Efficient Market Hypothesis Quotes

Some economists, when thinking about long memory, are concerned that it undercuts the Efficient Market Hypothesis that prices fully reflect all relevant information; that the random walk is the best metaphor to describe such markets; and that you cannot beat such an unpredictable market. Well, the Efficient Market Hypothesis is no more than that, a hypothesis. Many a grand theory has died under the onslaught of real data.

What the efficient market hypothesis doesn't account for is that people are not always rational. Just ask any divorce lawyer.

Be greedy when others are fearful and fearful when others are greedy.' Easier said than done for the vast majority of stock traders. ... On every stock trade there is someone who wants to sell and someone who wants to buy, at least at a particular price. ...the person who is selling thinks that she is getting out just in time while the person buying thinks that he is about to make good money. ... The truth is that the market doesn't really reflect some magical perfect valuation of a stock under the efficient market hypothesis. It reflects the mass consensus of how actual individual investors value the stock. It is the sum total of everyone's hopes and fears...

The truth is that the market doesn’t really reflect some magical perfect valuation of a stock under the efficient market hypothesis. It reflects the mass consensus of how actual individual investors value the stock. It is the sum total of everyone’s hopes and fears about what a company is capable of doing.

Markets are not efficient enough; that’s why we have market peaks and bottoms now and then.

Over the long term, and I mean a very long term, markets are efficient.

Why did so many smart people believe these laissez-fairey tales? It’s a good question. Some of the blame surely goes to the excessive faith in free markets that was the elixir of the day. Some goes to economists who believed and extolled the efficient markets hypothesis—and taught it to their students, many of whom wound up as financial engineers on Wall Street.

The concept that all useful information has already been factored into a stock's price, and that analysis is futile, is known as 'The Efficient Market Hypothesis' (EMH).

As former Chairman of the Federal Reserve Alan Greenspan grudgingly acknowledged in his testimony to Congress, there had been a ‘flaw’ in the theory underpinning the Western world’s approach to financial regulation. The presumption that ‘the self-interest of organisations, specifically banks, is such that they were best capable of protecting shareholders and equity in the firms’ had proved incorrect.8 Contrary to the claims of the ‘efficient markets hypothesis’ which underpinned that assumption, financial markets had systematically mispriced assets and risks, with catastrophic results.

. If the efficient-markets hypothesis were true, it would ironically mean that stock markets would necessarily be very inefficient, since no one would gather any information.36 In the aftermath of the Great Recession, the efficient-markets model has taken a beating.37 In the meanwhile, though, some market advocates continue to use the “price discovery” argument for defending changes in markets that were actually making it more volatile and less efficient.

A design sprint attempts to compress this work, from the initial debates all the way to receiving market feedback on the resulting decisions, into one highly efficient workweek. On the first day, you figure out the problem you’re trying to solve. On the second day, you sketch out competing solutions. On the third day, you make the tough decision about which solution you want to explore, transforming it into a hypothesis that can be tested. On the fourth day, you throw together a rough prototype that allows you to test the hypothesis, and on the fifth and final day, you put real clients in front of the prototype and learn from their feedback.

Phase 1: Discovery 1. Define the problem statement What is the challenge that will be solved? The problem statement is defined at this step and becomes the foundation of the project. Here is a sample problem statement: The company has more than one hundred thousand email addresses and has sent more than one million emails in the last twelve months, but open rates remain low at 8 percent, and sales attributed to email have remained flat since 2018. Based on current averages, a 2 percentage-point lift in email open rates could produce a $50,000 increase in sales over the next twelve months. It’s important to note that a strong and valid problem statement should include the value of solving the problem. This helps ensure that the project is worth the investment of resources and keeps everyone focused on the goal. 2. Build and prioritize the issues list What are the primary issues causing the problem? The issues are categorized into three to five primary groups and built into an issues tree. Sample issues could be: •​Low open rates •​Low click rates •​Low sales conversion rates 3. Identify and prioritize the key drivers. What factors are driving the issues and problem? Sample key drivers could include: •​List fatigue •​Email creatives •​Highly manual, human-driven processes •​Underutilized or missing marketing technology solutions •​Lack of list segmentation •​Lack of reporting and performance management •​Lack of personalization 4. Develop an initial hypothesis What is the preliminary road map to solving the problem? Here is a sample initial hypothesis: AI-powered technologies can be integrated to intelligently automate priority use cases that will drive email efficiency and performance. 5. Conduct discovery research What information can we gain about the problem, and potential solutions, from primary and secondary research? •​How are talent, technology, and strategy gaps impacting performance? •​What can be learned from interviews with stakeholders and secondary research related to the problem? Ask questions such as the following: •​What is the current understanding of AI within the organization? •​Does the executive team understand and support the goal of AI pilot projects?

Many have been supposedly foolproof but zany formulae that have made no one rich but the hucksters who sold them to the gullible. But over the years there have been some approaches that have enjoyed at least a modicum of success. These range from the Dow Theory first espoused by Wall Street Journal founder Charles Dow—essentially using technical indicators to try to identify and profit from different market phases—and David Butler’s CANSLIM system, to the value investing school articulated by Benjamin Graham. The earth-shattering suggestion of the research conducted in the 1960s and 1970s was that the code might actually be unbreakable, and efforts to decipher it were expensive and futile. Harry Markowitz’s modern portfolio theory and William Sharpe’s CAPM indicated that the market itself was the optimal balance between risks and return, while Gene Fama presented a cohesive, compelling argument for why that was: The net effect of the efforts of thousands upon thousands of investors continually trying to outsmart each other was that the stock market was efficient, and in practice hard to beat. Most investors should therefore just sit on their hands and buy the entire market. But in the 1980s and 1990s, a new round of groundbreaking research—some of it from the same efficient-markets disciples who had rattled the investing world in the 1960s and 1970s—started revealing some fault lines in the academic edifice built up in the previous decades. Perhaps the stock market wasn’t entirely efficient, and maybe there were indeed ways to beat it in the long run? Some gremlins in the system were always known, but often glossed over. Already in the early 1970s, Black and Scholes had noted that there were some odd issues with the theory, such as how less volatile stocks actually produced better long-term returns than choppier ones. That contradicted the belief that return and risk (using volatility as a proxy for risk) were correlated. In other words, loopier roller coasters produce greater thrills. Though the theory made intuitive sense, in practice it didn’t seem to hold up to rigorous scrutiny. This is why Scholes and Black initially proposed that Wells Fargo should set up a fund that would buy lower-volatility stocks (that is, low-beta) and use leverage to bring the portfolio’s overall volatility up to the broader stock market.7 Hey, presto, a roller coaster with the same number of loops as everyone else, but with even greater thrills. Nonetheless, the efficient-markets hypothesis quickly became dogma at business schools around the United States.

Ross’s “arbitrage pricing theory” and Rosenberg’s “bionic betas” posited that the returns of any financial security are the result of several systematic factors. Although seemingly stating the obvious, this was a seminal moment in the move toward a more vibrant understanding of markets. The eclectic Rosenberg was even put on the cover of Institutional Investor in May 1978, the bald, mustachioed man depicted as a giant meditating guru with flowers in his hair, worshipped by a gathering of besuited portfolio managers. The headline was “Who Is Barr Rosenberg? And What the Hell Is He Talking About?”8 What he was talking about was how academics were beginning to classify stocks according to not just their industry or their geography, but their financial characteristics. And some of these characteristics might actually prove to deliver better long-term returns than the broader stock market. In 1973, Sanjoy Basu, a finance professor at McMaster University in Ontario, published a paper that indicated that companies with low stock prices relative to their earnings did better than the efficient-markets hypothesis would suggest. Essentially, he showed that the value investing principles espoused by Benjamin Graham in the 1930s—which revolved around buying cheap, out-of-favor stocks trading below their intrinsic worth—was a durable investment factor. By systematically buying all cheap stocks, investors could in theory beat the broader market over time. Then Banz showed the same for small caps, another big moment in the evolution of factor investing. Follow-up studies on smaller stocks in Japan and the UK showed similar results, so in 1986 DFA launched dedicated small-cap funds for those two markets as well. In the early 1990s, finance professors Narasimhan Jegadeesh and Sheridan Titman published a paper indicating that simply surfing market momentum—in practice buying stocks that were already bouncing and selling those that were sliding—could also produce market-beating returns.9 The reasons for these apparent anomalies divide academics. Efficient-markets disciples stipulate that they are the compensation investors receive for taking extra risks. Value stocks, for example, are often found in beaten-up, unpopular, and shunned companies, such as boring industrial conglomerates in the middle of the dotcom bubble. While they can underperform for long stretches, eventually their underlying worth shines through and rewards investors who kept the faith. Small stocks do well largely because small companies are more likely to fail than bigger ones. Behavioral economists, on the other hand, argue that factors tend to be the product of our irrational human biases. For example, just like how we buy pricey lottery tickets for the infinitesimal chance of big wins, investors tend to overpay for fast-growing, glamorous stocks, and unfairly shun duller, steadier ones. Smaller stocks do well because we are illogically drawn to names we know well. The momentum factor, on the other hand, works because investors initially underreact to news but overreact in the long run, or often sell winners too quickly and hang on to bad bets for far longer than is advisable.

Whatever the reason, the existence of some persistent investment factors is today accepted by almost every (if not all) financial economist and investor. In an ingenious bit of marketing, factors are often called “smart beta.” Sharpe himself grew to hate the term, as it implies that all other forms of beta are dumb.10 Most financial academics prefer the term “risk premia,” to more accurately reflect the fact that they think these factors primarily yield an investment premium from taking some kind of risk—even if they cannot always agree what the precise risk is. An important milestone was when Fama and his frequent collaborator Ken French—another Chicago finance professor who would later also join DFA—in 1992 published a paper with the oblique title “The Cross-Section of Expected Stock Returns.”11 It was a bombshell. In what would become known as the three-factor model, Fama and French used data on companies listed on the NYSE, the American Stock Exchange, and the Nasdaq from 1963 to 1990 and showed that both value (the tendency of cheap stocks to outperform expensive ones) and size (the tendency of smaller stocks to outperform bigger ones) were distinct factors from the broader market factor—the beta. Although Fama and French’s paper termed these factors as rewards for taking extra risks, coming from the father of the efficient-markets hypothesis, it was a signal event in the history of financial economics.12 Since then academics have identified a panoply of factors, with varying degrees of durability, strength, and acceptance. Of course, factors do not always work. They can go through long fallow stretches where they underperform the market. Value stocks, for example, suffered a miserable bout of performance in the dotcom bubble, when investors wanted to buy only trendy technology stocks. And to DFA’s chagrin, after small caps enjoyed a robust year in DFA’s first year of existence, they would then undergo a long, painful seven-year period of trailing dramatically behind the S&P 500.13 DFA managed to keep growing, losing very few clients, partly because it had always stressed to them that stretches like this could happen. But it was an uncomfortable period that led to many awkward conversations with clients.

There is a conundrum at the heart of the efficient-markets hypothesis, often called the Grossman-Stiglitz Paradox after a seminal 1980 paper written by hedge fund manager Sanford Grossman and the Nobel laureate economist Joseph Stiglitz.22 “On the Impossibility of Informationally Efficient Markets” was a frontal assault on Eugene Fama’s theory, pointing out that if market prices truly perfectly reflected all relevant information—such as corporate data, economic news, or industry trends—then no one would be incentivized to collect the information needed to trade. After all, doing so is a costly pursuit. But then markets would no longer be efficient. In other words, someone has to make markets efficient, and somehow they have to be compensated for the work involved. This paradox has hardly held back the growth of passive investing. Many investors gradually realized that whatever academic theory one subscribes to, the cold unforgiving fact is that over time most active managers underperform their benchmarks. Even if they do beat the market, a lot of the “alpha” they produce is then often gobbled up by their fees. With his usual wit, Bogle dubbed this the “Cost Matters Hypothesis.”23 However, the truth of the Grossman-Stiglitz Paradox does raise some pertinent questions around whether markets may become less efficient as more and more investing is done through index funds.

Yet the best argument for the enduring value of the efficient-markets hypothesis comes from the eminent twentieth-century British statistician George Box, who is said to have quipped that “all models are wrong, but some are useful.” The efficient-markets hypothesis may not be entirely correct. After all, markets are shaped by humans, and humans are prone to all sorts of behavioral biases and irrationality. But the hypothesis is at the very least a decent approximation for how markets work—and helps explain just why they have in practice proven so hard to beat. Even Benjamin Graham, the doyen of many investors, later in his career became a de facto believer in the efficient-markets hypothesis.

According to investment theory, people are risk-averse by nature, meaning that in general they’d rather bear less risk than more. For them to make riskier investments, they have to be induced through the promise of higher returns. Thus, markets will adjust the prices of investments so that, based on the known facts and common perceptions, the riskier ones will appear to promise higher returns. Because theory says in an efficient market there’s no such thing as investing skill (commonly referred to today as alpha) that would enable someone to beat the market, all the difference in return between one investment and another—or between one person’s portfolio and another’s—is attributable to differences in risk. In fact, if you show an adherent of the efficient market hypothesis an investment record that appears to be superior, as I have, the answer is likely to be, “The higher return is explained by hidden risk.” (The fallback position is to say, “You don’t have enough years of data.”) Once in a while we experience periods when everything goes well and riskier investments deliver the higher returns they seem to promise. Those halcyon periods lull people into believing that to get higher returns, all they have to do is make riskier investments. But they ignore something that is easily forgotten in good times: this can’t be true, because if riskier investments could be counted on to produce higher returns, they wouldn’t be riskier.

People have realised that, in light of historical statistics, they have been too fearful of stocks. Armed with this new knowledge, investors have now bid stock prices up to a higher level.

The efficient market hypothesis (EMH) explains this phenomenon: current market prices reflect the total knowledge and expectations of all investors, and it is highly unlikely that one investor can know more than the market does collectively. For

The adjective “efficient” in “efficient markets” refers to how investors use information. In an efficient market, every titbit of new information is processed correctly and immediately by investors. As a result, market prices react instantly and appropriately to any relevant news about the asset in question, whether it is a share of stock, a corporate bond, a derivative, or some other vehicle. As the saying goes, there are no $100 bills left on the proverbial sidewalk for latecomers to pick up, because asset prices move up or down immediately. To profit from news, you must be jackrabbit fast; otherwise, you’ll be too late. This is one rationale for the oft-cited aphorism “You can’t beat the market.” An even stronger form of efficiency holds that market prices do not react to irrelevant news. If this were so, prices would ignore will-o’-the-wisps, unfounded rumors, the madness of crowds, and other extraneous factors—focusing at every moment on the fundamentals. In that case, prices would never deviate from fundamental values; that is, market prices would always be “right.” Under that exaggerated form of market efficiency, which critics sometimes deride as “free-market fundamentalism,” there would never be asset-price bubbles. Almost no one takes the strong form of the efficient markets hypothesis (EMH) as the literal truth, just as no physicist accepts Newtonian mechanics as 100 percent accurate. But, to extend the analogy, Newtonian physics often provides excellent approximations of reality. Similarly, economists argue over how good an approximation the EMH is in particular applications. For example, the EMH fits data on widely traded stocks rather well. But thinly traded or poorly understood securities are another matter entirely. Case in point: Theoretical valuation models based on EMH-type reasoning were used by Wall Street financial engineers to devise and price all sorts of exotic derivatives. History records that some of these calculations proved wide of the mark.

The Bayesian Invisible Hand … free-market capitalism and Bayes’ theorem come out of something of the same intellectual tradition. Adam Smith and Thomas Bayes were contemporaries, and both were educated in Scotland and were heavily influenced by the philosopher David Hume. Smith’s 'Invisible hand' might be thought of as a Bayesian process, in which prices are gradually updated in response to changes in supply and demand, eventually reaching some equilibrium. Or, Bayesian reasoning might be thought of as an 'invisible hand' wherein we gradually update and improve our beliefs as we debate our ideas, sometimes placing bets on them when we can’t agree. Both are consensus-seeking processes that take advantage of the wisdom of crowds. It might follow, then, that markets are an especially good way to make predictions. That’s really what the stock market is: a series of predictions about the future earnings and dividends of a company. My view is that this notion is 'mostly' right 'most' of the time. I advocate the use of betting markets for forecasting economic variables like GDP, for instance. One might expect these markets to improve predictions for the simple reason that they force us to put our money where our mouth is, and create an incentive for our forecasts to be accurate. Another viewpoint, the efficient-market hypothesis, makes this point much more forcefully: it holds that it is 'impossible' under certain conditions to outpredict markets. This view, which was the orthodoxy in economics departments for several decades, has become unpopular given the recent bubbles and busts in the market, some of which seemed predictable after the fact. But, the theory is more robust than you might think. And yet, a central premise of this book is that we must accept the fallibility of our judgment if we want to come to more accurate predictions. To the extent that markets are reflections of our collective judgment, they are fallible too. In fact, a market that makes perfect predictions is a logical impossibility.

Among his many accomplishments was inventing the “efficient market hypothesis” (EMH) which states, more or less, that all known information about a security has already been factored into its price.f This has two implications for investors: First, stock picking is futile, to say nothing of expensive, and second, stock prices move only in response to new information—that is, surprises. Since surprises are by definition unexpected, stocks, and the stock market overall, move in a purely random pattern.

Lawrence Summers, now the U.S. Treasury secretary , told The Wall Street Journal after the crash, "The efficient market hypothesis is the most remarkable error in the history of economic theory.

conventional wisdom had nothing to do with the truth and the efficient market hypothesis was deceptive.