Acknowledgement: In doing this series, I was immensely benefited by various blog posts by Ashish Kila , Neeraj Marathe, Ninad Kunder and Prof. Sanjay Bakshi. This does not mean that they will support this framework or will agree with some investment opportunities which I may identify. Needless, to mention that all errors or misinterpretation are solely mine.
I started using checklist a year back, when I came across this excellent presentation by Mohnish Pabrai. My research process was quite adhoc, checklists helped me to follow some standard procedure . Moreover, it forces one to go over all positives and negatives and avoid tendency to take impulsive decisions [Both for buy and sell]. But, off late my checklist became little cumbersome and sometime I was skipping going through it and then regret later. This is when I decided to go through Atul Gawande’s book The Checklist Manifesto: How to Get Things Right. Key takeaways from this book are:
- There are two types of errors. Errors of ignorance (mistakes we make because we don’t know enough), and errors of ineptitude (mistakes we made because we don’t make proper use of what we know). Failure in the modern world is really about the second of these errors. Checklists ensure people are applying all the knowledge and expertise THEY HAVE consistently well.
- Good checklists are precise. They provide reminders of only the most critical and important steps—the ones that even the highly skilled professionals using them could miss. They are NOT complete TO DO LIST.
- Checklist can be READ-AND-DO (where you read the item and then go do what’s specified)or CHECK-ONCE-DONE (where you confirm you’ve carried out the action specified
- Learn from mistakes by searching out the PATTERNS OF MISTAKES AND FAILURES and incorporating those in our CHECKLIST.
Most of the items in above checklist are self-explanatory and have been discussed in detail in earlier posts. Let’s discuss some key items:
Discovery of fair price:
In my view, this is the most important step. To ascertain the probability of discovery of fair price one need to check the incentives to delist and shareholding pattern.
- Incentive to delist: As explained by Neeraj Marathe here “Why should the promoters go through all the trouble to delist? How would they benefit? If there is high incentive for them to delist, they will do it by hook or crook. They will do it even if they have to be generous to the minority!”
- Shareholding pattern: Analyse the shareholding pattern very carefully to assess whether the top shareholders are professional shareholders or appear to be relatively unknown parties. In every situation where less than 10 shareholders hold the required quantity and they do not appear to be professional investors, discovery of fair price will be highly doubtful. Eg. Chettinad Cements, Amrit Banaspati and Binani Cements [See Part I: Failure has patterns]
Focus on expected return:
Source: Prof Sanjay Bakshi lecture on Adventures of a Risk Arbitrageur – Probabilistic Thinking
We have seen in APW President & other cases in Part I Failure has Patterns, what happens when one focus only on upside and ignore the downside completely. It’s also equally important to calculate expected returns factoring in the worst case scenario of withdrawal of delisting. To reiterate what Nassim Taleb mentioned in the book “Fooled by Randomness”, “Make sure the costs of being wrong are limited (and their probability is not derived from past data.” and “ It is not how likely an event is to happen that matters, it is how much is made when it happens that should be the consideration. How frequent the profit is irrelevant; it is the magnitude of the outcome that counts”
My benchmark return is absolute [not annualized] pre-tax expected return of 10% or higher [It needs to be adjusted upwards or downwards based on government bond yields]. I prefer absolute returns rather than annualized, to avoid time risk which is quite uncertain and to focus on most interesting opportunities.
No advantage of remaining invested after PA of RBB date
I think after public announcement of Reverse Book Building date [PA of RBB date], most retail investors will fall in EF matrix [Unknown to you, Known to others] of uncertainty as laid down by Richard Zeckhauser, in his excellent article on “Investing in the Unknown and Unknowable”
In other words by the time of PA of RBB, insiders would have better knowledge of of whether enough quantity will be tendered for book building to be successful, whether and what price promoter will accept for delisting. So it’s better to exit during by RBB date and NOT to tender shares in RBB. REMEMBER MOST DELISTING SUCCESS is BECAUSE OF COORDINATION & COOPERATION BETWEEN TOP 50-100 SHAREHOLDERS.
- According to this article by Shane Parrish “My main takeaway from Alan Greenspan’s latest book, The Map and the Territory: Risk, Human Nature, and the Future of Forecasting: Forecasters (and all those who rely on them) fail to realize that over the long run, even frequently accurate predictions mean little without taking into account the magnitude of relatively infrequent mistakes.”. Despite all the analysis it cannot be ignored that delisting situations are very dynamic and any failure to anticipate any factor can result in losses of more than 50%, if delisting fails or if promoter rejects the discovered price.
- As discussed in part I, Prisonners Dilemma kicks in – Two individuals might not cooperate, even if it appears that it is in their best interests to do so
Now the counter argument is, this is totally irrational. If it’s irrational for us to submit shares in RBB, then logically it should be irrational for everyone else. Let’s try to think who will buy from us. As Prof Baskhi explained in this post traders who measure their returns in annualized terms will buy from us [though this was with reference to open offer, same logic will hold good in case of delisting, in my view]
“Traders who want to buy today and tender in just a few days. Obviously if the stock has risen to Rs 96, this means that the offer is just about to close. The fellow who buys at 96 from you and sells it at 100 to the offerer makes 4 bucks on 96. That’s a 4.2% return. If he is going to make that return, say, in a week, then his annualised return comes to 217%. And you made a lower IRR in percentage terms, but look at it this way: who got most of the juice out of the trade? You! You let him have the rest of it because you figured that the remaining part of the return is just not worth the additional risk. There are two very important lessons here. One, don’t be fooled by a percentage”
Design safety into system [Capital allocation]
“Don’t find fault, find a remedy.” Don’t assign blame. Look for causes and preventive methods. Often it is better to prevent future errors by designing safety into systems than punishing individuals for past error. Blame does little to improve safety or prevent others from making the same mistake.” – Henry Ford
Despite all the checks and precautions, its undisputable that ACCIDENTS do happen in any special situation. I can say with confidence that sometime in next few years, I will incur loss of more than 40-70% in some situation. So to have an inbuilt safety into the system, one must have strict position limits. It depends on individual preferences, but I generally stick to allocation of between 4-7%. We can also understand this by reference to picking up pennies in front of roadroller metaphor.
Source: Prof Sanjay Bakshi lecture on Adventures of a Risk Arbitrageur – Probabilistic Thinking
Read-Do Mindmap and Check-once-Done checklist
I started using mindmaps after reading this and this post by Prof. Sanjay Bakshi. It’s more than a year that I have started using mindmaps. I can see significant improvement in my research process. Michael Taylor in his book Mind Maps: Quicker Notes, Better Memory, and Improved Learning 2.0, explains the advantages of mindmaps.
“Mind Maps speed up the process to review notes because they take advantage of the human mind’s ability to see an image as a whole instead of in isolated parts. When you look at a friend’s face, your eyes and mind process the face as a whole to recognize the person. It does not look at the eyes, nose, mouth, wrinkles, and every bit of information separately to distinguish your friend. This is the theory behind Mind Maps. With them, you are able to view the entire series of relationships between ideas with just one look. [Emphasis mine]”
[Source: Michael Taylor book on mindmaps]
Basic version of Xmind is the best mindmap software and is quite extensive. Moreover it’s free. Those who are interested can download Xmind mindmaps software from here.
You can download the Read-Do mindmaps for delisting from here [Xmind version from here ]and Check-once-Done checklist from here
Managed delisting cases
In cases of managed delisting cases, the above checklist has to be tweaked slightly. Let’s discuss some points which are not relevant in case of managed delisting cases.
Don’t have to worry about fundamentals
I think managed delisting cases like Amrit Banaspati, Binani Cements etc as discussed in Part I, fall within the parameters of template 1 as discussed by Prof. Sanjay Bakshi in his post Can you teach risk arbitrage? In this post in reply to a question [Though this was with reference to open offer, I believe same logic holds good for delisting] whether one needs to worry about fundamentals, Prof mentions that
“For the most part, no. In later templates, maybe but not in Template 1.Why? Because the offer is for all the shares, you are effectively buying, not the stock of Company A, but the bonds of offerer. Your analysis of this situation is the functional equivalent of doing credit risk analysis on the offerer. (This, by the way, is a very good example of a situation when a stock becomes a bond and where you should focus on underlying economic realities and not titles. [Emphasis mine]”
Rather than valuation, focus on floor price indicated by management
In case of managed delisting cases, there is no possibility of discovery of fair price, so valuations are irrelevant. Investors need to focus on floor price declared by promoter and buy only if one is able to buy stock at atleast 5-10% discount to floor price. In some cases like Binani cements, due to prior corporate events like open offer or buy back one may ignore floor price and give more importance to price at which corporate event happened.
I will suggest readers to avoid delisting situation with failure patterns i.e combination of steep valuation, pathetic fundamentals and scattered shareholding. Don’t be fooled by randomness by confusing between luck and skill, ALWAYS give more importance to expected return by calculating it after factoring in worse case scenario. Seek Uncertainty on Favorable Terms. At the same time there is no advantage to retail investors in holding shares after PA of RBB date as most RETAIL investors fall in EF matrix of uncertainty, so avoid tendering shares in RBB except in managed delisting cases. At the end I would like to quote Atul Gawande & Warren Buffet
“We have an opportunity before us, not just in medicine but in virtually any endeavor. Even the most expert among us can gain from searching out the patterns of mistakes and failures and putting a few checks in place. But will we do it? Are we ready to grab onto the idea? It is far from clear. [Emphasis mine]” – Atul Gawande
“We’re OK with losing a lot of money, as long as we’re being paid appropriately for the risk.” – Warren Buffet
This completes the four part series on delisting. In next post lets try to analyse some current delisting situation.
PS: Additional resources
Neeraj Marathe: Playing Delisting cases
Ninad Kunder: Special Opportunity Framework
Prof Sanjay Bakshi : Can You Teach Risk Arbritrage & Presentation on Adventures of a Risk Arbitrageur – Probabilistic Thinking