Listing Activity on the Singapore Exchange’s Main and Second Boards
Abstract
Listing activity on the Singapore Exchange fell following the Global Financial Crisis and remains low. We model listing activity on the main and second boards over a twenty-four-year period, including post-financial crisis. Negative binomial models are developed for the number of companies listing and OLS models for the value of capital raised. Concentrating on macroeconomic and market factors, including average first day returns, we find that main board listing activity is influenced by business expectations, the exchange rate, and follows an overall upwards trend despite the financial crisis. Second board listing activity is less influenced by macroeconomic factors which provides the exchange with limited protection from the effects of macroeconomic downturns.
1. Introduction
Corporate listings are central to a stock exchange’s business and profitability. Listings create direct income though listing fees while the resulting trading activity generates transaction fees, data and other information products the exchange can sell. As the Singapore market matures major new listings are becoming rarer, forcing the exchange to seek new sources of revenue, Wright (2016). The three general factors affecting exchange listing activity are: microeconomic factors, macroeconomic factors, and exchange structure. Microeconomic factors are company specific such as the need for funds or owners seeking liquidity. Macroeconomic factors span a range of issues, from the opportunity costs of various sources of funds, the general health of the economy and stock market, and general business confidence. Exchange structures, such as relaxed listing rules and offering alternative listing routes, such as second board markets, mean the exchange can influence factors shaping its attractiveness to potential issuers.
We concentrate on the macro-level reasons for listing as well as differences between main board and second board listings. This differs from other research on Singapore IPOs by seeking an explanation for IPO activity and using underpricing as an independent rather than the dependent variable. It also contributes to the research on Singaporean IPOs by using data over a long period, including before and after the 2008 financial crisis.
Listing activity, measured by the number of listings and value of capital raised in IPOs each quarter, is modelled for the period 1993 to 2016. Dividing the data into main board and second board sub-samples shows that while some macroeconomic factors, specifically the exchange rate and business expectations, have an impact on listing activity on the main board they have less impact on the second board. Differences between the main and second board are important in the relatively quiet IPO market following the 2008 financial crisis as the second board acts as a hedge against the slowdown in listing activity.
2. The Singapore Stock Exchange
Initially established as the Malayan Exchange in 1960 the Malaysian and Singaporean exchanges remained a single entity, the Stock Exchange of Malaysia and Singapore, following Singapore’s independence in 1965. However, in 1973 the Malaysian Government pushed for greater autonomy including no longer supporting Malaysian Ringgit and Singaporean dollar interchangeability, leading to the exchange splitting into the Stock Exchange of Singapore (SES) and the Kuala Lumpur Stock Exchange (KLSE). In 1999 the SES merged with the Singapore International Monetary Exchange (SIMEX) which was followed by demutualisation and self-listing of the new entity, Singapore Exchange Limited (SGX), in November 2000.
The merger and move from SES to SGX coincided with changes to market regulation.
Singapore’s financial development can be divided into a series of regulatory phases, including a ‘regulatory phase’ from 1981 to 1998 and a ‘supervisory phase’ since 1999, Lee and Phoon (2014). Both phases are characterized by regulatory support for financial development. They differ in the regulatory phase encouraging free capital flow and increasing investor confidence and trust in the market. The supervisory phase has seen greater liberalization of the financial markets, greater foreign participation, the growth of real estate investment trusts (REITs) and fund management in general. The Singaporean markets have also benefited from citizens and residents being able to invest their Central Provident Fund retirement savings accounts in a wide range of financial products including unit trusts, exchange traded funds, property funds and direct investment in shares.
2.1. International Listings
The Singapore exchange’s need for new listings is magnified by its complicated relationship with the Malaysian exchange which has often taken steps to stop Malaysian companies dual listing in Singapore. In 1989 the KLSE instructed around 180 dual listed Malaysian companies to delist from the SES main board. The SES responded by introducing a new market, the Central Limit Order Book (CLOB) International, an OTC market for foreign shares, in 1990.
This allowed Malaysian firms to retain Singaporean trading while complying with KLSE rules.
Following the 1997 Asian financial crisis the KLSE banned the trading of shares on unapproved exchanges and Bank Negara Malaysia introduced forex controls in 1998. These steps resulted in the removal of Malaysian shares from CLOB International, Tan (2005). Many Singaporean investors in Malaysian companies were stuck with illiquid holdings which were eventually sold at a significant discount.
Any remaining Singaporean demand for trading Malaysian shares is now satisfied through the ASEAN trading link. Since 2012 Singaporean, Malaysian and Thai investors have been able to trade directly on the SGX, Bursa Malaysia1 and Stock Exchange of Thailand (SET) through participating brokers in their home countries. Exchanges in Indonesia, the Philippines and Vietnam are also members of the ASEAN exchanges group and are expected to join the trading link in the future. Investment in other international companies occurs through SGX Global Quote, which provides American Depositary Receipt (ADR), depositary receipt and depositary share trading.
1 In 2004 the Kuala Lumpur Stock Exchange was demutualised and renamed Bursa Malaysia.
2.2. The Second Board
The SGX operates a second board market which caters to smaller, fast growing, companies through reduced standards for initial listing and continuous disclosure. Originally the second board was the Stock Exchange of Singapore Dealing and Automated Quotation (SESDAQ) system which commenced operations with its first listing in early 1987. Major changes to the structure and regulation of the second board were announced in December 2007, along with a new name for the second board: Catalist.
For an exchange, operating a second board offers companies a pathway to main board listing.
Second boards provide both a low cost entry point for smaller companies and a training ground where companies can experience and learn about the disclosure expectations on public companies. Saunders and Lim (1990) noted early concerns that SESDAQ would be a dumping ground for risky and low-quality companies, but found that this not supported by evidence.
Second boards also provide an exchange with diversification of its business and reduce the effects of macroeconomic factors on overall market activity.
Table 1 SGX Listings at September 2016
MAIN BOARD Primary Listings 548
Secondary Listings 32
Total 580
CATALIST Primary Listings 183
Secondary Listings 0
Total 183
SGX Total Listings 763
SGX GLOBAL QUOTE Quoted Securities 47
Source: SGX Monthly Market Statistics Report, September 2016 Second boards attract smaller companies by providing them with growth financing. Often second board companies concentrate in growth industries. For example, Wang (2000) argued that SESDAQ could contribute to Singapore’s development as a regional financial centre by attracting developing technology companies and becoming a local NASDAQ. The second board has succeeded as a growth market for the exchange; the number of main board companies increased fourfold between 1990 and 2010, while there are ten times the number of second board companies in 2010 compared to 1990, albeit from a low base, Witt (2012).
However, it has not become dominated by technology companies. Both Witt (2012) and Kitano (2015) observed that Catalist lists companies from a wide range of industries. This includes
many firms registered in Singapore, but mainly operating in other countries. Table 1 provides 2016 data on the number of companies listed on each board.
SESDAQ initially operated with relaxed versions of the main board’s listing rules. Both main and second board companies needed to meet quantitative targets for financial history, profitability, shareholder spread and share allocation. Except for the spread and allocation rules the quantitative requirements were later removed in favour of qualitative tests. For example, companies without an operating or financial history could list provided they could show there was a valid planned use for the proceeds. SESDAQ companies needed to show there was an expectation that the company would be viable, profitable, have growth prospects and show that their accounts were not qualified in any way.
Some SESDAQ rules were stricter than their main board equivalent. For example restrictions on share sales by insiders following an IPO applied for twelve months on SESDAQ but only six months for main board listings, Chong and Ho (2007). This rule helped ensure insiders were committed to their companies and were less likely to use an IPO to make a quick profit.
Catalist listing rules generally followed the SESDAQ rules, except with the addition of sponsors. SESDAQ companies were allowed a transition period, around two years, to either comply with Catalist rules, transfer to the main board or delist. The distinction between main board and Catalist companies is greater following changes to main board listing rules in 2012, which increased the minimum size and profitability requirements, arguably to push smaller companies onto Catalist.
Catalist listings are approved by a financial institution or sponsor similar to the system used by the London Stock Exchange’s Alternative Investment Market (AIM), Kitano (2015). Mizuno and Tabner (2008) compared Catalist to two similar Asian second boards: Hong Kong’s Growth and Enterprise Market (GEM) and Japan’s ‘Mothers’ and found Catalist has many similarities with AIM and is closer than GEM or Mothers in its resemblance to AIM.
3. Singapore IPO Research
Research on Singaporean IPOs generally follows that undertaken in other countries. First day underpricing and longer-term performance has been measured and modelled using common financial variables. Where sufficient data is available, researchers will compare the main board and second board market sectors, sometimes inferring lower quality investments or less informed investing in the second board market. However, there remain some relatively under- researched questions about the Singaporean market. For example, Yong (2007) identified the relationship between the number of IPOs and first-day returns as an issue generally
underexplored in Asian markets. This is an interesting omission that should be rectified, especially as some researchers, such as Chong and Ho (2007) and Van der Zahn (2008) are using associated concepts like ‘hot-markets’ without an established understanding of what constitutes a hot IPO market in Singapore.
Companies undertaking an IPO in Singapore usually determine the price of their offering through consulting an underwriter. Traditionally offers are fixed price, although a tender system is sometimes used. Companies using the tender system tend to have mixed offers, fixed price on one tranche and tender on the second tranche with the fixed price component used to ensure sufficient shareholder spread. Both main and second board companies are required to produce a prospectus containing details of the offering. For listings on the main board, the SGX vets and approves prospectus contents. For listings on Catalist an approved sponsor undertakes this task.
3.1. Investor Demand and Underpricing
Early studies of SESDAQ underpricing found little evidence that mispricing was any different from the main board. Saunders and Lim (1990) examined the first nine SESDAQ companies, where their small sample revealed no statistically significant difference between main board and SESDAQ listings. No evidence of statistical differences in underpricing was supported by Hameed and Lim (1998), despite perceiving the choice between main board and SESDAQ listing as an indicator of investment quality. As tender offers were more common for main board listings their classification suggested that the tender system suited better quality offers.
IPO research often examines microeconomic factors as determinants of mispricing. Firth and Liau-Tan (1997) concentrated on the role of microeconomic and deal factors in determining first day returns, their study included the stock market index’s three-month return as the sole macroeconomic factor. Unlike the other IPO research reviewed here, which measures underpricing as market value relative to offer price, they measured undervaluation as market value relative to net asset value. Retained ownership, profit margins, company age and stock market returns were all positively related to undervaluation. Less undervalued companies had less price volatility in the post listing period. More recently Van der Zahn (2008) examined a more specific determinant of underpricing; audit committee characteristics. He found that a committee with members holding accounting qualifications was associated with greater underpricing, while experience and diversity variables were not significant. It is not clear whether the sample includes SESDAQ IPOs, although given the sample size it is likely they were included. The one-month change in market index was used as a control variable, but was not statistically significant.
Differences between IPO regulations and underpricing in the United States, Hong Kong and Singapore were examined by Wang (2008). This showed that Singapore and the United States had comparable levels of underpricing once auditor and underwriter quality and other common underpricing explanations were accounted for. SESDAQ underpricing was comparable to NASDAQ, but greater than in Hong Kong’s GEM market.
Longer term performance, over the three years following listing, was examined by Tan and Mahmood (1993). However, due to the early sample period and different listing rules they excluded SESDAQ companies from their sample. They found IPO companies tended to underperform seasoned companies.
An interesting feature of prior Singapore IPO research is the examination of investor demand though subscription rates and distinguishing between demand from large and small investors.
For example Lee, et al. (1999) found that investors applying for larger allocations show some ability to select better IPOs in the short term, but not for longer term returns. It is not clear if any SESDAQ firms were included in their sample, but given the early sample period it is unlikely that many, if any, observations are SESDAQ companies. Differences between main board and SESDAQ IPO subscription rates were examined by Tan, et al. (1999). Using a sample from 1987 to 1993 the data for each market is not directly comparable as the main board included both fixed price and tender offers, while all SESDAQ IPOs during that period were fixed price. They found underpricing was common on average and SESDAQ offers had both lower subscription rates and lower underpricing. With a later sample, from 1998 to 2000, Reber and Fong (2006) found the opposite with SESDAQ IPOs being more oversubscribed than main board IPOs. Subscription levels were positively related to the level of mispricing which supports a winner’s curse, see Rock (1986), interpretation.
Large and small investors respond to IPO details differently. Eng and Aw (2000) found offers denominated in Singapore dollars attracted strong demand from large investors, and weak demand from small investors. Demand from large investors was also positively related to size and earnings yield and negatively related to the book to market ratio. Again, small investors had opposite preferences for earnings yield and size. Their sample included both main board and SESDAQ companies, but no comparison of groups was undertaken. In contrast Ho (2015) did not find a link between company factors and subscription rates, but showing some support for Lee, et al. (1999), did find that high subscription rates were not associated with post listing performance.
3.2. Hot Markets and Listing Activity
Research on hot IPO markets started with Ibbotson and Jaffe (1975), who define hot markets as those where average underpricing or aftermarket performance is abnormally high. In contrast, later researchers saw hot markets as periods with more IPOs than normal, or both high numbers of IPOs and above average underpricing. For example Loughran, et al. (1994) saw hot markets as a function of the number of IPOs, associating hot markets with the business cycle and a high market index. Strong stages of the business cycle were associated with more companies needing access to funds for growth, if the market index was high these companies would seek listing to take advantage of an overvalued market. Similarly Lowry (2003) argued that IPO volume reflects changing needs for capital by firms and investor optimism.
The combination of high initial returns with more companies than average listing raises issues about appropriate model specifications. While broadly agreeing with the arguments in Loughran, et al. (1994), Rees (1997) took issue with aspects of their methodology. Rees’
models of IPO activity in the United Kingdom found IPO value and the number of IPOs was related to index value and the opening of a second board market. The number of IPOs was also related to a business cycle indicator. Linking initial returns and IPO volume Lowry and Schwert (2002) found these were autocorrelated with a significant positive relation between average initial returns and subsequent volume. Unlike Loughran, et al. (1994) they did not see this as rational activity as it meant more companies were listing when underpricing was high, leaving too much money on the table. That this activity is irrational is reinforced by the fact that long book building periods in the United States, between 2-4 months, allowed partial learning by companies seeking listings during a hot market. These companies should take the opportunity to raise their issue price when there is high demand for IPO issues.
A behavioural explanation for this apparently irrational activity was provided by Loughran and Ritter (2002). They argued that companies could take advantage of hot markets by raising their offer price but still allow some underpricing. IPO investors still pay too much, relative to the company’s true value, but do not feel bad about it as their shares have still increased in value. They also note that overlapping offer periods limit the ability to learn from public information (recent IPOs), explaining the autocorrelation.
Figure 1 Second board listing activity and average first day returns 1993Q1-2016Q4
Figure 2 Main board listing activity and average first day returns 1993Q1-2016Q4
Inspection of both second board, Figure 1, and main board quarterly listings, Figure 2, show IPO volume picking up in the late 1990s before dropping around 2000 and 2001. This period is interesting as there was the boom, and bust, in technology companies but is also straight after the Asian financial crisis. There are hot periods on the main board, with both high first day returns and listing volumes, it is not sustained through the whole period. The hot main board is mostly unmatched by the second board. The dip in listings in 2000 and 2001 does
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2
0 2 4 6 8 10 12 14 16
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Average First Day Return
Number of Listings
Quarter
Second Board Listings Second Board Returns
-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
0 5 10 15 20 25
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Average First Day Return
Number of Listings
Quarter
Main Board Listings Main Board Returns
not last with strong IPO numbers returning and maintained until the 2008 global financial crisis.
The post crisis period is relatively cold in both markets. A relationship between average returns and IPO numbers is not apparent in these graphs.
4. Methodology
The number and value of listings on the Singapore Stock Exchange is modelled as a function of; average first day returns, four macroeconomic variables, a second board dummy variable and a trend variable. Quarterly data is used, with a sample period from the beginning of 1993 to the end of 2016. Over this period 614 companies listed on the main board and 344 on the second board, transfer between boards are not included. Around $85 billion of new capital was raised on the main board and $11 billion on the second board.
The models broadly follow Rees (1997), but adapted for the Singapore economy. Unlike Rees we do not include a composite index. While the Singapore Department of Statistics does calculate a Composite Leading Index, it is strongly correlated with the other independent variables, so it is excluded to reduce the risk of multicollinearity. We add first day returns as this factor is often associated with hot and cold markets and we add the Singaporean – United States exchange rates due to its importance in Singapore.
4.1. Dependent Variables
The two main dependent variables are listing count and size, for descriptive statistics see Table 2. Count is the number of new primary listings each quarter. There are three variations of this metric; main board listings (MainCount), second board listings (SecCount) and total listings (TotCount). Most listings are companies but investment funds, trusts and REITs are also included. Both capital raisings and compliance listings, where no new investment is sought, are included in IPO counts. Size is the total value of new listings each quarter, in millions of Singapore dollars. Again, this is calculated for main board listings (MainSize), second board listings (SecSize) and total listings (TotSize). Like Rees (1997) we tested the suitability of poisson and negative binomial models for the count data, finding negative binomial models were most appropriate. Size is modelled using multivariate ordinary least squares (OLS) regression.
Table 2 Dependent Variable Descriptive Statistics
MainSize SecSize TotSize MainCount SecCount TotCount
Mean $890.64m $119.41m $1010.05m 6.40 3.58 9.98
Standard Deviation $1064.26m $102.67m $1094.20m 5.08 2.66 6.48
Minimum $0.00m $0.00m $0.00m 0.00 0.00 1.00
Maximum $6497.25m $474.36m $6959.53m 22.00 14.00 31.00
Sample Size 96 96 96 96 96 96
MainSize, SecSize and TotSize is the amount of new capital raised each quarter on the main board, second board and combined boards respectively. MainCount, SecCount and TotCount are the number of companies listing each quarter on the main board, second board and combined boards respectively. Transfers between main and second boards are not included.
4.2. Independent Variables
The independent variables cover market, macroeconomic, and trend factors. The main market factor is Return. Return is the simple average of raw first day returns for all IPOs during the quarter. This is calculated for main board listings (MainReturn), second board listings (SecReturn) and total listings (TotReturn). Catalist is a dummy variable coded 1 for periods in which Catalist is the second board market, and coded 0 in periods were the SESDAQ was the second board. However, as the Catalist period largely overlaps with the global financial crisis and its aftermath care must be taken when interpreting this variable.
Macroeconomic variables are used to measure stockmarket performance, business expectations, and the costs of funds. STI is the FTSE Straits Times Index, which is the benchmark index for the Singapore stock market. Expect measures business expectations, for the following six months, in the manufacturing sector as measured by the Department of Statistics Singapore.2 PrimeRate is the average prime lending rate to individuals and businesses as measured and reported by the Monetary Authority of Singapore. SGDUSD is the Singapore dollar to United States dollar exchange rate as a direct quote from the Singaporean perspective, giving the Singapore dollar price of one US dollar. As Singapore is a small open economy, highly dependent on trade, the exchange rate is a very important economic indicator in Singapore. Furthermore, unlike many comparable economies, the Monetary Authority of Singapore uses exchange rate manipulation as its primary monetary policy instrument. For a fuller discussion of exchange rates in Singapore see Thia (2010).
2 The Singapore Department of Statistics also provides an index of business expectations in the services sector, but it is not used in this study as it is not available for the full sample period.
A trend variable, Trend, counts periods from 1 to 96 and accounts for general growth in the market unexplained by other variables. All Independent variables except for Trend and Catalist are lagged one quarter.
Table 3 Independent Variable Descriptive Statistics
MainReturn SecReturn TotReturn STI SGDUSD PrimeRate Expect
Mean 0.12 0.21 0.17 2376.49 1.51 5.63 7.04
Standard Deviation 0.19 0.23 0.22 651.82 0.18 0.52 17.14
Minimum -0.21 -0.26 -0.83 939.65 1.22 5.30 -57.00
Maximum 0.76 1.01 0.77 3706.23 1.85 7.79 39.00
Sample Size 96 96 96 96 96 96 96
Desciptive statistics for the continuous independent variables. MainReturn, SecReturn and TotReturn are the average first day returns for companies raising funds on the main board, second board and combined boards respectively. STI is the FTSE Straits Times Index, SGDUSD is the Singapore dollar price of United States dollars, PrimeRate is average prime lending rate and Expect measures
business expectations in the manufacturing sector.
Descriptive statistics for the independent variables are provided in Table 3 and their correlations in Table 4. Most of the high correlations are between the various return measures, as each is used in a separate model these correlations are not an issue. There are two other high correlations which require noting; the negative correlations between the Straits Times Index and the exchange rate and prime interest rate.
Table 4 Independent Variable Correlations
MainReturn SecReturn TotReturn STI SGDUSD PrimeRate Expect MainReturn 1
SecReturn 0.507 1
TotReturn 0.661 0.746 1
STI -0.001 -0.056 0.061 1
SGDUSD 0.195 0.154 0.065 -0.780 1
PrimeRate -0.098 0.030 -0.085 -0.439 0.137 1
Expect 0.410 0.246 0.297 0.173 0.041 -0.048 1
Correlation matrix for the continuous independent variables. Variables are as described in Table 3.
5. Results
Estimated models of the number of companies listed are in Table 5, and for value of capital raised in Table 6. The negative binomial models in Table 5 have significant LR-statistics, comparable to F-statistics in OLS regression, indicating the models are better than a null model overall. Pseudo R-squares are reasonable in the main board and full sample models but low in the weaker second board model. The OLS models in Table 6 have significant F- statistics and R-squares for the main board and combined board models. However, the second
board model has a low R-squared and the F-statistic indicates the model is not significantly better than a null model.
Table 5 Models of Quarterly Number of Companies Listed
Main Board Second Board Combined Boards
Estimate P-value Estimate P-value Estimate P-value (Intercept) -1.59614 0.3840 -2.40875 0.1964 -1.01662 0.4730
MainReturn 0.37009 0.1678
SecReturn 0.24140 0.4508
TotReturn 0.06319 0.7977
STI 0.39266 0.5101 -0.09265 0.8698 0.09707 0.8247
Expect 0.02579 0.0000 *** 0.00297 0.5364 0.01744 0.0000 ***
PrimeRate 0.03903 0.8288 0.01446 0.9332 0.01079 0.9370 SGDUSD 1.65458 0.0118 * 2.03589 0.0052 ** 1.76013 0.0008 ***
Catalist -0.26221 0.4469 -0.18493 0.6309 -0.29416 0.2879 Trend 0.00949 0.0381 * 0.00952 0.0631 0.00985 0.0078 **
Pseudo R2 0.43333 0.21573 0.44082
LR Statistic 54.2832 23.0481 55.6626
P-value 0.0000 *** 0.0017 *** 0.0000 ***
Sample Size 96 96 96
Negative binomial models of the number of companies listed on the Singapore stock exchange main board, second board and combined boards. Independent variables are as described in Table 3 and the text.
Two macroeconomic variables and the trend variable are significant in the main board and combined board models. As expected business expectations have a positive relationship with the number of listings and the amount of funds raised. The exchange rate variable also has a positive relationship, indicating a weaker Singapore dollar, corresponds to more new listings.
This could be due to companies preferring equity capital to debt when a weaker Singapore dollar makes repaying offshore borrowing more expensive, however we do not find the prime borrowing rate is significant. The trend variable is positively related to listing activity, indicating a general increase over time despite the fall in activity after the 2008 financial crisis.
Unlike the main board models, second board models are statistically weak and have few significant results. The one exception is the exchange rate variable showing the same relationship with the number of listings found in the main board and combined boards models.
Finding that the second board is not subject to the same macroeconomic factors as the main board indicates a benefit to the SGX in operating a second board. When macroeconomic indicators suggest a reduction in main board listing activity the SGX can expect that the second board will not necessarily be subject to the same downturn. This is evident in the data from 2009 to 2016. Whereas over the full sample period the main board has almost double
the number of listings as the second board, during the post-financial crisis there have been almost as many second board listings (90) as on the main board (107).
Table 6 Models of Quarterly Value of Capital Raised Through IPOs
Main Board Second Board Combined Boards
Estimate P-value Estimate P-value Estimate P-value (Intercept) 5.93363 0.0362 * 1.67982 0.5219 6.03203 0.0322 * MainReturn 0.55979 0.2009
SecReturn 0.38974 0.4043
TotReturn 0.47417 0.3576
STI 1.11316 0.2294 -1.05427 0.2100 0.06502 0.9394
Expect 0.02838 0.0004 *** 0.00561 0.4240 0.02805 0.0002 ***
PrimeRate -0.31556 0.2394 0.05301 0.8301 -0.28081 0.2885
SGDUSD 0.55110 0.5972 1.10967 0.2788 0.46918 0.6572
Catalist -0.41257 0.4706 -0.33414 0.5477 -0.88300 0.1239
Trend 0.02485 0.0023 ** 0.01832 0.0129 * 0.02662 0.0006 ***
Adj. R² 0.3458 0.06272 0.3107
F-stat. 7.494 1.822 7.052
P-value 0.0000 *** 0.0945 0.0000 ***
Sample Size 87 87 95
Ordinary least squares models of the value of funds raised (in millions of Singapore dollars) by companies listing on the Singapore stock exchange main board, second board and combined boards. Data is quarterly and quarters with no capital raised excluded from the sample. Independent variables are as described in Table 3 and the text.
There is no evidence of the relationship between initial returns and listing activity usually associated with hot and cold markets. While there are clear periods where listing activity is relatively higher or lower than other periods the Singapore market does not appear to be subject to the same concerns about managerial irrationality signalled by companies seeking listing when underpricing is common.
6. Conclusion
The Singapore exchange has faced many challenges; from Singapore’s split from Malaysia restricting the exchange’s ability to list companies from that country, to various international financial crises. One response to these challenges has been to operate both main board and second board markets, allowing smaller growth companies easier access to financial markets and reducing the effect macroeconomic factors have on listing activity. However, in the difficult period after the 2008 financial crisis both the main and second board IPO markets have been cold.
Two main contributions are made to the literature on Singapore IPOs. Firstly, most prior research has examined the role of microeconomic, or company specific, factors on IPO activity so macroeconomic factors have been largely ignored until now. Secondly, there has been very little examination of the Singapore IPO market after the financial crisis. The fact that listing activity is down should not mean it is not worth examination, instead it indicates that this is a topic needing attention. Further research should also be undertaken on companies moving between the main and second board, although such changes were excluded from this study there is enough activity to justify research, in collecting the data for this study movements in both directions were recorded but as expected most movement is from the second board to the main.
Despite the slowdown in listing activity following the 2008 financial crisis there has been an overall trend towards increased listing activity over the full sample period, 1993 to 2016. The main macroeconomic factors determining main board listings are the exchange rate and business expectations, but second board listings are largely independent of macroeconomic factors. Having a second board market driven by different factors provides the Singapore exchange with some protection from the effects of macroeconomic forces on listing activity.
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