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Agents interaction and price dynamics: evidence from the laboratory

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Abstract

Using data collected from an experimental double auction market, we study the dynamics of interaction among traders. Our focus is on the effect the trading network has on price dynamics and price-fundamental convergence. At the aggregate level, the network of empirical exchanges reveals properties that are dissimilar from random graphs and, in particular, high centrality and high clustering. Precisely, these properties are identifiable as the cause of price volatility and divergence from the fundamental value. At the microscopic level, we find out how the topological properties of the network derive from the behavior of traders. In fact, our findings show that it is the unbridled trading action of very centralized players who implement a minority game, to give rise to volatility clustering and arbitrage opportunities.

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Notes

  1. Here, we show the expected rate of correct signals from which the percentage is derived. Firstly, 62.5% is the percentage determined considering an expected fraction of 5 over 8 signals correct. Secondly 75% derives from 6 over 8 correct signals.

  2. The value p(d) is based on the number of subjects. To reach a proper level of market informativeness, we keep the expected value of the fraction correct signal higher that the one of incorrect (i.e., higher than 50%) in order to have enough information to predict the dividend at market level. On the other side, we exclude the possibility to have very high level of informativeness and precision to observe trading activity (as can be observed in Fig. 3). This lead us to opt for 5 or 6 expected correct signals per period.

  3. We are aware that our number of traders per market (i.e., 8), the number of sessions (i.e., 6) and the number of repetitions (i.e., 8) may be elements of weakness in this work. However, we are in line with other experimental work. Indeed, on the one hand, there are numerous studies with similar numbers of players per market (see, for example, Morone and Caferra 2020; Huber et al. 2008; Noussair and Xu 2015, with 8, 10 and 14 players, respectively). On the other hand, other studies use a similar number of sessions and repetitions (see, Noussair and Xu 2015, with 8 and 10 sessions and repetitions). There are other studies following a lower number of sessions per treatment in market context (3), as in List and Price (2005).

  4. Specifically, if the dividend is 20, \(d=20\), \(\bar{d}=10\), \(I_1=20\) and \(I_0=10\).

  5. Numerical solutions are obtained by applying the Newton–Rapson algorithm.

  6. The sample, made up of 168 observations, collects the information of the 8 subjects, in the 7 periods for each of the three sessions.

  7. We also estimate the empirical degree distribution with the \(\lambda \) parameter of the Poisson distribution. Results, omitted here, reconfirm the supremacy of the exponential distribution.

  8. Since we have time series, there might be serial dependence among observations. We consider this aspect observing that the network metrics and price variables are not serially correlated (1 lag). Additionally, we observe that all the metrics at time t are not dependent by the dividend drawn at time t and t-1. This partially justifies our choice to treat observations as “serial independent” for T-tests.

  9. By sampling every \(\tau =60\) s, we obtain a minimum of 20 and a maximum of 52 transactions every \(\tau \).

  10. We implements the “leading eigenvector” method.

  11. What we generate are not true time series, but more appropriately sequences of data. In fact, if it is true that there is temporal consecutiveness within each session, i.e., during the 7 trading periods that make up each session, there is no temporal consecutiveness between the different sessions. To be clear, the vertical solid lines identifying each session in Fig. 5 could be moved without modifying the analysis, which is not true for the vertical dashed lines corresponding to the trading periods.

  12. The traders descending order is also robust using other centrality measures such as the closeness and degree centrality.

  13. In this case, S1–S8 are the codes uniquely associated to each subject within each period considering the average strategy adopted by each player. Hence the code related to each subject changes depending on their order across different periods.

  14. We have also checked if the fraction of coherent agents impacts the network structure, obtaining no statistically relevant results.

  15. The 4 most peripheral agents have similar correlations to those reported here. Results are available upon request.

  16. S1 and S2 wealth at time t, \(W_{S,t}\), is given by \(W_{S,t}=C_{S,t}+A_{S,t}d_{t}\), where C and A is the amount of cash and stocks, respectively, and d the dividend. S2 is used as a proxy for the system due to the synchronization of the market orders of no-attacker traders. However, results are robust also compering S1 wealth with the system’s average wealth without the hub.

  17. C is calculated as the reciprocal of the sum of the length of the shortest paths between a player and all other subjects in the graph.

  18. The sample is made up of 338 observations, that is \(N=8\) subjects play two different scenarios (T1 and T2), repeated 3 independent times over a time span of 7 periods.

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Authors and Affiliations

  1. Department of Economics, Management and Business Law, University of Bari Aldo Moro, Bari, Italy

    Rocco Caferra, Gabriele Tedeschi & Andrea Morone

  2. Department of Economics and Law, Unitelma Sapienza University of Rome, Rome, Italy

    Rocco Caferra

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  1. Rocco Caferra
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Correspondence to Rocco Caferra.

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Caferra, R., Tedeschi, G. & Morone, A. Agents interaction and price dynamics: evidence from the laboratory. J Econ Interact Coord 18, 251–274 (2023). https://doi.org/10.1007/s11403-022-00366-5

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