Teplova Tamara, Gurov Sergei
National Research University Higher School of Economics, Moscow, Russian Federation.
Ann Oper Res. 2022 Apr 10:1-29. doi: 10.1007/s10479-022-04683-7.
Using high-frequency transaction-level data for liquid Russian stocks, we empirically reveal a joint nonlinear relationship between the average trade size, log-return variance per transaction, trading volume, and the asset price level described by the Intraday Trading Invariance hypothesis. The relationship is also confirmed during stock market crashes. We show that the invariance principle explains a significant fraction of the endogenous variation between market activity variables at the intraday and daily levels. Moreover, our tests strongly reject the mixture of distributions hypotheses that assume linear relationships between log-return variance and transaction intensity variables such as trading volume or the number of transactions. We demonstrate that the increase in the ruble risk transferred by one bet per unit of business time was accompanied by the rise in the average spread cost. Different aggregation schemes are used to mitigate the impact of errors-in-variables effects. Following the predictions of the Information Flow Invariance hypothesis, we also study the relationship between trading activity and the information process approximated by either the flows of news articles or Google relative search volumes of Russian stocks over the 2018-2021 period. The evidence suggests that a sharp increase in the number of retail investors who entered the Moscow Exchange in 2020 entailed a higher synchronization between trading activity and search queries in Google since February 2020, in contrast to the arrival rates of news articles. The changes are driven by the increasing influence of the trading behavior of individual investors using Google Search rather than professional news services as the main source of information.
利用俄罗斯流动性股票的高频交易层面数据，我们通过实证揭示了平均交易规模、每笔交易的对数收益方差、交易量与日内交易不变性假设所描述的资产价格水平之间的联合非线性关系。在股市崩盘期间，这种关系也得到了证实。我们表明，不变性原理解释了日内和日间市场活动变量之间很大一部分内生变化。此外，我们的检验强烈拒绝了分布混合假设，该假设假定对数收益方差与交易强度变量（如交易量或交易数量）之间存在线性关系。我们证明，每单位业务时间内一笔交易转移的卢布风险增加伴随着平均价差成本的上升。我们使用不同的汇总方案来减轻变量误差效应的影响。根据信息流不变性假设的预测，我们还研究了2018 - 2021年期间交易活动与由新闻文章流量或俄罗斯股票的谷歌相对搜索量近似的信息过程之间的关系。证据表明，2020年进入莫斯科证券交易所的散户投资者数量急剧增加，导致自2020年2月以来交易活动与谷歌搜索查询之间的同步性提高，这与新闻文章的到达率形成对比。这些变化是由使用谷歌搜索而非专业新闻服务作为主要信息来源的个体投资者交易行为的影响力不断增加所驱动的。
