Harvard Business Review (Jan08): Hedge funds are more likely than ever to demand that poorly performing managers change their company’s strategic direction. However, executives shouldn’t necessarily heed those demands. Our research shows that, on average, a company that alters strategy in response to activist shareholders won’t see stock gains that outperform the market – that is, unless the company is taken over. Hedge funds tend to target firms whose stock prices have lagged those of industry peers. The mere fact that a hedge fund has invested in a company is often enough to give the stock a boost, but more and more hedge funds see activism as a way to raise companies’ share prices further. Whereas hedge funds were involved in a handful of activist events targeting small public firms during the mid-1990s, by 2006 they participated in more than 90% of activist interventions, often setting their sights on large, well-established companies. The number of firms targeted by hedge funds for poor performance grew as well – more than 10-fold from 1994 to 2006. Hedge fund activism can range from asking for a stock repurchase or dividend increase to making more controversial requests, such as for seats on the company’s board, a change in corporate strategy, or the spin-off of a division. Executives at target companies often resist, arguing that these actions will not increase shareholder value, while passive investors sit on the sidelines wondering who is right. We collected data on every incident from 1994 to 2006 in which an activist investor became involved with a U.S. company. In the vast majority of these events, the investor was a hedge fund that had acquired more than 5% of the target company’s shares and had presented the company with a set of complaints. According to our research, activism created value for shareholders only if it succeeded in getting the target fi rm acquired. When activists were able to prompt a takeover,investors collected hefty premiums, sometimes as much as 40%. In cases where hedge funds were unable to find a buyer for he firm, the company’s 18-month stock market performance, on average, didn’t beat the market; these cases included companies where the activists had been able to force strategic “improvements” or get seats on the board.

Pirate Capital, for example, held 7.9% of James River Coal stock and in 2005 pushed the company into hiring an investment bank to explore “strategic alternatives” to increase shareholder value. James River’s stock lost nearly 75% of its value before the activist reduced its position to below 5% at the end of 2006. All this occurred despite Pirate Capital’s obtaining three seats on James River’s board. Our findings shouldn’t be surprising. Activists are investors, not managers, and their real talent lies in identifying undervalued assets, not in determining the right steps to fix them. If a buyer doesn’t step up to acquire a targeted company, the activist is stuck with a large position in a firm that it has no particular expertise in managing. We’e certainly not saying that activist hedge funds’ strategic demands should be ignored out of hand. Managers targeted by activism face a diffi cult tradeoff between acceding to demands they may disagree with and engaging in a costly, distracting fight with the investor. Sometimes giving in on certain points may be the wisest choice. In addition, managers should bear in mind that activist hedge funds come knocking when the stock is performing below expectations. If management cannot communicate to shareholders why the company has underperformed or why its stock is trading below fundamental value, then a showdown with an activist – and, ultimately, a change in control at the company – may soon follow.

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Robin Greenwood is an assistant professor in the finance area at Harvard Business School in Boston. Michael Schor is an analyst in the investment banking division of Morgan Stanley in New York.

related items:
Event Driven: Is the party over or did just the guests change? (mp3):
Christophe Grünig : Harcourt Investment Consulting
Karim Samii : Pardus
Guy Wyser-Pratte : Wyser-Pratte

Adam Monk is the Chicago Sun-Times' stock-picking monkey.

As of the close today, Mr. Monk's four-year market-beating streak has been snapped. His 2007 stock picks returned 3.72% this year compared to the S&P 500's total return of around 5.8%.

But wait, Mr. Monk can still hold his head up high because for the second year in a row, he has beaten the legendary (and I suspect, notorious index hugging) Legg Mason Value Trust run by Bill Miller. LMVTX is down 6.1% on the year.

Mr. Monk has made his stock picks for 2008: MVL, WCG, TWC, ATHR and TDC.

It will be interesting to see what happens at this same time next year. With heavy-weight marketing and Mauboussin in his corner, one can only hope Miller can beat the monkey (and the S&P) in 2008.

Ineichen writes (pdf): The drawdown measure can be graphically displayed as a value in percent of its previous all-time-high or "high water mark." These displays carry a lot of information that the volatility measure does not. The graph shows not only the extent of a loss (drawdown), which is important, it also shows the time it took for the index (or stock or NAV) to recover the losses and reach a new high. A value sticking to 100% would indicate an investment that only increases (or is flat). The figure below shows this drawdown measure for equities and bonds in the UK, again in real and total return terms:

The graph essentially shows the risk profile of the long-only buy-and hold investor in the 19th and 20th century. Since we are examining the UK we are looking at the risk profile of a country that has been the center of the universe for most of the 19th century and held up pretty well in the 20th century too. <> The next figure shows the drawdown measure for equities and bonds in Japan.

Check the paper for more interesting charts.

via AllAboutAlpha.com, Ineichen looks back (way back) to see future

"Islam's prohibition on paying interest has made it difficult for many Muslims living in the West to purchase homes. As a result, that prohibition has spurred some innovative home financing techniques using put-call parity. In one of the simplest of these transactions, the bank buys the house and sells it to its client for a higher price through an installment sale. Thus, instead of owning the house and taking out a mortgage, which would be written as S(t) - K*e^-r(T-t), the client has agreed to buy the house, which can be written as C(t) - P(t). Viewed from the bank’ s perspective, it owns the house subject to the agreement to sell it to its client. Thus, the bank’ s position can be written as S(t) + P(t) - C(t), which from put-call parity is equivalent to K*e^-r(T-t), a simple loan."

More interesting stuff here (pdf). Knoll concludes: Recent years has seen an explosion of financial innovation. Much of this innovation seeks to exploit inconsistencies in the regulatory environment, and one of the most popular techniques for doing so uses put-call parity. Nonetheless, regulatory arbitrage using put-call parity is not a new phenomenon, as is frequently suggested. It occurred in ancient Israel to evade the prohibition on charging interest. It also occurred in Medieval England, where it led to the development of the modern mortgage. And it is occurring today as Muslims in the West use mortgage substitutes to purchase homes. In addition, put-call parity is also being used today to try to evade the equity of redemption, the defining characteristic of the real estate mortgage, and statutory mortgagor protections.

related items:
Nontraditional Instruments, Mahalanobis

Anyone care to take a stab at refining the valuation formula?

May 8 (Bloomberg) -- A few months ago, Bloomberg News reporter Caroline Byrne chronicled a British divorce court's chilling new open-handedness with the ex-wives of rich businessmen. ...The situation for rich men has grown so dire that a U.K. divorce lawyer named Jeremy Levison now tells his clients: "Don't get married. If you must, make sure your other half is as rich as you."

British law has now put a fine point on a question that has been hovering over Wall Street for a long time now, and grown more interesting with each tic upward in hedge-fund pay: Why does Wall Street Man get married at all? ...I was curious if there might not be a more plausible explanation why so many young men who set out so single-mindedly to become rich on Wall Street ignore the British divorce lawyer's sage advice.

To find it I called a man who now runs a very successful hedge fund, and who, before he set off on his own, had a very successful career pricing complex securities for big Wall Street firms. He was a quant with horse sense -- just the sort of person the market would turn to if they wanted to put a price on something that seemed unpriceable.

I asked this man to value a new security: a call option on half the expected earnings of a Wall Street trader or investment banker. Not of a banker or trader who already has made huge sums of money, but a banker or trader who is just launching his career, with dollar signs in his eyes. What, in effect, would a smart hedge-fund manager pay to marry a first-year associate at Goldman Sachs Group Inc.?

Because this hedge-fund manager knew especially well the fancy end of Wall Street, his assumptions were as interesting as his analysis. He began with a back-of-the-envelope calculation of the present value of the expected future earnings of a banker who made it Big Time, or BT: $25 million was about the right number, he guessed.

He then further assumed -- again, drawing on his experience of many years watching young men trying to get really, really rich -- that about one in 25 who start out in Wall Street jobs actually make the BT. He then calculated something he called the Market Price of Risk, or MPR, which he described as "an adjustment that makes risky propositions just as attractive as other investments in the world.''

Formula for Truth
He felt reasonably sure -- here he drew some analogy to pricing of options on catastrophe bonds -- that in this case the MPR should range from 0.5 to 0.75. He conceded that he'd fudged quite a bit. He completely ignored the value of the intermediate cases -- the guys who didn't make the BT but still amassed several million dollars. His goal was not smart-bomb accuracy but to land in the general vicinity of truth.

When he was finished he had a formula: (BT/25 x MPR). And when you plugged into it all his assumptions about risk, likelihood of making the Big Time, etc., that formula yielded a number. But that number had to be divided by two, as the divorcing wife/ex-wife would be given at most a half share.

"In a reasonably competitive market for marriages to junior bankers,'' he concluded, "you might expect to see $187,500 to $375,000 being invested toward getting a junior banker to the altar.'' And then he realized: "I didn't even take taxes into account."

Who Got Stiffed?
People will quibble with his calculation. Valuation models will no doubt improve, if this market ever gets going. But the hedge-fund quant's number does raise an interesting possibility: that all these newly rich hedge-fund managers who now find themselves paying tens of millions of dollars to their ex-wives had a pretty clear idea, when they got married, of the value of what they were selling. And it wasn't that high.

It's not the Wall Street traders who failed to run the numbers and, as a result, got stiffed in the deal. It's the women who married them. -- Commentary by Michael Lewis

Telluride Asset Management, a fund run by Peter Hajas, is trying to enjoin one of its former portfolio managers from using "Markowitzian mean-variance optimization" in his future work because it constitutes a "trade secret" that is "owned in full" by Telluride, usage of which would generate "irreparable harm to Telluride". Defense asserts arguments do not pass the "giggle test".

A fairly readable set of briefs were filed on March 23 in Minneapolis.

Here's the Plaintiff: Tridecap (pdf, 884 KB)

Here's the Defense: defendant (pdf, 849 KB)

Update March 20, 2008: Millions of dollars later (see here), some legal docs (see here), no definition of the intellectual property with any particularity. Why Europeans should avoid America.

Quick. It's for free.

Recently, Teresa asked for my opinion regarding range-based volatility estimators because, for years, traders have incorporated Wells Wilders' Average True Range into many facets of their analysis and systems.

While I know nothing whatsoever about ATR, I had to figure out something to keep her happy (guys, you know how it is...): The most simple range-based volatility estimator is based on the difference between the maximum and minimum prices observed during a certain period. Parkinson [1980] showed that the daily high-low range, properly scaled, is also an unbiased estimator of daily volatility -- but five times more efficient than the squared daily close-to-close return when the underlying process is a random walk.

Many other estimators that include high, low, open, and close values have been developed (see Garman and Klass [1980], Rogers and Satchell [1991], Alizahdeh, Brandt and Diebold [2001] and Yang and Zhang [2002]). I think one has to play around with all these estimators to see how they perform in the wilderness. What I liked about Parkinson's paper was that it was a clear and easy-to-follow exposition, so I reprinted (slightly edited) the most interesting part for those of you who have never heard of range-based volatility estimators before and do not have acess to JSTOR:

Suppose a share price undergoes a continuous random walk with a diffusion constant D. Then, the probability of finding the share price in the interval (x, x + dx) at time t, if it started at point x₀ at time t = 0, is obviouslyBy comparison with the normal distribution, we see that D is the variance of the displacement x - x₀ after a unit time interval. This suggests the traditional way to estimate D: we measure x(t) for t = 1,2,...,n. Then, defining d_i = displacement during the ith interval, d_i = x(i) - x(i-1), i = 1,2,...,n, we haveas an estimate for D. However, instead of measuring x(n), for n = 0,1,2,..., suppose we have measured only the difference l between the maximum and minimum position during each time interval. These differences should be capable of giving a good estimate for D, for it is intuitively clear that the average difference will get larger or smaller as D gets larger or smaller.

A derivation of the probability distribution for l has already been published (see Feller 1951). Defining P(l,t) to be the probability that (x_max - x_min) ≤ l during time interval t, we havewhere erfc(x) = 1 - erf(x) and erf(x) is the error function. We can now show straightforwardly that(p real and ≥ 1), where ζ(x) is the Riemann zeta function. In particular, we have
andGiven a set (l₁, l₂, ..., l_n) of observed l values over n unit time intervals, we can write the extreme value estimate for D as follows:Comparison of D_x and D_l Estimates for D: Computing the variance of D_x and D_l, we find that
andwhere N is the number of observations. Thus, to obtain the same amount of variance using the two methods, we need N_x ≈ 5N_l. Clearly, the extreme value method is far superior to the traditional method and will be much more sensitive to variations in D.

There's so much hedge fund bashing these days that it makes you wonder: is Business Week readying one of their infamous "death of" cover stories?

We've got academics that think they can do better:

Computers 'will replace hedge fund managers'
The hedge fund industry is set for a move away from active fund management according to a new report because automated trading systems can outperform real managers.

Professor Harry Kat and colleague Helder Palaro from Cass Business School claim to have designed systems that would have outperformed real managers 82% of the time over the past 15 years.

This is the third time Kat has made such a claim in almost 18 months.

The two academics said they tested their mechanical futures trading strategies, which generate returns with the same risk as normal hedge funds, against 2,500 funds of funds.

Kat said index fund management will approach 40% of the hedge fund industry within 10 years, a similar proportion to that of the traditional long-only management industry today.

Kat said: "In the early days, the high fees came on the back of 15% to 20% returns. Things are very different now; hedge fund returns are routinely around 6% to 7%, basically the same as a glorified savings account. If fund managers are taken out of the picture, however, returns can be boosted by 2% or 3%.”

Kat also said that since synthetic funds trade in only the most liquid markets, they avoid other costs associated with alternative investments, including greater due diligence, transparency and "style drift", which refers to a manager's tendency to move away from his original stated investment strategy.

Critics of the research argue that Kat has not invested his own money in the system. However, a spokesman said that as an academic, the Cass professor "does not have the money to invest".

Then there is the "passive is the new active" crowd:

Taking all ego out of investing in hedge funds
"As the hedge-fund industry matures, and it becomes increasingly difficult for many investors to identify and invest in the top-performing hedge funds, passive management should gain wider appeal with hedge-fund investors," Merrill Lynch derivatives analyst Benjamin Bowler said in a recent report.

He notes that with the sharp growth of hedge funds, it is becoming tougher for active managers to excel. In that environment, he said, it's also more difficult for "investors to justify paying hedge fund fees for the performance of the average active manager."

So passive alternatives represent a natural evolution in an increasingly mature industry.

Even Ray Kurzweil is obliged to take a stab at it:

A Smarter Computer to Pick Stocks
Ray Kurzweil, an inventor and new hedge fund manager, is describing the future of stock-picking, and it isn’t human.

“Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence,” Mr. Kurzweil tells a room of investors who oversee enormous pools of capital. “Machines can observe billions of market transactions to see patterns we could never see.”

And of course, Andrew Lo (no relation) is going to show us all how it's done, by cloning them:

According to their paper, "Can Hedge Fund Returns Be Replicated? The Linear Case," the so-called hedge fund clones achieved higher risk-adjusted returns than certain types of hedge funds. But some strategies, such as convertible arbitrage, were not possible because investment products didn't apply. The passive clones provided an annualized return of 12.8 percent compared with 14.2 percent for the full array of hedge funds over 18 years.

Now, would these please stop talking and make money for the clients for a change?

Fenews: Some of the most important contributions to financial engineering in recent years have come from mathematical finance, and perhaps the most important of these is the theory of coherent risk measures proposed by Artzner et al (1999, 1997). Coherent risk measures have various desirable properties – most particularly, the property of subadditivity, which says that adding individual risks together does not increase overall risk – which make them demonstrably superior risk measures to the VaR.

The area is continuing to develop, and one of the most interesting newer developments is the theory of spectral risk measures (Acerbi 2004, 2002). The distinguishing feature of spectral measures is that they explicitly relate the risk measure to a user’s risk-aversion function.

To appreciate what these are about, let us define a class of risk measures M_φ that are weighted averages of the quantiles of our loss distribution. If p is a probability and q_(p) is the p-quantile of a loss distribution (i.e., so q_(p) is that loss such that the probability of a loss less than or equal to it is p), then our risk measure is
where the weighting function, φ(p), also known as the risk spectrum or risk-aversion function, remains to be determined.

It is interesting to note that both the Expected Tail Loss (henceforth ETL, aka expected shortfall, conditional VaR) and the VaR are special cases of this spectral measure. The ETL gives tail losses an equal weight (equal to 1/(1-α) if quantiles are spaced at equal increments of p) and gives other losses a weight of 0. The VaR is also a special case – albeit a highly degenerate one – of M_φ. Because the VaR is just a single quantile, the spectral risk measure is made equal to the VaR if places all its weight on a loss equal to the VaR and none of its weight on any other loss, including any higher loss. So one measure places equal weight on tail losses, and the other places none at all. Click here to learn more.

You may be also interested in this column in which Kevin Dowd addresses how to estimate these risk measures using Excel.

related items:
Presentation: Spectral Measures of Risk - Coherence in Theory and Practise
Paper: Progress in Risk Measurement