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Creation and Control of Bubbles: Managers Compensation Schemes, Risk Aversion, and Wealth and Short Sale Constraints

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Handbook of Financial Econometrics and Statistics

Abstract

Persistent divergence of an asset price from its fundamental value has been a subject of much theoretical and empirical discussion. This chapter takes an alternative approach of inquiry – that of using laboratory experiments – to study the creation and control of speculative bubbles. The following three factors are chosen for analysis: the compensation scheme of portfolio managers, wealth and supply constraints, and the relative risk aversion of traders. Under a short investment horizon induced by a tournament compensation scheme, speculative bubbles are observed in markets of speculative traders and in mixed markets of conservative and speculative traders. These results maintain with super-experienced traders who are aware of the presence of a bubble. A binding wealth constraint dampens the bubbles as does an increased supply of securities. These results are unchanged when traders risk their own money in lieu of initial endowments provided by the experimenter.

The primary method of analysis is to use live subjects in a laboratory setting to generate original trading data, which are compared to their fundamental values. Standard statistical techniques are used to supplement analysis in explaining the divergence of asset prices from their fundamental values.

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Notes

  1. 1.

    The paper was previously published as Ang et al. (2009). The creation and control of speculative bubbles in a laboratory setting. In Lee, A., and Lee, C.F. (Eds.), Handbook of Quantitative Finance and Risk Management (pp. 137–164). Springer, New York.

  2. 2.

    Stiglitz (1990), in his overview of a symposium on bubbles, defines the existence of bubbles to be: “if the reason that the price is high today is only because investors believe that the selling price will be high tomorrow – when ‘fundamental’ factors do not seem to justify such a price.” Similarly, he defines the breaking of a bubble as marked price declines which occur without any apparent new information.

  3. 3.

    Other notable examples of bubbles include the Dutch tulip mania in the seventeenth century, the South Sea Islands Company bubbles Voth and Temin (2003), John Law’s Mississippi Company scheme bubbles of the eighteenth century, the US stock market boom of the late 1920s, the Florida land price bubbles of the 1920s, the great bull market of the 1950s and 1960s, the high-tech stock boom of the early 1980s, and the boom and bust of the California and Massachusetts housing markets in recent years. However, due to the difficulties in specifying the fundamentals, there are still disagreements as to whether these cases could be explained by the fundamental, e.g., Garber (1990) versus White (1990).

  4. 4.

    Outstanding surveys of this literature are provided by Porter and Smith (2003), Camerer (1989), Sunder (1992).

  5. 5.

    Griffin et al. (2003) examine the extant theoretical literature about bubbles which includes models where naive individuals cause excessive price movements and smart money trades against (and potentially eliminates) a bubble versus models where sophisticated investors follow market prices and help drive a bubble. In considering these competing views over the tech bubble period on Nasdaq, they find evidence which supports the view that institutions contributed more than individuals to the spectacular Nasdaq rise and fall.

  6. 6.

    Becker and Huselid (1992), Ehrenberg and Bognanno (1990) have documented in field studies that such tournament compensation systems are effective in raising performance in professional golf and auto racing competitions.

  7. 7.

    It is possible that if there is sufficient number of short horizon portfolio managers herding in the manner described by Froot et al. (1992), a bubble can start on basis of any information. Shleifer and Vishny (1990) also propose that the portfolio managers have short horizon; however, it is the risk of uncertain return from investing in the longer horizon that prevented disequilibrium to be arbitraged away.

  8. 8.

    In the experiment, a trader has at least the following choices available:

    1. (a)

      Maintain the endowed position by not trading and receiving the stochastic payoffs at the end of each period.

    2. (b)

      Hold the securities through period A and sell in period B, in which case the investor will receive the first period dividend and the selling price.

    3. (c)

      Sell the initial holdings in period A to receive the sale price.

    4. (d)

      Buy additional shares in period A, receive dividends at the end of the period, and then sell the securities in period B.

    5. (e)

      Sell the securities in period A and then buy back securities in period B in order to receive the dividends.

    6. (f)

      Purchase a net amount of shares in both periods.

    7. (g)

      Purchase and sell shares within each period.

  9. 9.

    See Smith et al. (1988) for an example of when reinitialization is not used.

  10. 10.

    It is important to note that there is a difference between a one- and two-period horizon and a shortened horizon. In a one-period model, only a single dividend is valued. In a two-period model, two dividends are valued. In our shortened investment horizon, the trader is induced to operate within a horizon that is different from that of his operating environment. That is, within a two-period operating environment, the trader is given an incentive to operate with a shorter (possibly single)-period horizon. This is quite different from a single-period model. This shortened horizon is a stronger test of market efficiency, in that the pressures are away from rather toward rational equilibrium prices, (as defined in Eq. 73.4, subsequently). The methodology is meant to emulate modern portfolio managers operating in an environment of perpetual horizon stock securities yet receiving tournament incentives to outperform colleagues on a short-term basis.

  11. 11.

    Francs are the currency used within this study. They have been used successfully by Plott and Sunder (1982), Ang and Schwarz (1985), as well as others. Their primary benefit is to avoid the technical problem of dealing with small dollar amounts.

  12. 12.

    The compensation schemes depict the different ways portfolio managers are being rewarded: those who are above the average or beaten the market (Schedule Six) and those who are the superstars (Schedule Two).

  13. 13.

    The authors are aware of the work of Holt and Laury (2002) which was not available at the time of this study. According to Holt and Laury, their experiment shows that increases in the payoff level increase RRA. However, when estimating RRA, Holt and Laury assume that subject’s utilities depend only on payments in the experiment. They fail to account for the wealth subjects have from other sources (see Heinemann 2003).

  14. 14.

    The Jackson Personality Inventory is scientifically designed questionnaire for the purpose of measuring a variety of traits of interest in the study of personality. It was developed for use on populations of average or above average ability. Jackson states (1976, p. 9), “It is particularly appropriate for use in schools, colleges, and universities as an aid to counseling, for personality research in a variety of settings, and in business and industry.” Of the 16 measurement scales of personality presented, one scale directly measures monetary risk taking using a set of 20 true and false questions. Mean and standard deviation measures for 2,000 male and 2,000 female college students are provided. Jackson et al. (1972) demonstrate four facets of risk taking: physical, monetary, social, and ethical. The authors’ questionnaires are situational in that the respondent is asked to choose the probability that would be necessary to induce the respondent to choose a risky over a certain outcome. Jackson (1977) presents high internal consistency correlation between the risk measurement techniques.

  15. 15.

    Note that all odd-numbered experiments used the dividend design in Table 73.2. In order to differentiate between (1) learning about a stationary environment and (2) learning efficient valuation within laboratory markets, we created nonstationarity in equilibrium prices across experiments. In particular, for all even-numbered experiments, the dividend payoffs of Table 73.2 were simply cut in half so that rational equilibrium prices were also one-half that of the odd-numbered experiments. When this equilibrium dividend rotation is viewed in conjunction with the previously mentioned rotation of trader types, it becomes apparent that each individual trader was likely to view the environment (at least initially) as nonstationary. Consequently, any results that we show regarding equilibrium pricing and convergence would suggest that learning about valuation methods rather than a stationary environment creates rational valuation. That is, we are concerned about learning which takes place within the trader (how he values) not about the environment (stationary value). We are able to pursue this expanded question due to our debt to earlier authors who have already well established the presence of the latter.

  16. 16.

    While period A prices exceeded the calculated PFE price of 460, this price is somewhat unknown to traders at this point. Prior trading results had created a history of B period prices averaging 320. Consequently, it was rational for a PFE trader to pay up to 550 (230 for A period plus 320 for B period sales price). The last trade in period 5A of 505 was well below that level. A more detailed presentation of the experimental results further reveals the rationality of these prices and is available from the authors upon request.

  17. 17.

    Again, period A prices seem to drift upward due to initial excess pricing in period B.

  18. 18.

    Our design is to eliminate the bubble effect of miscalculation caused by inexperienced traders as suggested by White (1990) and King et al. (1990). It is more useful and realistic to study the formation and control of bubbles in markets of experienced traders.

  19. 19.

    To the author’s knowledge, this is the first time traders in an experimental market of this type have used their own money to trade and still produced bubbles.

  20. 20.

    For instance, new strategies were employed at various stages (which perpetuated continuing uncertainty in the markets). At one time, the market actually stood still for an extended period. Then traders began to liquidate at any price rather than to replicate their earlier strategy of waiting until late in the period to sell out at bubble prices. Other traders began to try and scalp the market by driving prices both up and down, thereby generating capital gains in both price directions. Even others began to try and force losses on traders with large inventories and thereby improve their relative ranking. This was accomplished successfully in period 2A by selling at a loss (at a price below market prices) in order to create a low settle price, M (the second to last trade). Other attempts at this strategy followed in all remaining A periods. Nevertheless, bubbles persisted and many traders were frustrated in their inability to arbitrage them away.

  21. 21.

    Traders completed survey questionnaire at the completion of experiments 4, 6, and 10.

  22. 22.

    Given that in experiment 6, period A prices averaged around 600, initial trading capital of 3,000 francs would provide buying power of roughly five securities. Consequently, the new buying power and selling power were a priori relatively equal. Even though period A prices turned out to be quite a bit lower in experiments 7–10, this did not create a great advantage to buyers since the supply of securities (5 traders × 12 traders = 60) was relatively large for a 6-min trading period. As such, there was an ample supply of securities relative to buying power in order to drive prices down should traders turn bearish.

  23. 23.

    We are unable to recruit all 12 traders back for experiments 7–10 due to graduation, taking of jobs, etc. We were, however, able to retain 7 of the original 12 traders. These traders had now participated in six previous experiments. The five replacements were drawn from the original pool of subjects that had completed the risk attribute questionnaires. These new traders were chosen to replace the risk types that had vacated so that in general, we maintained a wide dispersion of risk types within the market. In addition, some of these new traders had sat in as observers to previous experiments. Others viewed videos of the earlier experiments. All were instructed in the past experimental results, and the various strategies previously used were explained. As such, we do not believe that this change is a critical factor in the continuation of our investigation.

  24. 24.

    An analysis of many of the last trades of period A for experiments 7–10 often shows either a sharp spike up or down. This illustrates that the traders had become very efficient (through learning) in their manipulation of closing prices. Given the large supply of securities available to squelch a price bubble, speculators were no longer singularly (due to large initial endowments of trading capital) able to create capital gains by driving market prices up. With this constraint, they quickly learned that all they needed to accomplish was to purchase the most securities at present prices and then drive the market up on the final few trades. This was often easily accomplished in that 1) only the second to last trade needed to higher in line with the calculation rules of the TPI and 2) as no surprise, there were always many traders who were willing to sell their securities at a price above the current level. The art to this strategy became a matter of timing; do not try to buy the market too early lest you run out of capital, and do not be too late lest you be unable to make the second to the last trade. There did not appear to be too much of a problem for buyers in accomplishing this in experiments 7 and 8; however, starting in experiment 9, some short traders, having become annoyed at bullish traders getting the tournament prize, began jockeying in these last seconds with the long traders in order to drive prices down. The results of such feuds appear in periods 3A, 4A, and 5A of experiment 9 and each A period of experiment 10. The winner of these duels increasingly became the trader who was best able to execute his trade. Eventually, trading activity become so enraged in the last 15 s of trading that the open outcry systems of double-oral auction began to break down.

  25. 25.

    Experiments 11–14 were conducted at a second university, and therefore, the results provide information about the external validity of our experiments outside the setting of a single university.

  26. 26.

    A detailed examination of individual trades reveals the speculative group of traders who are found to be more innovative in designing new trading strategies both in the creating and bursting of bubbles. The finding is consistent with the observation made by Benjamin Friedman (1992) in his review of a dozen NBER working papers on asset pricing. He finds these recent research results demonstrate that rational speculative behaviors such as an attempt by investors to learn from other investors, to affect another’s opinion, or to simply engage in protective trading could in some context, such as imperfect information, magnify price fluctuations.

  27. 27.

    See the classic textbooks by Greene (2012), Wooldridge (2010), or Hayashi (2000) for details on the implementation and interpretation of OLS.

  28. 28.

    Furthermore, although insignificant, the p-value for I*S is equal to.14, suggesting that the speculative difference may be even greater under a shortened investment horizon.

  29. 29.

    In addition, we perform further OLS regressions and report the results in Table 73.5.

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Appendices

Appendix 1: Statistical Analysis

Table 73.4 summarizes the ordinary least squares regression analyses of the impact upon the divergence of asset prices from their PFE levels in period A.Footnote 27 In particular, we test the following relation:

$${\mathrm{P}}^{\mathrm{L}}-\mathrm{PFE}=f\left(\mathrm{I},\mathrm{E},\mathrm{I}*\mathrm{E},\mathrm{T},\mathrm{I}*\mathrm{E}*\mathrm{T},\mathrm{S},\mathrm{I}*\mathrm{S},\mathrm{A},\mathrm{I}*\mathrm{A}, \$ \right)$$
(73.5)

where:

  • PL – PFE = the deviation from equilibrium for period A of each trading year where PL is the last trade of the period and PFE is the perfect foresight equilibrium price,

  • ƒ = a linear additive model,

  • I = a dummy variable representing the shortened investment horizon according to Table 73.1 I = 1 for shortened horizon and 0 otherwise (i.e., experiments 4, 610, 12, 14),

  • E = a dummy variable representing the endowment effect according to Table 73.1. E = 1 when 2 securities are issued and 0 otherwise (i.e., experiments 1–6, 11–14),

  • I*E = an interaction dummy variable representing both a shortened investment horizon and two-security endowment (i.e., experiments 4, 6, 12, 14),

  • T = a dummy variable representing the tournament effect according to Table 73.1. T = 1 when there is a tournament prize for two traders only and 0 otherwise (i.e., experiments 4, 6, 12, 14 (years 4 and 5), 9, and 10),

  • I*E*T = an interaction dummy variable representing a shortened investment horizon, a two-security endowment, and a tournament effect (i.e., experiments 4, 6, 9, 10 (years 4 and 5)),

  • S = a dummy variable representing the extent to which speculators participated in the experiments according to Table 73.1. S = 1 for experiments 11 and 12 and 0 otherwise,

  • I*S = an interaction dummy variable representing the shortened investment horizon and a pure speculative trader market (i.e., experiment 12),

  • A = the ratio of end-of-period asset inventory for speculative traders to total asset holdings. Speculative traders are those who scored in the top one-half of the risk measurement questionnaires,

  • I*A = an interaction variable for shortened investment horizon and ratio asset holdings for speculators (experiments 4, 6–10, 12, 14),

  • $ = a dummy variable representing experiments where traders risked their own money according to Table 73.1. $ = 1 when their own money is used and 0 otherwise (i.e., experiments 5 and 6).

Fig. 73.2
figure 2

Experiment 2: bid ask, close and equilibrium prices, two dividends with five trading periods each, and mixed risk aversion. Subjects are from Las Vegas

Fig. 73.3
figure 3

Experiment 3: bid ask, close and equilibrium prices, two dividends with five trading periods each, and mixed risk aversion. Subjects are from Las Vegas

Fig. 73.4
figure 4

Experiment 4: bid ask, close and equilibrium prices, one dividend with 5 trading periods, and mixed risk aversion. Subjects are from Las Vegas. Mixed bonus: to top 6 traders in first half and top 2 traders only in the second half

Fig. 73.5
figure 5

Experiment 5: bid ask, close and equilibrium prices, two dividends periods with five trading periods each, and mixed risk aversion. Subjects are from Las Vegas and use own funds

Fig. 73.6
figure 6

Experiment 6: bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion. Mixed bonus: to top 6 traders in first half and top 2 traders only in the second half. Subjects are from Las Vegas and use own funds

Fig. 73.7
figure 7

Experiment 7: bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion, and increase supply of shares. Bonus to top six traders. Subjects are from Las Vegas

Fig. 73.8
figure 8

Experiment 8: bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion and increase supply of shares. Bonus to top six traders. Subjects are from Las Vegas

Fig. 73.9
figure 9

Experiment 9: bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion and increase supply of shares. Bonus to top two traders. Subjects are from Las Vegas

Fig. 73.10
figure 10

Experiment 10: bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion and increase supply of shares. Bonus to top two traders. Subjects are from Las Vegas

Fig. 73.11
figure 11

Experiment 11: bid ask, close and equilibrium prices, two dividends and five trading periods each, with speculative traders. Subjects are from FSU, experiment 1

Fig. 73.12
figure 12

Experiment 12 bid ask, close and equilibrium prices, one dividend with 5 trading periods, with speculative traders. Subjects are from FSU, experiment 1. Mixed bonus: to top 6 traders in first half and top 2 traders only in the second half. Subjects are from FSU, experiment 1

Fig. 73.13
figure 13

Experiment 13 bid ask, close and equilibrium prices, two dividends and five trading periods each, with speculative traders. Subjects are from FSU, experiment 1. Mixed bonus: to top 6 traders in first half and top 2 traders only in the second half. Subjects are from FSU, experiment 1

Fig. 73.14
figure 14

Experiment 14 bid ask, close and equilibrium prices, one dividend with 5 trading periods, with conservative traders. Mixed bonus: to top 6 traders in first half and top 2 traders only in the second half. Subjects are from FSU, experiment 1

Fig. 73.15
figure 15

Experiment 15 bid ask, close and equilibrium prices, two periods, mixed risk aversion. Subjects are from FSU, experiment 2. Considered most sophisticated

Fig. 73.16
figure 16

Experiment 16 bid ask, close and equilibrium prices, one dividend with 5 trading periods, and mixed risk aversion. Subjects are from FSU, experiment 2. Considered most sophisticated

Fig. 73.17
figure 17

Experiment 17 bid ask, close and equilibrium prices, two periods, and mixed risk aversion. Increase supply of shares. Subjects are from FSU, experiment 2. Considered most sophisticated

Fig. 73.18
figure 18

Experiment 18 bid ask, close and equilibrium prices, two period, and mixed risk aversion. Increase supply of shares. Subjects are from FSU, experiment 2. Considered most sophisticated

Fig. 73.19
figure 19

Experiment 19 bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion. Subjects are from Albania

Fig. 73.20
figure 20

Experiment 20 bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion, and bonus to top traders. Subjects are from Albania

Fig. 73.21
figure 21

Experiment 21 bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion and bonus to top traders. Subjects are from Albania

Fig. 73.22
figure 22

Experiment 22 bid ask, close and equilibrium prices, two periods, mixed risk aversion, and bonus to top traders. Increase supply of shares. Subjects are from Albania

Fig. 73.23
figure 23

Experiment 23 bid ask, close and equilibrium prices, two dividends with five trading periods each, and mixed risk aversion. Subjects are from Albania

Fig. 73.24
figure 24

Experiment 24 bid ask, close and equilibrium prices, one dividend with 5 trading periods, and mixed risk aversion. Subjects are from Albania

Fig. 73.25
figure 25

Experiment 25 bid ask, close and equilibrium prices, one dividend with 5 trading periods, mixed risk aversion. Subjects are from Albania

Fig. 73.26
figure 26

Experiment 26 bid ask, close and equilibrium prices, one dividend with 5 trading periods, and mixed risk aversion. Increase supply of shares. Subjects are from Albania

Table 73.4 The impact of investment horizon, credit/supply constraints, risk aversion, and other variables
Table 73.5 Negative bubble and single period results

Due to their differential design, the results for experiments 1–10 appear separately in Panel A and those for experiments 11–14 in Panel B.

Model 1 of Panel A tests the impact of (1) I = 1, a shortened horizon; (2) E = 1, a restricted endowment effect (wealth and supply effects); and (3) I = 1, E = 1, an interaction of a shortened horizon with restricted initial endowment. Given that the regression was run with no intercept, the coefficients represent estimates of each variable’s independent impact. The results suggest that neither a shortened investment horizon nor a biased endowment effect (advantage to “bulls” versus “bears”) is sufficient to induce bubble behavior. However, the interaction of these two variables is highly significant in explaining the bubble results of these experiments. That is, an environment that provides both the incentive and the ability to profit from a bubble will likely result in positive price divergence.

As hypothesized earlier, we test for the heightened effect of tournament incentives (i.e., T = 1) by examining the effect of “superstar” prizes paid to only the top two traders (as outlined in Tables 73.1 and 73.3). We also test for an interaction effect with a shortened horizon (I = 1) and restricted endowment (E = 1). Model 2 results are consistent with Model 1 in that a tournament effect is not sufficient in itself (t = 0.13 on T variable); however, in conjunction with a reduced horizon and restricted endowment, the tournament interacts to explain a significant part (t = 4.57 on I*E*T) of the bubbles in these experiments.

In Model 3, we observe the impact of speculative traders vis-á-vis conservatives by introducing a measure of asset purchase activity. The end-of-period asset holdings for the speculative group (the top one-half of traders in risk ratings) are compared to the total asset endowment for all traders. In the absence of any effect, assets should be evenly divided, and this ratio, A, should be equal to.5. The results of Model 3 indicate that speculators independently do not impact the presence of a bubble (t = 0.01 for A); however, when speculators operate within a shortened horizon (I*A), they do significantly differentiate themselves from conservatives by buying more and contributing to positive price bubbles. Finally, the impact of the use of the trader’s own money is shown not to significantly alter the effects of the price bubbles (t = −0.54 for $). The R2 of .80 suggests that the vast majority of price deviation from PFE levels can be explained by investment horizon, endowment effects, and risk aversion.

Panel B reports the results for experiments 11–14 where markets were composed of either all speculators (11, 12) or all conservatives (13, 14). Due to this makeup, variables A and I*A are not defined in these regressions although S and I*S are substituted in their place and represent the speculative markets (11 and 12) and the interaction of shortened horizon with a speculative market (12). In addition, a restricted endowment effect (E = 1) is imposed for experiments 11–14 since experiments 7–10 clearly established their necessity in creating bubbles. Model 4 results highlight the significant positive effect of the combined shortened horizon/restricted endowment effect (t = 6.98 for I). More importantly, the speculative group statistically differs from conservatives with an additional mean price difference of 89.9 (t = 4.92). Model 5 supports the results of experiments 1–10 in that 1) a shortened investment horizon with restricted endowments leads to price bubbles (t = 2.71) for I, 2) a heightened tournament incentive will heighten short-term horizons and lead to positive price effects (t = 2.83 for I*T), and 3) speculators contribute to positive price bubbles in restricted endowment environments (t = 3.17 for S).Footnote 28

The visual analysis of experiments 15–18 (negative bubble experiments) is confirmed by the regression results reported in Table 73.5. The variables are as defined earlier under Eq. 73.5 albeit the EN representing a dummy variable for the negative endowment effect. EN = 1 when the initial endowment equals 10 securities and 1,000 firms and 0 otherwise. In addition, since the shortened horizon variable I occurs for all years except 1A of each experiment, I and E are highly correlated. The design is therefore set to only measure the interaction effects of a shortened horizon and endowment. The four periods (1A of each experiment) are the control periods where a shortened horizon is not present (dummy NI = 1 for not I).

The parameter estimates of Model 6 show significant positive results for both positive and negative bubbles. The joint presence of a shortened horizon induced by a tournament payoff along with a buy side endowment (2 securities, 10,000 firms), that is, I*E = 1, leads to an average increase of 400.6 francs in price levels. The single alteration of the endowment to sell side (10 securities, 1,000 francs) in the presence of a tournament leads to an average decrease in price of 201.3 francs. The estimate for NI reflects the insignificant impact of the control periods where the endowment effect is present but without the tournament payoff inducing a shortened horizon. So as in the earlier results, the combined effect of the incentive (i.e., the tournament) and the ability (i.e., the endowment) works to create both positive and negative price bubbles.

Appendix 2: Additional Tests

To check the robustness of our results, we conduct eight final experiments in a unique and different setting, the former Communist country of Albania.Footnote 29 Of its many unique characteristics, one of the most important is its history of being the most isolated (politically and economically) country in Europe since World War II. Since democratic reforms opened in 1991, a new business school was opened in the second largest city of Albania, Shkodra, where the third year students served as traders. Would the students whose country didn’t have a securities market or a history of free market trade show the same results as we had found at US universities? While our previous experiments had the most experienced traders ever used in a study, these may indeed represent the least experienced traders examined to date which may be regarded as an extreme test of the validity of our results.

Because of the newness of the trading experience for these students, a single-period design was used in the first four experiments. For each experiment’s ten trading years (no period B), asset payoffs were for a single dividend payoff. The amounts used were the same as those of Table 73.2 so that equilibrium levels remained at 230 for each year. As shown in Table 73.1, Design 7 (experiment 19) consists of a single-period security without a tournament effect. Design 8 (experiments 20 and 21) introduces the tournament payoff of Table 73.3 (Schedule Two) within the single-period environment. This allows us to test for the presence of bubbles in the simpler pricing environment while also easing the learning experience of the Albanian students toward two-period tournament pricing.

The pricing results for these three experiments can be seen in Figs. 73.1973.21. Without the tournament in experiment 19, pricing is rational and typical showing a discount (risk premium) of about 30 francs from the equilibrium level of 230. Near the end of experiment 20, the tournament effect appears to have created some price movement above equilibrium. This pressure continues into experiment 21 where prices trade at an average premium of 30 francs. While these premiums do not constitute a bubble, it is clear they had a significant positive impact on pricing levels. Would this effect be eliminated (reversed) by changing the buy/sell pressure as was done earlier under Design 6 where negative bubbles were induced? Design 9 tests this proposition by changing the endowment from 2 securities and 5,000 francs to 20 securities and 500 francs. The results, reported in Fig. 73.22, show that even in these simple markets, the endowment effect combined with tournament payoff leads to pricing away from equilibrium. These observations are confirmed by the regression results of Model 7 in Table 73.5 where buy side preference (I*E = 1) leads to significant increase in prices, while sell side preference (I*EN = 1) leads to lower prices. The absence of a tournament payoff (NI = 1) leads to insignificant price effects as investment horizon cannot be altered in a single-period market.

The Albanian students had now participated in four single-period experiments and were ready to attempt two-period pricing. Experiment 23 was a simple two-period pricing environment without any tournament payoff as in Design 1 (control). The plot of prices in Fig. 73.23 shows that the students initially struggled with two-period pricing since period A prices (two payoffs) differed little from period B prices (single payoff), though by the end of the experiment enough learning had developed.

Experiments 24 and 25 introduce the shortened investment horizon (tournament effect) within the two-period framework as in Design 2 earlier. The price patterns in Figs. 73.24 and 73.25 show the creation of positive price bubbles to levels approaching 650 francs. Despite the historical background of this country and these students, they responded to market pressures in the same bubble-like manner. The last experiment, 26, alters the endowment to the sell side as before to see if negative bubbles can also be obtained. Price paths in Fig. 73.26 show a general downward trend of prices. The prices in period A show significant and growing discounts from the equilibrium levels of 460. These observations are confirmed by the regression results reported in Panel C of Table 73.5 with buy side endowment contributing 174.5 francs and sell side endowment reducing levels by 208.0 francs.

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Ang, J.S., Diavatopoulos, D., Schwarz, T.V. (2015). Creation and Control of Bubbles: Managers Compensation Schemes, Risk Aversion, and Wealth and Short Sale Constraints. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_73

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