本論文在分析 SP500 指數其中交易最為頻繁的 345 家公司在 1996年各月份的股票數據,市場模式的空間特性已經被證實出來[3][4][5],而我們利用卡忽南 -拉維展開來分解股價對數報酬的時間序列, 利用傅立葉分析,並考慮股價對數報酬是否有時間序列重疊,與比較高頻移動平均對時間序列的影響,藉由參考各股市系統的特徵參數來尋找相似或不同之處。 / We present the results of our analysis of time series for a collection of 345 stocks listed in S&P 500, to show that integrated information on collective fluctuations in financial data can be revealed quantitatively by combined analysis, focusing separately on either the deterministic or the stochastic contents of the system. In comparing the fluctuations of high frequency one-day moving averages (HF1MA) of the original prices of individual stocks with those inherited in the trajectories of Brownian particles [1], also comparing the log return with overlapping time interval with the log return without overlapping time interval, we can quantify the time characteristic properties of the whole system which would direct the motions of tracer particles. In this study, we decompose the fluctuations in Karhunan-Loeve expansions and reveal the system-specific collective properties by analyzing those collective modes in their time-wise as well as the stock-wise bases, obtained for either the original prices or those of HF1MA, and for the log return with or without overlapping time interval.