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方法。尤金·斯坦利，波士顿大学大卫·I。莱布森，哈佛大学和NBERXavier Gabaix，麻省理工学院和NBER问题的性质可以描述如下。 股票市场活动的各种措施表现出令人困惑的“普遍”的特点，最近吸引了大量的研究关注。 这些特征包括收益率、交易量、交易数量、交易机构管理的资产的幂律分布，以及将它们联系起来的其他幂律关系。 这些普遍的特征不仅对市场波动的模型提出了挑战，而且其特定的幂律性质也提出了新的建模方向，其中包括来自统计物理学的思想，这些思想被证明有助于理解临界现象物理学中发生的类似关系。我们试图通过探索以下假设来理解这些波动的起源：（i）当少数大型机构在流动性相当小的市场上进行交易时，就会产生巨大的回报。 (ii)当市场流动性下降时，无论涉及的机构规模大小，都会产生巨大的回报。 (iii)由于波动反馈机制也可以引起幂律分布，并在交易活动中产生短期和长期相关性，因此会产生大回报。我们通过开发简单的理性和有限理性模型，并分析超大数据集来解决这个问题。 这种紧凑易处理的模型和大量数据集的探索之间的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率、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。 建模将涉及设计财务决策模型，这可以提供我们的实证研究将研究的可测试的影响。 首先，我们将了解是什么造成了巨大的经济波动。 特别是，我们的工作将划定之间的竞争性假设，试图解释幂律分布的回报，并系统地探讨量和流动性在解释特定的指数值的作用。其次，更一般地说，我们的工作可能会提供一些见解，量化和理解集体人类行为使用的概念，统计物理学，行为金融学和心理学。我们还将表明，制定模型的一个有用的方法是借鉴复杂环境中决策的研究，这本身就借鉴了认知心理学。

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.