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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/39005完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇永成(Yong-Chern Su) | |
| dc.contributor.author | Wei-Ling Tseng | en |
| dc.contributor.author | 曾惟苓 | zh_TW |
| dc.date.accessioned | 2021-06-13T16:57:00Z | - |
| dc.date.available | 2008-06-14 | |
| dc.date.copyright | 2005-06-14 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-05-30 | |
| dc.identifier.citation | 1.Chan, K. and W. M. Fong, 2000, “Trade Size, Order Imbalance, and the Volatility-Volume Relation,” Journal of financial Economics, 57, 247-273.
2.Chordia, T., A. Subrahmanyam, 2004, “Order Imbalance and Individual Stock Returns: Theory and Evidence,” Journal of Financial Economics, 72, 485-518 3.Chordia, T., R. Roll, and A. Subrahmanyam, 2002, “Order Imbalance, Liquidity, and Market Returns,” Journal of Financial Economics, 65, 111-130. 4.Conrad, J., A. Hameed, and C.M. Niden, 1992, “Volume and Autocovariances in Short-Horizon Individual Securit Returns,” Journal of Finance, 49, 1305-1329. 5.Foster, D. F. And S. Viswanathan, 1995, “Strategic Trading with Asymmetric Informed Traders and Long-Lived Information.” Journal of Financial and Quantitative Analysis, 29, 499-518. 6.Foster, D. F. and S. Viswanathan, 1996, “Strategic Trading When Agents Forecast the Forecasts of Others,” Journal of Finance, 51, 1437-1478. 7.Grossman, S., 1976, “On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information,” Journal of Finance, 31, 573-585. 8.Holden, C. W., and A. S. Subrahmanyam, 1992, “Long-Lived Private Information and Imperfect Competition.” Journal of Finance, 117, 247-265. 9.Ho, T., Stoll, H., 1983, “The dynamics of dealer markets under competition,” Journal of Finance,38, 1053-1074. 10.Karpoff, J. M., 1987, “The Relation between Price Changes and Trading Volume: A Survey.” Journal of Financial and Quantitative Analysis, 22, 109-126. 11.Kyle, A., 1985, “Continuous Auctions and Insider Trading,” Econometrica, 53, 1315-1335. 12.Lamoureux, C., and W. Lastrapes, 1990, “Heteroskedasticity in Stock Return Data: Volume Versus GARCH Effects,” Journal of Finance, 45,221-229. 13.Lee, C. M. C., and B. Swaminathan, 2000, “Price Momentum and Trading Volume,” forthcoming in Journal of Finance. 14.Lin, C. M., 2003, “Information Asymmetry and Return-Volume Relation: A Time Varying Model based upon Order Imbalance and Individual Stock,” Graduate Institute of Finance of National Taiwan University. 15.Lin, J. C. 2004, “Price-Volume Relation—A Time Varying Model with Censored and Camouflage Effects,” Graduate Institute of Finance of National Taiwan University. 16.Llorente, G., R. Michaely, G. Saar, and J. Wang, 2002, “Dynamic Volume-Return Relation of Individual Stocks,” Review of Financial Studies, 15, 1005-1047. 17.Morse, D., 1980, “Asymmetric Information in Securities Markets and Trading Volume,” Journal of Financial and Quantitative Analysis, 15, 1129-1148. 18.Stickel, Scott E., and Robert E. Verrecchia, 1994, “Evidence that Volume Sustains Stock Price Changes,” Financial Analyst Journal, November-December, 57-67. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/39005 | - |
| dc.description.abstract | 根據以前的研究指出,投資者為什麼交易與根據不同動機而有不同交易型態,可以由量指標觀察出端倪。投機型交易會使報酬產生正的自我相關及股價通常會在高量能支持下而有持續性。因此,本研究選擇股價創52週新高的NASDAQ股票當作投機型個股,這類型股票具有基本面支撐,通常也會有較多資訊不對稱且尚未完全反應在股價上。
本研究以一種量的指標”買賣單不對稱”做為資訊不對稱的代理變數,來研究資訊不對稱對日內股價報酬的影響。先以日內每筆交易的買賣單不對稱當作GARCH(1,1)模型中的解釋變數。另外,因內部資訊者為了避免市場中其他交易者模仿,傾向將較大的交易單拆成好幾筆小單,所以本研究設定三種時間區間,分別為90秒、3分鐘與15分鐘,用時間序列迴歸來檢驗買賣單不對稱與股價報酬之間的關係。實證結果發現無論在任何時間區間下,同期的買賣單不對稱與股票報酬呈正向關係,尤其在90秒與3分鐘區間測試中呈現顯著正向關係。差一期的結果雖然大部分呈現負相關,但是卻不顯著,也就是股價在下一期會稍微拉回但是幅度小,因好消息仍未完全反映在股價上,推論此為資訊不對稱產生的效果。另外,在控制同期的買賣單不對稱後,檢驗是否有預測效果存在,90秒區間測試中觀察到持續性關係,而在3分鐘與15分鐘區間不具有任何關係。 最後,本研究觀察到傳統的資訊不對稱代理變數”公司大小”,對於同期買賣單不對稱對股價報酬的影響是顯著為負,推論小公司含有較多不對稱的資訊,較易產生異常報酬。 | zh_TW |
| dc.description.abstract | According to previous studies, we can learn from volume depends on why investors trade and how trades with different motives relate to prices. Speculative trades generate positively autocorrelated returns. There is a phenomenon that returns of individual stocks on high-volume days are more sustainable. Therefore, we focus on speculative stocks which reach to 52-week new high records. There might be possibly with more information inside because they have kept good performance in earnings or revenues. Order imbalance is employed as a proxy of information asymmetry in this article. Relation between intraday return and order imbalance is investigated.
Every trade order imbalances is used as our explanatory variable in GARCH (1,1) model. Besides, because informed traders tend to split orders into small size, we also examine relation between order imbalance and return in 90-second, 3-minute, or 15-minute interval time-series regression tests. The major findings are as follows. A contemporaneous significant positive relation exists in all kinds of interval tests, especially for 90-second and 3-minute interval tests. The coefficients of lagged order imbalances are negative but not significant. It can be explained as asymmetric information effect. After controlling contemporaneous order imbalance, it shows continuation in lag-one relations in 90-second interval test. In 3-minute and 15-minute interval tests, the results are mixed. Finally, the relation of contemporaneous coefficients and market capitalization is significantly negative in all kinds of interval tests. It implies that there is more information asymmetry in smaller firms. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T16:57:00Z (GMT). No. of bitstreams: 1 ntu-94-R92723011-1.pdf: 459939 bytes, checksum: a36c4386eb393e3f824b6257ead139d3 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | Chapter 1 Introduction
1.1 Motives and Purposes- 4 - 1.2 Framework of the Thesis - 7 - Chapter 2 Literature Review 2.1 Trading Behavior and Information Asymmetry- 8 - 2.2 Price-Volume Relations- 10 - Chapter 3 Data 3.1 Sampling Criteria- 13 - 3.2 Descriptive Statistics of Order Imbalances- 14 - Chapter 4 Methodology 4.1 GARCH(1,1) Model- 16 - 4.2 Intraday Time-Series Regressions- 17 - Chapter 5 Empirical Results 5.1 Dynamic Return-Order Imbalance Relations- 20 - 5.2 Intraday Time-Series Regressions - 21 - 5.2.1 Contemporaneous Effect- 22 - 5.2.2 Lagged Effect- 23 - 5.2.3 Conclusions - 24 - 5.2.4 Predictability- 24 - 5.3 Relation between Coefficients of Order Imbalance and Firm Sizes- 25 - Chapter 6 Conclusions- 27 - References- 66 - FIGURE 1 NASDAQ INDEX –APR.29TH,2004~APR.29TH,2005- 29 - FIGURE 2 DISTRIBUTION OF MARKET CAPITALIZATION- 30 - FIGURE 3 DISTRIBUTION OF EPS- 31 - FIGURE 4 DISTRIBUTION OF ESTIMATES OF RETURN-ORDER IMBALANCE RELATION-CONTEMPORANEOUS IN GARCH(1,1) - 32 - FIGURE 5 DISTRIBUTION OF ESTIMATES OF RETURN-ORDER IMBALANCE RELATION- IN 90 SECOND TEST- 33 - FIGURE 6 DISTRIBUTION OF ESTIMATES OF RETURN-ORDER IMBALANCE RELATION- IN 3 MINUTE TEST - 34 - FIGURE 7 DISTRIBUTION OF ESTIMATES OF RETURN-ORDER IMBALANCE RELATION- IN 15 MINUTE TEST- 35 - TABLE 1 BASIC INFORMATION OF 67 SAMPLE STOCKS - 36 - TABLE 2 DESCRIPTIVE STATISTICS OF ORDER IMBALANCES- 39 - TABLE 3 SAMPLE DATA IN 90S/3MIN/15MIN PERIODS - 40 - TABLE 4 NUMBER OF SIGNIFICANCES AND INSIGNIFICANCES IN CONTEMPORANEOUS PERIOD IN GARCH(1,1) UNDER DIFFERENT CONFIDENCE LEVELS. - 43 - TABLE 5 ESTIMATES OF RETURN-ORDER IMBALANCE RELATION- CONTEMPORANEOUS IN GARCH(1,1)- 44 - TABLE 6 NUMBER OF SIGNIFICANCES AND INSIGNIFICANCES IN CONTEMPORANEOUS AND LAGGED PERIODS IN TIME-SERIES REGRESSION UNDER DIFFERENT CONFIDENCE LEVELS. - 49 - TABLE 7 ESTIMATES OF CONTEMPORANEOUS AND LAGGED RETURN- ORDER IMBALANCE RELATION IN 90-SECOND INTERVAL FOR THE 67 SAMPLE STOCKS- 51 - TABLE 8 ESTIMATES OF CONTEMPORANEOUS AND LAGGED RETURN- ORDER IMBALANCE RELATION IN 3-MINUTE INTERVAL FOR THE 67 SAMPLE STOCKS- 55 - TABLE 9 ESTIMATES OF CONTEMPORANEOUS AND LAGGED RETURN- ORDER IMBALANCE RELATION IN 15-MINUTE INTERVAL FOR THE 67 SAMPLE STOCKS- 59 - TABLE 10 NUMBER OF SIGNIFICANCES AND INSIGNIFICANCES IN LAGGED PERIODS IN TIME-SERIES REGRESSION UNDER DIFFERENT CONFIDENCE LEVELS.- 63 - TABLE 11 ESTIMATES OF COEFFICIENT –MARKET CAPITAL RELATION- 65 - | |
| dc.language.iso | en | |
| dc.subject | 買賣單不對稱 | zh_TW |
| dc.subject | 價量關係 | zh_TW |
| dc.subject | GARCH模型 | zh_TW |
| dc.subject | 資訊不對稱 | zh_TW |
| dc.subject | Price-Volume Relation | en |
| dc.subject | GARCH Model | en |
| dc.subject | Order Imbalance | en |
| dc.subject | Information Asymmetry | en |
| dc.title | NASDAQ新高投機型個股之日內報酬率-買賣單不對稱關係 | zh_TW |
| dc.title | Intraday Return – Order Imbalance Relation in NASDAQ Speculative New Highs | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖咸興,王耀輝 | |
| dc.subject.keyword | 價量關係,GARCH模型,資訊不對稱,買賣單不對稱, | zh_TW |
| dc.subject.keyword | Information Asymmetry,Price-Volume Relation,Order Imbalance,GARCH Model, | en |
| dc.relation.page | 66 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-05-30 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位: | 財務金融學系 | |
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