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

• 批准号: 0527688
• 负责人: H. Eugene Stanley
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0527688&HistoricalAwards=false
• 关键词: DRU; Collaborative; Research; Econophysics; Behavioral

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.

DRU合作研究：金融波动的经济物理学和行为方法H。波士顿大学的尤金·斯坦利哈佛大学的大卫·I·莱布森和麻省理工学院和NBER的哈维尔·加拜克斯问题的性质可以描述如下。股票市场活动的各种衡量标准显示出令人费解的“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、成交量、交易数量、交易机构管理的资产的幂规律分布，以及将它们联系在一起的其他幂规律关系。这些普遍特征不仅对市场波动模型提出了挑战，而且它们特定的幂定律性质也暗示了新的建模方向，其中包括来自统计物理学的观点，事实证明，这些观点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规律的起源：(I)当几家大型机构在流动性相当小的市场进行交易时，会产生巨大的回报。(Ii)当市场流动资金下降时，无论所涉机构的规模如何，都会产生巨额回报。(3)由于波动率反馈机制产生的高收益也可以产生幂定律分布，并在交易活动中产生短期和长期相关性。我们通过建立简单的理性和有界理性模型来解决这个问题，并分析极大的数据集。紧凑、易处理的模型和对海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、交易量、交易数量和流动性的联合分布。这包括详细研究贸易的价格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，这些模型可以为我们的实证研究提供可检验的含义。我们工作的潜在影响包括以下几个方面。首先，我们将了解是什么造成了巨大的经济波动。特别是，我们的工作将在寻求解释收益的幂规律分布的相互竞争的假设之间进行划分，并系统地探索交易量和流动性在解释特定指数值中的作用。其次，更广泛地说，我们的工作可能会让我们有一些洞察力，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。我们还将展示，建立模型的一个有用的方法是借鉴复杂环境下的决策研究，而复杂环境下的决策研究本身就来自认知心理学。