Notes From Mauboussin’s Who Is On The Other Side?

Excerpts from Michael Mauboussin’s research: Who Is On The Other Side?

In this report, Michael describes a taxonomy of inefficiencies, supported by a rich vein of academic research. The goal is to have a clear idea of why efficiency is constrained and why we believe we have an opportunity to generate an attractive return after an adjustment for risk.

There’s a reward for trying to outperform but most are not equipped to compete for it.

• The Market for Information and the Market for Assets: In 1980, a pair of finance professors, Sanford Grossman and Joseph Stiglitz, wrote a paper called “On the Impossibility of Informationally Efficient Markets.” They argue that markets cannot be perfectly efficient because there is a cost to gathering information and reflecting it in asset prices and therefore there must be a proportionate benefit in the form of excess returns. Because collecting information is costly, active investors need exploitable mispricings to provide a sufficient incentive to participate. Lasse Pedersen, a professor of finance, says that markets must be “efficiently inefficient.” In this market, investors seek to “buy” information and sell” profit. The market for assets concerns the price at which investors buy and sell fractional stakes in various assets. Some investors trade based on information, others trade on data or drivers not relevant to value, and still others free ride. For instance, investors in portfolios that mirror indexes or follow specific rules rely on active managers for proper price discovery and liquidity. The market’s ability to translate information into price is limited by costs. These are commonly called “arbitrage costs” and include costs associated with identifying and verifying mispricing, implementing and executing trades, and financing and funding securities. These costs create frictions that are commonly understated in academic research. That said, many of these costs have come down over time, which has contributed to greater efficiency in many markets. For example, Regulation Fair Disclosure, implemented in 2000, seeks to quash selective corporate disclosure. In addition, trading costs have dropped precipitously in recent decades as a result of deregulation and advances in technology.
• This suggests a useful distinction between “prices are right” and “no free lunch. Prices are right means that price is an unbiased estimate of value. No free lunch says that there is no investment strategy that reliably generates excess returns. A common argument for market efficiency is that very few investment managers consistently deliver excess returns. If prices are right, it stands to reason that there is no free lunch. But the opposite is not true. There can be no free lunch even when prices are wrong if the cost and risk of correcting mispricing are sufficiently high. Identifying and exploiting these pockets of inefficiency should be the main focus of active managers
• To be an active investor, you must believe in inefficiency and efficiency. You need inefficiency to get opportunities and efficiency for those opportunities to turn into returns.

Sources of Edge

Behavioral edge

• Only a fraction of asset price moves can be directly linked to changes in fundamentals, such as revisions in cash flow or interest rate expectations. This has been established by studies of the biggest moves in the stock market since the 1940s that looked to the media for a fundamental explanation after the fact. In many cases, there is no clear fundamental driver of value.
• We observe certain patterns in nearly all markets. For example, we have seen bubbles and crashes in a multitude of geographies (e.g.,Americas, Europe, and Asia) and asset classes
• Beware of Behavioral Finance: The interaction of investors with little information or rationality can yield prices with surprising efficiency. The lesson is that you cannot extrapolate from individuals, who fail to operate according to the rules of rationality, to markets. The reason is that individual errors can cancel out, leading to accurate prices. You can be an overconfident buyer and I can be an overconfident seller and the net result is a correct price. The key is understanding when the wisdom of crowds flips to the madness of crowds. And the essential insight is that it has to do with a violation of one or more of the core conditions for a wise crowd.

  The essential conditions include the presence of investors with sufficiently heterogeneous views and decision rules and having an effective way to aggregate the information. When and how the wisdom of crowds, where markets are efficient, transitions to the madness of crowds, where markets are inefficient. This may be the most important recurring behavioral opportunity.

The importance of  heterogeneous views

• • Blake LeBaron, a professor of economics at Brandeis University and an expert in agent-based modeling, built such a model. He included 1,000 agents with well-defined objectives for portfolio allocations, a risk-free asset, an asset that pays a dividend at a rate calibrated to the empirical record in the last half-century, and 250 active decision rules. The agents made or lost money as they traded and he eliminated those with the lowest levels of wealth. He also evolved the decision rules by removing those the agents did not use and replacing them with new ones. The beauty of LeBaron’s model is we can observe the interaction between diversity and asset prices. LeBaron’s model replicates many of the empirical features of markets, including clustered volatility, variable trading volumes, and fat tails. For the purpose of this discussion, the crucial observation is that sharp rises in the asset price are preceded by a reduction in the number of rules the traders used. LeBaron describes it this way: “During the run-up to a crash, population diversity falls. Agents begin to use very similar trading strategies as their common good performance begins to self-reinforce. This makes the population very brittle, in that a small reduction in the demand for shares could have a strong destabilizing impact on the market. The economic mechanism here is clear. Traders have a hard time finding anyone to sell to in a falling market since everyone else is following very similar strategies. In the Walrasian setup used here, this forces the price to drop by a large magnitude to clear the market. The population homogeneity translates into a reduction in market liquidity.” Because the traders were using the same rules, diversity dropped and they pushed the asset price into bubble territory. At the same time, the market’s fragility rose.

  • The model underscores some important lessons about behavioral inefficiency
    • as the agents lose diversity by imitating one another, the initial impact is that they get richer. This is why betting against a bubble is so hard.
    • Second, the market’s reaction to a reduction in diversity is non-linear. As diversity falls, the market’s fragility rises. But the higher asset price obscures the underlying vulnerability. At a critical point, however, an incremental reduction in diversity leads to a large drop in the asset price. Crowded trades work until they don’t.

How beliefs spread

• • You need to understand a model of how ideas or information propagate across a network. Epidemiologists use a model to describe the spread of disease that is analogous to the spread of beliefs, including fads and fashions. The model considers:
     
    • contagiousness
    • degree of interaction
    • degree of recovery

When seasoned investors stop betting against the investment or investment theme, they contribute to the lack of diversity. With no countervailing opinion voting in the market, decision rules converge and diversity suffers.

Analytical Edge

• Better analysis or info weightings
  • info weighting
    • requires giving a signal the appropriate strength. Be Bayesian. If a coin comes up head 4x in a row you might extrapolate based on the signal but you will be overconfident because the sample size is small and should not move your prior too much (bias towards the strength and also recency bias can both lead to overreaction)
  • time arbitrage
    • Benartzi and Thaler attempt to explain the historical equity risk premium by combining two ideas. The first is loss aversion, which says humans suffer losses roughly twice as much as they enjoy equivalent gains. That you should be twice as upset at losing $100 as you are happy at winning $100 is inconsistent with classical utility theory. The second idea is myopia, which means “nearsightedness.” This reflects how frequently you look at your investment portfolio. The stock market tends to go up over time, but it rises by fits and starts. Based on nearly a century of data, the probability you will see a gain in your diversified U.S. stock portfolio is roughly 51 percent for a day, 53 percent for a week, and 75 percent for a year. Look out a decade or more and the probability of a profit is very close to 100 percent. Both ideas are well established on their own, but together they address the issue of investor time horizon in a new way. The more frequently an investor looks at his or her portfolio, the more likely he or she is to observe losses and suffer from loss aversion. As a result, an investor examining his or her portfolio all the time requires a higher return to compensate for suffering from losses than one who looks at his or her portfolio infrequently and hence suffers less. A long-term investor is willing to pay a higher price for the same asset than is a short-term investor.71 Evidence from the field suggests that professional investors are not immune from myopic loss aversion.
    • Studies showing that participants in a study playing a positive EV game bet less after a loss despite the game being being positive ev.

Exploiting inefficiencies

• play games you are relatively better at
• weight and update info effectively
• “make time your friend”
• understand the story embedded in the price
• be faster, pay attention esp at things that are neglected (info in less covered areas is relatively less likely to be reflected in prices), find edges in secondary or higher-order effects which take longer to reason about
• exploit technical inefficiencies: Forced buyers and sellers (ie hedgers, restrictions on what a funds can own, margin calls/leverage)