Study of Statistical Correlations in Intraday and Daily Financial Return Time Series

Abstract

The aim of this article is to briefly review and make new studies of correlations and co-movements of stocks, so as to understand the "seasonalities" and market evolution. Using the intraday data of the CAC40, we begin by reasserting the findings of Allez and Bouchaud [1]: the average correlation between stocks increases throughout the day. We then use multidimensional scaling (MDS) in generating maps and visualizing the dynamic evolution of the stock market during the day. We do not find any marked difference in the structure of the market during a day. Another aim is to use daily data for MDS studies, and visualize or detect specific sectors in a market and periods of crisis. We suggest that this type of visualization may be used in identifying potential pairs of stocks for "pairs trade". 1 1 "The existence of seasonal asset returns may be an indicator of market inefficiencies. . . The presence of seasonal returns, however, does not necessitate market inefficiency" [4]

Impact of monetary policy changes on the Chinese monetary and stock markets

“…The largest eigenvalue λ 1 is also believed to be an index representing the behavior of the whole market, here in our case the 13 banks. It is also observed in previous literatures [29,[31][32][33] that the largest eigenvalue λ 1 is associated with the so-called ''market mode'', when all participants share a similar sentiment or expectation of the market, making the financial assets behave in a collective way, fluctuating up or down synchronically. In our case, the 13 banks will quote prices well correlatively in a market mode.…”

Sectoral Co-movements in the Indian Stock Market: A Mesoscopic Network Analysis

“…Naturally, such correlation coefficients satisfy the usual condition of 1 ≤ C ij ≤ 1 and we can create an N × N correlation matrix C by collecting all values [15,16]. By construction, the matrix is symmetric and it serves as the basis of the rest of the present article.…”

Market microstructure – a high frequency analysis of volume and volatility intraday patterns across the Brazilian stock market.

“…Then Jain and Joh (1988) find that average returns differ across trading hours and some hours of the day present higher volume. More recently Tilak et al, (2013) found the same U-shaped patter investigating intraday data the main index in France 5 . The variables investigated were volume and volatility, seeking out for patterns during the trading session of Brazilian stock market, the BM&FBovespa trying to match up results of stock markets around the world that display a similar behavior, a U or L shaped pattern.…”