Adaptive Market Hypothesis

Interesting paper by Andrew W. Lo

From the Abstract:

The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and little consensus exists as to which side is winning or the implications for investment management and consulting. In this article, I review the case for and against the Efficient Markets Hypothesis and describe a new framework—the Adaptive Markets Hypothesis—in which the traditional models of modern financial economics can coexist alongside behavioral models in an intellectually consistent manner. Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants. Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, I show that the Adaptive Markets Hypothesis yields a number of surprisingly concrete applications for both investment managers and consultants.

Read the paper here

Replacing Modern Portfolio Theory

Via The Aleph Blog:

I have never liked using MPT [Modern Portfolio Theory] for calculating the cost of equity capital for two reasons:

• Beta is not a stable parameter; also, it does not measure risk well.
• Company-specific risk is significant, and varies a great deal.  The effects on a company with a large amount of debt financing is significant.

What did they do in the old days?  They added a few percent on to where the company’s long debt traded, less for financially stable companies, more for those that took significant risks.  If less scientific, it was probably more accurate than MPT.  Science is often ill-applied to what may be an art.  Neoclassical economics is a beautiful shining edifice of mathematical complexity and practical uselessness.

I’ve also never been a fan of the Modigliani-Miller irrelevance theorems.  They are true in fair weather, but not in foul weather.  The costs of getting in financial stress are high, much less when a firm is teetering on the edge of insolvency.  The cost of financing assets goes up dramatically when a company needs financing in bad times.

Read the full post here

Why Markets Make Mistakes

Via Simoleon Sense:

Introduction (Via SSRN):

The research described in this article calls into question the assumptions of rationality, transparency, efficiency, and homogeneity on which many models of markets are based (see for example Samuelson 1948; Bass 1969; Fisher and Pry 1971, Malkiel 1973). The classic models assume, at least implicitly, that decision makers understand the structure of the market and how it produces the dynamics which can be observed or might potentially occur. Are these models acceptable simplifications, or can they be seriously misleading?
This article explains why markets routinely and repeatedly make “mistakes” that are inconsistent with the simplifying assumptions and often produce disastrously wrong business decisions. The undesirable outcomes could include vicious cycles of investment and profitability, market bubbles, accelerated commoditization, excessive investment in dead-end technologies, giving up on a product that becomes a huge success, waiting too long to reinvent legacy companies, and changes in market leadership. The article illuminates the effects of bounded rationality, imperfect information, fragmentation of decision making, and extrapolating past trends.

Click Here To Learn Why Markets Make Mistakes

What We Talk About When We Talk About the Efficient Market Hypothesis

Via The Curious Capitalist:

Eugene Fama said that in order to test whether markets were efficient in this sense you needed an economic theory of how prices were determined. He chose the Capital Asset Pricing Model, devised a few years before by Jack Treynor, Bill Sharpe and John Lintner. It said risky stocks would outperform less-risky ones, with the risk that mattered being something called beta—the correlation of a stock’s movements to those of the overall market.

Roll started pointing out issues with CAPM in the 1970s, and Fama and French concluded in 1992 that the conjunction of CAPM and the EMH simply didn’t match the data. They chose to jettison CAPM, not the EMH (Fischer Black made more or less the opposite choice). But without an economic theory of how stock prices should move, there’s no way of testing the claim that markets are efficient in the “price is right” sense. Pricing models like the arbitrage pricing theory or the Fama-French factor models simply assume that prices are right, then extrapolate from that what the relevant risk factors must be that determine prices. But this assumption that prices are right is now based on no empirical evidence at all. In fact, both Fama and Roll have said that there’s just no way to tell whether prices are right or not.

That leaves us with an efficient market hypothesis that merely claims, as John Cochrane puts it, that “nobody can tell where markets are going.” This is an okay theory, and one that has held up reasonably well—although there are well-documented exceptions such as the value and momentum effects. But if “we can’t tell where the markets are going” was all the finance professors had to offer, they wouldn’t have had much influence.

The price-is-right combo of EMH and CAPM allowed finance professors to say much more than “we dunno.” They may not have known exactly where a stock’s price was headed, but thanks to CAPM they could confidently predict the bounds within which it would move. Thus armed they went on to conquer the world, eventually transforming MBA curricula, legal thinking, corporate governance, financial regulation and many aspects of investment practice. It’s admirable that finance scholars—especially Fama, since it was his theory in the first place—kept sniffing around and eventually concluded that the EMH/CAPM combo didn’t match the evidence. It’s not so great that some of them now pretend that the price-is-right version of the efficient market hypothesis never existed, and fail to fully confront what its demise means for a lot of the other things taught in finance and investment classes.

Read the full post here

Do Hedge Fund Managers Have Stock Picking Skills?

Via SSRN (paper by Wesley R Gray):

I study novel data from a confidential website where a select group of fundamentals-based hedge fund managers privately share investment ideas. These value investors are not easily defined: they exploit traditional tangible asset valuation discrepancies such as buying high book-to-market stocks, but spend more time analyzing intrinsic value, growth measures, and special situation investments. Evidence suggests that the managers’ long recommendations earn economic and statistically significant long-term abnormal returns. Oddly enough, these managers share their profitable ideas with other skilled investors. This evidence is puzzling in a world where there is an efficient market for fund managers and asset prices.

Read the paper here

Markets After the Age of Efficiency

John Kay writing in the FT:

As anyone who has taken Finance 101 knows, there are three versions of the efficient market hypothesis. The strong version claims that everything you might know about the value of securities is “in the price”. It is closely bound up with the idea of rational expectations, whose implications have dominated macroeconomics for 30 years. Policy interventions are mostly futile, monetary policy should follow simple rigid rules, market prices are a considered reflection of fundamental values and there can be no such things as asset-price bubbles.

These claims are not just empirically false but contain inherent contradictions. If prices reflect all available information, why would anyone trouble to obtain the information they reflect? If markets are informationally efficient, why is there so much trade between people who take different views of the same future? If the theory were true, the activities it purports to explain would barely exist.

Yet although efficient market theory is not true, it may nevertheless be illuminating. The absurdities of rational expectations come from the physics envy of many economists, who mistake occasional insights for universal truths. Economic models are illustrations and metaphors, and cannot be comprehensive descriptions even of the part of the world they describe. There is plenty to be learnt from the theory if you do not take it too seriously – and, like Mr Buffett, focus on the infrequent inefficiency rather than the frequent efficiency.

…The strong version of the efficient market hypothesis is popular because the world it describes is free of extraneous social, political and cultural influences. Yet if reality were shaped by beliefs about the world, not only would we need to investigate how beliefs are formed and influenced – something economists do not want to do – but models and predictions would be contingent on these beliefs. Of course, models and predictions are so contingent, and an understanding of how beliefs form is indispensable. Economics is not so much the queen of the social sciences but the servant, and needs to base itself on anthropology, psychology – and the sociology of ideologies. The future of investing – and economics – lies in that more eclectic vision.

Read the full article here

Further reading: Culture and Prosperity: Why Some Nations Are Rich but Most Remain Poor

Adapative Asset Allocation

Via Abnormal Returns:

One of the tenets of modern portfolio management most damaged due to the financial crisis has been asset allocation.  We have discussed how during a bear market correlations tend to one, the myth of the all-weather portfolio and how investors may need a more dynamic approach to asset allocation.  It seems we are not alone in our opinion(s).

Noted finance professor Andrew Lo of MIT has a piece in the Financial Times discussing how the practice of portfolio management has been upturned in part due to the financial crisis – asset allocation included.  While we recommend you read the entire piece, the bottom line is that the investment world is now much more complicated post-crisis.  Lo writes:

Diversification is still a good idea, but it has become much harder to achieve. Thanks to the increasing competition for additional yield, every type of investment vehicle and strategy has experienced substantial growth in assets under management.

The asset classes (and dynamic) strategies that have been touted as portfolio diversifiers have seen an influx of capital and managers.  Lo cites the case of the “carry trade” that has become popular enough to have spawned an ETF that follows the strategy.

Read the full post here

The Myth of the All-Weather Portfolio

Via Abnormal Returns:

For quite some time now financial advisers of all stripes have been in search of the elusive “all-weather portfolio.”  That is, an asset allocation that serves to protect investors in bad times (bear markets) and performs well in good times (bull markets).  Does an all-weather portfolio really exist?

Prior to the economic crisis many would have answered in the affirmative and would have pointed to the large university endowment funds as examples of investors who had achieved this goal.  However the aftermath of the credit crisis and ensuing bear market indicate these funds have failed to achieve this goal.

Maybe it isn’t that case that asset allocation models are broken.  It may simply be the case that we are asking too much of asset allocation as a discipline.  In what other investing endeavor do we expect to have the best of all possible worlds?

Read the full article here

Economists Need to Study Bubbles, Reinvent Models

A great editorial by Robert Shiller:

The widespread failure of economists to forecast the financial crisis that erupted last year has much to do with faulty models. This lack of sound models meant that economic policymakers and central bankers received no warning of what was to come.

As George Akerlof and I argue in our recent book Animal Spirits, the current financial crisis was driven by speculative bubbles in the housing market, the stock market, energy and other commodities markets. Bubbles are caused by feedback loops: rising speculative prices encourage optimism, which encourages more buying and hence further speculative price increases — until the crash comes.

You won’t find the word “bubble,” however, in most economics treatises or textbooks. Likewise, a search of working papers produced by central banks and economics departments in recent years yields few instances of “bubbles” even being mentioned. Indeed, the idea that bubbles exist has become so disreputable in much of the economics and finance profession that bringing them up in an economics seminar is like bringing up astrology to a group of astronomers.

The fundamental problem is that a generation of mainstream macroeconomic theorists has come to accept a theory that has an error at its very core — the axiom that people are fully rational. As the statistician Leonard “Jimmie” Savage showed in 1954, if people follow certain axioms of rationality, they must behave as if they knew all the probabilities and did all the appropriate calculations.

So economists assume that people do indeed use all publicly available information and know, or behave as if they know, the probabilities of all conceivable future events. They are not influenced by anything but the facts, and probabilities are taken as facts. They update these probabilities as soon as new information becomes available and so any change in their behavior must be attributable to their rational response to genuinely new information. If economic actors are always rational, then no bubbles — irrational market responses — are allowed.

Abundant psychological evidence, however, has now shown that people do not satisfy Savage’s axioms of rationality.

Read the full article here

Wall Street’s Math Wizards Forgot a Few Variables

From the NYT:

IN the aftermath of the great meltdown of 2008, Wall Street’s quants have been cast as the financial engineers of profit-driven innovation run amok. They, after all, invented the exotic securities that proved so troublesome.

But the real failure, according to finance experts and economists, was in the quants’ mathematical models of risk that suggested the arcane stuff was safe.

The risk models proved myopic, they say, because they were too simple-minded. They focused mainly on figures like the expected returns and the default risk of financial instruments. What they didn’t sufficiently take into account was human behavior, specifically the potential for widespread panic. When lots of investors got too scared to buy or sell, markets seized up and the models failed.

That failure suggests new frontiers for financial engineering and risk management, including trying to model the mechanics of panic and the patterns of human behavior.

“What wasn’t recognized was the importance of a different species of risk — liquidity risk,” said Stephen Figlewski, a professor of finance at the Leonard N. Stern School of Business at New York University. “When trust in counterparties is lost, and markets freeze up so there are no prices,” he said, it “really showed how different the real world was from our models.”

In the future, experts say, models need to be opened up to accommodate more variables and more dimensions of uncertainty.

The drive to measure, model and perhaps even predict waves of group behavior is an emerging field of research that can be applied in fields well beyond finance.

Financial markets, like online communities, are social networks. Researchers are looking at whether the mechanisms and models being developed to explore collective behavior on the Web can be applied to financial markets. A team of six economists, finance experts and computer scientists at Cornell was recently awarded a grant from the National Science Foundation to pursue that goal.

“The hope is to take this understanding of contagion and use it as a perspective on how rapid changes of behavior can spread through complex networks at work in financial markets,” explained Jon M. Kleinberg, a computer scientist and social network researcher at Cornell.

Read the full article here