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The influence of investment experience on market prices: laboratory evidence

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We run laboratory experiments to analyze the impact of prior investment experience on price efficiency in asset markets. Before subjects enter the asset market they gain either no, positive, or negative investment experience in an investment game. To get a comprehensive picture about the role of experience we implement two asset market designs. One is prone to inefficient pricing, exhibiting bubble and crash patterns, while the other exhibits efficient pricing. We find that (i) both, positive and negative, experience gained in the investment game lead to efficient pricing in both market settings. Further, we show that (ii) the experience effect dominates potential effects triggered by positive and negative sentiment generated by the investment game. We conjecture that experiencing changing price paths in the investment game can create a higher sensibility on changing fundamentals (through higher salience) among subjects in the subsequently run asset market.

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  1. In all treatments the \({\mathrm {FV}}\) of period \(p\) is disclosed on a history screen after trading ended in period \(p\). In prior studies the development of the \({\mathrm {FV}}\) is mostly displayed in a table in the experimental instructions (e.g., see the experimental instructions of Noussair et al. 2001, Dufwenberg et al. 2005, Huber and Kirchler 2012, and Kirchler et al. 2012). We find no statistical differences between our baseline treatment \({\mathrm {T1}}\) and the comparable baseline treatment in Kirchler et al. (2012) which uses the \({\mathrm {FV}}\)-table in the instructions of the experiment. See also Stöckl et al. (2015) as a reference study for displaying the \({\mathrm {FV}}\) on the history screen instead of displaying it in the experimental instructions.

  2. Specifically, the price path was taken from Stöckl et al. (2015), market 1 of treatment R3(\(/\)).

  3. Subjects earned 12.13 Euro in the investment game preceding \({\mathrm {T3_{POS}}}\), 8.96 Euros in the investment game preceding \({\mathrm {T4_{NEG}}}\), 12.05 Euros in the investment game preceding \({\mathrm {T5_{FV POS}}}\), and 8.53 Euros in the investment game preceding \({\mathrm {T6_{FV NEG}}}\) with an average of 10.66 Euros. Average earnings in the subsequent laboratory market were 13.50 Euros, hence the investment game on average made up some 44 percent of total earnings from these two experiments.

  4. See Online Appendix B for instructions and Online Appendix C for information on the price (index) paths used in the NEG and POS experience treatments of the investment game.

  5. The robustness checks are presented in greater detail in Appendix A. Note that we ran 10 markets for Treatment \({\mathrm {T3_{POS}}}\).

  6. The calculation of DEV yields one observation per treatment.

  7. Additionally we provide results from two-sample Kolmogorov–Smirnov tests to test for differences in distributions. Tests for treatment differences in DEV are based on period values. Note, however, that these values do not constitute independent observations.

  8. Mann–Whitney U tests, N = 28 (60 for DEV). RAD: z = −1.393; p value = 0.1637. RD: z = −1.300; p value = 0.1936. SPREAD: z = −1.346; p value = 0.1782. ST: z = 0.511; p value = 0.6096. VOLA: z = −0.650; p value = 0.5157. DEV: z = −1.552; p value = 0.1206. Kolmogorov–Smirnov tests, N = 28 (60 for DEV). RAD: p value = 0.442. RD: p value = 0.442. SPREAD: p value = 0.237. ST: p value = 0.442. VOLA: p value = 0.802. DEV: p value = 0.183.

  9. We would like to stress that we have also investigated the correlations between behavior in the investment game (e.g., average percent invested, final Taler holdings, emotions after the investment game) and subsequent trading behavior on the market (e.g., final cash holdings, number of limit or market orders posted, the ratio of shares bought minus shares sold) on an individual level. We find no systematic correlations between behavior in the investment game and subsequent market activity on an individual level. These findings are in line with our major finding that no differences in price efficiency emerge between positive and negative investment experience. Detailed results can be provided upon request.

  10. Note that treatments \({\mathrm {T5_{FV POS}}}\) and \({\mathrm {T6_{FV NEG}}}\) also show significantly lower variability of price paths compared to treatment \({\mathrm {T2_{FV}}}\). This is due to one outlier market in the latter treatment. When running the analyses without this market differences are insignificant.


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We thank Charles Noussair (the Editor) and two referees for their very constructive and helpful comments. We thank participants of SAET 2011 and Experimental Finance 2011 for helpful comments. Financial support by the Austrian National Bank (OeNB-Grants 12789 and 14953), the Austrian Science Foundation (FWF-Grants 20609 22400, START-Grant Y617-G11), and the University of Innsbruck (Nachwuchsförderung Stöckl) is gratefully acknowledged.

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Correspondence to Thomas Stöckl.

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Appendix A: Robustness checks

Appendix A: Robustness checks

Figure 3 pictures the evolution of individual market prices (grey lines), of mean treatment prices (bold line with circles), and of \({\mathrm {FV}}\)s (bold line). Table 5 provides treatment averages for RAD, RD, SPREAD, and ST. Difference in treatment averages along with results from pairwise two-sided Mann–Whitney U tests are presented in Table 6.

Table 5 Treatment averages for RAD (mispricing in percent of \({\mathrm {FV}}\)), RD (overvaluation in percent of \({\mathrm {FV}}\)), SPREAD, ST (share turnover), VOLA (standard deviation of log returns), and DEV (the average deviation of period mean prices) for robustness check treatments

Treatment \({\mathrm {T7_{MAXFV}}}\) demonstrates that prices are also quite efficient when a reliable proxy, i.e., information closely related to the \({\mathrm {FV}}\), is shown. Compared to \({\mathrm {T1}}\) the display of the maximum \({\mathrm {FV}}\) reduces RAD and RD, though insignificantly because of high variance, by 9.7 and 20.6 % points, respectively. At the same time there are no significant differences in RD compared to Treatment \({\mathrm {T2_{FV}}}\), while visual inspection of Fig. 3 indicates that variations between markets are markedly higher in \({\mathrm {T7_{MAXFV}}}\) compared to \({\mathrm {T2_{FV}}}\). Thus, an unbiased proxy for the \({\mathrm {FV}}\) has a positive effect by decreasing mispricing, but less so than precise information on the \({\mathrm {FV}}\). In contrast, displaying irrelevant “noise” by showing average period prices of another cohort in Treatment \({\mathrm {T8_{NOISE}}}\) neither serves as anchor nor leads to more efficient prices. Values of RAD and RD of 57.0 percent and 53.0 percent are significantly higher compared to treatments \({\mathrm {T2_{FV}}}\) and \({\mathrm {T7_{MAXFV}}}\), and indistinguishable from the baseline \({\mathrm {T1}}\).

To summarize, we observe that displaying precise information about the \({\mathrm {FV}}\) (\({\mathrm {T2_{FV}}}\)) or useful proxies thereof (\({\mathrm {T7_{MAXFV}}}\)) on the trading screen eliminates or at least reduces mispricing and overvaluation. These results are in line with Corgnet et al. (2010) who show that qualitative messages about the level of mispricing can play a significant role in bubble abatement, or rekindling. Displaying other non-relevant information on the trading screen (\({\mathrm {T8_{NOISE}}}\)) does not significantly influence price efficiency. Thus, we can rule out that simple anchoring drives our results.

Table 6 Differences between market averages (column minus row) in percentage points (except ST) for measures of mispricing (RAD), over-valuation (RD), average bid-ask-spread (SPREAD), share turnover (ST), standard deviation of log returns (VOLA), and the average deviation of period mean prices (DEV)
Fig. 3
figure 3

Fundamental value (\({\mathrm {FV}}\), bold line), mean prices (bold line with circles) and volume-weighted mean prices for individual markets (grey lines) as a function of period of \({\mathrm {T1}}\) (top left), \({\mathrm {T2_{FV}}}\) (top right), \({\mathrm {T7_{MAXFV}}}\) (bottom left) and \({\mathrm {T8_{NOISE}}}\) (bottom right)

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Huber, J., Kirchler, M. & Stöckl, T. The influence of investment experience on market prices: laboratory evidence. Exp Econ 19, 394–411 (2016).

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