effortsaround common views and goals, it is less likely to overlook important de-tails. Dramatic market movements can easily grab the attention of thecommunity of investors and encourage them to act in ways that amplifythe movement, even if there is no fundamental reason for it.
Robert Shiller has studied the way dramatic price movements captureinvestor attention and trigger herd behavior. Even institutional investors,who would be expected to select stocks in systematic ways based on sta-tistically valid characteristics, are prone to buying a stock simply becauseit has experienced a rapid price increase.” This mechanism can explainhow a market boom can be triggered just by a vivid initial price movementthat calls attention to the stock market. Moreover, Shiller’s studies showthat investors are often not even aware that it was a dramatic price move-ment that motivated them to act.
The Role of Feedback in Systematic Price Movements. Socialinteraction among investors creates feedback. Feedback refers tothe channeling of a system’s output back into the system as input. SeeTheories of Nonrandom Price Motion 367
one with no feedback
 
Figure 7.9, where two systems are comparedand one with feedback.
There are two types of feedback, positive and negative. One way tothink of the difference is as follows. In the case of negative feedback, thesystem output is multiplied by a negative number and the result is feed-back in. In the case of positive feedback the multiplier is a positive num-ber. This is illustrated in Figure 7.10.
SystemSc with —> OutputNo FeedbackSystemExternalinput with —> Output
Feedback
    
 
Feedback Loop
 
FIGURE 7.9 Feedback: output becomes input.
    
   
arate —_—_—__—» System —> OutputMultiplyby <0Negative Feedback Loop.—— System —> Output
       
Multiplyby>0
Positive Feedback Loop
FIGURE 7.10 Positive and negative feedback loops.368 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Negative feedback has the effect of dampening system behavior, dri-ving the output back toward a level of equilibrium. Positive feedback hasthe opposite effect, amplifying a system’s behavior, thus driving its outputfurther from equilibrium. This is illustrated in Figure 7.11.
An example of a negative feedback system is a household heating &air conditioning system. When the temperature falls below a desired orequilibrium level, the thermostat turns on the furnace, returning the sys-tem’s output, the household temperature, back to the desired level. Tem-perature above the desired level turns on the A/C, bringing the temperatureback down. This is all accomplished by feeding the system’s output backinto the system as an input. This allows the system to self-regulate its waytoward equilibrium.
Positive feedback is illustrated by the growing screech in a public-address system when the speakers are placed too close to the micro-phone. The normal hum coming out of the speakers (output) gets fedback into the audio system via the microphone, where it is amplified bythe system. With each cycle, the sound comes out louder, developing intoaloud screech. Positive feedback is also known as the snowball effect orvicious cycle.
Financial markets, like other self-organizing self-regulated systems,rely on a healthy balance between negative and positive feedback.*! Ar-bitrage provides negative feedback. Prices that are too high or too low
NegativeFeedbackSystem
PositiveFeedback
 
FIGURE 7.14 > Positive and negative feedback loops: dampening versusamplifying.Theories of Nonrandom Price Motion 369
trigger arbitrage trading that pushes prices back toward rational levels.Positive feedback occurs when investor decisions are dominated by im-itative behavior rather than independent choice. In this regime, in-vestors will hop aboard an initial price movement, buying after firstsigns of strength or selling after first signs of weakness, thus amplifyingan initial small price movement into a large-scale trend. A TA approach,known as trend following, depends on large-scale price moves for itsprofitability and thus is most effective during times when positive feed-back dominates.
Positive feedback can cause a vicious cycle of price increases or de-creases, which take prices far beyond rational levels even if the initialprice move that triggered the whole affair was justified. In other words,price trends can go far above or below rational levels simply due to theamplifying effect of positive feedback.
Positive feedback can also result from they way people adapt their ex-pectations to recent changes. Although it generally makes sense to alterexpectations when conditions change, people do it imperfectly. At times,they adjust insufficiently (the conservatism bias), whereas at other timesthey are overly sensitive to change and alter their expectations more thanis justified. Investors are especially likely to overreact to new informationthat is prominent. Either a single large price change or a sequence of simi-lar changes can induce investors to alter their expectations too much.These price changes can feed on themselves, thus generating price mo-mentum that may lead to a bubble or crash.
A judgment error that sometimes contributes to positive feedback ismental accounting. This is the irrational tendency to think about money asif it belonged in separate accounts, which should be treated differently.Gains from prior speculative ventures may be assigned to the hot-actionaccount, whereas money accumulating in the home-equity account istreated with greater conservatism. Rationally, all of an investor’s money,regardless of how it was made or in which asset it is invested, should betreated in the same way. A dollar is a dollar. However, speculative gainsfrom a recent market rise are often, irrationally, earmarked for furtherspeculation, thus adding fuel to a rising market.
At some point, the increasing expectations that fuel a bubble runout. Common sense would suggest, and some analysts insist, that aprice bubble necessarily ends in market crashes (i.e., a negative bub-ble). At times they do, but other bubble termination patterns are possi-ble. Computer simulations of systems that incorporate feedback loopsshow that bubbles not only develop in a jagged fashion with many inter-vening pauses, but they can also deflate in a similar manner. If the late1990s was a price bubble, it seems to be ending with many interveningpauses.370 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Self-Organizing Ponzi Schemes
The feedback theory of price bubbles is appealing because of its plausibil-ity and the numerous historical examples that seem to confirm its validity.However, it is hard to prove that a simple feedback mechanism involvingheightened investor focus, imitative behavior, and exaggerated investorconfidence is truly operational in financial markets.
Nevertheless, Yale economist Robert Shiller believes actual cases ofpyramid frauds known as Ponzi schemes offer evidence that confirms thefeedback theory. The scheme, named after the infamous Charles Ponzi,who invented the idea in the 1920s, involves promising investors a highrate of return from some sort of business venture or investment. However,the investors’ money is never put to work in the way promised. Instead,the commitments of new investors are used to pay off earlier investors.This serves two purposes: it makes the venture look legitimate and it en-courages the first investors to spread the story of their success. As the talegains popularity, new investors are motivated to make investments. Theirfunds are, in turn, used to pay off a prior round of investors, who now goout and tell their story. This cycle of using Peter’s investment to pay offPaul to prove the success of the enterprise continues and grows. Thenumber of people influenced by the scam follows a curve similar to thediffusion of an infection throughout a population. Ultimately, a saturationlevel is reached. The supply of noninfected speculators gets exhausted,and so there are no new investments with which to pay off the last roundof investors. At this point, the scheme crashes.
Recently a housewife from a small town in Alaska collected an esti-mated $10 to $15 million over a six-year period by promising a 50 per-cent return. Her business was purportedly based on unused frequentflyer miles accumulated by large companies. In fact, this was very simi-lar to Ponzi’s original scheme, which was based on international postagecoupons.
Ponzi scams evolve in a typical pattern. Initially, investors are skepti-cal and invest only small amounts, but once early investors reap profitsand tell their success story, later-stage investors gain confidence and theflow of capital into the scheme gains momentum. Frequently there are ra-tional skeptics issuing warnings, but the greedy pay no heed. Well-founded cynical arguments cannot compete with vivid accounts of fatprofits. The moths dive into the flame.
Shiller contends that speculative bubbles are naturally occurring Ponzischemes that emerge spontaneously in financial markets, without requiringthe machinations of a fraudulent promoter.” Such self-organizing phenom-ena are common in complex systems. There is no need for false stories be-cause there is always some buzz about the stock market anyway. A trendTheories of Nonrandom Price Motion 371
of rising prices acts as a call to action much like the gains of early in-vestors in a Ponzi scheme. The message is then amplified by Wall Street’ssalesmen, and they need not tell lies. They simply pitch the upside poten-tial while downplaying the risk side of the story. The parallels betweenPonzi schemes and speculative bubbles are so clear in Shiller’s opinionthat the burden of proof is on those who deny the similarity.
Competing Hypotheses of Behavioral Finance
A mathematical model quantifies a scientific hypothesis about some as-pect of the world, This allows quantitative predictions to be made that canbe tested against future observations. Of course, those proposing themodel hope its predictions will coincide with said observations. Neverthe-less, if they should clash, a true scientist stands ready to formulate a newmodel that both explains the discordant observations and makes addi-tional predictions that can be tested against further observations. This cy-cle of refinement never ceases because knowledge is never complete.
Pseudoscientific stories resemble legitimate scientific hypotheses, butonly superficially. A scientific hypothesis is compact, yet it accounts for awide range of observations. Pseudoscientific stories tend to be compli-cated. Scientific hypotheses make precise predictions that can be checkedagainst future observation. Pseudoscientific accounts make vague predic-tions that are consistent with prior observations but never make predic-tions specific enough to permit clear refutation. Thus, pseudoscientificaccounts survive in the minds of the gullible, no matter how far off themark they may be.
On this basis, behavioral finance qualifies as science, albeit a youngone. Although as yet, there is no agreement on a single theory, there hasbeen substantial progress. Within the last decade behavioral-finance prac-titioners have proposed several hypotheses to explain investor behaviorand key features of market dynamics. These are important to TA becausethey provide a rationale for the occurrence of systematic price move-ments, the “without-which-not” of TA.
The behavioral-finance hypotheses have been formulated as mathe-matical models, thus allowing them to generate testable predictions ofhow markets would behave if the hypothesis were correct. This opens thehypotheses to refutation if markets do not manifest the predicted behav-iors. For example, if a model predicts the occurrence of trend reversalssimilar to those actually observed in market data, the hypothesis gainssome confirmation. However, if the model’s predicted scenarios are con-tradicted by actual market behavior, then the hypothesis would be re-futed. This makes the hypotheses of behavioral finance scientificallymeaningful.372 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Recall that the random-walk model of EMH predicts that systematicprice motion should not be observed in market behavior. In contrast, thebehavioral-finance hypotheses described later predict the occurrence ofsystematic price motion and offer an explanation about why they shouldoccur. Even though none of the hypotheses provides a comprehensivetheory of market behavior, they have a common a message of importanceto TA: There is good reason to believe financial markets are not entirelyrandom walks.
Biased Interpretation of Public Information: The Barberis,Shleifer, and Vishny (BSV) Hypothesis. Empirical studies showthat investors display two distinct errors in response to news releases.Sometimes they underreact, and consequently prices change less than thenew fundamentals say they should. At other times, investors overreact,causing prices to change more than is justified by the news. Ultimately,however, these pricing errors are rectified by systematic price movementstoward price levels that accurately reflect the new information.
The puzzle is: Why do investors sometimes overreact while at othertimes they underreact? A hypothesis proposed by Barberis, Shleifer, andVishny* (BSV) offers an explanation. First, BSV asserts that two distinct.cognitive errors are involved: conservatism bias and sample-size neglect.Second, they assert that the particular error that is operative at a givenpoint in time depends on circumstances.
To briefly recap: The conservatism bias describes the tendency to al-ter a prior belief less than new information would warrant. Existing opin-ions are sticky. Sample-size neglect, also known as the crime of smallnumbers, is the tendency to draw too grand a conclusion from a smallsample of observations. Thus, the conservatism bias is the opposite of thecrime of small numbers. In the former, new evidence is given too littleweight; in the latter, it is given too much.
The conservatism bias is at work when investors underreact to news,leaving prices too low after good news or too high after bad news. How-ever, as investors gradually realize the new information’s true signifi-cance, prices make a gradual systematic march to a level that properlyreflects the news. Thus, some price trends can be explained by investorunderreaction.
The crime of small numbers explains investor overreaction. For ex-ample, if investors observe a small number of positive earnings changes,they may conclude, too hastily, that a valid growth pattern has been estab-lished. This stimulates overly aggressive buying that pushes prices higherthan warranted. Investors can overreact in the same way to a few negativeobservations and push the price too low.Theories of Nonrandom Price Motion 373
The mathematical model put forward by BSV makes the followingsimplifying assumptions: (1) the market has a single stock, (2) its earningsstream follows a random walk, but (3) investors do not realize this,*! so(4) investors labor under the false impression that the stock’s earningsstream shifts back and forth between two distinct regimes, a growth trendphase and a mean-reverting (oscillating ) phase, but they do not considerthe possibility that the earning may follow a random walk, (6) therefore,investors believe their job is to figure out which of these two regimes isoperative at any given point in time.
Let’s see how BSV’s mythical investor might react to the releases ofnew earnings information. At any given time, the investors hold a beliefthat the earnings are in either one of two nonrandom regimes: a growthtrend or mean reverting process. The possibility of a random walk is notconsidered. Recall that a random walk displays less mean reversion thana true mean-reverting process and also displays streaks that are shorterin duration than those found in a truly trending process. As we pick upthe story, an investor holds some prior belief about the earnings regimeand hears news about earnings. Suppose the news announcement contra-dicts the investor’s belief. For example, if the investor had believed thestock’s earnings had been in a growth trend, and a negative earnings re-port came out, the investor would be surprised. Similarly, if the investorhad been under the belief that earnings were in a mean-reverting regime,a streak of similar changes would also be surprising. According to theBSV hypothesis, both kinds of surprises would trigger an underreactionby the investor due to the conservatism bias. In other words, regardlessof whether the investor's prior belief was in a mean-reverting regime or ina growth regime, news contradicting the belief would be given too littleweight causing prices to underreact to the new information. However, ifnews that is at odds with a prior belief continues to occur, the investorwill become convinced that the regime has switched, either from meanreverting to growth or from growth to mean reverting. In this case, theBSV hypothesis predicts that investors will switch from the mistake ofbeing too stuck with their prior beliefs and underreacting (the conser-vatism bias) to the mistake of committing the crime of small numbersand overreacting. Thus, the BSV model accounts for both kinds of in-vestor error as well as the systematic price movement that corrects bothforms of errors.
I illustrate the type of behavior predicted by BSV in Figure 7.12.Here I posit the case where investors initially believe that earnings arein a mean-reverting regime. Thus, they expect positive earnings reportsto be followed by a negative report. For this reason, they underreact tothe first two positive earnings reports. However, after the third positiveB74 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
      
Conservatism Bias Crime of Small Numbers
' Price {
    
Belief:Earnings Mean Revert
Belief:Positive Earnings Trend
+ News +News + NewsEvent Event Event#1 #2 #3
FIGURE 7.42  BSV: underreaction followed by overreaction.
report, not at all unusual in a random process (three heads in a row),they abandon their earlier belief and adopt the idea that earnings haveentered a growth regime and have established an uptrend. Conse-quently they now expect that future reports will also be positive. Thiscauses investors to overreact, pushing the stock above a rational level.The systematic price movements resulting from both types of investorerror are highlighted in grey.
Thus BSV’s model also shows how a feedback loop emerges from thecrime of small numbers. When investors see earnings move in the same di-rection several times in succession, they switch from doubting the trendto assuming it is real.*° However, the conservatism bias initially inyppedesthe adoption of the new belief. The interaction between the crime of smallnumbers and the conservatism bias determines the speed at which specu-lative feedback develops.* Simulations of the BSV model show severalkinds of actual market behavior such as price trends following surprisingearnings, price momentum, long-term reversals, and the forecastingpower of fundamental ratios such as price-to-book. Thus, the BSV modelexplains how investors’ biased interpretations of public information canpredict the occurrence of phenomena that are observed in actual marketdata. This suggests that the model does capture some of the dynamics ofreal financial markets.Theories of Nonrandom Price Motion 375
Biased Interpretation of Private Information: The Daniel, Hir-shleifer, and Subrahmanyam (DHS) Hypothesis. An alternativehypothesis proposed by Daniel, Hirshleifer, and Subrahmanyam (1998,2001)*" (DHS) suggests a different explanation for systematic price move-ments such as trend reversals and trend persistence (momentum). In con-trast to BSV, the DHS hypothesis is founded upon investors’ biasedinterpretations of private research. In addition, DHS emphasizes some-what different cognitive errors: the endowment bias, the confirmationbias (see Chapter 2), and the self-attribution bias (see Chapter 2).
Private research refers to an investor’s independently derived inter-pretations of public information (i.e., proprietary research). The hypothe-sis put forward by DHS predicts that investors will be overly confidentabout the quality of their private research and hence will have a tendencyto overreact to it. This prediction is based on the endowment bias—thepredisposition to place too high a value on what we own or create. Thisoverreaction pushes prices beyond rational levels, which ultimately leadsto a price reversal back toward rational prices. So in DHS’s view of theworld, price reversals are an effect of overconfidence in privately derivedsignals.
DHS also explains price momentum (nonrandom price trends) as theresult of two other cognitive biases: confirmation bias and self-attributionbias. They assert price momentum develops because news releases andprice movements impact investors in a biased manner. Because of theconfirmation bias, news or price movements that confirm an investor'sprior beliefs, (i-e., those founded on their private signals and research),will be given excessive weight. News events and/or price movementsthat are confirmatory have the effect of increasing the investors’ confi-dence that their private research was indeed accurate. This inducesthem to take additional action (buying or selling), which amplifies themomentum of the current price trend. The self-attribution bias alsocauses investors to view confirmatory news or price movement as a signthat their private research is good. Recall that self-attribution causespeople to take credit for outcomes, even if they occur because of goodluck, but to deny responsibility for bad outcomes by attributing them tobad luck. Thus, both the confirmation bias and self-attribution bias in-duce investors to overreact to confirmatory evidence, thereby amplify-ing the market’s positive feedback.
Conversely, if public information or price behavior contradicts theinvestor’s private research, it will be given too little weight (confirma-tion bias) and investors will attribute it to bad luck rather than to poorprivate research (self-attribution bias). Therefore, regardless of whetherthe news or the price movement confirms or contradicts the investor's37@ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
private information, the DHS model predicts that investors’ confidence intheir private research will increase. “This unequal treatment of new infor-mation means initial overconfidence is on average followed by evengreater overconfidence, thus generating price momentum.”* However, af-ter a sequence of contradictory public news signals, the investors’ confi-dence in their private information will be shattered (crime of smallnumbers), and price trends will reverse.
Both DHS and BSV predict that investors are sometimes excessivelyoptimistic and sometimes excessively pessimistic. In addition, these be-liefs are predicted to be subject to reversal when a streak of disconfirm-ing information arrives. If DHS and BSV are valid, then the profits earnedby strategies that exploit trend reversals should be concentrated at thetimes of information releases. This predicted consequence, which opensboth hypotheses to refutation, has been confirmed. Indeed, a largefraction of the excess returns earned by buying value stocks (low priceto book, low P/E, and so forth) and buying the weakest stocks of thepast three to five years do occur near the times of earnings informationreleases.
News Traders and Momentum Traders: The HS Hypothesis.As just described, certain instances of price momentum can be explainedby prices catching up with fundamentals due to an initial underreaction tonews. Other trends can be explained by positive feedback, in which aprice increase stimulates additional buying and price weakness stimulatesadditional selling.” Thus, some investors look for signals in recent pricechanges rather than news.
From the perspective of TA, trends spawned by positive feedback canbe viewed as a cascade of progressively less sensitive trend-following sig-nals being triggered. An initial price movement (news inspired or random)triggers the most sensitive trend signals (e.g., a very short-term moving av-erage crossover signal). Transactions triggered by that signal push pricesenough to trigger a somewhat less sensitive trend signal (e.g., medium-term moving average), which in turn triggers even less sensitive signalsand so on. Whatever the mechanism, investor actions become correlated,forming a herd effect.
The hypothesis proposed by Hong and Stein®! (HS) explains threemarket phenomena: momentum, underreaction, and overreaction. To doso, they postulate two classes of investors, news watchers (fundamental-ists) and momentum traders (technicians). The HS hypothesis asserts thatan interaction between these two classes of investors creates positivefeedback and, therefore, price momentum. The HS assumption that thereis more than one class of investors is grounded in the notion that in-Theories of Nonrandom Price Motion 377
vestors, by dint of their intellectual limits, must confine their attention to arestricted portion of the available information. News watchers confinetheir attention to fundamental developments. On the basis of this informa-tion, they derive private estimates of future returns. In contrast, momen-tum traders confine their attention to past price changes and derive theirprivate forecast of future trends.
Hong and Stein posit that news watchers pay little or no attention toprice changes. As a result, their private assessments of the news fail tospread quickly throughout the community of news watchers. To under-stand how an exclusive focus on the fundamental news impedes the dif-fusion of information, consider what would happen if the news watchersalso paid attention to price action. Watching price movements would al-low them to infer, to some degree, the private information of other newswatchers. For example, rising prices might imply that other news watch-ers were interpreting the news as favorable, whereas falling prices mightimply the opposite. As we have seen, any investor behavior that slowsthe process by which prices incorporate new information can causeprice trends. That is, instead of prices instantaneously adjusting to newinformation, they move gradually to a higher or lower level justified bynew information. For momentum (trend-following) strategies to work,prices must change in a gradual enough fashion to allow signals basedon an initial price change to predict additional price change. Thus, thefailure of news traders to extract information from price action slowsthe rate at which prices adjust to new information, thus resulting inprice trends.
Now consider the effect on market dynamics caused by momentumtraders (trend-followers). The HS hypothesis claims that momentumtraders only pay attention to signals from price behavior. Trend follow-ing assumes that newly emerging price trends are an indication that im-portant new fundamental information is starting to diffuse throughoutthe market. Momentum traders make the bet that the diffusion is in-complete and that the price trend will continue after the initial momen-tum signal. Actions taken by subsequent waves of trend followers keepprices moving, thus generating positive price feedback. Ultimately, asmore trend followers join the party, prices may carry further than fun-damentals justify. The overshooting occurs because momentum tradersare unable to judge the extent to which the news has been diffused andunderstood by all investors. When the news is fully diffused and all in-vestors have acted on it, no further price movement is fundamentallyjustified. Thus, HS assert that the overshooting of rational price levels,resulting from the positive feedback of momentum traders, sets up theconditions for a trend reversal back toward rational levels based onfundamentals.37B_~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
The Bottom Line. Behavioral finance has offered a number oftestable hypotheses that attempt to explain systematic price motion. It isnot yet clear which, if any, of these are correct. No doubt new hypotheseswill be offered. So long as they generate testable (falsifiable) predictions,they should be entertained. What is of importance to TA is that these theo-ries suggest that systematic price movements should occur in financialmarket prices.
NONRANDOM PRICE MOTION IN THE CONTEXTOF EFFICIENT MARKETS
 
The preceding section explained how systematic price movement canarise in markets that are not fully efficient. However, even if markets arefully efficient, the case for TA is not lost. A case cam be made for the exis-tence of systematic price motion in markets that are fully efficient. Thissection presents this case.
How Systematic Price Motion andMarket Efficiency Can Coexist
Some economists contend that systematic price movements are possibleeven if markets are fully efficient.’ In other words, the existence of non-random price behavior and the possibility of price prediction does notnecessarily imply that financial markets are inefficient.’
Other economists state the case more strongly, asserting that somedegree of predictability is not only possible but necessary for markets tofunction properly. In their book A Non-Random Walk Down Wall Street,Andrew Lo and Craig MacKinlay say, “predictability is the oil that lubri-cates the gears of capitalism.”™ A similar position is taken by Grossmanand Stiglitz.°° They contend that efficient markets must offer profit oppor-tunities to motivate investors to engage in the costly activities of informa-tion processing and trading. For it is these very activities that drive pricestoward rational valuations.
In fact, there is only one special condition under which market effi-ciency necessarily implies that price movement must be random and,therefore, unpredictable. This occurs when all investors have the sameattitude toward risk, a condition that is simply not plausible. Is thereany type of risk about which all people have the same attitude? To seehow far-fetched this assumption is, imagine for a moment a world inwhich all people have the same attitude toward risk. There would be ei-ther too many test pilots and high steel workers or not enough of them.Theories of Nonrandom Price Motion 379
Either everyone would be into skydiving and alligator wrestling, or noone would.
It is far more realistic to assume a world of investors with highly di-verse attitudes toward risk. In such a world, some investors would bemore adverse to risk than others, and they would be willing to pay otherinvestors to accept the burden of additional risk. Also, there would likelybe risk-tolerant investors looking to profit from the opportunity to assumeadditional risk. All that would be needed in such a world is a mechanismby which risk can be transferred from the risk adverse to the risk tolerantand for compensation to be paid to those willing to accept said risk.
Let’s step away from markets for a moment to consider one mecha-nism by which the risk adverse can compensate the risk inclined—the in-surance premium. Homeowners dread the possibility of a fire, so theyenter into a transaction with a fire insurance company. The homeownertransfers the financial risk of a fire to the insurance company and, in ex-change, the insurance company earns a premium. This is a profitable busi-ness if the insurance company can predict the likelihood of a houseburning down (over a large number of homes—Law of Large Numbers)and charge a premium that compensates it for that risk plus a bit more forprofit. The bottom line: The insurance company provides a risk accep-tance service and is compensated for doing so.
Participants in financial markets have exposures to various risks andvary in their tolerance for them. These ingredients motivate transactionsthat transfer risk from one party to another. In this situation, the investorswilling to bear greater risks can reasonably demand compensation in theform of a higher return than that offered by riskless investments, just astest pilots and high steel workers can demand a premium salary for theadditional risks they bear. In financial market terms, compensation for ac-cepting increased risk is called a risk premium or economic rent.
Financial markets offer several kinds of risk premiums:
¢ The equity market risk premium: Investors provide working capi-tal for new business formation and are compensated by receiving a re-turn that is above the risk-free rate for incurring the risk of businessfailure, economic downturns, and such.
¢ Commodity and currency hedge risk transfer premium: Specula-tors assume long and short positions in futures to give commercialhedgers (users and producers of the commodity) the ability to shedthe risk of price change. Compensation is in the form of profitabletrends that can be captured with relatively simple trend-followingstrategies.
¢ Liquidity premium: Investors assume positions in less liquid securi-ties and in securities being aggressively sold by investors with acute38O_ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
short-term needs for cash. Compensation is in the form of higher re-turns for holding illiquid securities or in the form of short-term gainsfor buying stocks that have recently been very weak (