DRU -- Collaborative Research: An Econophysics and Behavioral Approach to Financial Fluctuations

DRU合作研究：金融波动的经济物理学和行为学研究[j]。Eugene Stanley，波士顿大学david I. Laibson，哈佛大学和NBERXavier Gabaix，麻省理工学院和nber问题的本质可以描述如下。衡量股市活动的各种指标显示出令人费解的“普遍”特征，这些特征最近引起了许多研究的关注。这些特征包括收益、交易量、交易数量、交易机构管理下的资产的幂律分布，以及连接它们的其他幂律关系。这些普遍的特征不仅对市场波动模型提出了挑战，而且它们特定的幂律性质也提出了新的建模方向，其中包括统计物理学的思想，这些思想被证明对理解关键现象的物理学中发生的类似关系很有用。我们试图通过探索以下假设来理解这些规律的起源：(i)当一些大型机构在流动性相当小的市场上进行交易时，会产生高额回报。（ii）当市场流动性下降时，无论相关机构的规模如何，都会产生高额回报。（iii）由于波动率反馈机制也可以产生幂律分布，并在交易活动中产生短期和长期相关性，从而产生大回报。我们通过开发简单理性和有界理性模型，以及分析超大数据集来解决这个问题。紧凑的可处理模型和对大量数据集的探索之间的这种协同作用一直是我们研究小组的主要方法。我们的实证研究将研究收益、交易量、交易次数和流动性的联合分布。这包括详细研究贸易对价格的影响及其在不同市场条件下的变化。建模将涉及设计金融决策模型，这可以为我们的实证研究提供可测试的含义。我们工作的潜在影响包括以下几点。一是搞清经济大起大落的原因。特别是，我们的工作将描述试图解释收益幂律分布的相互竞争的假设，并系统地探索在解释特定指数值时体积和流动性的作用。其次，从更广泛的意义上讲，我们的工作可能会对使用统计物理学、行为金融学和心理学的概念来量化和理解人类集体行为提供一些见解。我们还将表明，制定模型的一个有用方法是借鉴复杂环境下的决策研究，这本身就来自认知心理学。

DRU Collaborative Research: An Econophysics and Behavioral Approach to Financial FluctuationsH. Eugene Stanley, Boston UniversityDavid I. Laibson, Harvard University and NBERXavier Gabaix, MIT and NBERThe nature of the problem can be described as follows. Various measures of stock market activity exhibit puzzling "universal" features that have recently attracted much research attention. These features include the power law distributions of return, volume, number of trades, assets under management of trading institutions, and other power law relations linking them. These universal features not only present a challenge to models of market fluctuations, but their specific power-law nature also suggests new modeling directions, which include ideas from statistical physics which proved useful in understanding similar relationships that occur in the physics of critical phenomena.We seek to understand the origins of these regularities, by exploring the following hypotheses: (i) Large returns arise when a few large institutions trade in a market of fairly small liquidity. (ii) Large returns arise when market liquidity drops, independent of the size of the institutions involved. (iii) Large returns arise because of volatility feedback mechanisms can also give rise to power law distributions, and generate short and long term correlations in trading activity.We approach this problem by developing simple rational and boundedly rational models, and analyzing extremely large data sets. This synergy between compact tractable models and the exploration of vast data sets has been the prime method of our research group. Our empirical research will study the joint distributions of returns, volume, number of trades, and liquidity. This includes studying in detail the price impact of trade and its variations under different market conditions. The modeling will involve designing models of financial decision making, that can provide testable implications that our empirical research will study.The potential impact of our work includes the following. First, we will understand what creates large economic fluctuations. In particular, our work will delineate between competing hypotheses that seek to explain the power-law distribution of returns, and systematically explore the role of volume and liquidity in explaining the specific exponent value. Second, more generally our work may give some insight into quantifying and understanding collective human behavior using concepts of statistical physics, behavioral finance, and psychology. We will also show that a useful way to formulate the models is to draw from research in decision making in complex environments, which itself draws from cognitive psychology.