依據之前的研究，我們知道日買賣單不對稱對股價報酬率有顯著的解釋能力。然而我們認為，日內買賣單不對稱對於股價報酬率應有更好的解釋效果，因為日內資料所帶來的應是最新的市場資訊，對於股價報酬率的影響力應該較過去的日買賣單不對稱更強。因此於本研究中，我們採用日內資料做為樣本。

利用GARCH(1,1)、與時間序列複迴歸模型，我們發現在日內買賣單不對稱與股價報酬率間，確實存在著同期效果，亦即同期之日內買賣單不對稱對於股價報酬率有良好的解釋能力。然而，我們卻無法於其中發現預測能力，亦即前一期之日內買賣單不對稱對於當期股價報酬率並無顯著之預測能力，此結果與我們的預期不相符合。

最後，我們建構一簡單迴歸模型來偵測小型股效果。迴歸結果顯示，資本額愈小的公司，其同期日內買賣單不對稱對於股價報酬率的影響力愈大，表示確實存在著小型股效果。

By former researches we learn that daily order imbalance has significant explanatory power to daily return. And we think that intraday order imbalance is more useful information to investors because it may contain the latest market information and will have greater influence to stock price than those of previous transaction days. Thus we adopt intraday data in our research, to investigate the relation between intraday return and order imbalance.

In this research we try to see if intraday order imbalance has explanatory power to return. By using dynamic time and sale data in GARCH(1,1) model and by 90-second data in time-series regression models, we find out that there is significant contemporaneous effect, that is, the contemporaneous order imbalance has explanatory power to return, both in dynamic and 90-second time and sale data. On the other hand, we do not see significant predictability in lag-one period order imbalance to return, that is, the lag-one period order imbalance does not show predictability to contemporaneous return.

At last, we build a cross-sectional regression model to test the small-firm effect. We found out that there is small-firm effect. It means that the smaller the firm’s capital expenditure, the greater the influence of order imbalance to its stock return.

Chapter 1 Introduction 1
1.1 Motives and Purposes 1
1.2 Framework of the Thesis 4
Chapter 2 Literature Review 5
2.1 Information Asymmetry 5
2.2 Price-Volume Relation 7
Chapter 3 Data 11
3.1 Data Sample and Sources 11
3.2 Descriptive Statistics 13
Chapter 4 Methodology 15
4.1 Dynamic Return-Order Imbalance Relationship 15
4.2 Contemporaneous Effect and Predictability 17
4.3 Size Effect 18
Chapter 5 Empirical Results 20
5.1 Dynamic Return-Order Imbalance Relationship 20
5.2 90-Second Return-Order Imbalance Relationship 21
5.2.1 Contemporaneous Effect 21
5.2.2 Lag-One Period Effect - Predictability 23
5.3 Size Effect 24
Chapter 6 Conclusion 26
References 55

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