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

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

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