跳到主要內容

臺灣博碩士論文加值系統

(34.236.36.94) 您好!臺灣時間:2021/07/24 22:23
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:黃家興
研究生(外文):Chia-Hsin Huang
論文名稱:不同身分別投資人之優勢資訊內涵是否為臺灣證券市場之資產定價因子?
論文名稱(外文):Are Superior Information Inherent in Different Investors Determinants of Asset Pricing in the Taiwan Equity Market?
指導教授:李忠榮李忠榮引用關係盧陽正盧陽正引用關係
指導教授(外文):Chung-Jung LeeYang-Cheng Lu
學位類別:碩士
校院名稱:銘傳大學
系所名稱:財務金融學系碩士在職專班
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:79
中文關鍵詞:投資人身分別動量效應私有訊息資訊交易機率
外文關鍵詞:Probability informed tradingPrivate information
相關次數:
  • 被引用被引用:0
  • 點閱點閱:253
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本研究擬援用臺灣證券交易所之日內逐筆成交檔(trade book)、揭示檔(display book)及委託檔(limit order book),探討參與掛牌公司交易之不同類型投資人(自然人與法人(包含外資、投信、自營商與其他一般法人)),其買方賣方委託單成交方向之正確性,並驗證何種身份別投資人之委託成交具優勢資訊內涵,以進一步確認臺灣證券市場之投資人行為模式。首先依據Lee and Ready(1991),Ellis,Michaely and O’Hara(2000),Odders-White(2000)及Finucane(2000)建立具身份別之買賣盤成交方向判斷法則,並估計法人與自然人之買賣盤成交方向正確率,據以解析不同身份別投資人買賣盤成交方向正確率之資訊內涵,並完成Easley, Hvidkjaer and O’Hara (2002)(以下簡稱EHO(2002))之 PIN模型在臺灣證券市場定價能力之檢驗;其次,延伸Easley, Kiefer,O’Hara and Paperman(1996)(以下簡稱EKOP(1996))及EHO(2002)之優勢資訊交易機率(probability of information-based trading, PIN)測度,重行構建具身份別(法人及自然人)之PIN模型,並依Fama and French(1992,1993)系列多因子定價模型之實證研究方法,確認具身份別PIN模型在臺灣證券市場之定價能力。
本研究實證結果發現,法人引起之優勢資訊交易機率較自然人引起者為高。換言之,法人引起之PIN能捕捉到較高之優勢資訊交易者在市場交易的比例。最後本研究由因子檢測分析發現,EHO(2002)提出的不考慮投資人身分別之優勢資訊交易機率,確實為臺灣證券市場超額報酬的解釋因子,其原因可能在於身分別之PIN僅為部分優勢資訊交易之集合,而不考慮身分別之PIN則可完全捕捉到優勢資訊交易之行為,優勢資訊交易機率確實可應用於臺灣證券市場之資產定價。而就PIN在策略性交易及投資組合管理上之應用性而言,PIN的動量效應確實存在。
This study applies the tick data from TSEC, include trade book, display book and limit order book. In order to discuss trade from different investor types and examine which kind of trader trades more information. Furthermore, we can comfirm investor behavior in the Taiwan equity market. In line with the law of buyer or seller with identify advanced by Lee and Ready(1991), Ellis, Michaely and O’Hara(2000), Odders-White(2000) and Finucane(2000), and estimate the correct probability of institution and individual, which is buyer or seller. This study extending the work of Easely, Hvikjaer and O`hara (2002), they examine probability of information -based trading (PIN) estimating individual stocks in NYSE having private information, and derive a measure of the probability of information-based trading. Specifically, we use transactions and quote data of Taiwan Stock Exchange to measure PIN. Cross-sectional asset pricing tests show that PIN is a significant pricing factor in Taiwan stock market, and we estimate this measure using Lee and Ready (1991) tick rules. Aktas, Bodt, Declerck & Oppens (2007) indicated that PIN had measured the proportion of the informed traders trade, and PIN can’t find the difference between private or public information.
Our results reveal that PIN caused by institution is higher than individual. In the other words, PIN caused by institution can catch higher proportion of the informed traders trade in the market. Finally, our factor test analysis find that PIN without considering investor types is a determinant of abnormal return in the Taiwan equity market, which was also advanced by EHO (2002). Because of PIN caused by some investor types is only a portion of PIN. Furthermore, PIN without considering investor types is a determinant of asset pricing in the Taiwan equity market. By the way, the PIN’s momentum effect is certainly exists.
目 錄
目 錄 iii
圖目錄 iv
表目錄 v
第壹章 緒論 1
一、研究背景及目的 1
二、研究目的與問題 4
三、研究流程 5
第貳章 文獻回顧 6
一、資訊交易 6
二、資產定價模型 9
第參章 研究方法 12
一、資料描述 12
二、臺灣證券市場優勢資訊交易機率(PIN)模型之構建 15
三、臺灣證券市場具投資人身份別之優勢資訊交易機率模型之構建 18
四、多因子資產定價模型 23
第肆章 實證結果分析 25
一、 資料分析 25
二、EHO(2002)參數估計結果 27
三、臺灣證券市場優勢資訊交易機率模型之定價能力確認 29
四、臺灣證券市場具身份別優勢資訊交易機率定價能力之確認 34
五、優勢資訊交易機率與其他因子之定價能力驗證 44
六、動量生命周期循環(Momentum Life Cycle;MLC)觀念落實在大富投資決策系統之策略應用 48
第伍章 結論與建議 63
一、結論 63
二、建議 64
參考文獻 65
英文部分 65
中文部分 71
圖目錄
圖1 研究流程圖 5
圖2 優勢資訊交易機率模型圖 15
圖3 區分法人與自然人之優勢資訊交易機率模型樹狀圖 19
圖4 EHO(2002)參數估計結果之各年分佈圖 27
圖5 EHO(2002)資訊交易機率參數分配圖 28
圖6 不考慮身分別之買方力道與賣方力道之各年分佈圖 29
圖7 資訊交易機率參數各年分佈圖 30
圖8 資訊交易機率參數分配圖 31
圖9 考慮身分別之買方力道與賣方力道各年分佈圖 34
圖10 考慮投資人身分別之資訊交易機率參數各年分佈圖 36
圖11 考慮投資人身分別之資訊交易機率參數分配圖 37
圖12 價格與成交量週轉率之二維動量效應 48
圖13 與報酬率之二維投組構建概念圖 49
圖14 與報酬率之投資組合二維動量效應 50
圖15 與報酬率之投資組合二維動量效應 51
圖16 與報酬率之投資組合二維動量效應 51
圖17 利吉發股票分析系統主畫面 52
圖18 決策系統功能畫面 53
圖19 參數設定畫面-PIN與報酬率之二維投資組合(3×3) 54
圖20 參數設定畫面-PINI與報酬率之二維投資組合(3×3) 55
圖21 參數設定畫面-PINN與報酬率之二維投資組合(3×3) 56
圖22 參數設定畫面-PIN與週轉率之二維投資組合(3×3) 57
圖23 參數設定畫面-PINI與週轉率之二維投資組合(3×3) 58
圖24 參數設定畫面-PINN與週轉率之二維投資組合(3×3) 59
圖25 參數設定畫面-週轉率與報酬率之二維投資組合(3×3) 60
圖26 MLC決策系統股票選擇類別 61
圖27 月K線與PIN,PINI及PINN圖形-以台泥為例 62
圖28 月K線與PIN,PINI及PINN圖形-以鴻海為例 62
表目錄
表1 變數定義 14
表2 敘述性統計分析 25
表3 相關分析 26
表4 不考慮身分別之買方力道與賣方力道敘述統計量 29
表5 資訊交易機率統計量 32
表6 優勢資訊交易機率與營收市值比之投資組合超額報酬分析表 32
表7 優勢資訊交易機率與淨值市值比之投資組合超額報酬分析表 33
表8 PIN 與PPIN 和Fama-French 3 因子之定價模型檢測結果 33
表9 考慮身分別之買方力道與賣方力道敘述統計量 34
表10 考慮投資人身分別之資訊交易機率統計量 38
表11 法人優勢資訊交易機率與營收市值比之投資組合超額報酬分析表 39
表12 法人優勢資訊交易機率與淨值市值比之投資組合超額報酬分析表 39
表13 自然人優勢資訊交易機率與營收市值比之投資組合超額報酬分析表 40
表14 自然人優勢資訊交易機率與淨值市值比之投資組合超額報酬分析表 40
表15 法人優勢資訊交易機率與Fama-French 3 因子定價模型檢測結果 41
表17 不同身分別之優勢資訊交易機率與Fama-French 3 因子定價模型檢測結果 43
表18 Panel A: 、 、 與其他因子之定價能力驗證 45
表19 Panel B: 、 、 與其他因子之定價能力驗證 46
表20 Panel C: 、 、 與其他因子之定價能力驗證 47
表21 報酬與PIN之二維投組構建概念 49
英文部分
1.Abad, D. and A. Rubia (2004), “Estimating the Probability of Informed Trading: Further Evidence from an Order-driven Market,” Working Paper, Department of Financial Economics University of Alicante.
2.Aktas, N., E. Bodt and F. Declerck, H. V. Oppens (2007), “The PIN Anomaly around M&A Announcements,” Journal of Financial Markets, Vol. 10, pp. 169-191.
3.Alexander, G. J., M. A. Peterson (2007), “An analysis of trade-size clustering and its relation to stealth trading,” Journal of Financial Economics, Vol. 84, pp. 435-471.
4.Amihud, Y. and H. Mendelson (1986), “Asset Pricing and the Bid-Ask Spread,” Journal of Financial Economics, Vol.17, pp.223-249.
5.Anand, A. and S. Chakravarty (2007), “Stealth Trading in Options Markets,” Journal of Financial & Quantitative Analysis, Vol. 42, pp.167-187.
6.Barclay, M. J. and J. B. Warner (1993), “Stealth trading and volatility: Which trades move prices?” Journal of Financial Economics, Vol. 34, No. 3, pp. 281-305.
7.Brown, S., S. A. Hillegeist and K. Lo (2006), “The Effect of Meeting or Missing Earnings Expectations on Information Asymmetry,” Working Paper, Department of Accounting, Goizueta Business School, Emory University., Accounting and Control Area, INSEAD and Sauder School of Business, The University of British Columbia.
8.Biais, B., L. Glosten and C. Spatt (2005), “Market Microstructure: A Survey of Microfoundations, Empirical Results, and Policy Implications,” Journal of Financial Markets, Vol. 8, No. 2, pp. 217-264.
9.Brown, S. and S. A. Hillegeist (2006), “How Disclosure Quality Affects the Level of Information Asymmetry,” Working Paper, Department of Accounting, Goizueta Bussiness School, Emory University and Accounting and Control Area, INSEAD.
10.Bagehot, W. (1971), “The Only Game in Town,” Financial Analysts Journal, Vol. 27, No. 2, pp. 12-22.
11.Cai, B. M., C. X. Cai, K. Keasey (2006), “Which trades move prices in emerging markets? Evidence from China''s stock market,” Pacific-Basin Finance Journal, Vol. 14, pp. 453-466.
12.Chan, W. H. (2004), “Conditional Correlated Jump Dynamics in Foreign Exchange,” Economics Letters, Vol. 83, pp. 23-28.
13.Carhart, M. M. (1997), “On Persistence in Mutual Fund Performance,” Journal of Finance, Vol. 52, pp. 57-82.
14.Chakravarty, S. (2001), “Stealth-trading: Which traders'' trades move stock prices?” Journal of Financial Economics, Vol. 61, No. 2, pp.289-307.
15.Chen, Q., I. Goldstein and W. Jiang (2007), “Price Informativeness and Investment Sensitivity to Stock Price,” The Review of Financial Studies, Vol.20, pp. 619-650.
16.Chung, K. H. and M. Li(2003), “Adverse-Selection Costs and the Probability of Information-Based Trading,” The Financial Review, Vol.38, pp. 257-272.
17.Chung, K.H. and M. Li (2005),“Information-based trading, price impact of trade, and trade autocorrelation,” Journal of Banking & Finance, Vol.29, pp. 1645-1669.
18.Copeland, T. E., and D. Galai (1983), “Information Effects on the Bid-Ask Spread,” Journal of Finance, Vol.38, pp. 1457-1469.
19.De Bondt, W. F. M., and R. Thaler (1985), “Does the Stock Market Overreact?” Journal of Finance, Vol.40, pp. 793-805.
20.Ellis, K., M. Roni and M. O''Hara (2000), “The Accuracy of Trade Classification Rules: Evidence from NASDAQ,” Journal of Financial and Quantitative Analysis, December, Vol 35, pp. 529-541.
21.Easley, D., N. M. Kiefer and M. O’Hara (1996), “Cream-Skimming or Profit Sharing? The Curios Role of Purchased Order Flow,” Journal of Finance, Vol. 51, pp. 811-833.
22.Easley, D., N. M. Kiefer, M. O’Hara and J. B. Paperman (1996), “Liquidity, Information, and Infrequently Traded Stocks,” Journal of Finance, Vol. 51, pp. 1405-1436.
23.Easley, D., N. M. Kiefer, and M. O’Hara (1997), “The Information Content of the Trading Process,” Journal of Empirical Finance, Vol. 4, pp. 159-186.
24.Easley, D., N. M. Kiefer and M. O’Hara (1997), “One Day in the Life of A Very Common Stock,” Review of Financial Studies, Vol. 10, No. 3, pp.805-835.
25.Easley, D., M. O’Hara and J. Paperman (1998), “Financial Analysts and Information-base Trade,” Journal of Financial Markets, Vol. 1, No. 2, pp. 175-201.
26.Easley, D. and M. O’Hara (1987), “Price, trade size and information in securities market,” Journal of Financial Economics, Vol.19, pp.69–90.
27.Easley, D., S. Hvidkjaer and M. O’Hara (2002), “Is Information Risk a Determinant of Asset Returns,” Journal of Finance, Vol. 57, No. 5, pp. 2185-2221.
28.Escribano, A. and R. Pascual (2005), “Asymmetries in bid and ask responses to innovations in the trading process,” Empirical Economics, Vol. 30, pp. 913-946.
29.Fama, E. F. and K. R. French (1992), “The Cross-section of Expected Stock Returns,” Journal of Finance, Vol. 47, pp. 427-465.
30.Foster, F. D. and S. Viswanathan (1993), “Variations in Trading Volume, Return Volatility, and Trading Costs: Evidence on Recent Price Formation Models,” Journal of Finance, Vol. 48, pp. 187-211.
31.Grammig, J. and E. Theissen (2002), “Estimating the Probability of Informed Trading – Does Trade Misclassification Matter,” Bonn Graduate School of Economics - Discussion Paper, No. 37, pp. 1-22.
32.Glosten, L. R. (1985), “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders,” Journal of Financial Economics, Vol. 14, No. 1, pp. 71-100.
33.Hamdan, M. A., and H. A. AL-Bayyati, (2006), “A Note on the Bivariate Poisson Distribution,” The American Statistician, Vol. 23, pp. 32-33.
34.Hansen, L. P., and R. J. Hodrick (1980), “Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis,” Journal of Political Economy, Vol. 88, pp. 829-853.
35.Harries, L. (2003), “Trading and Exchanges:Market Microstructure for Practitioners” Oxford University Press, N.Y.
36.Hasbrouck, J. (1991), “Measuring the Information Content of Stock Trades,” Journal of Finance, Vol. 46, pp. 179-207.
37.Hasbrouck, J. (1998), “Trades, Quotes, Inventories, and Information,” Journal of Financial Economics, Vol. 22, pp. 229-252.
38.Heflin, F. and K. W. Shaw (2000), “Blockholder Ownership and Market Liquidity,” Journal of Financial & Quantitative Analysis, Vol. 35, pp. 621-633.
39.Heidle, H. G. and R. D. Huang (2002), “Information-Based Trading in Dealer and Auction Markets An Analysis of Exchange Listings,” Journal of financial and quantitative analysis, Vol.37, pp. 391-424
40.Henke, H. (1995), “Correlation of Order Flow and the Probability of Informed Trading,” The Review of Financial Studies, Vol. 8, pp.579-603.
41.Huang, R. D., J. Cai and X. Wang (1999), “Information-Based Trading in the Treasury Note Interdealer Broker Market,” Journal of Financial Intermediation, Vol.11, pp. 269-296.
42.Huson, Mark R., Youngsoo Kim, and Vikas C. Mehrotra (2006), “Did Decimalization Benefit Members of the Toronto Stock Exchange?” Quarterly Journal of Business & Economics, Vol. 45, pp. 49-67.
43.Iwasaki, M. and H. Tsubaki (2005), “A New Bivariate Distribution in Natural Exponential Family,” Mathematics and Statistics, Vol. 61, pp. 323-336.
44.Jaffe, J. F. (1974), “Special Information and Insider Trading,” Journal of Business, Vol. 47, pp. 410-428.
45.Jegadeesh, N. and S. Titman (1993), “Returns to buying winners and selling losers: Implications for stock market,” Journal of Finance, Vol. 48, pp. 65-91.
46.Jegadeesh, N., and S. Titman (1995), “Overreaction, delayed reaction, and contrarian profits,” Review of Financial Studies, Vol. 8, pp. 973-993.
47.Jegadeesh, N. and S. Titman (2001), “Profitability of Momentum Strategies: An Evaluation of Alternative Explanations,” Journal of Finance, Vol. 56, pp. 699-720.
48.Finucane, T. J. (2000), “A direct test of methods for inferring trade direction from intra-day data,” Journal of Financial and Quantitative Analysis, Vol 35, pp. 553-576
49.Kyle, A. S. (1989), “Informed Speculation with Imperfect Competition,” Review of Economic Studies, Vol. 56, pp. 317-358.
50.Kocherlakota, S. and K. Kocherlakota (2001), “Regression in the Bivariate Poisson Distribution,” Communications in Statistics: Theory and Methods, Vol. 30, pp. 815-825.
51.Lee, C. M. C., and M. J. Ready (1991), “Inferring trade direction from intraday data,” Journal of Finance, Vol. 46, pp.733-746.
52.Lee, C. M. C., B. Mucklow, and M. J. Ready (1993), “Spreads, depths, and the impact of earnings information: an intraday analysis,” Review of Financial Studies, Vol. 6, pp. 345-374.
53.Lee, C. M. C. and B. Swaminathan (2000), “Price Momentum and Trading Volume,” Journal of Finance, Vol. 55, pp. 2017-2069.
54.Lee, C. M. C. and B. Radhakrishna (2000), “Inferring Investor Behavior: Evidence from TORQ Data,” Journal of Financial Markets, Vol.3, pp. 83-111.
55.Lin, C. F. (2006), “Transparency— An Empirical Study Using Taiwan Stock Exchange Data,” Review of Pacific Basin Financial Markets & Policies, Vol. 9, pp. 129-147.
56.Litzenberger, R. H. and K. Ramaswamy (1979), “The Effect of Personal Taxes and Dividends on Capital Asset Prices: Theory and Empirical Evidence,” Journal of Financial Economics, Vol. 7, pp. 163-195.
57.Madhavan, A. (2000), “Market Microstructure: A Survey,” Journal of Financial Markets, Vol. 3, pp. 205-258.
58.Madhavan, A., D. Porter and D. Weaver (2005), “Should Securities Markets Be Transparent?” Journal of Financial Markets, Vol. 8, pp. 265-87.
59.Mayer, W. J. and W. F. Chappell (1992), “Determinants of Entry and Exit: An Application of the Comounded Bivariate Poisson Distribution to U.S. Industries, 1972-1977,” Southern Economic Journal, Vol. 58, pp. 770-778.
60.McInish, T. and R. Wood (1989), “An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks,” Working Paper, Fogelman College of Business and Economics, Memphis State University.
61.Papke, L. E. and J. M. Wooldridge (1996), “Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates,” Journal of Applied Econometrics, Vol. 11, pp. 619-632.
62.Rhee, S. G., and S. H. Chan (2000), “Information Asymmetry, Informed Trading, and Order Imbalance Around Daily Limit-hits: Evidence from Transactions Data and The Limit Order Book of The Kuala Lumpur Stock Exchange,” Working Paper, University of Hawai’I , SSBCiti Asset Management Group and University of Wisconsin-Milwaukee.
63.Rosenberg, B., K. Reid and R. Lanstein (1985), “Persuasive Evidence of Market Inefficiency,” Journal of Portfolio Management, Vol. 11, pp. 9-16.
64.Saar, G. (2001), “Price Impact Asymmetry of Block Trades: An Institutional Trading Explanation,” Review of Financial Studies, Vol. 14, pp. 1153-1181.
65.Sharpe, W. F. (1964), “Capital Asset Price: A Theory of Market Equilibrium Under conditions of Risk,” Journal of Finance, Vol. 19, pp. 425-442.
66.Sias, R. W. (1996), “Volatility and the Institutional Investor,” Financial Analysts Journal, Vol. 52, pp. 13-20.
67.Stoll, H. R. (1978), “The Pricing of Security Dealer Services: An Empirical Study of NASDAQ Stocks,” Journal of Finance, Vol. 33, pp. 1153-1172.
68.Tonda, T. (2005), “A Class of Multivariate Discrete Distributions Based on An Approximate Density in GLMM,” Hiroshima Mathematical Journal, Vol. 35, pp. 327-349.
69.Vega, C. (2006), “Stock price reaction to public and private information,” Journal of Financial Economics, Vol.82, pp.103-133
70.Venter, J. H. and D. CJ De Jongh (2004), “Extending the EKOP Model to Estimate the Probability of Informed Trading,” Working Paper, North-West University of Center for Business Mathematics and BMI .
71.Yuxing, Y. and S. Zhang (2006), “An Improved Estimation Method and Empirical Properties of the Probability of Informed Trading,” Working Paper, University of Pennsylvania and Nanyang Technological University.



中文部分
1.江掌珠 (2004),「資訊交易機率測度與動能生命週期策略」,碩士論文,私立銘傳大學財務金融研究所。
2.周賓凰、劉怡芬 (2000),「臺灣股市橫截面報酬解釋因子:特徵、單因子、或多因子?」,證券市場發展季刊,第12卷第1期,1-32。
3.邵靄如(2001),「臺灣地區新上市/上櫃公司資訊結構與股價行為之研究」,碩士論文,國立政治大學企業管理研究所。
4.孫佩儀(2002),「臺灣股市成交量與報酬序列相關之研究-資訊不對稱」,碩士論文,私立銘傳大學金融研究所。
5.郭政麟 (2004),「資訊交易機率測度、資產定價及資產管理策略」,碩士論文,私立銘傳大學財務金融研究所。
6.郭維裕、胡桂華 (2003),「臺灣股市資訊交易之實証研究」,證券市場發展季刊,第14卷第4期,39-72。
7.陳健宏(2000),「資訊交易機率對股市績效的影響」,碩士論文,國立中山大學財務管理研究所。
8.陳榮昌(2002),「臺灣股票報酬之結構分析」,碩士論文,國立中山大學財物管理研究所。
9.黃仁甫、劉玉珍 (1995),「臺灣股市交易資訊不對稱之實證研究-VAR模型之應用」,中國財務學刊,第3卷第1期,95-117。
10.黃理哲(1994),「臺灣股票市場股票報酬解釋因素之探討」,碩士論文,國立中山大學企業管理研究所。
11.黃俊傑 (2003),「私有資訊提前反應與風險性資產報酬-臺灣證券市場之實証」,碩士論文,私立銘傳大學財務金融研究所。
12.詹場(2000),「臺灣證券市場交易方向之推導與資訊含量」,博士論文,國立臺灣大學財務金融學研究所。
13.趙偉翔 (2006),「優勢資訊交易估計、行為探勘及其在投資組合策略構建上之運用」,碩士論文,私立銘傳大學資訊管理研究所。
14.楊清芬(2001),「資訊交易機率之測度及其決定因素探討」,碩士論文,國立中山大學財物管理研究所。
15.劉家榮(2007)、「資訊風險是否為臺灣證券市場之資產定價因子」,碩士論文,私立銘傳大學財務金融研究所。
16.鄭景綸 (2005),「臺灣證券市場資訊交易機率測度、資產定價及交易成本考量下之策略性資產管理策略」,碩士論文,私立銘傳大學財務金融研究所。
17.謝宓頤 (2002),「臺灣低流動性股票訊息交易之研究」,碩士論文,國立清華大學科技管理研究所。
18.顧廣平 (2005),「單因子、三因子或四因子模式?」,證劵市場發展季刊,第17卷第2期,101-146。
電子全文 電子全文(本篇電子全文限研究生所屬學校校內系統及IP範圍內開放)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top