Experimental Business Research II springer 2005 phần 2 docx

Return rate Average Aggregate Producer per period per per lease used-good surplus per revenue per of the same setting, and to compare results from different experimental settings in amea

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12 Experimental Business Research Vol II

Table 3 Theoretical predictions with the intended parameters of the residual quality distribution: µ = −1 and σ = 0.2

Theory New-lease prob Return rate Average Aggregate Producer

per period per per lease used-good surplus per revenue per

of the same setting, and to compare results from different experimental settings in ameaningful manner

In order to have an appreciation of how finite sampling correction affects thetheoretical prediction, we first list these predictions with the originally chosenparameters for the residual quality distribution µ = −1 and σ = 0.2 in Table 3.Typically, the finite sampling implies about 5% corrections to the mean and 10%corrections to the volatility As we will see shortly, all aggregate variables, exceptreturn rate, are not very sensitive to the finite sampling correction

Table 4 lists the results of Experiments 1 to 4, along with the correspondingtheoretical predictions corrected by the ﬁnite-sampling effect Since Experiments

2, 3 and 4 share the same k= 160, we first average the aggregate results from thesethree experiments and then compare the average to the theory The differencesbetween these three experiments also serve as a crude measure of behavior fluctuationsfrom rather small sample sizes of subjects Given the fact that there is no fitting pro-cess involved in the comparison, the level of the agreement between experimentalresults and theoretical predictions in Table 4 is quite remarkable Quantitatively, theworst case is the return rate, in which the experimental values are systematicallylower than that of the theory by about 30% One way to interpret this systematicdifference is risk aversion The only uncertainty in this model is the consumption inthe first period of a new lease, represented by an unknown residual quality that isonly realized at the lease-end Thus, risk averse agents may be inclined to keep theleased unit, whose value is known at the time of exercising the option, instead ofstarting another new lease Consequently, return rate will be lower than the theorythat assumes risk neutral consumers Another possible way to interpret the systematicdiscrepancy may be traced to ownership effects However, to settle the true cause,additional theoretical modeling and experimental investigation are needed

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D URABLE E G OODS S L L EASE E C ONTRACTS AND U U SED -G OODS S M M ARKET T B EHAVIOR 13

Table 4 Experimental results and theoretical predictions with the ﬁnite-sample parameters

of the residual quality distribution realized in each experiment

Experiment New-lease prob New-lease prob Return rate Return rate Average Average Aggregate Aggregate Producer

per period per per lease per lease used-good used-good surplus per surplus per surplus per consumer price price period per period per period per

the strike price from k = 80 to k = 160 at a ﬁxed lease price will lead to a slight

decrease in total lease volume, a substantial increase in the return rate, an increase inaverage used-good price, a reduced aggregate surplus for consumers, and an increase

in producer revenue All these directional changes are conﬁrmed in Table 4, with theexception of producer revenue, which went the opposite way of the theoreticalprediction We attribute this deviation to the fact that there are too few new leases

in Experiments 2 and 3, caused by issues of market rules and subject samplingmentioned earlier It is worth noting that the theory predicted a substantial changeonly in the return rate while all other changes are more moderate Experimentalresults conﬁrmed this substantial change in the return rate

We chose not to report standard deviation statistics Since the game is dynamic

in nature, data across periods were not independent Thus, calculating standarddeviations, or any other variance estimates, across periods would not be useful.Furthermore, variations in subject behavior were mostly driven by their differ-ent willingness-to-pay parameter θ Therefore, reporting variance estimates acrossθindividuals would not truly reveal heterogeneous individual characteristics such asrisk aversion However, most of the comparative static holds true between any of

Experiment 2, 3, or 4 (with k = 160) and Experiment 1 (with k = 80) Thus, we have

some conﬁdence that the comparison is valid

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4.3 Detailed Level Comparison

We now examine how the experimental results and theoretical predictions compare

at a detailed level In particular, we are interested in seeing how patterns of sumer behavior emerge as a function of willingness-to-pay We are also interested inseeing how used-good prices change with variations of residual quality For the sake

con-of space limitation, we will only use the results for Experiment 1 as illustratingexamples In most of the cases, the results of Experiment 1 are quite typical Due tothe fact that the used-good market is treated tersely in the theory, we expect thatthe theory will fare less well at a detailed level than at an aggregate level

In the following we treat the same subjects with a different θ essentially as aθdifferent consumer If all the data were used, each subject would yield two points.Thus, we observe a total of twice as many consumers as the number of subjects

in each experiment It can be argued that the data in the ﬁrst two periods withfreshly assigned θ values should be thrown away because of start-game effects.θHowever, we found that the conclusions are not dependent on whether we exercisethis option

4.3.1 Average Payoff and Used-good Price

Figure 1 shows average payoff per period as a function of consumer heterogeneity θ

In the left panel of the figure, the theoretical payoff curve tracks very closely theexperimental payoffs The right panel of the figure indicates that the observed used-good prices are clustered around the theoretical prediction The trend that higherresidual quality implies a higher used-good price is reproduced, though with largefluctuations There is a small number of observations whose residual qualities arehigher than the point where the theory curve ends This signals a slight behaviordeviation from the theory, which predicts that there is an upper limit in residualqualities in the used-good market due to the presence of the option Nevertheless,Figure 1 allows us to conclude safely the following results

Residual Quality

Figure 1 Average payoff as a function of consumer heterogeneity (left panel) and

used-good price as a function of residual quality (right panel) Curves are theoretical predictions and diamond points are experimental observations in Experiment 1.

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D URABLE E G OODS S L L EASE E C ONTRACTS AND U U SED -G OODS S M M ARKET T B EHAVIOR 15Result 1: Observed payoffs are consistent with the theory.p y y

Result 2: Observed used-good prices are consistent with the theory.g p y

4.3.2 Behavioral Segmentation

The theoretical model predicts that subjects would be segmented endogenously intothree classes of behavior Lower valuation consumers θ 僆 (0, θm) are priced out ofthe market Medium valuation consumers in θ 僆 (θm, θθ ) participate in the used-M

good market High valuation consumers θ 僆 (θθ , 1) lease new goods and occasion-M

ally exercise the option embedded in the lease contract at lease-end

Behavior segmentation can be captured in two measures: new-lease probabilityand auction-winning probability Figure 2 shows these probabilities as functions of

θ In Experiment 1, the theory predicts

can see from Figure 2, both new-lease probabilities and auction winning ities are quite low whenθ < 0.3 This supports the conclusion that on average, lowvaluation consumers are priced out of the market New lease probabilities begin torise at around θ = 0.4 and become quite close to the theoretical curve from around

probabil-θ = 0.5 onward On the other hand, though still roughly concentrating at aroundthe right region, auction-winning probabilities are much more spread than thetheory’s prediction From time to time, consumers who would be theoretically thepure used-good buyers also enter the new-lease market, and consumers who would

be theoretically pure lessees venture into the used market One interpretation is thatthe fundamental economics forces were operating correctly However, the perfectrationality assumption in the theory is obviously violated, leading to the smearing

in consumer segmentation

Interestingly, the smeared behavior does not cause a substantial payoff gap,

as can be inferred from the left panel in Figure 1 This implies that the economicincentive that is responsible for the sharp segmentation in theory is not very strong

Figure 2 New-lease probability (left panel) and auction winning probability (right panel)

as functions of consumer’s heterogeneity Lines are theoretical predictions and diamond points are experimental observations in Experiment 1.

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for those consumers whose willingness-to-pays are in the middle, and occasional

“mistakes” are gracefully tolerated In addition, Figure 2 also provides evidence onwhy several subjects have their payoffs much lower than the theoretical curve Forexample, consumers whose θ values lie between 0.8 and 0.9 should have leasedθmore new goods rather than participated in auctions Nevertheless, the followingconclusion can be drawn

Result 3: Strong but Imperfect Patterns of Behavioral Segmentation.g p g

4.3.3 Cherry Picking

Theoretically, units with a higher residual quality have a higher chance of beingpurchased by the consumer exercising his lease-end option Thus, the units returned

to the producer would have a distribution skewed towards the low-end compared to

the original distribution of residual qualities This kind of cherry picking phenomenon

is also observed in the experiment Figure 3 shows the distribution of residualqualities for all the units and the distribution for those units that were returned tothe producer and subsequently entered the used-good market Notice that not allhigh residual quality units were returned to the producer as predicted

Furthermore, Kolmogorov-Smirnov Tests (Table 5) show that, in three out offour experiments, the distribution of residual qualities of the returned units is con-sistent with model predictions Experimental evidence not only conﬁrms the cherry

picking phenomenon in a qualitative fashion, but also suggests that the theory is sound quantitatively despite all the handicapping factors mentioned before.

Result 4: Cherry Picking Observed and Consistent with Theory.y g y

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Table 5 Kolmogorov-Smirnov Test to see if residual qualities of the returned units were consistent with the theoretical distributions

Experiment Observations K-S Statistics P-Value

on their willingness-to-pay parameters Subjects at the low end of willingness-to-paywere priced out of both the used- and the new-goods markets Subjects at the highend leased with increasing frequencies They sometimes exercised their optionsdepending on the realization of the residual quality and the potential value achiev-able at the used-goods market The last segment of the subjects lived in the middleand primarily participated in the used-goods market The sizes of these three groupswere qualitatively consistent with the theoretical model Furthermore, when weincreased the strike price in a different treatment, the experimental market mostlyresponded in the direction predicted by the model This result is robust even withsmall variations of market rules and sampling of subjects Given the fact that thetheoretical model has largely grossed over issues of market rules in the used-good market, the near agreement between the theory and experiment is highlynon-trivial

On the other hand, in all the experiments, the subjects with high valuationare more likely to exercise the option relative to the theoretical prediction Thereare multiple possible explanations One such possibility is risk aversion that isnot addressed by the theoretical model With risk aversion, a leasing subject hasthe tendency to keep the used unit that entails no uncertainty relative to lease anew good that has an unknown consumption in the ﬁrst period Other explana-tions such as ownership effects may also account for the discrepancy between

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theory and experimental results More evidence is needed to pinpoint the correctexplanation

The effect of learning in the experiment appears to manifest mostly in whethersubjects are used to the economic context of the experiment Once subjects familiar-ize themselves with the decision-making process, there is no obviously discernableeffect associated with progressive stages of the experiment However, due to thecomplex setting of the experiment, less experienced subjects, as exemplified inExperiment 2, took a long time to figure out what they ought to behave and henceearned significantly less payoffs comparing to more experienced subjects

There are several directions that can be viewed as natural extensions of thecurrent work To settle whether the aforementioned systematic bias in return rate iscaused by risk aversion or something else can be pursued by extending the theory toinclude risk aversion and conducting additional experiments that are specificallydesigned for this purpose Another interesting direction is to treat the residual qualitybeing only partially observable, which in turn will allow the possibility of studyingthe interplay between optionality and adverse selection Investigations of lease con-tracts with more sophisticated options and under oligopoly market structure are othertopics for future exploration In addition, it is important to realize that the setting ofthe current experiment is not very far from many realistic business environments.Adapting the experiment described in this paper to field studies has the potential toprovide useful business insights Finally, work has already begun to use a modifiedversion of this experiment to examine business strategies in other aspects of theautomotive market

NOTES

1 If the residual quality were known to the lessee at the signing of the lease contract, there would have been no risk factor in each consumer’s decision-making process, at least theoretically This, in turn, would have made the option embedded in the lease contract meaningless.

2 The ﬁnite-sample parameters of the residual quality distribution realized in Experiment 1 are µ = −0.95 and σ = 0.18.

3 The ﬁnite-sample parameters of the residual quality distribution realized in Experiments 2, 3 and 4 are

µ = −0.96 and σ = 0.22.

REFERENCES

Black, F and Scholes, M., (1973) “The Pricing of Options and Corporate Liabilities.” Journal of ical Economy, 81, 637–659.

Polit-Camerer, C., “Individual Decision Making.” In The Handbook of Experimental Economics, edited by

Kagel, J and Roth, A Princeton Univ Press, 1995.

Huang, S and Yang, Y., (2002) “Pricing Lease Contracts with Options in Imperfect Markets of Durable Goods.” Technical Report, Ford Research Laboratory.

Huang, S., Yang, Y and Anderson, K., (2001) “A Theory of Finitely Durable-Goods Monopoly with

Used-Goods Market and Transaction Costs.” Management Science, 56, 549–569.

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Maskin, E and Tirole, J., (1988) “A Theory of Dynamic Oligopoly: I and II.” Econometrica, 56,

549–569 and 571–599.

Merton, R., (1973) “Theory of Rational Option Pricing.” Bell Journal of Economics, 4, 141–183.

Milgrom, P., (1981) “Rational Expectations, Information Acquisition, and Competitive Bidding.”

Econometrica, 49, 921–943.

Wilson, R., (1977) “A Bidding Model of Perfect Competition.” Review of Economic Studies, 44,

511–518.

APPENDIX: PAYOFF CURVES

The following ﬁgures show payoffs of all four experiments Each point representsthe average payoff of a subject under the same willingness-to-pay parameter It isinteresting to note that earnings for all subjects in Experiments 1 and 4 and formost subjects in the other two experiments are very close to the predicted values

As pointed out in section 4, some subjects in Experiments 2 and 3 were earningsubstantially less money

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M ICROECONOMIC

M AND F F INANCIAL L P P RICE E A DJUSTMENT T P P ROCESSES 21

21

Chapter 2

TOWARDS A HYBRID MODEL OF

MICROECONOMIC AND FINANCIAL PRICE

ADJUSTMENT PROCESSES: THE CASE OF A

MARKET WITH CONTINUOUSLY REFRESHED

SUPPLY AND DEMAND

a resulting classical efﬁciency of allocation Yet, the informational efﬁciency

of market prices, often treated as a starting axiom for financial market theory,requires instead that current prices represent fair gambles over an unknown distri-bution of future prices: financial price processes are idealized as random walks withindependent increments perhaps modified by some notion of heteroskedasticitysuch as stochastic volatility Unlike prices following a Marshallian path, randomwalks do not generally converge towards an equilibrium price The conflict betweenthese two views of market processes is explored and a model that is a hybrid

of the microeconomic and ﬁnancial approaches is constructed and comparedagainst data from laboratory markets involving continuously refreshed supply anddemand

1 INTRODUCTION

This chapter is a very rough ﬁrst attempt to integrate some ideas from acrossmicroeconomics and ﬁnance about the price dynamics of competitive markets.The research is from the point of view of an experimental economist interested inlaboratory market equilibration, not from the point of view of general asset pricing

or ﬁnance in general The goal is not to resolve all the questions one might haveabout the nature of price dynamics, convergence or the differing approaches orassumptions that may be involved across various ﬁelds

A Rapoport and R d Zwick (e (( ds.), Experimental Business Research, Vol II, 21–45.

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Instead, the goal is more modest, to put forward the notion that the noisy bration of a fairly simple single market is still a subject worthy of study There are

equili-no “states of the world” in the sense of classical ﬁnance and, correspondingly, equili-nolaboratory bets on securities whose values are based on coin tosses or dice rolls.Instead, there is a pair of markets, a private market and a public market Buyers andsellers receive private, seemingly random opportunities to buy or sell a good fromthe “experimenter” in their private market and are able to trade with each other inthe public market Subjects are not told anything about the distribution of theseopportunities The supply and demand curves representing the aggregate of theseprivate market opportunities are held stationary and the experimenter observes thetime series of voluntary trading prices in the public market

Since the market participants do not know ex-ante what the public market priceshould be, there is a kind of endogenous heterogeneity and complexity of beliefs andknowledge about market conditions more typical to the experimental economicsliterature than the classical ﬁnance literature It is the general success of experi-mental economics in providing a means of studying this peculiar kind of complexitythat is hoped to make such a laboratory approach worthwhile

Although a broad view of some of the problems one encounters in mergingideas from different ﬁelds is important, ultimately the research reported here is muchmore narrowly focused upon a particular data set and a particular form of time-series analysis One can then attempt to ask questions about the adequacy of simple,stationary models: Can price equilibration be described by a simple mathematicalequation with ﬁxed parameters or is a model with two or more regimes more appro-priate? Does something happen when markets equilibrate that we can detect in thetime-series properties of the data? The data reported here is an attempt to get at thesequestions, among others The research is not expected to answer many questions atthis stage, but instead it is an attempt to stimulate new questions and to begin a longprocess of obtaining answers made possible through the continued work of futureresearchers

The remainder of this section will provide an overview of some literature, butdoes not pretend to be a guide to this subject for newcomers nor can it even hope

to even brieﬂy credit all those whose research formed the present understanding ofmarkets The introduction concludes with a brief road map organizing the research

to be presented

Early laboratory studies into market behavior, beginning with Smith (1962),were not designed merely to conﬁrm or demonstrate known principles of economics.Early experimental environments by design violated three common assumptions

once thought appropriate for the applicability of competitive models: (i) perfect

information was violated as student subjects typically knew only their own costs and

values when trading, not the costs or values of others, the aggregate supply and

demand, or the distributions from which costs and values were drawn; (ii) continuity

was violated at the unit level and the agent level because the units traded in themarket are indivisible and because agents were not trading small quantities relative

to the aggregate market; (iii) perfect rationality was probably violated because the

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M ICROECONOMIC

student subjects often had no previous exposure to trading and therefore could not be

expected to trade as well as the perfectly rational homo economicus, and possibly

not even as well as a businessman or professional trader

High efﬁciencies of allocation and convergence of observed prices and tradedquantities to the predictions of competitive theory nevertheless occurred in earlylaboratory markets A critic who only saw the experiments as a misconstruction relat-ive to the requirements of existing theories, inadequately “simulating” a larger market,

or relying “too much” on data from students instead of experienced businessmen orprofessional traders would potentially miss an interesting result regarding the robust-ness of competitive processes These early laboratory markets were real markets.The results showed that the details of trading institutions matter: the prices in themarkets of Chamberlin (1948) did not converge nearly as well as Smith (1962)because Smith included speciﬁc kinds of trading structure – the publicly observablebids and asks recorded on a blackboard and the improvement requirement (bids go

up, asks come down until a trade occurs) inherent to double auction rules – whileChamberlin’s completely unstructured approach left traders on their own to decidewhat to do as they walked around and searched out trades with others in the room.Charles Plott and a number of other researchers duplicated Smith’s early laboratoryresults, going on to laboratory investigations involving multiple markets, trans-formation and production, and other complex scenarios

The broad pattern of these results demonstrated that markets could functionfairly well – given the proper structure and a bit of learning by repetition – withlumpy goods and only a few inexperienced traders in a variety of situations andapplications Plott (2000) has argued that laboratory research on market processesand equilibration can support a modernization of Hayek’s view of the market Hayek(1945) viewed markets as human institutions providing a means of imperfect, butself-correcting, coordination and solution to a demand/supply problem withouthaving to convey all the information about market conditions to a single mind.Previous laboratory studies reveal market equilibration likened to a rational butalmost mechanical process, possibly unrecognized by the market participants,attempting to ﬁnd the solution of an equation balancing supply and demand Eventhough no one (except the experimenters) knows the equations or has full knowledge

of the parameter values needed to solve the equations, the rationality inherent inproﬁt-seeking behavior would drive the process to equilibrium

In contrast, the framework Gode and Sunder (1993) developed as an alternativeexplanation for market equilibration by way of their Zero Intelligence (ZI) robotalgorithm demonstrated a strong potential for a mechanical, non-rational converg-ence processes based only on budget constraints and not on proﬁt maximization.The ZI robot framework is still a popular environment for beginning a study ofmore complex phenomena (see Farmer, Patelli and Zovko (2004) for a recentexample or Duffy (2004) for a review) Prices in markets populated by the ZI robotsappear to converge towards competitive equilibria and exhibit negative autocorrela-tion of price changes Cason and Friedman (1996) ﬁnd negative autocorrelations

of price changes in laboratory markets populated by inexperienced human traders,

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with the autocorrelations moving towards zero and positive autocorrelation withmore experienced subject pools Both studies also show that large surplus tradeswould occur earlier in the market, with convergence being driven by the fact thatthis leaves the price-constrained low surplus trades to occur later in the marketperiod

While laboratory microeconomics has developed a body of empirical regularitiessurrounding imperfect – but functional – markets, standard ﬁnancial theory generallybegins with a set of axiomatically deﬁned perfect markets and derives further prop-erties under various conditions in an uncertain world using mathematical probabilitytheory Take, for example, the case of a popular and regularly traded stock on theNYSE or NASDAQ Analysts follow the business closely Therefore, at least amongthe major market participants setting prices, one might assume perfect information

or at least homogeneous information Millions of shares are traded, so continuity isvirtually satisﬁed The major market participants are generally expert traders, and soshould be acting rationally If one assumes that all known information has been fullyprocessed by a perfect market, the prediction of ﬁnance in the short term is amaz-ingly simple: the share price should represent a fair gamble based on the probabilitydistribution of possible share prices in the near future

Over time, prices should exhibit the properties of a Martingale process, such aszero autocorrelation of price changes Field tests on ﬁnancial market data yieldvarious non-zero results.1

However, a careful theorist can still argue that the

Martin-gale property is an ex-ante property related to historical expectations about future prices and therefore impossible to test ex-post based solely on observed prices alone t

without some additional assumptions – see for instance, Bossaerts (2002; pp 42–43) Without certain simplifying assumptions, one would would instead need to beable to somehow record what the “market was thinking” about future prices, and testwhether the price at each moment in time equaled this expectation as beliefs evolve

Of course, this ex-ante kind of Martingale theory is much more difﬁcult tofalsify, and also causes the ﬁne details of beliefs to become important Are beliefsactually homogeneous so that all market participants have the same expectations or

is this merely a convenient approximation? If beliefs are homogeneous, are theycorrect or at least unbiased? Does it matter if homogeneous, correct beliefs do notinitially exist but do form over time as the market converges? Or do the correctbeliefs exist because the market participants exist in a world of stationary probab-ilities where the frequency of various kinds of events, and their effects on prices, arewell known? Without evoking criticism of any microeconomic or financial theoryand shying away for now from the technical details that make the approaches ofmicroeconomics and finance to equilibration so different, it is interesting to note thatthe Hayekian view of market equilibration as a process of solving for prices withoutconveying all the necessary information to a single mind is in such stark contrast tothe view of more widely studied theories in finance that assume that all marketparticipants are indeed of a single mind in the sense of holding identical, correctbeliefs These questions are already well known but are tricky and quite technical todeal with, and are beyond the immediate scope of this work

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M ICROECONOMIC

Several earlier laboratory experimental approaches to financial economics arereviewed by Sunder (1995) Information aggregation from insiders to the generalmarket and belief formation were common areas for exploration More recently,Bossaerts (2002) reviews many theoretical issues in finance and discusses laboratoryexperiments structured specifically to test asset-pricing models in a multi-asset riskyenvironment Bossaerts (2002; p 129) notes that laboratory markets converge slowly,and this slow convergence in prices may require models with adjustments or biases

of “market” beliefs away from the perfect beliefs assumed by the efﬁcient markethypothesis

Most previous work in finance-based laboratory experiments, including the workcited above, required experiments with many markets and many uncertain states ofthe world in order to fit the mold of the financial models Instead, the research to bereported here focuses on equilibration of a single market The connection to finance

is in the efﬁcient market hypothesis and its implication for Martingale or randomwalks in prices

Irregardless of whether changes in ﬁnancial market prices are due to randomshocks to the proﬁtability of an underlying business or random noise traders, if there

is a pattern to the price changes then there is a potential for proﬁt that should notexist in a perfect market The Martingale or random walk hypothesis can be thought

of as an axiomatic description of perfect market prices without reference to anunderlying firm or asset or any specific requirement limiting the scope to onlyfinancial markets

Does the notion of price as a Martingale process apply to laboratory markets? Ifprices do not follow a Martingale or random walk, is the notion of a random walkstill useful somehow? Can the random walk somehow be reconciled with the notion

of an imperfect market that is attaining competitive equilibrium over time? Theanswer to the ﬁrst question will be no, both on principle and empirically prices inlaboratory markets clearly do not follow a Martingale process But the initial answer

to the latter two questions will surprisingly be yes

The process of reaching this result is as follows: Section 2 describes the ously Refreshed Supply and Demand (CRSD) Environment that is used to generatelong data sets and disrupt the means by which ZI robot populated markets converge

Continu-to equilibrium Human-populated CRSD markets still appear Continu-to converge Continu-towards

an equilibrium price, so something more is happening with the humans that do nothappen with the ZI robots Section 3 identifies the microeconomic and financialapproaches to market convergence Section 4 compares and contrasts these twoapproaches and identifies some issues that would appear to prevent the financialmodel from describing the behavior of laboratory markets Section 5 shows how touse the random walk to design a new kind of trading robot that captures some of, butnot all of, the dynamics of the human-populated market in the CRSD environment.Markets populated by the random walk robots show price dynamics that can bedescribed fairly well as an AR(1) process However, markets populated by humansalso show a kind of outlier-correction whereby prices deviate from the convergencepath and then pop back up to near the previous price Outliers and corrections can be

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