从风险管理角度看扰动对美国股市价差和投资者情绪的影响

Maria-Cristina Zwak-Cantoriu, L. Anghel, Simona Ermiş
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摘要

本文旨在解决一个感兴趣的话题,即:近年来对最大的重要股票市场之一的重大中断的影响和影响。本文的目的是显示这些干扰对美国股票市场的影响,考虑市场效率和衡量估计的买卖价差。使用每日和每周的数据集的13年里,基于10家公司的股价收盘类别的纳斯达克和纽约证券交易所上市股票指数和计算返回(t)和(t + 1)为每个股票的协方差两个返回(t)和(t + 1)和使用t (t + 1) 21天的“滚动窗口”,代表了交易日,以及利用每周的数据系列以同样的方式,我们得到了差值测量与其大小之间的关系,这是一种很强的负截面关系,为此我们进行了一系列统计检验,总结在本文中。之后,我们将每一年的数据分开,这样我们就可以对每一年的规模的对数值进行横向回归,我们注意到两者之间存在很强的负相关关系。根据所获得的结果,可以观察到,对于交易成本为零且市场价格包含所有相关信息的信息有效市场,在每日频率数据和2020年数据的情况下,2019年和2021年数据的负相关性最强,而在每周频率数据的情况下,2021年数据的负相关性最强。所记录的强烈负相关性可以用以下事实来解释,即在这些时期发生了强烈的负面影响,这导致了股票市场的混乱,而不仅仅是。同时,上一段时间分析的股票市场的这些负相关也显示出更大的价差增加,理论上表明流动性较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Impact of Disturbances on the US Stock Market’s Spread and Investor Sentiment Through the Perspective of Risk Management
Abstract The paper aims to address a topic of interest, namely: the influence and effect of the major disruptions from recent years on one of the largest important stock markets. The purpose of the paper is to show the influence of these disruptions on the US stock market, considering market efficiency and measuring the estimated Bid-Ask spread. Using daily and weekly data sets over a period of 13 years, based on the closing stock prices of 10 companies listed in the category of the NASDAQ and NYSE stock indexes and calculating the return at (t) and (t+1) for each stock, the covariance of the two returns at (t) and (t+1) and using at t and (t+1) a "rolling window" of 21 days, which represents the trading days, as well as using the weekly data series in the same way, we obtained the relationship between the spread measurement and its size, a strong negative cross-sectional relationship, for which we performed a series of statistical tests summarized in the paper. Later, we split the data for each year separately so that we’d be able to use for each year a cross-sectional regression of the spread over the logarithmic values of the size and we noticed that there is a strong negative relationship between the two of them. According to the results obtained, it can be observed that the strongest negative correlations are in 2019 and 2021 in the case of data with daily frequency and 2020, and 2021 in the case of data with weekly frequency, for an informationally efficient market, where transaction costs are zero and in which the market price contains all the relevant information. The strongly negative correlations recorded can be explained by the fact that strong negative influences took place during these periods, which contributed to the disruption of the stock market and not only. At the same time, these negative correlations on the stock market analyzed in the last period also show a wider spread increase which theoretically shows low liquidity.
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