The premise of counter-trading by sophisticated investors “has been the main argument for why bubbles could not happen,” Nagel said in an interview. Yet, the study’s results importantly support recent theories of the limits of arbitrage. According to these theories, rational investors reasonably refuse to short or trade against even plainly overpriced securities if they believe most investors will continue to act irrationally, such that the security’s trading price will continue to rise. These, of course, are the very conditions of a market bubble.

“There is no evidence that hedge funds as a whole exerted a correcting force on prices during the technology bubble,” Nagel and Brunnermeier write. Indeed, “among the few large hedge funds that did resist the bubble], the manager with the least exposure to technology stocks—Tiger Management—did not survive until the bubble burst.” Nagel and Brunnermeier note in the study that Tiger Management was an example of a classically rational investor. Tiger declined to take major positions in technology stocks, believing them to be overpriced. While Tiger Management was proved right in the long run, its results fell far behind other funds that soared with the “irrational” approach of buying technology issues. Tiger was compelled to close up shop.

“The key to this is that if you feel you can predict what the irrational guys are doing, then it may be entirely rational to buy irrationally priced stocks,” Nagel said. In part, these possibilities arise because of time factors in hedging. Hedge traders generally are unwilling to hold short positions for a long period. Instead of betting on long-run reversal to fundamentals, they may prefer to follow short-run trends in the behavior of “noise traders,” as economists call them. “It seems that the hedge funds did exploit such a predictability during [the bubble],” noted Nagel.

The abstract in their own words:

The efficient markets hypothesis is based on the presumption that rational speculators would find it optimal to attack price bubbles and thus exert a correcting force on prices. We examine stock holdings of hedge funds during the time of the Technology Bubble on NASDAQ and find that the portfolios of these sophisticated investors were heavily tilted towards (overpriced) technology stocks. This does not seem to be the result of unawareness of the bubble: At an individual stock level, hedge funds reduced their exposure before prices collapsed, and their technology stock holdings outperformed characteristics-matched benchmarks. Our findings do not conform to the efficient markets view of rational speculation, but they are consistent with models in which rational investors can find it optimal to ride bubbles because of predictable investor sentiment and limits to arbitrage. Moreover, frictions such as short-sales constraints do not appear to be sufficient to explain why the presence of sophisticated investors failed to contain the bubble.

Read more:

• Stanford Research: Make Day Traders Act Rationally Rather Than Regulate Hedge Funds
• Hedge Funds and the Technology Bubble, by Markus Brunnermeier and Stefan Nagel

Treating Investors Like Customers

Seen from the perspective of the financial markets, a company’s ultimate product is its equity. So companies need to start applying to their shareholders the same kind of strategic disciplines they typically apply to customers. Treating investors more like customers does not mean employing misguided, and increasingly discredited, techniques for “managing earnings.” Nor does it mean that corporate executives should let investors determine business strategy any more than they should let customers determine product strategy. What it does mean: developing a detailed process for ensuring that a company’s strategy is informed by the perspectives and requirements of its investor base, and then working over time to create alignment between strategy and shareholders.

In marketing practice, customer segmentation is perhaps the cornerstone to creating any successful campaign. Segmentation is the process of surveying a pool of potential customers, segmenting them into manageable groups based on shared traits, analyzing those traits to understand what product attributes are important to each segment (e.g. style, convenience, price), and finally aligning your products and strategies with one or more of those segments. An extension of customer segmentation is that not all customers are equally valuable to a company. It is not uncommon for a minority of a company’s customers to generate the majority of their profits. What would it mean to apply the process of customer segmentation to one’s shareholders?

Just as some customers are more profitable than others, some investors are more attractive than others–whether because of their timeframe (long horizon, low churn), investment objectives (more in tune with future direction than past portfolio), or interdependence (insiders, employees, and alliance partners). Cultivating these aligned investors will help the company migrate toward an owner base that supports the long-term strategy and will reduce unnecessary volatility as short-term investors move into and out of the stock.

The presence of this type of misalignment between shareholders and corporate strategy raises some troubling questions related to market efficiency. How would such a misalignment arise? We could guess that investors have a poor understanding of a company’s strategic direction, but this is unlikely in the case of institutional investors. A more likely explanation is what I’ll call “strategy drift”, where investors and corporate executives had the same objectives at the time of purchase, but over time the positioning of the company has changed. Various factors, behavioral and otherwise, could cause institutional shareholders to maintain their holdings, at least in the short run, despite the misalignment.

In the end, BCG argues that shareholder value can be created directly by resolving these misalignments where they occur. Companies need to understand who their shareholders are, what attributes of the company’s stock they value, and if these values conflict with corporate strategy, either the strategy needs to shift, the shareholder base needs to be “migrated” towards a better fit, or a combination of both. This is an intriguing approach to value creation, which seems to run counter to orthodox financial theory.

Read more: BCG Perspectives: Treating Investors Like Customers

Do these emails have any real effect on the market? New research claims they do. Laura Frieder and Jonathan Zittrain compared a database of collected stock spam against historical market activity to examine the effects of spam on both market volume and price. They found that stock spam does make a significant impact on the market. From their abstract:

Based on a large sample of touted stocks listed on the Pink Sheets quotation system, we find that stocks experience a significantly positive return on days when they are heavily touted via spam, and on the day preceding such touting. Volume of trading also responds positively and significantly to heavy touting. Indeed, on a day when no tout has been detected in our database, the likelihood of a touted stock being the most actively traded stock that day is only 6%. On the other hand, on days when there is touting activity, the probability of a touted stock being the single most actively traded stock is 81%. Returns in the days following touting are significantly negative. The evidence accords with a hypothesis that spammers “buy low and spam high,” purchasing penny stocks with comparatively low liquidity, then touting them – perhaps immediately after an independently occurring upward tick in price, or after having caused the uptick themselves by engaging in preparatory purchasing – in order to increase or maintain trading activity and price enough to unload their positions at a profit. Selling by the spammer then results in negative returns following touting. Investors who respond to touting are losing, on average, 5.25% in the two day period following touting. For the quintile of stocks in our sample that are touted most heavily, this 2-day loss approaches 8%. These estimates are conservative, as they do not account for transaction costs.

For a nontechnical review of the paper, see Spammers Make a Sound Investment in Stocks. The original paper is here: Spam Works: Evidence from Stock Touts and Corresponding Market Activity.

It’s important to note that timing is everything when it comes to profit or loss from temporary market manipulations like these. The Spam Stock Tracker is a mock portfolio of penny stocks touted in spam received by the author. As of today, his portfolio has lost over $47,000 (on paper), based on an investment of $70,987.

Via kottke

UPDATE: Roger Ehrenberg at Information Arbitrage has an insightful post on the same topic: Stock Spamming for Profit – A Sucker Born Every Day

The most likely answer is overconfidence. We all think that we know better than simple models. My own confession at the start of this note is a prime example of such hubris. The key to the quant model’s performance is that it has a known error rate, whereas our error rates are unknown.

And furthermore:

Grove and Meehl suggest many possible reasons for ignoring the evidence presented in this note; two in particular stand out as relevant to the discussion here. Firstly, the fear of technological unemployment. This is obviously an example of a self serving bias. If, say, 18 out of every 20 analysts and fund managers could be replaced by a computer, the results are unlikely to be welcomed by the industry at large.

Secondly, the industry has a large dose of inertia contained within it. It is pretty inconceivable for a large fund management house to turn around and say they are scrapping most of the processes they had used for the last 20 years, in order to implement a quant model instead.

Another consideration may be the ease of selling. We find it ‘easy’ to understand the idea of analysts searching for value, and fund managers rooting out hidden opportunities. However, selling a quant model will be much harder. The term ‘black box’ will be bandied around in a highly pejorative way. Consultants may question why they are employing you at all, if ‘all’ you do is turn up and run the model and then walk away again.

It is for reasons like these that quant investing is likely to remain a fringe activity, no matter how successful it may be.

Read more:  Painting By Numbers: An Ode To Quant

Earlier:

• James Montier: Quantitative Strategies Rule
• All posts tagged James Montier

Shhh! Whisper it quietly, but what if the whole active fund management business is a con? What if the multi-billion pound investment game, employing thousands of highly-paid money managers in the City, is based on a myth? What if the Emperor’s got no clothes?

Maybe that’s overstating it, but there is compelling evidence that all but the luckiest fund managers are doomed to underperform not just the averages but simple quantitative approaches to stock selection. If machines can really do it better, why do we accept an expensive, inefficient system that makes us pay through the nose for mediocrity?

You are probably aware that most active fund managers underperform their benchmark. You may even be aware that 90pc of investment returns are nothing to do with stock selection but a product of being in the right or wrong market or asset class each year. You may not have considered that there might be something hard-wired into the human brain that makes active investment a mug’s game.

That is the implication of an interesting piece of research by Dresdner Kleinwort’s behavioural strategist James Montier, in which he questions why the City offers so few funds based on simple quantitative approaches when the data suggest these models significantly outperform human judgment.

His work is based on a study of 136 different decision-making situations in which mechanistic models were compared with approaches relying on an assessment of the facts by supposed experts. Just eight of the 136 found in favour of human judgment and in each of these cases the people had extra information that the quantitative models did not. On average the experts made accurate or successful judgments in 66.5pc of situations, while the quantitative models had a hit rate of 73.2pc.

Read more: The unsaid truth: machines are better stock pickers

Our answers to each of these questions can radically change the outcome of a decision making process, for better or for worse. In his latest Mauboussin on Strategy article, Michael Mauboussin surveys the many behavioral factors that go into forming comparisons, and offers some advice for making comparisons which are appropriate to the situation.

Read more: Mauboussin on Strategy: How Do You Compare?

Previously:

• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

When someone who’s supposed to be looking out for public shareholders is instead mulling over wallpaper samples for staterooms, it’s time to sell. The yacht has long been the classic indicator of someone who has so much money that he doesn’t need to make any more. Unlike a jet, which can speed busy executives to their offices efficiently, a yacht has no useful purpose. And who has time to play with such an over-the-top toy? Someone who doesn’t work weekends figuring out how to make money for other people. A classic 1940 investment book, aimed at debunking the practices of Wall Street, was called Where Are the Customers’ Yachts? Today, you should ask: Where are the shareholders’ yachts? If you look at the recent record of CEOs who have become yachtsmen, it’s clear that when they buy a boat, it’s the shareholders who usually get soaked.

Gross makes note of the recent performance of asset management legend Bill Miller, one of the few fund managers to have consistently beaten the S&P 500 over the past 15 years. Second quarter results of the Legg Mason Value Trust fund, which Miller runs, were released earlier this month and show the fund lagging the S&P significantly this year¹. Also noted is Miller’s recent purchase of a huge yacht. Coincidence? Yes, according to Gross; while there is plenty of evidence of serious corporate downturns occurring around the time of yacht purchase, Miller’s record is long and strong enough to be strong evidence of his superior skill as a fund manager.

In the end, the results are less than heartening. The successes in the bunch don’t come close to making up for the disasters. Does the yacht warning mean investors should dump shares in Bill Miller’s Legg Mason Value Trust? No. It’s very difficult for small individual investors to find managers with excellent long-term records. And Miller’s long-term record is still phenomenal. Besides, there are signs that his approach to yacht ownership differs from those of Allen and Perelman. Many gazillionaires are content to see their boats and crew sit idle while they party elsewhere. Miller, according to Barron’s, plans to rent his vessel out for charters. After all, he’s a value investor.

Read more: The CEO bought a yacht? Then it’s time to sell.

[1] A noteworthy excerpt from Bill Miller’s letter to shareholders:

We had a dreadful second calendar quarter. The Value Trust lost 5.67% compared to the market’s fall of 1.44%, for the quarter ending June 30, 2006. Our results, as you may know, bounce around quarter-to-quarter and don’t correlate terribly closely with those of the major indices, nor should they. Our portfolio does not look like the S&P 500 and should not act like it. Deconstructing near-term results has little predictive value in our opinion; the market is too efficient and the results of long-term investment decisions are only evident long-term. We have been doing this a long time and have been here before (way behind the market), but for those who are newer, or nervous, or whose psychological equilibrium is disturbed by non-linearity, some context might be helpful.

(emphasis added)

Does exceptionally negative news coverage predict hard times for a company and its stock price? In their August 2006 paper entitled “More Than Words: Quantifying Language to Measure Firms’ Fundamentals”, Paul Tetlock, Maytal Saar-Tsechansky and Sofus Mackassy test whether they can predict a company’s future performance and stock returns by quantifying the sentiment in its financial news coverage. Their sentiment measure is a standardized level of negativity based on word counts and the Harvard psychosocial dictionary. Using Wall Street Journal (WSJ) and Dow Jones News Service (DJNS) stories about individual S&P 500 firms during 1980-2004 (350,000 significant articles), along with contemporaneous financial and stock price data.

Read more: Financial News Sentiment Predicts Stock Returns?

In the book’s conclusion you mention some of the things the experts still don’t understand about investing. Can you talk about the directions for future research?

If you look at the world of finance, there are many, many open questions. For example, we don’t really understand how capital markets get to efficiency. There are some theories that are widely used in the world of finance, including mean-variance and no-arbitrage assumptions. I suspect these traditional ideas will eventually be superseded by this idea of complex adaptive systems, or the wisdom of crowds.

I think that the recent developments in neuroscience and decision making are absolutely fantastic. Another area that is really intriguing are the statistical regularities, like the power laws, that have come out of the study of physical systems, like earthquakes. In biological science, we know things like body mass and metabolic rate also follow a power law, a scaling property, and we have ways to explain those phenomena reasonably well. We see many of those same power laws in social sciences, yet we really have no causal mechanisms. So we don’t know why city sizes follow a power law or why the sizes of corporations follow a power law.

The last idea I’d mention is the flight simulator for the mind. One of the challenging things about investing is it’s very difficult to get timely and clear-cut feedback. If you’re a handicapper at the racetrack or you’re a weather forecaster, you get feedback pretty immediately on the decisions that you make, and that helps you calibrate and improve your decision-making process. When you purchase or sell a stock, you really don’t know in a timely fashion whether that decision was a good or a bad one. So an interesting question is whether we could create some sort of artificial environment that allows people to get better feedback on their decisions.

Read more: Guppies, ants and golf swings: Mental models for investors

Previously:

• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

SpreadPlayer relies on sound to revise the concept of financial visualization. The installation translates concepts used in finance into auditive ideas such as frequency and volume. It transmits the state of the stock market through sound, liberating the user’s eyes and stimulating alternative senses such as the ear, the musical sense or the sense of rhythm.

The installation includes an MP3 player, a proprietary software package, a real time connection to the capital markets and the packaging of the product. The player displays variations in prices of specific stocks, as well as fluctuation in indices in real time. It offers the option of modifying the sound output with the “melody” tool (which changes the average price of a stock), cancelling the “noise” of a stock (eliminating its financial peaks) or calculating an average of the up and down movements. Visually, it evokes the aesthetics of other players (iPod, Creative Zen, etc.) as well as their software (iTunes, Windows Media Player), emphasizing the idea of a mass market product.

SpreadPlayer offers a new concept, the notion of “auditive representation”. It reintroduces, at an individual and portable level, the usual reliance of brokers of multi-tasking while working from trading rooms. Traders, for example, use their sight to watch the piece of the market in which they are buying or selling, but remain connected to the broader market by overhearing the conversations of their fellow traders. By reintroducing sound in the individual experience of the market, SpreadPlayer redefines the traditional concept of financial visualization.

Derivart’s resident economic sociologist is Daniel Beunza, who just joined the faculty of the Columbia Business School as an Assistant Professor. For more information on Derivart, including some of their other projects, check out this post at We Make Money Not Art: Art, Finance and Technology.

MarketSound is designed to generate a realistic mix of background noises that fluctuate in time with the market, as well as spoken prices in a choice of three voices. Its automated voices read out stock quotes and other market data. A companion product, Virtual Pit, generates fake sounds of people shuffling around, talking and, occasionally, yelling in the background.

One voice, recorded by a British nightclub singer, clears her throat, saying “ahem, excuse me,” before reading her next price. It is programmed to happen randomly, once every month or so. In 2003, the program’s creators received patent No. 6,507,818 on the algorithms used to produce natural-sounding crescendos that rise and fall smoothly in line with the peaks and troughs of actual, real-life trading.

Read more: Traders Are All Over, But for the Shouting, Some Use Software

Via Mahalanobis

Just took lots of huge losses to offset some gains, made my stomach turn.

Why is it that realizing what had previously been only a paper loss is so painful for investors? Shouldn’t it be the declining market price that is most painful, not simply the sale that realizes our loss? This bias hurts the performance of the portfolios of many investors. We are too slow to sell losers, letting money languish in a bad investment instead of selling it off and putting the money to more productive use. Financial advisors employ some psychological tricks to try and soften the pain of these realized losses. One is the well-known strategy of selling off losers in December for tax purposes. A sale that would be framed as an investment loss in November can be re-framed as simply a tax deducation if sold in December. Another technique is to “swap assets” by selling the losing position and immediately using the proceeds to buy into a new position.

Normal investors like swaps because swaps blur mental accounts and distract their attention from the fact that they are realizing losses. Consider the mental accounting benefits of swaps recommended by Gross in his manual for brokers: “The two separate transactions (moving out of the loss and moving into a new position) are made to flow together by the magic words ‘transfer your assets.’ The prospect thought he was making a single decision, switching one investment into another. He was not being asked to think in terms of selling XYZ…”

What can investors do to avoid the trap of holding losing positions too long? Statman proposes one such strategy — “loss harvesting”:

…a rule that mandates loss harvesting at the end of every quarter makes it easier to realize losses because it makes loss realization automatic. (Note also how the term “harvesting” creates a positive frame. Harvesting connotes sweet fruit, not bitter losses.)

Read more: Martha Stewart’s Lessons in Behavioral Finance, by Meir Statman

Alter and Oppenheimer did a second study looking at 89 real stocks that were traded on the New York exchange between 1990 and 2004. They asked 16 undergraduates to grade the fluency of the stock names on a sliding scale. Then they checked on the stocks’ performance.

As anticipated, the more complex a share’s name, the poorer it performed on the first day of trading. The effect appeared to wane as time went on; after 6 months, when more information about the stock was presumably available, the name alone couldn’t be used to predict a single stock’s performance.

But the overall impact on a portfolio of stocks was, in this case at least, substantial. Alter and Oppenheimer calculated how much a US$1,000 investment would have fared if it were invested in either the ten most fluent, or ten least fluent, shares. After just one day, the fluent portfolio was $118 ahead of the tongue-twisters; and after a year, it was US$333 up.

Read more: Simple sounds make for sound investments - Easily pronounced stocks do better on the market

via kottke

Read more: Mauboussin on Strategy: Common Errors in DCF Models

Previously:

• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin