'Underwriter' Theory of Subscription to IPOs

[lonewolf]

I decided to start this thread to continue the discussion which sgd started started over in APPT thread.

Basically, it was his contention that an IPO is worth subscribing only if it has 2 or fewer underwriters:

how to tell "before hand" whether an IPO will be good to subscribe or not? I learned from very experienced sifu many years ago. Generally If there are 2 underwriters is a good IPO. If there is only 1 underwriter for the IPO then just sell your farm and buy that stock as much as you can.

Underwriters have a lot of risk they don't earn commission for nothing, if the IPO launch is not well taken up they have to absorb all the unsold shares. So if IPO prospectus indicate there is only 1 sole underwriter means that underwriter is very confident of the launch to be a success. And If is good deal why need/want to share your commission with others?

But if underwriter is not confident they will try to get others to co-underwrite means if the launch is not successful everybody has to take a share to absorb unsold stock. In this case there were so many underwriters.

So rather than they absorb and stuck with it, they throw it to those kiasu people who oversubscribed.

I then summaried his observations as follows:

In summary:

1 underwriter: DIE DIE MUST BUY!
2 underwriters: CAN BUY!
3 or more underwriters: YOU BUY YOU DIE!

I decided to test the validity of his observations by using data available from shareinvestor. I plotted the 1st day % change / 1st week % change against the no of underwriters for IPO since Mar 2012. I concluded that the 'underwriter' theory is without merit based on the fact that there appears to be no correlation in the scatter plots.

This is what I got:

[Image: UnderwritersVsGains_zps3a255348.jpg]

kopikat made the following observations from my chart:

1. During bullish times (since 2012), the chances of making $$ from IPO stags (1st day) is very high. In your charts, only 1 is underwater. Almost no one (those who applies for the IPOs anyway) bothers too much about company fundamentals..

2. After only 1 week, we start to see more of them going underwater (4 in your charts, with 2 from Catalist). Perhaps, the smarter ones have already taken profits, especially from the stocks of poorer fundamentals (?). Guess who'll usually be left carrying the babies? Most likely the less informed and "uneducated" (as in stocks "education") ones... I used to be one of them...

It'll be interesting to see the longer term performance, especially when we hit a negative mkt sentiment period of time...

Finally, zf87 was of the opinion that "if you plot the average gain Vs numbers of underwriters probably you will appreciate the merit. Moreover, the non-underwriter case may not be included in the plots, since the "theory" says the fewer the underwriters, the more responsibility/risk they take."

Feel free to add on to the discussions and other observations.

zf87 suggested I plot average gain instead; but how should we calculate average gain? I'm prepared to refine the charts based on suggestions and other inputs.

[safetyfirst]

if you really want to play around with these figures, i was thinking perhaps you can incorporate the mood of the public in the week before the ipo, i.e. assign -1 for pessimism, 0 for neutral and +1 for optimism

humans like to find pattern when given data, haha, sometimes even when there is no pattern, we would just like to prove that it exists, so do be very careful

[lonewolf]

if you really want to play around with these figures, i was thinking perhaps you can incorporate the mood of the public in the week before the ipo, i.e. assign -1 for pessimism, 0 for neutral and +1 for optimism

humans like to find pattern when given data, haha, sometimes even when there is no pattern, we would just like to prove that it exists, so do be very careful

Wah. That would be way too much work considering that some of the IPOs go back at least a yr.

I also dun like subjective parameters. So how do I incorporate the mood of the public? Everyone has a subjective view of the opinion.

There may perhaps be value in using the STI percentage change as a proxy of market pessimism and optimism; but my views is that incorporating a third element (market sentiment) to the theory now will make it overly complicating.

After it this may merely meant to be a simple thought-provoking exercise and not a thesis. But I appreciate the inputs. Thanks!

[zf87]

yes, its better to start a new thread for more specific discussion.

IMHO, there are a few ways to improve the plots, in the order of simplicity to professionalism.

1 there are some overlapping data points on the plots. Maybe its better for the eye to reduce the size of the dots/squares, so that there are not cluttered. (It can be done by changing the setting in excel easily.)

2 if possible, the average and standard deviation of the data can be calculated, so that for each # of underwriter, you only have one data point, but with an error bar(standard deviation). For example, for the case of 4 underwriters, let's say the gain/loss are 15%, 5%, -1%, so the average is 6.33%, and standard deviation is 8.08%. (AVERAGE and STDEV are the two functions in excel). do the same for 1 underwriter, 2 underwriters, etc.

3 maybe it is easy to compare 2 groups of data, A and B, if A has AVG of 30%, STD of 2%, and B has AVG of 10%, STD of 1%. A is definitely better than B . But if the difference in AVG are not big enough comparing to the STDs, we are not 100% sure that A has certain advantage over B.

By doing a Student's t-test, we can estimate the the confidence level (95% sure or 99% sure) if the two data sets are statistically different (in other words if there is significant merit between the two sets). excel can also do so. pls refer to the link below.

http://click4biology.info/c4b/1/2007.htm

It seems complicated at first look, but since the software can do Student's t-test for us, we only need to learn how to interpret the results of the Student's t-test, which is much easier if one can refer to some worked examples.

Anyway, this is what the academia has been doing. I did the test to determine if one proposed antibiotics has "statistically significant" effect on certain bacteria.

[lonewolf]

Gosh. I feel like I'm answering my undergrad statistical question all over again.

I can appreciate what you are driving at. But for it to be statistical meaningful, the sample size need to be bigger. Right now, there is less than 30 data pts (including the catalist IPOs). To get a barely decent sample size, I will probably need at least 10 yrs worth of IPOs and even then, I dun think it will be statistically conclusive one way or another.

Like I said, this was never meant to be a thesis but I may continue to work on it in my spare time.

Thanks for your input. I really appreciate it!

[zf87]

Gosh. I feel like I'm answering my undergrad statistical question all over again.

I can appreciate what you are driving at. But for it to be statistical meaningful, the sample size need to be bigger. Right now, there is less than 30 data pts (including the catalist IPOs). To get a barely decent sample size, I will probably need at least 10 yrs worth of IPOs and even then, I dun think it will be statistically conclusive one way or another.

Like I said, this was never meant to be a thesis but I may continue to work on it in my spare time.

Thanks for your input. I really appreciate it!

Since you have learn it during yr undergrad, just do it as a review.

There is no minimum sample size for the t test to be valid.

Believe it or not, in the antibacterial tests I mentioned, I only have 3 duplicates for each sample and 3 duplicates for the control (without antibacterial treatment). Consider the tedious work involved in biological experiments, it is a well-accepted common practice (to not having 30 duplicates for each sample), and my work was published in international peer-reviewed journal. And yes, it was for my thesis.

[wsreader]

Didn't know there was such an interesting discussion going on in the APPT thread as I was not interested in APPT IPO and did not follow that thread. Thanks for creating a separate thread for this discussion.

The hypothesis is that that one can predict the post-IPO performance of a company by looking at the risk that underwriters are willing to undertake. Perhaps what we need to distill from the data is the size of the IPO divided by the number of underwriters, giving the risk each underwiters are willing to take. As the size of the IPO increases, it is common for underwriters to pool the risk by inviting more underwriters. Such risk pooling actions may not have any predictive value, I think.

[specuvestor]

^^ This was what I wrote:

The book building process is more complicated than that. Sometimes more underwriters and bookrunners are needed because 1) issue is big 2) sales channel needed. I think focus on the lead underwriter track record is important.

The underwriters need to have a rough gauge where the pricing is "attractive" ie may not be reasonable (think dot com) hence the big gap in valuation range. During the roadshow they will access where the demand level is coming from, and usually it's based on comparative PE, anchors and of course market sentiments. And the pricing will also depend on whether management wants every ounch of flesh or leave some on the table, which is contingent to their own objectives, and sometimes personal objectives.

Usually of course low allocation means high demand and good performance, whereas high allocation makes the buyer fearful The irony of IPO.

My experience is that IPOs after a lull are usually better bets because sentiments are generally rough so valuations and expectations are lower. IPOs in the heat of euphoria are flips and soon the second tier losers will IPO too.

[zf87]

For those who are interested, there is journal article about the topic.

They found the higher the number of managing underwriters, the better long-run performance.

Underwriter Quality and Long-Run IPO Performance


Abstract: We analyze the relationship between the quality of underwriters and the long-run performance of initial public offerings (IPOs) in light of underwriter marketing, certification and screening, and information production. We find that higher underwriter quality (measured by the number of managing underwriters, underwriter reputation, and absolute price adjustment) predicts better long-run performance, even when returns are value weighted. We compare underwriter quality measures and find that the effects of the number of managing underwriters and underwriter reputation are mutually complementary and are especially strong among IPOs with high uncertainty, while absolute price adjustment, which is more likely to be associated with information production than marketing or certification/screening, loses significance. Our findings are consistent with the marketing and certification and screening roles of investment banks but lend little support for the information production role of underwriters.

[sgd]

woah there's even a study on it means this argument was disputed by somebody else before.

very subjective lah, I been using this advice for years and more often than not I use it to screen for good IPO stocks. Good here refers to confidence in the underwriter that may translate into IPO trading at higher prices on opening day and in the weeks beyond it. These days if you play IPO punters are getting very agile and clever, on morning of opening day within the first 10-15 minutes they will dump everything. If you take your own sweet time eat breakfast drink kopi - gameover already

Usually I see those stocks IPO with many underwriters their b/s very leveraged. Which may influence confidence of underwriter in their ability to sell. So if they cannot sell well they need more sales channel in the form of bringing in more underwriters and also to mitigate their risk.

But I guess as long as it makes you money you can believe what you like.