In the Age of the information revolution, statistics has taken an essential position in decision making procedure. A number of statistical models have been developed during the pre-information age such as ARIMA, VAR, and GARCH with several other child models such as FAR, CFAR, etc. for time dependent information, which mainly focus on finding the best model that represents the given data with the smallest value of errors while all models have the same flaw which is the fanning out problem of prediction intervals over time. This incurable problem in conjunction with emerging of big data requested us to consider new methods to analyze time dependent information more efficiently. Kim and Tsokos recently proposed new fundamental methods to analyze time dependent information based on investigating lag relationship among objects. In the present study, we modify and apply LTTC and MFTC to SP500 stock price data in order to investigate the sensitivity of financial time dependent objects to an event specific market shock such as presidential election, etc. Then, multivariate time series models are presented at the end based on similarity in sensitivity to an event specific market shock.