Algorithms for merging tick data and data analysis for Indian financial market

Pankaj Sinha, Gopalakrishna Sharma, Akash Shah and Abhijeet Singh

MPRA Paper from University Library of Munich, Germany

Abstract: This paper discusses the problem of ‘merging’ financial tick data available from data sources such as Bloomberg, NSE, and Thomson Reuters etc. Different derivative securities are traded on the exchange with different frequencies in each unit of time such as second or minute in intraday trading , therefore, it is difficult to form ‘ordered pairs’, which are essential for any financial analysis, of tick data representing the simultaneous trades of the different derivative securities. Merging refers to the conversion of intraday tick data of different securities of varying frequencies, as provided by data sources, into the form in which the tick data of all traded derivative securities have same frequency, so that it is possible to form ordered pairs of data (in every unit time period) in such a way that the original nature of the data is preserved. The four merging algorithms: Truncation, Weighted mean, median and all-combinations algorithm are compared with Dropdown algorithm, which is being used widely by the trading firms. Using NSE intraday tick data for various trading days, it is found that ‘Truncation’ and ‘Weighted Mean’ algorithms are more efficient merging algorithms.

Keywords: tick data; merging algorithm; minute data; financial market; derivative securities; high frequency financial data; intraday trading (search for similar items in EconPapers)
JEL-codes: C15 C61 C8 D53 (search for similar items in EconPapers)
Date: 2011-07-03
New Economics Papers: this item is included in nep-mst
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