本文採用傅利葉轉換與小波轉換以探討非線性現象：長期相依的碎形結構與混沌現象。藉由傅利葉轉換與小波轉換兩種研究方法，所得到臺灣股票市場加權股價指數的實證結論如下：1.藉由傅利葉轉換所得到的H值為0.4632；藉由小波轉換所得到的H值為0.4750。這兩種研究方法皆顯示臺灣股票市場具有負的長期相依的碎形結構。2.藉由傅利葉轉換的研究方法，臺灣股票市場加權股價指數的頻譜由初始向下與寬的連續的頻帶所組成；臺灣股票市場加權股價指數的自我相關函數則隨著時間差距的增加而遞減。此顯示臺灣股票市場具有混沌現象。3.小波轉換可以檢測出臺灣股票市場加權股價指數的奇異之處，並且指出存有一能說明臺灣股票市場碎形結構的複雜性的機制。藉由以上的實證結論，可以得知臺灣股票市場具有反持續性的碎形結構，股票價格的變動來自於臺灣股票市場尺度上的自我相似性。即使如此，由於混沌不可預測性的本質，使得股票價格的預測似乎是不可能的。 / The Fourier transform and the wavelet transform are utilized in this research to explore the nonlinear phenomena: the fractal structure of long trem dependence and the phenomenon of chaos. 　　In terms of the two research methods of the Fourier transform and the wavelet transform, the empirical conclusions of the Taiwan stock exchange weighted stock index are derived as follows: 　　1. The $H$ value of the research method of the Fourier transform is 0.4632; the $H$ value of the research method of the wavelet transform is 0.4750. The two research methods show that the Taiwan stock market has a fractal structure of negative long term dependence. 　　2. In terms of the research method of the Fourier transform, the power spectrum of the Taiwan stock exchange weighted stock index consists of initially downward and wide continuous band of frequencies; the autocorrelation function of the Taiwan stock exchange weighted stock index decreases as the time lag increases. These observations show that there exists the phenomenon of chaos in the Taiwan stock market. 　　3. The wavelet transform can detect out the singularities of the Taiwan stock exchange weighted stock index and can point out the heirarchy that illustrates the complexity of the fractal sturcture in the Taiwan stock market. 　　By the above empirical conclusions, there exists the antipersistent fractal structure in the Taiwan stock market. The variations of stock prices result from the self-similarity of the scales of the Taiwan stock market. Even so, the prediction of stock prices seems very impossible as a result of the unpredictability of chaotic nature.

本研究的目的在比較研究中國大陸與台灣股市，其上市公司的財務資訊特性，以及這些財務資訊在這兩個股市中的有用性。大陸股市迄今年齡尚輕，僅七歲。唯大陸股市的法規，散戶多，同文同種等與台灣股市相近；不同之處在於，例如國家對上市公司所持有的股份仍佔一定比例、法人極少（中國大陸自五月份始方開放共同基金的發行）、股市分為 A、B 及 H 股等。因此在不同的股市特性及股市環境下，到底公開資訊被使用的情形如何？這種比較分析可令我們深入瞭解，在不同股市特性及股市環境（包括證劵管理環境）下，投資者使用公開財資訊的深度與廣度。因此本研究以公開財務報表為例，探討並比較台灣股市與中國大陸間資訊有用性及有用程度。 本研究採用傳統盈餘／股價關係的研究設計來探討其間的關係，根據　Easton and Harris(1991)及 Lev(1989)的建議，在傳統的盈餘反應係數模式中加入二個與盈餘相關變數，一為「當期盈餘水準除以期初股價」的盈餘水準變數；另一為「當期盈餘變動除以期初股價」的未預期盈餘變數，以此二盈餘變數實證比較兩岸股市，其相對盈餘資訊內涵。其次，依據 Lipe(1986)的建議及其所建立的實證模式，本研究測試盈餘組成分子是否具備增額資訊內涵，並比較兩岸盈餘組成分子增額資訊內涵是否顯著不同。最後，本研究借用 ERC 模式，並依據 Collins and kothari(1989)加入 ERC 決定因素如公司系統風險、盈餘持續度、公司成長機會等，來增進盈餘／股會間關係的解釋能力，以期在最具解釋力的模式下，比較兩盈餘資訊內涵。本研究台灣方面以台灣證劵交所股票上市公司為對象，研究期間自民國 77 年至 85 年止，共計 9 年為樣本期間，大陸則以在上海、深圳交易所上市的股票上市公司樣本，研究期間則為 1994 年至 1996 年研究結果顯示： 1.兩岸之資訊環境存在顯著差異性。 2.兩岸之盈餘資訊皆具有用性。 3.盈餘組成項目均具增額資訊內涵。 4.考慮 ERC 模式後可增加模式之解釋力。 / This study mainly applies ERC (Earnings Response Coefficient) model to compare the information contents of earnings in Taiwan and China stock markets. In addition, this study investigates the disclosure rules of these two markets in order to develop related hypotheses. This study includes four hypotheses: (1) information environment hypothesis, (2) earning level hypothesis, (3) earnings components hypothesis, and (4) ERC determinants hypothesis. The samples strategically select from. listed companies in Taiwan and China to facilitate our comparisons. The findings can be summarized as follows. 1. In the Taiwan stock market, the firm size is not a significant factor for determining optimal return window; but in the China stock market, the response of larger firms is earlier than smaller firms to the earnings announcement (information environment hypothesis). 2. The Easton-Harris earnings level variable can significantly increase the explanatory power of ERC in the Taiwan stock market; but it cannot significantly increase the explanatory power of ERC in the China stock market (earnings level hypothesis). 3. The earnings components can increase the explanatory power of earnings/return relationship in both Taiwan and China stock markets. In addition, the non-operating components have better explanatory power than operating components in earnings (earnings components hypothesis). 4. The determinants of ERC can increase the explanatory power of earnings/return relationship; but the determinants of ERC have negative effects of the explanatory power of earnings/return relationship.