What is Behavioral Finance? Toward a Definition


Behavioral finance is a true “mutt” that continues to evolve and adapt, perhaps more quickly than ever as interest and resources allocated to the science increase. It sits at the crossroads of finance, economics, psychology, social psychology, decision-making science and neurology, to name but a few of the disciplines that make up its strange brew. The earliest recorded usage of the term “behavioral finance” is as recent as 1958 but unofficial adherents can be found hundreds of years before. To truly understand the seminal nature of the work done at Nocturne Capital, it may be useful to first understand the theory that underpins it.

Adam Smith is best known for his work, “The Wealth of Nations” in which he popularized the notion of an “invisible hand” and forever changed the formal study of economics. However, his lesser known, “Theory of Moral Sentiments”, first published in 1759, presages the formation of behavioral finance as a true sub-discipline. “Moral Sentiments” touches on Smith’s notion of economic behavior being driven by a struggle between “passions” versus the “impartial spectator”; a dichotomy that mirrors the current discussion on “rational” versus “irrational” investor behavior.

By 1841, Scottish journalist Scott Mackay had published his groundbreaking work, “Extraordinary Popular Delusions and the Madness of Crowds” which covered everything from financial bubbles to the more sensational elements of herding behavior found in witch hunts, religious crusades and fortune telling. Despite the unscientific nature of some of Mackay’s work, the chapters on financial delusions remain prescient and relevant to this day.

Legendary economist John Maynard Keynes and value-investing darling Benjamin Graham both emphasized the impact of emotions on investing in their work in the early to mid-20th century. 1979 saw the publication of "Prospect Theory: An Analysis of Decision under Risk" in Econometrica, an event that few would debate heralds the formal birth of behavioral economics (behavioral finance can be viewed as a sub-discipline of behavioral economics) as Kahneman would go on to win the Nobel Prize for his work on decision-making under uncertainty. Sadly, Amos Tversky had passed away at the time of the award and Nobel Laureates are not awarded posthumously.

Angner and Loewenstain posit that there have been three distinct phases of behavioral economics. The first, they argue, began in 1980 with the cataloguing of behavioral phenomena inconsistent with the economic models of the day. The second phase, which began roughly a decade later, involved the incorporation of behavioral considerations into mathematical models of economic behavior. The third phase began during the early aughts and incorporated behavioral economics into public policy considerations. This third phase is embodied by the work “Nudge” by Richard Thaler and Cass Sunstein; a book that popularized the notion of “libertarian paternalism” as a third way of persuading that purports to be a middle ground between laissez-faire and coercive approaches. So widespread is the popularity of the ideas in “Nudge” that the British government has now established a “Nudge Unit” and President Obama retained the authors of the book to assist in his reelection efforts.


Ask five people familiar with behavioral finance what it is and you’re likely to get just as many answers. However, one common thread to the answers is that they are likely to speak of behavioral finance in relation to efficient market hypothesis. The traditional finance paradigm is undergird by a belief in markets with “rational” participants. This rationality has two primary features: First, rational market participants have access to information and update their beliefs immediately upon gaining access to new information. Second, rational market participants make decisions consistent with Subjective Expected Utility (SEU) as outlined by Savage. The appeal of this elegant, simple approach is obvious; if investor behavior can be reduced to two simple rules, the predictive power of market experts would be enormous (not to mention enormously lucrative).

Sadly, the lived experience of hundreds of years of observing financial markets tells us that the Efficient Market Hypothesis (EMH), for all its elegance, is an oversimplified model that fails to accurately map the behavior of market participants. Let’s quickly examine the two assumptions of EMH in light of actual events. The assumption that market participants are “informationally efficient”, that is, that they incorporate new information into a rational decision making process is one with which behavioral finance theorists take issue on anecdotal grounds. If investors are indeed informationally efficient, it would mean that dislocations from true value would soon be arbitraged away by investors who incorporated this new information into their reasonable preferences.

There are two problems with this notion of efficiency. The first is that the history of the stock market tells us that values can deviate wildly from true value for a protracted period of time. The second is that even when such a dislocation may occur, limits to arbitrage may exist that make correcting the dislocation impossible or undesirable.

One of the primary reasons the market may deviate from fundamental values over time is a belief that “this time it’s different”, an idea that explains everything from stock bubbles to why some people choose to get married for a fourth or fifth time. In the case of marriages, the oft-tossed-about statistic about half of marriages ending in divorce is a little misleading. In reality, less than half of first marriages end in a split, but 67% of second marriages and 73% of third marriages end in divorce.

There are certainly a number of things that make second and third marriages difficult but foremost among them is the tendency to rebound without accurately addressing the problems underlying the past failure. Dating coaches call this “Liz Taylor Syndrome”, psychologists call it “New Era Thinking” – both involve a belief that the unique circumstances of this time will make it different than the past.

Four hundred years ago, in one of the first speculative bubbles on record, a Dutch commodity traded for 10 times the annual salary of a skilled laborer. In some cases, this commodity fetched as much as 12 acres of prime farmland and could even be traded for a single family dwelling. The commodity of which I’m speaking is a single tulip bulb. You see, it was thought that tulips were an investment that would always appreciate in value and were immune to the ups and downs of comparable tradable goods.

It’s comforting to think New Era Thinking as a relic of the past, a trick of the mind that fooled investors less savvy than ourselves. But as recently as the Great Recession of the past few years and the tech bubble of the turn of the century, New Era Thinking has been more present than ever. The advent of the internet was greeted by Wall Street with great enthusiasm. The thought that the web would revolutionize the way we do business was largely correct, but the notion that financial fundamentals no longer mattered was not.  In 1998,, an internet upstart, had sales of $30M, profits of -$28.6M and a total stock value of $8 billion. Toy veteran Toy’s R Us on the other hand, had more than 40 times the sales but only ¾ of the total stock value. Such massive dislocations from true value over prolonged periods of time erode the EMH notion of informational efficiency. Perhaps John Maynard Keynes was right, the markets really can stay irrational longer than we as investors can stay solvent.

The second consideration corrosive to a belief in investor efficiency is “limits to arbitrage” or the theory that due to restrictions, traders that would typically arbitrage away pricing inefficiencies are unable to do so. The poster-children for the impact of limits to arbitrage are Long-Term Capital Management (LTCM), a hedge fund set up by future Nobel laureates Myron S. Scholes and Robert C. Merton. LTCM used complex mathematical models to arbitrage away pricing inefficiencies in treasury bonds brought about by differences in liquidity. Through what is called a convergence trade, LTCM would short a more expensive bond and buy less expensive bond and profit from the regression of their prices toward the mean over time. Since the pricing differences in the bonds were so small, the company needed to take on significant leverage to make attractive profits, as high as 25:1 at the time of their eventual demise.

In 1998, LTCM made bets on bonds that were guaranteed to converge over the long run, but acted erratically given the East Asian debt crisis and the Russian government’s default on its debt. As a result of these two circumstances, harried investors traded against LTCM forcing them toward margin calls that eventually led them to close out their positions at catastrophic losses, even though they would have eventually resulted in large gains. Once again, we see how the shortsighted panic experienced by stressed investors can thwart the calculations of those attempting to arbitrage mispricing. As a result of the manias and panics mentioned above, all but the staunchest proponents of EMH have relinquished their belief in informational efficiency.

The second tenet of EMH is a belief that investors act so as to maximize Subjective Expected Utility (SEU). An intuitive understanding of SEU can be achieved by a simple dissection of the word. The “subjective utility” portion of SEU attempts to give a behavioral patina to the concept by recognizing that value is an individual consideration and that the worth of something will vary from person to person. The “expected” part of SEU means that an individual’s assessment of the likelihood of an event will serve as another part of the equation. Simply put, the utility of an outcome to a person, weighted by its perceived likelihood will determine the SEU (

One of the most consistent violations of SEU is driven by what is termed, “ambiguity aversion” or the preference for humans to make bets on known instead of unknown risks. The quintessential lab proof of ambiguity aversion is the Ellsberg Paradox, where people are shown to prefer betting on the likelihood of selecting a certain color ball from an urn with 50 red and 50 blue balls rather than an urn with 100 red and blue balls of unknown quantities. Even though the odds of the “unknown urn” are as likely to lean in our favor as against, we are loathe to wager our hard earned cash (or the probability of winning some researchers hard earned cash) on something so foggy.

The presence of ambiguity aversion is hardly limited to the lab, however. Indeed, it is succinctly explained in the folksy aphorism that we prefer, “the Devil that you know.” Experientially, “the Devil that you know” logic has some explanatory power: Why else would the child of an alcoholic parent marry an alcoholic herself? Why would someone gutted by the inanity of his job remain there year after year? In these cases, the behavioral preference for certainty, even a negative certainty, is shown to trump the EMH notion of people being programmed to maximize subjective utility.

The financial markets have also given us ample proof of ambiguity aversion in what is called the “equity premium puzzle.” The equity premium puzzle “refers to the empirical fact that stocks have outperformed bonds over the last century by surprisingly large margin”, much more than we would expect from rational investors seeking to maximize utility. As found in a Yale survey of behavioral finance using annual data from 1871–1993, Campbell and Cochrane (1999) report that the average log return on the S&P 500 index is 3.9% higher than the average log return on short-term commercial paper. If efficient market theorists are correct, investors would notice such a pattern and begin to allocate away from bonds and toward stocks, after all, if stocks consistently provide superior risk adjusted returns such an anomaly should be arbitraged away by informationally efficient, utility maximizing market participants. Yet, decade after decade, the equity premium exists.

In the examples above, we have set forth the two fundamental axioms of efficient market theory and provided empirical refutations of each. While a more detailed dismantling of EMH is not useful for the purposes of our book, we hope the reader is sufficiently convinced that market participants do not always act in ways that dispassionately take advantage of all extant information for the maximization of subjective utility. While they often do act in such a way, they just as certainly act in ways that ignore important pieces of information and can be self-sabotaging in their efforts to avoid uncertainty and loss. Events as unrelated to market fundamentals as plane crashes, sunshine and soccer team performance have been reliably shown to impact markets and as long as we remain influenced by such externalities, adherents to a belief in rational market participants will have some explaining to do.


Positioning itself as distinct from, and in some cases in opposition to, efficient market theory is a necessary but not sufficient first step for behavioral finance. Like an adolescent, establishing distance from a parent is a crucial first step, but one must now create an identity in order to thrive as an autonomous entity and not just a rebel without a cause. A less oppositional definition of behavioral finance is, “a discipline that attempts to increase the explanatory and predictive power of financial theory by providing it with more psychologically plausible foundations. Put succinctly, Behavioral finance takes the position that financial phenomena can be better understood by realizing that some agents are not fully rational.

Behavioral finance has risen to the public consciousness through at least three primary channels: an academic spat with traditional finance, a collection of pithy anecdotes about human irrationality, and an explanatory framework for a series of recent financial catastrophes beginning with the bubble and ending with the Great Recession.

This confluence of circumstances has made behavioral finance popular, but it will also make it obsolete if it does not move beyond mere parlor trickery and integrate itself into a more cohesive whole. After all, professorial fights grow tiresome after a season as does the erosion of confidence in our ability to make rational decisions. What began as a useful critique of a flawed paradigm could quickly turn to petulant whining if behavioral finance experts do not quickly look for ways to apply their learning for the benefit of the investing public. It is just such holism and applicability that is our aim here.

One of the nagging, if not entirely unfair critiques of behavioral finance is that it is a collection of observed biases without any sort of underlying theory. In one sense this is fair, if behavioral finance wants to hold itself up against the worst of EMH, it must also consider the best of traditional finance which is that it is undergird by a consistent set of theoretical assumptions. In other ways, this criticism may be nothing more than a reflection of the differences between economics and psychology (see Rabin, 1998, 2002, for a detailed comparison). As we read in Rabin, “the field of psychology has its roots in empirical observation, controlled experimentation, and clinical applications. From the psychological perspective, behavior is often the main object of study, and only after carefully controlled experimental measurements do psychologists attempt to make inferences about the origins of such behavior. In contrast, economists typically derive behavior axiomatically from simple principles such as expected utility maximization, resulting in sharp predictions of economic behavior that are routinely refuted empirically”

A full discussion of whether or not a discipline with roots in psychology can ever achieve the kind of theoretical parsimony (however flawed) found in finance is beyond the scope of this work. What’s certain, however, is that the current system could benefit from a move in the direction of greater cohesiveness. At last count, a popular blog listed 117 different behavioral biases that impact investment decision-making. 117! Obviously, lists of that size are unwieldy and of limited usefulness to advisors or clients when trying to plan and make financial decisions, often under duress. In addition to the sheer difficulty of managing this quantity of information, financial professionals are suffering from what we call “bias fatigue.” Simply put, bias fatigue is weariness with being told how endlessly irrational investors are and a hunger for applying behavioral ideas to more positive ends. Investors and financial professionals alike are telling us, “We make mistakes, we get it. Now what?”

In the final analysis, behavioral finance will be judged by its ability to produce measurable outcomes. At Nocturne, we are excited to be pioneers in behavioral asset management and believe that the study of human behavior can do much to improve the often outdated practice of money management. Behavioral finance has done much to prove that we can make poor financial decisions. Attempting to prove that it can also improve our ability to select securities is our professional raison d'être. 

Portions of this piece are drawn from Personal Benchmark, co-authored by Nocturne Capital founder Dr. Daniel Crosby and Brinker Capital founder Chuck Widger.