Skip navigation

DSpace JSPUI

DSpace preserves and enables easy and open access to all types of digital content including text, images, moving images, mpegs and data sets

Learn More
DSpace logo
English
中文
  • Browse
    • Communities
      & Collections
    • Publication Year
    • Author
    • Title
    • Subject
    • Advisor
  • Search TDR
  • Rights Q&A
    • My Page
    • Receive email
      updates
    • Edit Profile
  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 財務金融學系
Please use this identifier to cite or link to this item: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38041
Full metadata record
???org.dspace.app.webui.jsptag.ItemTag.dcfield???ValueLanguage
dc.contributor.advisor蘇永成
dc.contributor.authorYen-Tzu Chenen
dc.contributor.author陳晏慈zh_TW
dc.date.accessioned2021-06-13T15:58:16Z-
dc.date.available2011-06-25
dc.date.copyright2008-06-25
dc.date.issued2008
dc.date.submitted2008-05-30
dc.identifier.citation1. Admati, A. and P. Pfleiderer, 1988, “A Theory of Intraday Patterns: Volume and Price Variability,” Review of Financial Studies, 1, 3-40.
2. Barclay, M. and J. Warner, 1993, “Stealth Trading and Volatility,” Journal of Financial Economics, 34, 281-305.
3. 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.
4. Bernard, B. S., 2002, “An Empirical Study of the Mixture of Time and Movements in Prices,” Department of Finance and Statistics, Swedish School of Economics and Business Administration.
5. Bessembinder, H. and M. Kaufman, 1997, “A Cross-exchange Comparison of Execution Costs and Information Flow for NYSE-listed Stocks,” Journal of Financial Economics 46, 293-320.
6. Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307-327.
7. Booth, G. G., J. C. Lin, T. Martikainen, and Y. Tse, 2002, “Trading and Pricing in Upstairs and Downstairs Stock Markets,” Review of Financial Studies, 15, 1111-1135.
8. Brown, P., D. Walsh, and A. Yuen, 1997, “The interaction between order imbalance and stock price,” Pacific-Basin Finance Journal, 5, 539-557.
9. Campbell, J. Y., S. J. Grossman, and J. Wang, 1993, “Trading Volume and Serial Correlation in Stock Returns,” Quarterly Journal of Economics, 108, 905-939.
10. Chakravarty, S., 2001, “Stealth-trading: Which Traders’ Trades Move Stock Prices?” Journal of Financial Economics, 61, 289-307
11. Chan, K. and W. Fong, 2000, “Trade size, order imbalance and the volatility-volume relation” Journal of Financial Economics, 57, 247-273
12. Chordia, T. and A. Subrahmanyam, 1998, “Order Imbalance and Individual Stock Returns,” the eScholarship Repository, University of California.
13. Chordia, T., R. Roll, and A. Subrahmanyam, 2002, “Order Imbalance, Liquidity, and Market Returns,” Journal of Financial Economics, 65, 111-130.
14. Chordia, T., R. Roll, and A. Subrahmanyam, 2004, “Order Imbalance, Liquidity, and Market Returns,” Journal of Financial Economics, 72, 486-518.
15. Christie, A. A., 1982, “The stochastic behavior of common stock variances,” Journal of Financial Economics, 10, 407-432.
16. Ciner C., 2003, “Return-Volume Dynamics of Individual Stocks: Evidence from an Emerging Market,” College of Business Administration of Northeastern University.
17. Copeland, T. E., 1976, “A model of Asset Trading under the Assumption of Sequential Information Arrival,” Journal of Finance, 31, 1149-1168.
18. Easley, D., Kiefer, N. and O’Hara, M., 1997b, “One Day in the Life of a Very Common Stock,” Review of Financial Studies 10, 805-835.
19. Easley,D., Kiefer, N. and O’Hara, M., 1997a, “The Information Content of the Trading Process,” Journal of Empirical Finance 4, 159-186.
20. Epps, T. and M. Epps, 1976, “The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-distributions Hypothesis,” Econometrica, 44, 305-321.
21. F. A. Wang, 1998, “Strategic Trading, Asymmetric Information and Heterogeneous Prior Beliefs,” Journal of Financial Markets, 1,321-352.
22. Foster, D. F. and S. Viswanathan, 1994, “Strategic Trading with Asymmetric Informed Traders and Long-Lived Information,” Journal of Financial and Quantitative Analysis, 29, 499-518.
23. Foster, D. F. and S. Viswanathan, 1996, “Strategic Trading When Agents Forecast the Forecasts of Others,” Journal of Finance, 51, 1437-1478.
24. French K. R. and R. Roll, 1986, “Stock return variances: The arrival of information and the reaction of traders,” Journal of Financial Economics, 17, 5-26.
25. Gallant, R., P. Rossi, and G. Tauchen, 1992, “Stock Prices and Volume,” Review of Financial Studies, 5, 199-242.
26. Grossman, S., 1976, “On the efficiency of competitive stock markets where traders have diverse information,” Journal of Finance, 31, 573-585.
27. He, H., and J. Wang, 1995, “Differential Information and Dynamic Behavior of Trading Volume,” Review of Financial Studies, 8, 919-972.
28. Heflin, F., and K. W. Shaw, 2000, “Trade Size and the Adverse Selection Component of the Spread: Which Trades Are 'Big'?”
29. Holden, C. W. and A. S. Subrahmanyam, 1992, “Long-lived private information and imperfect competition.” Journal of Finance, 117, 247-265.
30. Hong, H., and J. Wang, 2000, “Trading and Returns under Periodic Market Closures,” Journal of Finance, 55, 297-354.
31. Jennings, R. H., L. T. Starks, and J. C. Fellingham, 1981, “An Equilibrium Model of Asset Trading with Sequential Information Arrival,” Journal of Finance, 36, 143-161.
32. Jones, C., G. Kaul, and M. Lipson, 1994, “Transactions, Volume and Volatility,” Review of Financial Studies, 7, 631-652.
33. Kahneman, D. and A. Tversky, 1979, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47, 263-291.
34. Karpoff, J., 1987, “The Relation between Price Changes and Trading Volume: A Survey,” Journal of Financial and Quantitative Analysis, 22, 109-126.
35. Kyle, A., 1985, “Continuous Auctions and Insider Trading,” Econometrica, 53, 1315-1335.
36. Lamoureux, C., and W. Lastrapes, 1990, “Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,” Journal of Finance, 45, 221-229.
37. Lee, C. and M. Ready, 1991, “Inferring trade direction and components of the bid-ask spread,” Review of Financial Studies, 8, 1153-1184.
38. Lee, Y. T., Y.J. Liu, R. Roll and A. Subrahmanyam, 2003, “Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange,” Journal of Financial and Quantitative Analysis, 20 01
39. 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.
40. Lin, J.C., 2004, “Price-Volume Relation: A Time Varying Model with Censored and Camouflage Effects,” Graduate Institute of Finance of National Taiwan University.
41. Lin, J. C., G. C. Sanger, and G. G. Booth, 1995, “Trade Size and Components of the Bid-Ask Spread,” Review of Financial Studies, 8, 1153-1183.
42. Llorente, G., R. Michaely, G. Saar, and J. Wang, 2002, “Dynamic Volume-Return Relation of Individual Stocks,” Review of Financial Studies, 15, 1005-1047.
43. Lo, A. and J. Wang, 2000, “Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory,” Review of Financial Studies, 13, 257-300.
44. Lo, A. W. and A. C. MacKinlay, 1990, “An Econometric Analysis of Nonsynchronous Trading,” Journal of Econometrics, 45, 181-211.
45. Lo, A. W., and A. C. MacKinlay, 1988, “Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test,” Review of Financial Studies, 1, 41-66.
46. Morse, D., 1980, “Asymmetric information in securities markets and trading volume,” Journal of Financial and Quantitative Analysis, 15, 1129-1148.
47. Spiegel, M. and A. Subrahmanyam, 1995, “On intraday risk premia,” Journal of Finance, 50, 319-339.
48. Stoll, H., 1978a, “The supply of dealer services in securities markets,” Journal of Finance, 33, 1133-1151.
49. Wang, J., 1993, “A Model of Intertemporal Asset Prices Under Asymmetric Information,” Review of Economic Studies, 60, 249-282.
50. Wang, J., 1994, “A Model of Competitive Stock Trading Volume,” Journal of Political Economy, 102, 127-168.
51. Yu, Y. H., 2002, “Information Asymmetry and Price-Volume Relations,” Graduate Institute of Business Administration of National Taiwan University.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38041-
dc.description.abstract本研究採用鉅額跌幅投機型個股的日內資料取代過去研究者所使用的日資料來進行研究。在延續前人的研究探討日內買賣單不平衡對於個股報酬率的影響外,另加入了波動性的因子以探討日內買賣單不平衡對於波動性的影響。此外也嘗試建立以買賣單不平衡為基礎的交易策略以期獲得超額報酬。
首先我們以GARCH(1,1)模型及複迴歸模型來研究日內買賣單不平衡與個股報酬率間的關係,發現兩者呈現正向的顯著關係,與前人的研究結論相同。而在不考慮當期下,前一期的買賣單不對稱與股價報酬率間則呈現負向的顯著關係。接著我們以修正過的GARCH(1,1)模型進行日內買賣單不平衡與波動性間關係的探討,發現結果呈現正向的顯著關係,較大量買賣單不平衡會使得報酬率波動較激烈。接著我們以簡單迴歸模型來驗證買賣單不平衡和公司規模間是否存在著小型股效果。實證結果顯示兩者之間僅有相當微弱的負顯著關係。
最後,本研究嘗試以買賣單不平衡為基礎發展交易策略並檢視其獲利性。由於本研究是以鉅額跌幅投機型個股為樣本,故以放空後回補做為我們的策略,結果發現,在進行交易量篩選之後,此交易策略能夠替投資者賺取超額報酬。
zh_TW
dc.description.abstractThis study adopts intraday return instead daily return used by previous researches to examine the effect of order imbalance not only on the individual stock return but also volatility among extreme losers. After that, we build up order imbalance-based trading strategies to gain profit.
First, the contemporaneous order imbalance-return relation is examined by GARCH (1,1) model and time-series regression model. The data presents significantly positive relation in both models as previous studies. Second, we focus on the lagged effect of the return and find that such relation is negatively significant while contemporaneous imbalance has positive significant. Third, we examine the volatility-order imbalance relationship by revised GARCH (1,1) model. The positive relationship is consistent with our expectation that larger imbalance would make return more volatile. Then, our empirical test of the small firm effect shows the weakly negative relation between order imbalance and market capital.
At last, we design two order imbalance-based trading strategies based on different price matched to the imbalance: the trading price and bid-ask price, separately and test the profitability. Due to the characteristics of our extreme losers, we adopt short selling strategy. Our results show the huge profitability of the two strategies when we pick up only the extreme volume.
en
dc.description.provenanceMade available in DSpace on 2021-06-13T15:58:16Z (GMT). No. of bitstreams: 1
ntu-97-R94723018-1.pdf: 513288 bytes, checksum: 65158148b5da85a2c538f316452cb095 (MD5)
Previous issue date: 2008
en
dc.description.tableofcontentsChapter 1 Introduction 1
1.1 Motives and purpose 1
1.2 Framework of the thesis 4
Chapter 2 Literature Review 5
2.1 Trader’s Behavior Under Information Asymmetry 5
2.2 Relation Between Return and Trading Volume 9
2.3 Volatility 13
Chapter 3 Data 15
3.1 Data sample and sources 15
3.2 Descriptive Statistics 17
Chapter 4 Methodology 19
4.1 GARCH (1,1) Model 19
4.2 Intraday Time-Series Regression model 21
4.3 Dynamic Volatility-Order imbalance Relationship 23
4.4 Size effect 23
4.5 Order Imbalance-based Trading Strategies 24
Chapter 5 Empirical Results 27
5.1 Dynamic Return-Order Imbalance Relationship 27
5.2 Intraday Time-Series Regression Model 28
5.3 Dynamic Volatility -Order Imbalance Relationship 33
5.4 Size effect 36
5.5 Order Imbalance-based Trading Strategies 37
Chapter 6 Conclusion 41
References 75
Figure 1 Distribution of Market Capital of Our 53 Samples 44
Figure 2 Distribution of α1, The Contemporaneous Coefficient of GARCH (1,1) Model 45
Figure 3 Distribution of γi0, The Contemporaneous Coefficient of Time-Series Regression Model 46
Figure 4 Distribution of δi1, The Lag-One Period Coefficient of Time-Series Regression Model 47
Figure 5 Distribution of C1, The Contemporaneous Coefficient of GARCH (1,1) Model In Testing Volatility 48
Table 1 The Summary Statistics of The Data of 53 Samples 49
Table 2 The Results and Fitness of GARCH (1,1) Model 50
Table 3 Significance of The Coefficients Estimated By Contemporaneous Time-Series Regression Model 51
Table 4 Significance of The Coefficients Estimated By Lag-One Period Time-Series Regression Model 52
Table 5 GARCH (1,1) Model In Testing Volatility 53
Table 6 Size Effect 54
Table 7 The Intraday Return of The Order Imbalance-based Trading Strategies 55
Table 8 The Profitability of The Order Imbalance-based Trading Strategies 56
dc.language.isoen
dc.subject買賣單不平衡zh_TW
dc.subject波動性zh_TW
dc.subject資訊不對稱zh_TW
dc.subjectinformation asymmetryen
dc.subjectvolatilityen
dc.subjectorder imbalanceen
dc.title鉅額跌幅投機型個股報酬率、波動性與買賣單不平衡之動態關係研究zh_TW
dc.titleDynamic relations between order imbalance, return and volatility of extreme losersen
dc.typeThesis
dc.date.schoolyear96-2
dc.description.degree碩士
dc.contributor.oralexamcommittee胡星陽,王耀輝
dc.subject.keyword買賣單不平衡,資訊不對稱,波動性,zh_TW
dc.subject.keywordorder imbalance,information asymmetry,volatility,en
dc.relation.page80
dc.rights.note有償授權
dc.date.accepted2008-06-03
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept財務金融學研究所zh_TW
Appears in Collections:財務金融學系

Files in This Item:
File SizeFormat 
ntu-97-1.pdf
  Restricted Access
501.26 kBAdobe PDF
Show simple item record


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved