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Share Market Sectoral Indices Movement Forecast with Lagged Correlation and Association Rule Mining

Abstract : This paper analyses the correlation between two different sectoral indices (e.g. between Automobile sector index and between Metal sector index, between Bank sector index and IT sectoral index etc.) in a time lagged manner. Lagging period is varied from 1 day to 5 days to investigate if any selected sector has lagged influence over any other sectoral index movement. If any upward/downward movement of a sectoral index (sector A) is correlated with similar upward/downward movement of another sectoral index (Sector B) with a time lag of ‘d’ days, then with association rule mining support and confidence is calculated for the combination. If d is the lag for which support and confidence is maximum then depending on the higher correlation as well as higher support and confidence value it is possible to forecast future (d days ahead of current day) movement of sector B based on present day movement of sector A. This model first uses correlational analysis to identify the level of dependence among two different sectors, then considers only those sectors having higher value of correlation for association rule mining. Those sector are not considered for which combination correlation is very low or 0.This model has been tested with Indian share market data (NSE sectoral index data of 6 sectors) of 2015. Result shows it is possible to predict in short term (1 to 5 days in future) price movement of sectoral indices using other lagged correlated sector price index movement.
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Giridhar Maji, Soumya Sen, Amitrajit Sarkar. Share Market Sectoral Indices Movement Forecast with Lagged Correlation and Association Rule Mining. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. pp.327-340, ⟨10.1007/978-3-319-59105-6_28⟩. ⟨hal-01656264⟩

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