A Bayesian approach to experimental analysis: trading in a laboratory financial market
A Bayesian approach to experimental analysis: trading in a laboratory financial market
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实验分析的贝叶斯方法：实验室金融市场中的交易
DOI：
10.1007/s10058-012-0124-8
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发表时间：
2012
影响因子：
0.7
通讯作者：
Cipriani M
中科院分区：
文献类型：
--
作者：
Cipriani M
我们采用贝叶斯方法来分析金融市场实验数据。我们估计了一个顺序交易的结构模型，其中交易决策分为五种类型：基于私人信息、噪音、羊群、逆向和犹豫。通过蒙特卡罗模拟，我们估计了结构参数的后验分布。这种技术使我们能够比较几种非嵌套的贸易到达模型。我们发现，最适合数据的模型是，一旦先前的买入和卖出决策数量之间的差异至少为两次，那么一部分交易就来自于不仅仅依赖其私人信息的主体。在这个模型中，大多数交易都源于主体遵循其私人信息。还有很大一部分噪音交易活动偏向于购买资产。正如理论所表明的那样，我们很少观察到羊群效应和逆势操作。最后，我们观察到很大一部分（犹豫不决的）主体在自己的私人信息与公共信息一致时遵循，但在与公共信息不一致时放弃交易。
We employ a Bayesian approach to analyze financial markets experimental data. We estimate a structural model of sequential trading in which trading decisions are classified in five types: private-information based, noise, herd, contrarian and irresolute. Through Monte Carlo simulation, we estimate the posterior distributions of the structural parameters. This technique allows us to compare several non-nested models of trade arrival. We find that the model best fitting the data is that in which a proportion of trades stems from subjects who do not rely only on their private information once the difference between the number of previous buy and sell decisions is at least two. In this model, the majority of trades stem from subjects following their private information. There is also a large proportion of noise trading activity, which is biased towards buying the asset. We observe little herding and contrarianism, as theory suggests. Finally, we observe a significant proportion of (irresolute) subjects who follow their own private information when it agrees with public information, but abstain from trading when it does not.
实验室金融市场中的噪音交易：最大似然法
影响因子：
3.6
作者：
Marco Cipriani;A. Guarino
通讯作者：
A. Guarino