NASDAQ新低投機型個股之日內報酬-買賣單不對稱關係

Intraday Return-Order Imbalance Relation in NASDAQ Speculative New Lows

李馥吟(Fu-Yin Lee)

指導教授 ： 蘇永成

國立臺灣大學/管理學院/財務金融學研究所/碩士(2005年)

https://doi.org/10.6342/NTU.2005.02847

摘要

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。

關鍵字

價量關係 ； 買賣單不對稱 ； 資訊不對稱

並列摘要

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.

並列關鍵字

information asymmetry ； price-volume relation ； order imbalance

參考文獻

1.Admati, A. and P. Pfleiderer, 1988, “A Theory of Intraday Patterns: Volume and Price Variability,” Review of Financial Studies, 1, 3-40.

2.Back, K., C. H. Cao, and G. A. Williard, 2000, “Imperfect Competition among Informed Traders,” Journal of Finance, 55, 2117-2155.

3.Barclay, M. and J. Warner, 1993, “Stealth Trading and Volatility,” Journal of Financial Economics, 34, 281-305.

4.Barclay, M. J., T. Hendershott and D. T. Mccormick, 2003, “Competition Among Trading Venues: Information and Trading on Electronic Communications Networks,” Journal of Finance 58, 2637-2666.

5.Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307-327.