market. To monitor and compare the long-run performance, the researchers will compute for the average CAR until year 1 and until year 3 from its offering date. The cumulative market-adjusted long-run performance will be measured by Equation 6:
(1) Computing for the market-adjusted return
= −
(4) Where:
market-adjusted return for stock “ ” in the week
=
return for a firm “ ” in the trading week
=
market return on the trading week
=
(2) Computing for the average market-adjusted return of each IPO stock
=
1∑
=1 ,
(5) Where:
arithmetic average of the market-adjusted returns
=
(3) Computing for the cumulative market-adjusted long-run performance
,
= ∑
=
(6) Where:
cumulative abnormal return from event month “ ” to event month “ ”
, =
summation of the average market-adjusted returns
∑
=
=
This measurement is consistent with a study done by Ritter (1991) when he likewise assessed the long-run performance of IPOs.
Lastly, Table 12 shows a summary of the potential determinants of underpricing including their description and/or measurement. The data for these variables will be obtained from the sources mentioned above: Singapore Exchange website (www.sgx.com), Bursa Malaysia website (www.bursamalaysia.com), Refinitiv Eikon, or in the company’s respective IPO prospectus. To measure the firm industry, dummy variables will be allocated to represent the various industries in SGX and KLSE.
Table 12.
Determinants of Underpricing (IV) Description
Ownership structure Measured by the annual free float shares percentage during its IPO year Firm size Measured by the total assets in the last year prior to the year of IPO Firm age Determined by the number of years from incorporation to listing year Offering price Price of a stock set by the underwriter during the IPO process
(in USD/share)
Offering size Amount to be raised based on final IPO prospectus (in USD)
Financial leverage Debt-to-equity ratio during its IPO year (Firm’s total liabilities ÷ Shareholders’ equity)
Return on Equity (ROE) Measured by the Return on Equity formula during its IPO year (Net income ÷ Shareholders’ equity)
Energy industry (qv) Companies belonging to the energy industry based on the Global Industry Classification Standard (GICS)
Materials industry (qv) Companies belonging to the materials industry based on the Global Industry Classification Standard (GICS)
Industrials industry (qv) Companies belonging to the industrials industry based on the Global Industry Classification Standard (GICS)
Consumer discretionary industry (qv) Companies belonging to the consumer discretionary industry based on the Global Industry Classification Standard (GICS)
Consumer staples industry (qv) Companies belonging to the consumer staples industry based on the Global Industry Classification Standard (GICS)
Healthcare industry (qv) Companies belonging to the healthcare industry based on the Global Industry Classification Standard (GICS)
Financials industry (qv) Companies belonging to the financials industry based on the Global Industry Classification Standard (GICS)
Information technology industry (qv) Companies belonging to the information technology industry based on the Global Industry Classification Standard (GICS)
Communications services industry
(qv) Companies belonging to the communications services industry based on the Global Industry Classification Standard (GICS)
Utilities industry (qv) Companies belonging to the utility industry based on the Global Industry Classification Standard (GICS)
Real estate industry (qv) Companies belonging to the real estate industry based on the Global Industry