Intraday seasonalities and nonstationarity of trading volume in financial markets: Collective features

Abstract

Employing Random Matrix Theory and Principal Component Analysis techniques, we enlarge our work on the individual and cross-sectional intraday statistical properties of trading volume in financial markets to the study of collective intraday features of that financial observable. Our data consist of the trading volume of the Dow Jones Industrial Average Index components spanning the years between 2003 and 2014. Computing the intraday time dependent correlation matrices and their spectrum of eigenvalues, we show there is a mode ruling the collective behaviour of the trading volume of these stocks whereas the remaining eigenvalues are within the bounds established by random matrix theory, except the second largest eigenvalue which is robustly above the upper bound limit at the opening and slightly above it during the morning-afternoon transition. Taking into account that for price fluctuations it was reported the existence of at least seven significant eigenvalues—and that its autocorrelation function is close to white noise for highly liquid stocks whereas for the trading volume it lasts significantly for more than 2 hours —, our finding goes against any expectation based on those features, even when we take into account the Epps effect. In addition, the weight of the trading volume collective mode is intraday dependent; its value increases as the trading session advances with its eigenversor approaching the uniform vector as well, which corresponds to a soar in the behavioural homogeneity. With respect to the nonstationarity of the collective features of the trading volume we observe that after the financial crisis of 2008 the coherence function shows the emergence of an upset profile with large fluctuations from that year on, a property that concurs with the modification of the average trading volume profile we noted in our previous individual analysis.

General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures

“…Sinha and Pan (2007) discovered the industrial correlation structures of the Indian stock market using the rest components [ 10 ]. Graczyk and Drarte (2017) studied the trading volume in financial markets, and they found the behavioral homogeneity of the trading volumes [ 11 ]. The loss of information embedded in the remaining components is a major limitation of PCA when studying systematic information, but it has received limited attention [ 1 , 2 , 6 – 8 ].…”

Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features

“…For the sake of conciseness, we split our work into two parts: analysing the statistical properties related to the first four order cumulants of the trading volume individually and cross-sectionally. In a subsequent work [28], we will discuss the collective intraday features of our data as well as its nonstationarity.…”