(FORTUNE Magazine) – BLACK MONDAY discomfited not only stockbrokers and portfolio managers, but also an influential set of academics whom they have long considered their archenemies -- the efficient market crowd. These academics, you may recall, claim that buying high-priced investment advice is essentially a mug's game. Their bedrock belief, the efficient market hypothesis (EMH), holds that stock prices always reflect everything known about a company's prospects and the direction of the economy, so individuals cannot beat the market. The notion that markets actually operate with such God-like omniscience and clockwork logic had been taking a licking even before October's crash. If the hypothesis were literally true, after all, Warren Buffett could not exist. The wizard of Omaha and a handful of other high-visibility investors, mostly disciples of the late Benjamin Graham, have outperformed the market averages with EMH-defying regularity. In recent years scholars have documented a host of other anomalies -- persistent moneymaking opportunities that an unfailingly efficient market should long ago have arbitraged into oblivion. They have found that small-company stocks consistently yield higher returns than large ones, even after adjusting for risk. Year after year, share prices rise in early January. More often than not, they fall on Mondays. But these anomalies pale beside the mega-event that occurred on October 19. How could a rational, up-to-the-minute model of the market value it on Friday at $2.8 trillion and decide by Monday evening that it was worth $500 billion less? Some of the faithful see no contradiction. Says Eugene F. Fama, a finance professor at the University of Chicago and one of the original prophets of EMH: ''The appropriate response to the October performance of the market is applause.'' Fama contends that investors had long been gleaning news that made them nervous. After digesting the details, they simply forced prices to a new, more efficient level -- in record time. Neither Fama nor anyone else can identify precisely what new information sparked this rational stampede. But that only proves, he contends, that the market is wiser than mere mortals. In general, however, the Dow's one-day, 508-point plunge has fostered a new uncertainty among finance professors. For two decades they have taught EMH as if it were as indisputable as the laws of gravity. Now, says James Van Horne, A. P. Giannini Professor of finance at Stanford University's graduate school of business, ''the crash makes us realize that prices are not entirely efficient. You can get abrupt discontinuities. I think by and large all of us in finance are somewhat more humble.'' Emboldened by October 19, critics of EMH are stepping up the hunt for new models that could more accurately describe the real world. Much of their work starts with the persuasive premise that a lot of investor behavior is less than rigorously rational. If that's so, then efficiency must suffer. Cornell economist Richard Thaler, a leading advocate of this approach, draws upon psychological research that shows people tend to overemphasize recent events when making decisions. This propensity, he believes, could explain why stock prices tend to swing above and below rough estimates of their underlying value. In a three-year-old study, Thaler and co-author Werner DeBondt reviewed 50 years of stock-price data. They found that a portfolio of ''losers'' -- companies that significantly underperformed the market for three years -- turned around and outperformed it by almost 20% during the following three years. Over the same period, a portfolio of previously highflying stocks underperformed the market by about 5%. To Thaler, these regular cycles of undue optimism and pessimism are evidence of irrationality. Lawrence Summers, a Harvard economist and top adviser to Michael Dukakis, strongly supports Thaler's thesis. Last winter, Summers and three co-authors published a paper arguing that a class of irrational investors known as ''noise traders'' move markets more successfully than EMH would allow. In financial parlance, noise is news with little bearing on a company's fundamental value. Noise traders are folks who tend to buy and sell on bad information, or who view the world, like Thaler's investors, wearing very short-term blinders. IN A TRULY EFFICIENT market, noise traders should consistently lose out to smart investors and eventually be driven from the market. But in the world according to Summers, noise traders can be wrong and still make big bucks. When noise traders grow bullish on a stock, he reasons, rational investors, ( acutely aware that the noisy know-nothings could also drive the price down if they grow bearish, sell out. This leaves noise traders holding large quantities of shares whose price fluctuates more than less risky assets. Over time, Summers asserts, these riskier assets should tend to pay above average rates of return, so at least some noise traders will earn profits superior to those with sounder strategies. Efficient marketers rightly complain that Summers is all theory and no evidence. He admits that he has yet to find the data that document how noise traders actually garner their hypothetical gains. But Black Monday has only confirmed his belief that the prices set in the stock market are sometimes bonkers. Says Summers: ''The crash proves that dramatic movements in stock prices occur on things other than news about fundamental values.'' Yale economist Robert J. Shiller, a longtime foe of EMH, sees human emotion as the culprit. Shiller polled 1,000 investors before and immediately after Black Monday. Not surprisingly, he discovered that shareowners don't focus overmuch on fundamental values when the market is crumbling around them. In the week before the crash, both buyers and sellers told Shiller the market was overvalued. When the crash came, more than 60% described it as a clear case of a change in investor psychology. Instead of displaying cool rationality on October 19, many institutional investors spent their time reacting to other investors' jitters. That day, over 40% of those Shiller surveyed suffered symptoms of emotional stress -- sweaty palms, tightness in the chest, irritability, or rapid pulse. In search of an alternative stock market model that, unlike EMH, allows for the possibility of panic, Shiller has been exploring how epidemics and plagues spread. Two years ago he and co-author John Pound published a piece suggesting that individuals infect one another with enthusiasm for certain stocks. As a stock grows popular and its price rises, some investors are cured -- that is, they sell out or at least buy fewer shares -- and the progress of the disease slows. While the notion of contagion holds tremendous metaphorical appeal, Shiller and Pound have so far failed to reduce their idea to a mathematical formula. After examining one year of trading data on ten stocks and polling the owners of those shares, they concluded that investors do infect one another with stock-purchasing ideas, but this contagion of interest is difficult to measure with any precision. Nor did the price movements they found conform to the hump-shaped pattern that an epidemic should produce. Shiller's contagion model is typical of the fascinating, though fledgling, academic effort to develop far more complex mathematical models of the economy and markets than currently exist. Many of these models are being spawned by the arcane new field of chaos theory. Though in its infancy in the social sciences, chaos theory has been used in the physical sciences to provide insight into phenomena as diverse as the creation of weather patterns, the curling of cigarette smoke, and the stresses that cause spasmodic heart fibrillations. In nature the apparently inexplicable onset of turbulence is a common and fundamental occurrence. A smooth flow of water suddenly bursts into thousands of droplets, or a waving flag shifts abruptly from gentle undulations to short, sharp snaps. What chaos theorists have discovered is that the origin of turbulence, though it appears a random event, follows patterns that can be predicted and reduced to a complex set of mathematical equations. Might economists discover a set of equations that predict or at least closely resemble the gyrations of the stock market? Researchers claim their first excursions show promise. They are much more certain now than in the past that the ups and downs of stock prices follow patterns that are not random. Unfortunately, their ability to devise formulas that numerically capture this dimly perceived underlying order is still years away -- and may never come. ''Our main insight so far is that chaos theory helps explain stock behavior,'' says Jose Sheinkman, a professor of economics at the University of Chicago who did some pioneering work in the field. ''But the theory gives no guidance on what departures from the random walk consist of.'' Agrees William Brock, an economist at the University of Wisconsin and another connoisseur of chaos: ''As yet, chaos theory doesn't do anything for you formally. But it gives you inspiration for models and helps you think through ways to solve problems.'' ONE INSIGHT Brock has already garnered from the physical sciences is the powerful effect of ''positive feedback'' -- a term that describes the tendency of an existing trend to reinforce itself. Brock reasons that the events of October 19 were a classic example of how positive feedback can distort normal buying patterns. That day investors saw the heavy volume of trading, couldn't get information from their brokers, and so decided to mimic what other investors were doing and sell, thus pushing the market in the direction it was already headed. The most intriguing insight chaos theory has to offer, however, is the notion that in a system as complex as the stock market, the quest for sweeping explanations of its more violent swings may be misguided. In the equations that have proved their ability to predict the onset of turbulence in nature, the dramatic transition from stability to chaos is often occasioned by a tiny shift in a single variable. Scientists call this the ''butterfly effect'' -- a reference to the theoretical prospect that the beating of a butterfly's wings over Mount Fuji could ultimately produce a hurricane in the Caribbean. In similar fashion October's crash may well have been triggered by some event that at the time either was imperceptible or seemed insignificant. For the denizens of Wall Street, this new idea, like EMH, offers cold comfort. Their business demands that they behave as if they can outguess the market. But what chaos theory suggests is that the market's movements may ultimately prove neither rational nor irrational, but inscrutable -- and certainly not beatable. A butterfly over Mount Fuji is not a scenario you can sell to a pension fund. On balance the current assault on EMH is unlikely to have large policy consequences. If markets are somewhat less efficient and more irrational than EMH hard-liners hold, further tinkering -- imposing trading halts on future Black Mondays, say -- may be called for. No one is seriously pushing a radical regulatory agenda. The changes, if any, will come in the academy. Like Newtonian physics in the years before Einstein, the efficient market hypothesis may be showing its theoretical shortcomings. But despite its recent battering, EMH should continue to hold sway for now. No one is close to devising a better model of how the financial world works. ''It's like the old saw, the wheel may be crooked, but it's the only game in town,'' says Merton H. Miller, a University of Chicago business school professor and leading capital markets theorist. Still, what finance professors appear more likely to acknowledge in the future -- and Wall Streeters will chortle that they already knew -- is that, to paraphrase Hamlet, more things move markets than were dreamt of in the professors' philosophy.