NYSEARCA: IPO vs NYSEARCA: SPY, NYSEARCA: VOO, NYSEARCA: IVV, MUTF: SWPPX
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This report evaluates whether investing in initial public offerings is better than investing in the market at large. The returns and risks of the two are analyzed to evaluate whether one investing strategy is better than the other. The report utilizes 360 weeks of data (90 months | 7.5 years) for this analysis.
Some analysts and researchers have argued that investing in IPOs is more profitable relative to investment in listed shares more generally. For example, Ballard, R. (2015) stated that there were 30 IPOs on the London stock exchange in 2014, and that an investor that had taken up some of these shares would have made a return of 12.4%, while generally listed shares made a return of -2.0% in the same period (Source).
(While data does support that these stocks are highly likely to rise on the first day of trading (See report for in-depth insights), this report examines whether the ETFs in this space perform better than the overall market)
While many opinions and viewpoints may be in circulation, this report analyzes whether the data supports this claim. An analysis is also included in this report.
One of the most prominent ETFs in this space, the Renaissance IPO-ETF, is used in this analysis.
For this analysis, hypothesis testing has been conducted; a paired comparison test is done to evaluate the returns, and an F test has been conducted to evaluate the risk. (calculations and data attached at the end of the report).
For the period analyzed, the average results of the two in question were as follows:
The initial analysis does present a favorable case for the IPO ETF; however, further testing reveals a different case:
Hypothesis testing reveals that the returns do not exhibit any statistically significant difference. i.e., the two subjects have comparable monthly returns, statistically.
Risk is another story, nonetheless; the risk of IPO-ETF, as measured by variance (sample standard deviation^2), is 183% higher than that of S&P 500; the F test similarly reveals that the risk of the two is not similar.
If you invested in the IPO-ETF, for the time series analyzed, you would have earned a return 39% higher than the S&P 500; however, you would also be exposed to substantially higher, statistically significant risk.
The Beta of Renaissance IPO ETF in relation to the S&P 500 stands at 1.252, meaning a ± 1% change in the overall market would result in a ± 1.252% change in this ETF.
Why are IPOs generally considered a good investment?
The underwriters of IPOs have a strong incentive to choose a low price for the new issue so that the book building (securing subscribers for the new issue) would be an easier task, and they wouldn’t have to hold/buy a significant number of shares due to under subscription; also, to ensure that their responsibility of making the market (i.e., providing liquidity for the issue) and supporting the price of the shares in the secondary market, months after the issue, isn’t financially burdening due to a selling pressure, if it arises.
If a significant selling pressure arises after the issue in the secondary market, the investment bank (working as the underwriter) would have to support the price in the months after the issue, as (or if) defined in the prospectus of the issue. Doing so would significantly cost the bank, and the bank may lose more than the usual rate of commissions earned on such deals of about 7%.
The bank also has an incentive to price the issue lower so the bank can allocate valuable shares to its clients to benefit them, and thus, benefit itself indirectly. A higher issue price would be incompatible with such an objective.
First-time issuers also accept a slightly lower price because if the offering is priced high, and the new issue is undersubscribed, it sends negative information to the market about the company and its future prospects. If the issue is priced lower, and the share price rises in the secondary markets, the firm’s management, business model, and future prospects may be viewed favorably. Appreciation of share price may also directly benefit management, as they may be awarded significant performance benefits etc., and receive recognition regarding their efficient performance by peers/analysts outside the business.
All the factors mentioned above usually tend to lower initial offering prices; prices often rise in secondary markets immediately following an IPO due to the factors explained in this section. Therefore, many market participants regard IPOs as a good investment.
What's the final verdict, then? Is it a buy?
The most important factor that we must consider is the business cycle (the cycles of expansion and contraction of the broader economy), and its impact on new share offerings. Inclusion of risk in such consideration would also aid critically.
During the peak of the cycle, multiples for stock valuations are usually at the highest, and this means that firms issuing new share would already be demanding a premium price on the issue, as the multiples on revenue, EBITDA, etc., would already be high across the board in the market.
In such an environment acquiring this ETF wouldn't be a recommended choice, as usually, shares issued at a high multiple, and thus price, at the peak of the cycle, would decline precipitously as the economy and the market enters the stagnation and then decline stage.
With an average market decline standing at about 25.5% (see report for further information), and the beta of this ETF standing at 1.252, it would fall approximately 32% in an average market crash/decline, a very substantial figure, indeed. Understanding the stage of the cycle is crucial for this investment, therefore.
Alternatively, after a decline or crash, or during the market troughs, the multiple on EBITDA or revenues, etc., for stocks is low. Due to multiples on stocks being low in such market conditions, new issues are also issued at reasonable prices, unlike new issues at the peak of the market.
New issues or the IPO ETF acquired in such conditions should perform better than the overall economy as the market marches upwards and multiples on stocks increase. As the market starts to move up from the troughs, moves up, say, 85%, as it did from the 2008 troughs till 2011 peaks, this ETF, as per its beta, would increase by 106.4%, a very handsome return. This viewpoint is also aligned with the point presented by Ballard, R. (2015), as the period assessed by Ballard is also after the troughs of the market in 2008 and a relative decline in 2011.
Put simply, acquiring an IPO ETF, given its high risk, would be a viable strategy if the market is in the early, modest recovery stage of the business cycle, and upward price movement is very likely; for example, after a recent market crash (1-2 months after the troughs of the market). As the market moves upwards, with a high beta, this ETF should yield a handsome return.
However, as the market reaches the expected peak of the expansion, or is around the expected peak of the business cycle, holding this instrument would expose the investor to significant risks.
Estimating the business cycle requires expert judgment and may be difficult for retail investors. Investors should know that the National Bureau of Economic Research (NBER) is a well-known organization that dates business cycles in the US; investors can use NBER's Business Cycle Dating Committee's determinations to understand the stage of the business cycle. Nonetheless, it's important to note that there may be a delay in the determinations of the committee, and determinations may also be reversed.
It would, therefore, be ideal to use other economic indicators in conjunction with the determinations of NBER's committee on business cycles (see report on an indicator of a market crash).
Lastly, it is worth noting that price movements of public offerings in 2019, such as by Uber (NYSE: UBER), and Lyft Inc. (NASDAQ: LYFT), also support the viewpoint presented in this report. They floated their issues at the peak of the market in 2019; large investment firms knowing that the cycle was close to the peak, did not acquire these stocks, as they presented a high risk if the market faced downward pressure due to cyclical trend change.
Hence, these stocks did not perform well till late 2020, when considerable quantitative easing by the FED had loosened the investment conditions.
Calculations and Data
The paired t test is conducted as:
H0: µ1- µ2 = 0, vs H1: µ1 - µ2 ≠ 0
The mean returns of IPO, mean returns of S&P 500 (comparison) are analyzed for equivalence.
Paired T test:
Paired sample T-test, using T distribution (df=89) (two-tailed) (validation)
1. H0 hypothesis Since p-value > α, H0 cannot be rejected. The average of S&P500&IPO's population is assumed to be equal to the μ0. In other words, the difference between the average of the two and the μ0 is not big enough to be statistically significant. 2. P-value The p-value equals 0.3076, ( p(x≤T) = 0.1538 ). It means that the chance of type I error, rejecting a correct H0, is too high: 0.3076 (30.76%). The larger the p-value the more it supports H0. 3. The statistics The test statistic T equals -1.0262, which is in the 98% region of acceptance: [-2.369 : 2.369]. x=-0.005, is in the 98% region of acceptance: [-0.0116 : 0.0116]. The standard deviation of the difference, S' equals 0.0049, is used to calculate the statistic. 4. Effect size The observed effect size d is small, 0.11. This indicates that the magnitude of the difference between the average and μ0 is small.
F test has been conducted as:
H0: σ1 = σ2, vs H1: σ1 ≠ σ2
The variance of the returns of IPO is compared to the variance of the returns of S&P 500 (comparison) to assess equivalence.
F test for variances, using F distribution (dfnum=89,dfdenom=89) (two-tailed) (validation)
1. H0 hypothesis Since p-value < α, H0 is rejected. The sample standard deviation (S) of IPOETF's population is considered to be not equal to the sample standard deviation (S) of S&P 500's population. In other words, the difference between the sample standard deviation (S) of the two populations is big enough to be statistically significant. 2. P-value The p-value equals 0.00000125, ( p(x≤F) = 1 ). It means that the chance of type I error (rejecting a correct H0) is small: 0.00000125 (0.00012%). The smaller the p-value the more it supports H1. 3. The statistics The test statistic F equals 2.8675, which is not in the 98% region of acceptance: [0.6083 : 1.6439]. S1/S2=1.69, is not in the 98% region of acceptance: [0.7799 : 1.2822]. The 98% confidence interval of σ12/σ22 is: [1.7443 , 4.714].