One of the pillars of modern portfolio theory is the efficient market hypothesis (EMH), a theoretical framework for understanding how securities are valued in the marketplace. According to the EMH, the market is composed of well-informed and rational investors simultaneously making decisions that drive assets to their true value.

Competing theories and volumes of research suggest, however, that the market is driven by other forces as well — among them, the irrational behavior of investors acting upon persistent biases in their perceptions. These biases may create anomalies in the marketplace that astute investors can exploit to their advantage. This is what people refer to as “behavioral finance.”

By ascribing a market role to investor psychology, behavioral finance theory embraces premises that fly in the face of the EMH. One is that economic decision makers are not always rational but are predisposed to making errors in judgment. A second is that these behavioral patterns may be so systematic among market participants that they can influence the movement of security prices. A third is that because such movements are predictable and recurring, it may be possible to develop strategies that exploit these biases and enable investors to reap excess returns.

While not all students of the financial markets subscribe to the theories that underpin behavioral finance, the field entered the mainstream in the 1980s and has been gaining the attention of a growing number of investment professionals. Indeed, many believe complementary strategies built on behavioral models may be useful in improving a portfolio's overall risk and return profile. Behavioral finance acolytes offer an investment perspective and stock selection process radically different from traditional core equity strategies — and therein lie potential benefits. But investors have a number of issues to consider when evaluating whether behavioral finance investing can add value to their particular portfolios.

Advisors shouldn't ignore behavioral finance investing: Increasingly, proposals made to investors include a significant allocation to behavioral finance strategies. It's best to understand the theories and the practice.


All humans carry cognitive biases within them in any endeavor, whether it is investing or listening to the news or conducting themselves in relationships. We tend to anchor our expectations of what will come based on a string of events that we interpret as a trend. We seek confirmation of our positions by giving greater weight to opinions that support our arguments and discounting those that weaken or contradict them. We shade our responses to situations depending on how the issues are framed.

Even sophisticated financial market observers acknowledge that emotion can sometimes be a dominant force in the markets. Examples of investors' less-than-rational behavior date back at least to Europe's tulip mania in the mid-1600s. But intuition and anecdotal evidence did not provide a sufficient foundation for challenging EMH. A theory had to be developed, and one that, like any good theory, had some predictive ability. Furthermore, this theory needed to provide a framework for modeling the conditions under which investors would become risk-averse and when they would become risk-seeking. It is the strength of this dynamic, after all, that causes investors to drive asset prices away from their “true values,” as computed by price-to-book, price-dividend or other valuation measures.

It was only in the late 1970s, when Amos Tversky and Daniel Kahneman introduced behavioral finance as a subset of the cognitive sciences (the interdisciplinary study of perception, memory, judgment and reasoning) that it entered the mainstream. In their groundbreaking study,1 Tversky and Kahneman observed the choices of individuals under uncertainty in an analysis of how they responded when faced with lottery-like odds. Individuals tended to interpret the odds differently depending upon a personal benchmark. If the subjects were in the “domain of losses,” they were likely to become risk-seeking and would accept the lottery-like odds; if they were in the “domain of gains,” they would become risk-averse and reject them. Stated another way, the subjects' positions at the start of the experiment influenced their perceptions of what were, in fact, identical odds. If they started from a losing position, they were more likely to be risk takers; if they started out ahead of the game, they were more likely to want to protect their gains.

The researchers coined the term “prospect theory” to describe how investors will evaluate risk when viewed within the context of their individual biases. Tversky and Kahneman did more than publish a theory of investors' asymmetrical behavior toward risk. They opened the debate that had been simmering ever since the efficient market hypothesis was circulated more than a decade before. Investors, the proponents of behavioral finance were saying, are rational — only up to a point. The theoretical investor views losses the same way he or she may view gains and rationally takes into account the probability of outcomes. The behavioral investor, however, views various situations differently depending on where he is positioned.

Ever since Tversky and Kahneman published their findings in 1979, research into how emotion impacts economic decision making has gradually worked its way into the investment community through the financial press, investment texts and industry conferences. If humans are irrational at the margins, the evidence seemed to say, then behavioral finance might be able to capture the maximum point of irrationality. At the beginning, managers gave behavioral finance a low profile, applying the findings only in subtle ways. With the establishment during the 1980s of actively managed funds that deployed strategies rooted in behavioral finance theory, investment professionals were ready to put their beliefs to the test.


Tversky and Kahneman had shown that investors' appetite for risk changed according to heuristics, that is, shortcuts to decision making that were operating in the background of the investor's mental processes. While heuristics can be indispensable tools that humans use to process large amounts of information, their utility is compromised by their potential to lead thinkers to false conclusions.

The first job of behavioral analysts, then, was to link suspected anomalies to specific heuristics, or cognitive biases. Simply observing that asset prices did not always reflect their true value was not enough. A market pricing anomaly had to be “explained” by specific behavioral tendencies. Otherwise, it would be discounted as a chance occurrence. Then, the association had to be modeled into a strategy that could be replicated across time periods. Finally, the honed approach would lead to the development of technical screens that could be applied across thousands of stocks to identify actual or potential mispricings to be exploited.

How does this play out in practice? Behavioral finance helps to establish a missing link between investor decision making and market movements. Since the cognitive sciences focusing on human intellect and the mind were formally recognized in the 1950s, many cognitive biases have been studied and documented. Some of those most frequently identified with investors are, in alphabetical order:

  • anchoring — the tendency to anchor expectations based on reference points that may not have any relevance to the value that is being projected but that nevertheless influence quantitative decision making;

  • availability bias — the propensity to form judgments about the probability of an event based primarily on the availability of information that favors a certain outcome;

  • confirmation bias — the seeking of information that supports an investor's belief while disregarding evidence that may be inconsistent or contradictory;

  • disposition effect — the tendency to hold losing securities too long and to sell winners too quickly because of an aversion to loss;

  • framing and reference dependence — the tendency to take into account irrelevant information (such as a previous high in the share price) when determining return expectations for an asset; these irrelevant reference points can lead an individual to be risk-seeking when holding a security at a loss, but risk-averse when holding a security at again;

  • illusion of control — individuals' tendency to overestimate the control they have over outcomes;

  • optimism bias — people's tendency to believe that they are better than average and that misfortunes are more likely to befall other people than themselves;

  • overconfidence bias — the tendency to be overconfident in the ability to predict the behavior of the markets, particularly as it relates to the section of winning stocks;

  • overreaction — seeing patterns in random events, such as projecting current trends into the future forever;

  • representativeness — the tendency to find similarities among prospects whose resemblances are only superficial, thereby taking the sample to be representative of the whole population; and

  • underreaction — a reluctance to adjust one's expectations to new information.


When analysts use traditional approaches to value securities, they focus on company fundamentals and place a lower priority on feedback from the marketplace. These researchers dig deeply into a company's “story” to arrive at the present value of its expected future cash flows. Dividend discount and discounted cash flow models are constructed to identify incorrect prices. While factors such as price momentum are not ignored, the emphasis is placed on the company's revenue growth rate, margin analysis, tangible assets and other components that get to the issue of the firm's sustainable competitive advantage and its deployment of free cash flow.

In contrast, behavioral finance teams are not as strongly wedded to the fundamentals of the securities on their screens. Quantitative data and, increasingly, mathematically modeled quantitative information are loaded into a stock-selection engine. The stock's ranking according to the manager's proprietary scoring system takes top priority. Some funds choose a hybrid approach by running companies through their screens but then applying a qualitative overlay to the results. In an effort to avoid acquiring biases, there are also things that many managers of behavioral finance funds will not do. For example, they may not visit companies, make qualitative assessments about companies' prospects, or do company-specific research.


So what exactly does behavioral finance add?

In theory, the principles of behavioral finance can be used in any asset class — equities, fixed income, convertible securities, real estate investment trusts — those that are large and face relatively few liquidity restraints. Behavioral finance investing can be applied across styles (growth and value), industry sectors and domains (domestic and international stocks).

Within a strategic asset allocation, an approach like behavioral finance can give investors the flexibility to pursue their performance objectives in a changing climate. For more aggressive investors, the tactic may be a means of moving further along the efficient frontier, that collection of portfolios offering the highest expected return at given levels of risk. Behavioral finance strategies can be a possible source of correlation benefits in a portfolio — correlation can encompass the relative movement of one asset class to another, one manager to another, or one strategy to another. Because the pattern of excess returns from behavioral finance investing moves slightly differently than the rest of the large cap portfolio — particularly the growth and value components — these strategies hold the potential for increasing portfolio efficiency.

To be sure, in times of prosperity and rising markets investors are less likely to be interested in delving into a relatively new approach that might add value to their core portfolios. But with the current outlook for moderately low returns at near-historical levels of volatility, complementary investments that could boost the expected return or lower the expected risk of an asset class gain favor. Investors are looking for ways to gain exposure to equities without having the same volatility profile as basic stock indexing. One way to do that is by mixing complementary strategies within asset classes — such as adding behavioral finance to fundamental core strategies.


Not everyone is convinced, however, of the value that behavioral finance adds. Empiricists frequently dismiss the theory as a catchall for any phenomenon that will in time be understood and factored out. This is where “back testing” comes in. Before any financial strategy is given serious consideration, it is imposed retroactively on many years of historical data. Such back tests are not highly reliable, but they do represent one of the few tools that professionals have to check their theories. That said, any back testing of behavioral finance — or any other strategies — has to be looked at with a certain degree of skepticism. Questions have to be raised if you're going to rely on a back test to give you confidence: Were transaction costs included? Were the back tests run on sample data? To what extent were the results influenced by the frequency of trading or rebalancing?”

When back testing seems to corroborate the existence of sustainable market anomalies, champions of behavioral finance hail the results as proof that the theory works. Critics remain unconvinced. They contend that such findings are likely to prove spurious and cite errors in research methodology (time-specific results, for example). Another misgiving centers on investors' expected risk premiums: Because it is impossible to observe individuals' risk tolerances, trading profits may be mistaken for excess returns. That is, they may simply be rewards that are commensurate with the level of risk incurred by investing in strategies that seek to exploit market anomalies.


Perhaps for these reasons, the acceptance of behavioral finance as a portfolio management approach is still not widespread. Behavioral-finance-driven funds, just like any other strategy, can be designed around any return and/or volatility requirements. Those with more aggressive risk and return objectives typically experience higher alpha volatility in the short run relative to a traditional “core” portfolio. Wide swings in shorter-term performance are likely to test investors' attitudes toward risk, particularly in a down market.

Of all the challenges that behavioral fund managers face, time is the greatest danger. Call it the quarterback's refrain: “I never lost a game in my life, I just ran out of time.” Given their comparatively limited performance history, behavioral analysts, in some respects, are in a race against time to prove that their insights can add value to a portfolio. While some funds seek to beat their benchmarks on a continuing basis, others may need a full market cycle, or as much as three to five years. Investors must be willing to exercise patience with a strategy that could be out of favor during half that time before they see the results of behavioral finance investing. Obviously, their goal is to beat the market over the long time frame, and many of them do that — but the key word here is long.


Despite the hurdles, there may be opportunities for a behavioral finance strategy to transform a client's risk and return profile in equities. From a portfolio construction standpoint, behavioral-finance-driven investing can be used as part of a diverse toolbox of complementary investments, to a core portfolio. While the core may continue to dominate an investor's large cap stock allocation, these modifiers can be anywhere from 10 percent to 50 percent of a private investor's large cap exposure. And in this sense, behavioral finance can play a part in enhancing the portfolio's risk and return profile.

Before jumping on the bandwagon, however, investors need to evaluate the trade-offs alongside the potential benefits of behavioral finance investing. These strategies typically require hefty turnover, which can result in considerable transaction costs. High turnover can also be tax-inefficient for individuals concerned with short-term gains. The trade-off could largely offset the diversification benefits and incremental returns from implementation. And there can be liquidity issues.

Despite these and other considerations, interest in behavioral finance is on the rise — which ultimately could pose a hazard to its success. If a growing number of individuals apply behavioral finance thinking to their investing, the opportunities that bubble up to the surface could quickly dissipate. Despite the distant threat of diminishing returns, it is unlikely that the field of behavioral finance will disappear altogether. Perhaps the market really is, as Graham and Dodd put it, a huge voting machine, where investors make their preferences known through myriad choices driven by both logic and emotion.


  1. Daniel Kahneman and Amos Tversky, “Prospect theory: An analysis of decision making under risk,” Econometrica, (1979) 47:263-291. Together, Tversky and Kahneman revolutionized economics; finance being just a part of what they influenced. In 2002, six years after Tversky died, Kahneman won the Nobel Prize in Economic Sciences for the study he did with Tversky.