Equilibrium Data Mining and Data Abundance

J. Dugast, Thierry Foucault
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引用次数: 6

Abstract

We model of the search for predictors by speculators (active asset managers) and use it to analyze how the improvement in data processing power and the growth in available data ("data abundance") affect the diversity of trading signals used by speculators, the dispersion of their profits and the similarities of their holdings. Our central message is that data abundance and computing power do not have the same effects. In particular, an improvement in computing power always raises the bar for the quality of predictors that managers consider good enough to exploit while more data lower it when data becomes sufficiently abundant. When this happens, the diversity of speculators' signals and the dispersion of their trading profits increase in equilibrium while their holdings become less correlated.
平衡数据挖掘和数据丰度
我们建立了投机者(主动资产管理公司)寻找预测者的模型,并用它来分析数据处理能力的提高和可用数据的增长(“数据丰度”)如何影响投机者使用的交易信号的多样性、他们的利润分散和他们持有的相似性。我们的核心信息是,数据丰富和计算能力不会产生同样的效果。特别是,计算能力的提高总是会提高预测器的质量标准,管理人员认为这些标准足够好,可以加以利用,而当数据足够丰富时,更多的数据会降低预测器的质量标准。当这种情况发生时,投机者信号的多样性和交易利润的分散性在均衡状态下增加,而他们持有的股票相关性降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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