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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許耀文(Yao-Wen Hsu) | |
dc.contributor.author | Yi-Chun Liu | en |
dc.contributor.author | 劉怡均 | zh_TW |
dc.date.accessioned | 2021-06-08T01:55:49Z | - |
dc.date.copyright | 2016-07-26 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-07-04 | |
dc.identifier.citation | 1. Ball, R., & Brown, P. (1968). An Empirical Evaluation of Accounting Income Numbers. Journal of Accounting Research, 6(2), 159-178.
2. Brent, A., Morse, D., & Stice, E. K. (1990). Short Interest: Explanations and Tests. The Journal of Financial and Quantitative Analysis, 25(2), 273-289. 3. Desai, H., Ramesh, K., Thiagarajan, S. R., & Balachandran, B. V. (2002). An Investigation of the Informational Role of Short Interest in the Nasdaq Market. The Journal of Finance, 57(5), 2263-2287. 4. Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465. 5. Finnerty, J. E. (1976). Insiders and Market Efficiency. The Journal of Finance, 31(4), 1141-1148. 6. Jaffe, J. F. (1974). Special Information and Insider Trading. The Journal of Business, 47(3), 410-428. 7. Jegadeesh, N., & Livnat, J. (2006). Post-Earnings-Announcement Drift: The Role of Revenue Surprises. Financial Analysts Journal, 62(2), 22-34. 8. Kim, Y., Choi, Y.-K., & Emery, S. (2013). Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages. The American statistician, 67(3). 9. Ou, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11(4), 295- 329. 10. Penman, S. H. (1982). Insider Trading and the Dissemination of Firms' Forecast Information. The Journal of Business, 55(4), 479-503. 11. Pinheiro, J., & Chao, E. C. (2006). Efficient Laplacian and Adaptive Gaussian Quadrature Algorithms for Multilevel Generalized Linear Mixed Models. Journal of Computational and Graphical Statistics, 15(1), 58-81. 12. Senchack, A. J., & Starks, L. T. (1993). Short-Sale Restrictions and Market Reaction to Short-Interest Announcements. The Journal of Financial and Quantitative Analysis, 28(2), 177-194. 13. Seneca, J. J. (1967). Short Interest: Bearish or Bullish? The Journal of Finance, 22(1), 67-70. 14. Sivakumar, K., & Waymire, G. (1994). Insider Trading Following Material News Events: Evidence from Earnings. Financial Management, 23(1), 23-32. 15. Woolridge, J. R., & Dickinson, A. (1994). Short Selling and Common Stock Prices. Financial Analysts Journal, 50(1), 20-28. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19370 | - |
dc.description.abstract | 現今社會之投資管道日益漸增,其中股票放空即利用融券之機制向授信機構 融券,先行於股票市場賣出,待日後股價下跌時進行融券回補,此投資方式不僅 說明股市看空亦可獲利,打破單向市場之限制,融券保證金之訂定亦顯示投資者 可利用較低的資本獲取相同的利潤,也就是信用交易的槓桿效應。
本研究欲規劃一完整之股票放空策略以提供投資者在重要之決策點有效且即 時之投資建議,當預測的股價趨勢為大幅下跌,則建議投資者進行股票放空,反 之亦然。本研究參考 (Seneca, 1967)等研究,文獻指出融券餘額與未來股價在統計 上呈顯著的負相關,此外,比較過去曾有炒作行為之股票與穩定成長之股票,由 歷史數據顯示具炒作行為之股票其董監持股比例與十大股東持股比例在股價最高 點前已有出脫持股的情形,此為炒作類型股票之特性之一,另外列舉股價漲跌與 成交量增減之純價量變因以作為待篩選之重要變因,以縱貫性研究分析 2009 年十 月至 2013 年十月間台灣上市與上櫃公司股票之週資料。由於影響短期內未來股價 之因素瞬息萬變,且容易受人為操控與市場環境影響,因此本研究將短期內未來 股價分為兩個類別,一類是價格將大幅下跌,故要融券放空者;另一類是價格保 持平穩,甚至是上漲,不進行融券放空者,另外,融券放空策略中包含停損點與 期望投資報酬率標準之設定。 本研究同時考量股票資料之縱向與橫向之關係,因此設定為混合效應邏吉斯 迴歸模型,出象僅有價格下跌與價格上漲兩種分類,經過一百次的重複抽樣,每 次分層抽樣五十筆作為建立模型之基礎,最終模型預測價格分類之平均準確率為 72.34%,平均陽性預測值為 44.13%,若完全依據預測結果進行虛擬投資,平均報 酬率為 11.08%,平均年化報酬率則為 30.19%,準確率與報酬率結果均佳,陽性 預測值亦大幅超越投資虧損之標準,但仍有 0.19 的機率為虧損的投資組合。 本研究之股票放空策略預測模型之計算簡易,且節省投資人對於眾多投資標 的之前置作業分析時間,例如對於各公司詳細財務基本狀況之調查與比較,此外, 抽樣樣本之各項指標如 McFadden pseudo R2、MCC 與 ROC 曲線下之面積 AUC 值 皆不差,亦通過皮爾森卡方適合度檢定及 Hosmer-Lemeshow 適合度檢定,因此根 據研究結果顯示,本研究之股票放空策略在統計上具顯著之效益。 | zh_TW |
dc.description.abstract | Many kinds of investment channels have sprung up these years. One of these is to sell short in the stock market. The most important concept of shorting is to sell short with high price and to cover it as the price collapses. Therefore, while investors expect the price to fall down, they may sell short at the decision point and vice versa. The purpose
of this study is to help investors forming a shorting mechanism which includes stop-loss point and proper time for short covering. Under longitudinal study, the best set of predictor variables includes the maximal price-rising rate, the price-falling rate and the acceleration of volume. The output of mixed effects logistic regression model shows both the predicted price-classification in the near future and the investment advice. After one- hundred-time stratified sampling, the average accuracy rate is 72.34%, the average positive predictive value is 44.13%, the average return is 11.08%, the average annualized return is 30.19%, and may still make a loss in series of investments with probability 0.19. To summarize, it’s an easy and time-saving strategy for shorting with statistical significance according to the research. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T01:55:49Z (GMT). No. of bitstreams: 1 ntu-105-R03h41009-1.pdf: 8608310 bytes, checksum: b12777cc134c4004058934864b1b3d36 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 目錄
致謝 ................................................................................................................ i 摘要 ................................................................................................................ ii Abstract .......................................................................................................... iii 第一章 緒論 .................................................................................................... 1 第一節 研究背景與動機 .................................................................................. 1 第二節 研究目的 ............................................................................................. 2 第二章 文獻回顧 ............................................................................................. 3 第三章 研究材料與研究對象 ........................................................................... 8 第一節 研究材料 ............................................................................................. 8 第二節 研究期間與研究對象 ........................................................................... 11 第四章 研究方法 ............................................................................................. 12 第一節 前置作業—炒作股票與穩定成長股票之區分........................................ 12 第二節 重要變因之探討與選擇 ........................................................................ 14 第三節 模型設定與分層抽樣 ............................................................................ 16 第四節 決策模型之建立、預測與虛擬投資 ...................................................... 18 第五節 模型之有效性與適合度 ........................................................................ 19 第六節 重複分層抽樣與新資料之測試 ............................................................. 21 第五章 實證結果 .............................................................................................. 23 第一節 原始模型 .............................................................................................. 23 第二節 改善後之模型 ....................................................................................... 25 第三節 模型之指標與適合度檢定 ..................................................................... 26 第四節 新資料之測試 ....................................................................................... 28 第六章 結論 ..................................................................................................... 30 第七章 參考文獻與附錄 ................................................................................... 33 | |
dc.language.iso | zh-TW | |
dc.title | 以統計方法規劃股票放空之策略 | zh_TW |
dc.title | A Statistical Shorting Strategy | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡宛珊(Wan-Shan Tsai),盧信昌(Hsin-Chang Lu) | |
dc.subject.keyword | 融券放空,融券回補,縱貫性研究,混合效應邏吉斯迴歸模型,停損點, | zh_TW |
dc.subject.keyword | Selling short,Short covering,Panel study,Mixed effects logistic regression model,Stop-loss point, | en |
dc.relation.page | 58 | |
dc.identifier.doi | 10.6342/NTU201600619 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2016-07-07 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
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