Birthday No. 24

It was really hot. Although it was my 7th cup of coffee, I feel that there is some problem with the coffee machine. Strangely, it has dispensed coffees of all different temperatures in past 6 hours. I bet this was the hottest of the 7. Suddenly I am called again in the board room…………….In my mind…..Ahhh the break is over.

I entered the room, Guess what everybody (and that includes the CEO, the COO, the CFO and the Executive Directors etc etc) in the board room wishing, “HAPPY BIRTHDAY TO YOU”. I looked at my Team Leader with a silent question, “Why did you tell them…….Its so damn embarrassing? He  is silent with a 360^O smile on his face  Which is certainly not helping me………..I tried to control my not so happy emotions…….(For record, I don’t feel this way when I am wished in normal circumstances).

Flashback…….My boss tells me, that I will have to stay for one month in Delhi for a super critical strategy assignment for one of our most esteemed clients. It’s a make or break situation for the company. Its going to be their bread and butter for years to come and some other related stuff.

Next comes: I will have to lead the assignment and ensure one of the team members, with no prior domain experience comfortably transitions into the team, until my team leader is back from an international assignment.

Did you see his language, FLAWLESS, I actually started to feel happy about all of this…..Its not that his words were not true….but this is what consulting teaches you….you need to communicate the news to the person (who is going to get screwed) in a manner that he feels happy about it …..or better, he looks forward to it.

Ok so coming back to where we were……..It was my birthday and the calls from family and friends were still pouring in……At one point, I thought it is better to switch it off……..I mean it is far better than screwing the bidding strategy for a company…….Repercussions: my company loosing the client and the CEO loosing his job……better concentrate on financial model and the random scenarios that the people (Read stakeholders) want me to test and discussions on strategy (Phew….). We finally finished. It is 1:30 A.M. We move back to the rooms…..which by the way I get to see only in nights.

Final thoughts before dozing off: I am 24……shit I am actually 24. One year later and it would never be early 20’s and the zone of mid or late 20’s will start…….And then the repeated re-telecast of the question and answers

The first half of me: “Have I achieved something in my life …..Anything?”

The other half: Yaaah …why not, I am consulting CEO’s …….@24….thats cool + I got the award for the best bla bla.

The first half: yaah but you are not in Mckinsey or Bain….They do cool stuff……

The other half again (feeling sleepy): Ya but I might some day …..I have not yet done my MBA

The first half: but some people at your age do that………

The other half (trying hard not to sleep): yaah but they don’t get to lead engagements so early in their career

The first half: Ya but they earn more than you…..dumbo

The other half: Zzzz Zzzzz

The first half: Looser….. Looser ……..Is anybody there???

Have a nice day,

Anshum Saxena

In Economics, Asymmetric Information refers to decision in transactions, when one party has more information than the other party involved in the transaction. This imbalance in access to information creates an information divide, which may lead to market collapse.

In this article, we would be discussing the impact of asymmetric information on markets and finance leading to problems of adverse selection.

Small Lemons - big problem

 

Lemons Problem – One of the prominent papers written on asymmetric information is by George Akerlof on “The Market for Lemons: Qualitative Uncertainty and Market Mechanism”. The main point of the paper was how asymmetric information creates the problem of adverse selection.

STRUCTURE:

There is a market of potatoes with good and bad potato sellers. (GROUP OF POTENTIAL TRANSACTIONS)

The buyer of potato cannot identify between good and bad potatoes. (INFORMATION ASYMMETRY) This might not be a practical assumption in case of potatoes but might stand well for more complex commodities and financial instruments.

PROBLEM:

Since buyer cannot identify the bad potatoes, they would be willing to pay only average price for the potatoes. Let’s say following are the prices:

Price of good potatoes – Rs. 10 per kg

Price of bad potatoes is Rs. 5 per kg

Thus the buyer would be willing to pay around Rs. 7 or 8 per kg for potatoes. This is a common scenario, isn’t it? If we know that the price charged for a specific product is not up to the quality desired, we generally would be willing to pay only an average price for that product (average of the bad and good).

However this average price is very attractive for bad potato sellers (potato value is lower than the market price that is being offered) and not at all satisfying for good potato sellers (value of potato is higher than market price being offered). This is called adverse selection. The market is selecting and incentivizing bad potato sellers.

Now since the price offered to bad potato is higher than the value, the supply of bad potato will increase, leading to more bad potatoes than good potatoes in the market. Given this scenario all the bad potato sellers would be willing to sell their potatoes, as they are getting a good price for their relatively bad quality of potatoes. This will make potato buyers more suspicious of the quality of the potato and they will feel that a randomly selected potato has a higher probability of being bad. This will further reduce the offer price from the buyers.

Going forward the market price will be increasingly unfeasible for good potato sellers and remaining unsatisfied they will start packing their shops. Very soon the customers will lose the trust as many of the bad potato sellers would now be willing to sell the product at the price that is being offered. So the buyers will start leaving the market ultimately leading to the collapse of the market. The main reason behind the breakdown of the market was asymmetry of information. The potato buyers did not have adequate information to distinguish between good potatoes and bad potatoes, ultimately leading to collapse of the market.

The idea of lemons problem is also extendable to area of corporate finance. Personally I find it equally difficult to buy a stock and potato, but logically speaking since there are far many complex factors that determine stock quality than potato quality, so let us assume for a minute that buying a stock is complex than buying potato (no offences to housewives)

Putting the same analogy, if investors cannot determine the value of the firm before they buy the equity, they would be willing to pay only an average price for the stock (equity) of the firm. Given the price is average, selling equity in the market for bad firms is far more incentivizing and attractive than it is for the good firms. Hence more and more bad firms would be offering equity in the market (Adverse selection). Very soon the investors will become suspicious and if they are offered equity than it must mean that the firms’ value will be below average. Hence investor will no longer be willing to pay average price for the equity, which will ultimately lead to collapse of market with no transactions taking place.

Practically speaking, in real life scenarios we don’t see efficient markets, rational buyers and many a time buyers can distinguish between good value stocks and bad value stocks, using the understanding of valuation techniques or equity research reports. Thus what can be deduced is that companies with more R&D based activities which create more information asymmetry between investors and company’s existing owners have a higher probability or reason of high owners’ or promoters stake. This is one example that explains the reasoning in the real life scenario.

Can you find some more real-life examples (business or non-business) justifying the lemons problem – Asymmetric information?

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George Arthur Akerlof (born June 17, 1940) is an American economist and Koshland Professor of Economics at the University of California, Berkeley. He won the 2001 Nobel Prize in Economics (shared with Michael Spence and Joseph E. Stiglitz). His father was Swedish and his mother a Jewish/German-American.

George Akerlof at Berkeley, 1966

Akerlof is perhaps best known for his article, “The Market for Lemons: Quality Uncertainty and the Market Mechanism”, published in Quarterly Journal of Economics in 1970, in which he identified certain severe problems that afflict markets characterized by asymmetrical information.

 

 

 

Good day,

Anshum Saxena

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More on ‘Lemons Problem’

Writing the “The Market for Lemons”: A Personal Interpretive Essay by George Akerlof

Asymmetric Info Models of Capital Structure

George Akerlof and Lemons Problem

The LTCM Story

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 “Long-Term Capital Management (LTCM) was a U.S. hedge fund which failed spectacularly in the late 1990s, leading to a massive bailout by other major banks and investment houses. LTCM was founded in 1994 by John Meriwether (the former vice-chairman and head of bond trading at Salomon Brothers). Board of directors members included Myron Scholes and Robert C. Merton, who shared the 1997 Nobel Memorial Prize in Economics. Initially enormously successful with annualized returns of over 40% in its first years, in 1998 it lost $4.6 billion in less than four months and became a prominent example of the risk potential in the hedge fund industry. The fund folded in early 2000.”

- Wikipedia

In this post, I have tried to put a short summary of LTCM’s strategies and the reason for their failure. LTCM was John Meriwether’s baby. He was the pioneer in the arbitrage concept. LTCM drew heavily from the Arbitrage group he set up in Soloman Brothers in the late 1970’s. Meriwether set up LTCM after he left Solomon Brothers in a cloud. Even the core team of LTCM composed mainly of former Arbitrage Group members.

John Meriwether

 

I was intrigued by the fact that how could the firm evolve into such a size so that its failure would threaten the markets as a whole. The fund was filled with academicians from Chicago, MIT, Stanford, LSE and the likes. The firm boasted of names like David Mullins, (former vice-chairman of the US Fed) Merton and Scholes (of the Black-Scholes fame) who later went on to receive the Nobel Prize for their contribution to the world of finance.

LTCM strategy was to concentrate on “relative value” trades in bond markets, i.e. they would buy some bonds and sell some. It would bet on spreads between bonds to either contract or widen. It was envisaged from the start that LTCM would leverage its capital 20-30 times or more. This was a necessary part of the strategy because the spreads it would make would be miniscule. The fund raised $1.25 billion equity to start off. It added a $1 billion capital at a later stage.

 

One of the first trades involved the thirty-year US Treasury bonds. Six months old “off the run” bonds were traded at slight discount to the freshly issued “on the run” bonds. There was a 12-point gap between the two bonds. LTCM bet on the spread to narrow. After all, the US government is no less likely to pay off a bond that matures in 29.5 years and one in 30. The spread in actuality is too low. But LTCM leveraged this arbitrage 25 times. No sooner did LTCM buy off the run bonds than it loaned them to some other Wall Street firm to buy on the run bonds against it.

 

The major trades done by LTCM were:

 

• Russia and other emerging markets: LTCM thought that the spreads between Russian government bonds and corporate bonds was too high. They bet that these spreads were going to converge. Unfortunately for the fund in the summer of 1998 Russian government announced a debt moratorium, devaluation of the Ruble and a selective default. This led to the spreads widening to monstrous proportions. Also, since one of the legs of the trade defaulted, LTCM suffered huge losses on this trade. This also led to what would be famously called “flight to liquidity”. Investment flocked to the ‘safe’ US t-bills thus increasing the spreads in every region.

 

• Equity pairs: This trade involved in taking advantage of the difference between different classes of shares of same/similar companies. For example Volkswagen has two shares with different voting rights. LTCM would take advantage of the difference in prices of such shares.

 

• Yield Curves arbitrage: This trade is about taking advantage of the mispricing in various stages of the yield curves caused mainly due to lack of liquidity, like the 29.5 year t-bill described above.

 

• S&P 500 stocks: Stocks which were going to be part of S&P 500 had to be bought by the index funds which usually lead to an increase in their price on the day they would be a part of the index. LTCM would buy such stocks.

 

• Merger arbitrage: This trade is about taking advantage of the difference between the merger share price and prevailing market price.

 

• Swaps: LTCM mostly maintained a leverage of around 30x. This was mainly possible through their heavy use of swaps.

 

• Equity volatility: LTCM figured the average volatility of the market (S&P 500) was about 15%. So, if the market valued volatility at 20% (volatility is an important factor determining options prices in the Black Scholes formula) LTCM would short the index. This was referred to as “selling volatility”.

 

All these strategies were very sound on a fundamental basis. Some say that if the firm had enough cash and hadn’t leveraged so much it might have even survived as the positions ultimately turned profitable. The Russian default was the beginning of its troubles. This led to a rise in bonds spreads across all geographies. Since LTCM bet that the spreads were going to converge, it lost a lot of money on such trades. Also the stock indexes volatility rose as high as 38% and LTCM again lost tonnes of money on this trade. The nail in the coffin was when the other banks came to know about LTCM’s losses and tried to bleed it further to death. Also LTCM had to open its books to Goldman and others as it needed an equity investment. The deal never went through but the whole street came to know of LTCM’s various positions. They bled the firm to death.

 

The firm had built up about $100 billion of assets. It traded with almost every single bank/fund, thus threatening a systemic collapse on its demise. This led to FED orchestrating a rescue plan consisting of 14 banks and $3.65 billion.

 

All this makes me think even the best can make horrible mistakes out of overconfidence generated by their successes. And my most important take away from this whole story is that “leverage is a dangerous double edge sword”. It can either propel a firm’s returns to stratosphere or push it or the depths of bankruptcy.

 

 

By: Praful Katta

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