Abstract
In this paper, we explore the problem of establishing a network among the stocks of a market at high frequency level and give an application to program trading. Our work uses high frequency data from the National Stock Exchange, India, for the year 2014. To begin, we analyse the spectrum of the correlation matrix to establish the presence of linear relations amongst the stock returns. A comparison of correlations with pairwise mutual information shows the further existence of non-linear relations which are not captured by correlation. We also see that the non-linear relations are more pronounced at the high frequency level in comparison to the daily returns used in earlier work. We provide two applications of this approach. First, we construct minimal spanning trees for the stock network based on mutual information and study their topology. The year 2014 saw the conduct of general elections in India and the data allows us to explore their impact on aspects of the network, such as the scale-free property and sectorial clusters. Second, having established the presence of non-linear relations, we would like to be able to exploit them. Previous authors have suggested that peripheral stocks in the network would make good proxies for the Markowitz portfolio but with a much smaller number of stocks. We show that peripheral stocks selected using mutual information perform significantly better than ones selected using correlation.
Highlights
High frequency trading is the buying and selling of large numbers of stocks in very short intervals of time, usually fractions of seconds
We analysed the spectrum of the correlation matrix to study the randomness
More than 40% deviations were observed from the random matrix theory (RMT) indicating that the pairwise correlation coefficients are not random
Summary
High frequency trading is the buying and selling of large numbers of stocks in very short intervals of time, usually fractions of seconds. With advancement in computing and technology, it is possible for investors to carry out such trades using algorithmic trading. A good trading strategy should be equipped to understand the movement of stocks even at a tick-by-tick level. Among various factors, which influence the change in a stock price, change in the prices of other stocks is one of the most significant. Many researchers [1,2,3,4,5,6,7,8,9,10,11] have used random matrix theory (RMT) on the empirical correlation matrix to understand the co-.
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