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研究生:蔡佳樺
研究生(外文):Chia-Hua Tsai
論文名稱:投資交易因子之灰色多準則評選-以 S&P500 指數期貨為例
論文名稱(外文):Grey Multiple Criteria Evaluation of Trading Variables for Investing S&P 500 Stock Index Futures
指導教授:胡為善胡為善引用關係胡宜中胡宜中引用關係
指導教授(外文):Wei-Shan HuYi-Chung Hu
學位類別:碩士
校院名稱:中原大學
系所名稱:企業管理研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:86
中文關鍵詞:多準則決策灰關聯分析利率決策實驗室法美元指數網路分析程序法Granger 因果關係檢定S&P 500 指數期貨
外文關鍵詞:ANPDEMATELGRAGRDInterests RatesMultiple decision-makingS&P 500 index futuresUS Dollar Index
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台灣自 2015 年 12 月起,先後引進 S&P 500 指數之正反 ETF 與日本東証單日正向 之兩倍 ETF,以期加速與國際市場的連結,並提供投資人多元投資之管道。本研究期望 除了在學術上從不同的觀點來預測 S&P 500 指數期貨之變動外,也能在實務上為投資人 在擬定投資策略、作避險規劃及資產配置等方面均提供重要的參考依據。
本研究先透過德爾菲法建立影響投資人購買 S&P500 指數期貨的關鍵因素之研究架 構,並運用決策實驗室網路程序分析法(D-ANP)向 13 位財務專家進行問卷資料分析, 進而探討投資人在交易 S&P500 指數期貨時,應考量的五項關鍵因素以及這五項因素彼 此間的關聯性,最後再以 Granger 因果關係、逐步迴歸分析與灰關聯分析(GRA)進行 驗證,本研究獲得之結論如下:
1. 本研究發現投資人在投資 S&P 500 指數期貨時,已從過去的只關注傳統技術層 面,逐漸轉移到總體經濟層面;而影響其決策的五個關鍵因素為「美元指數」、 「利率」、「製造業採購經理人指數」、「波動率指數」及「失業率」。
2. 本研究透過 Granger 因果關係檢定,以驗證各準則間之因果關係,發現「美元 指數」、「利率」與「製造業採購經理人指數」皆為影響 S&P 500 指數的重要因 素。
3. 本研究從逐步迴歸分析的驗證結果中,亦發現「波動率指數」、「美元指數」、「利 率」及「失業率」等四個準則都具有預測 S&P 500 指數的能力。
4. 本研究從灰關聯分析的驗證結果中發現,透過 D-ANP 獲得的五項準則,對於 S&P 500 指數期貨比道瓊指數期貨及 Nasdaq 指數期貨的解釋力都高;進一步 檢視 S&P 500 指數期貨中所含成份股之解釋力,又發現以工業股為最高,因此 本研究建議投資人在交易 S&P 500 指數期貨時,除了參考這五項關鍵因素外, 也應留意 S&P 500 指數工業成份股中所含之各股的股價波動。
Since December 2015, Taiwan’s Yuanta Daily S&P 500 Bull 1x ETF, Bull 2x ETF, and S&P 500 Bear 1x ETF, as well as Tokyo Stock Price Index 1x ETF have been listed at Taiwan Stock Exchange one after another. These listings not only connect Taiwan stock markets with international ones, but also provide international financial instruments with Taiwanese investors. This study not only attempts to provide a new perspective to forecast the fluctuations of S&P 500 index in academics, but also assists the investors to make their investment’s strategic decisions on hedging planning and asset allocations in practice.
This study first employs the Delphi method to construct a research framework and applies the D-ANP method to evaluate the weight of the influential factors on the S&P 500 index futures and explores the relevant relationship among these factors. Additionally, the Granger causality test, stepwise regression analysis and the Grey relational analysis (GRA) methods are used to verify the empirical results. The conclusions are summarized below.
1. This investigation finds that various investors of S&P 500 index futures have changed
their focuses from the technical analysis to the macroeconomic parameters when making their investment decisions. Five key factors, selected by the D-ANP method, affecting the fluctuations of S&P 500 index are US dollar index, interests rates, purchase management index, volatility index and unemployment rate.
2. Granger causality test is utilized to verify the causal relationship proposed by the D-ANP method. Empirical findings confirmed that US dollar index, interests rates and purchase management index are the main factors that causing the fluctuations of the S&P 500 index.
3. The stepwise regression result confirms that the predictive power of US dollar index, interests rates, volatility index and unemployment rate on the fluctuations of S&P 500 index.
4. The GRA result also confirms that the predictive power of the determinants suggested by 13 financial experts via D-ANP method on the fluctuations of S&P 500 stock index futures. The Grey relational grade (GRD) also proves that the S&P 500 index futures being evaluated as the most effective one among Dow Jones index futures, S&P 500 index futures and NASDAQ index futures. Furthermore, this study examines the explanatory power of the components of S&P 500 stock index futures and finds that the explanatory power of the S&P 500 industrial’s index outperforms the other S&P 500 indices, suggesting that the investors of trading S&P 500 index futures should not only consider the above five key factors, but also pay serious attention to the fluctuations of the price of each individual company of S&P 500 stock industrial index futures when making their investment decisions.
摘要 ........................................................................................................................ I ABSTRACT ......................................................................................................... II
致謝詞 ................................................................................................................. IV
目錄 ...................................................................................................................... V
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
第三節 研究流程............................................................................................3
第二章 文獻探討 ............................................................................................... 4
第一節、S&P 500 股價指數期貨 .................................................................... 4
第二節、影響 S&P 500 指數期貨投資人交易決策之因素 ........................... 5
第三節、以決策實驗室法為基礎的網路分析程序法 ................................... 8
第四節、灰關聯分析 ..................................................................................... 11
第三章 研究方法 ............................................................................................. 12
第一節 研究架構建立..................................................................................12
第二節 資料分析方法..................................................................................19
第三節 計量模型驗證..................................................................................24
第四節 灰關聯分析......................................................................................28
第四章 研究結果與分析................................................................................. 30
第一節 問卷設計..........................................................................................30
第二節 問卷分析..........................................................................................30
第五章 研究結果驗證 ..................................................................................... 38
第一節 計量模型驗證..................................................................................38
第二節 灰關聯分析......................................................................................45
第六章 結論與建議 ......................................................................................... 52
第一節 結論..................................................................................................52
第二節 研究限制............................................................................................56
第三節 研究建議............................................................................................56
參考文獻 ............................................................................................................. 57 附錄 A................................................................................................................62
附錄 B................................................................................................................66
附錄 C................................................................................................................69
附錄 D................................................................................................................74
圖1. 研究流程......................................................................................................3
圖2. AHP層級架構圖和ANP網路架構圖.................................................................8
圖3. AHP階層式網路圖及其對應之超級矩陣..........................................................9
圖4. 研究雛型架構...........................................................................................12
圖5. 修正後研究雛型架構......................................................................................16
圖6. 正式研究架構.............................................................................................. 18
圖7. 本研究之資料分析與方案評選模式..................................................................19
圖8. 準則因果關係圖範例................................................................................23
圖9. 準則因果關係圖.......................................................................................36
圖10. 各變數原始序列圖...................................................................................... 39
圖11. 各變數原始序列圖(續)........................................................................... 40
表1. 總體經濟指標之相關文獻彙整表.....................................................................6
表2. 技術層面之相關文獻彙總表............................................................................7
表3. 技術層面之相關文獻彙總表...........................................................................7
表4. 專家問卷調查成員..........................................................................................13
表5. 雛型架構範圍增減與說明................................................................................14
表6. 第一次準則重要性評分表..............................................................................17
表7. 第二次準則重要性評分表.............................................................................. 17
表8. 範例各準則對應之重要度及原因度..................................................................22
表9. Borda法則排序之準則權重範例........................................................................23
表10. 評分尺度說明................................................................................................30
表11. 對應準則之直接關係矩陣................................................................................31
表12. 對應準則之正規化直接關係..............................................................................31
表13. 對應準則之總影響關係矩陣..............................................................................32
表14. 對應準則之重要度及原因度..............................................................................32
表15. 各準則的因果類別彙整....................................................................................33
表16. 對應準則之超級加權矩陣................................................................................33
表17. 對應準則之極限超級矩陣...............................................................................34
表18. 基於D-ANP產生之準則權重............................................................................34
表19. Borda法則排序之準則權重..............................................................................35
表20. 準則間之影響關係確認..................................................................................36
表21. 變數名稱定義表............................................................................................38
表22. 敘述統計彙總表.............................................................................................39
表23. ADF單根檢定結果..........................................................................................40
表24. Johansen共整合檢定結果..............................................................................41
表25. VECM分析結果..............................................................................................41
表26. 修正誤差項調整速度....................................................................................42
表27. Granger因果關係檢定結果......................................................................42
表28. 逐步迴歸分析結果..........................................................................................43
表29. 各組報酬率T檢定結果比較............................................................................44
表30. 各成份股原始資料數列...................................................................................46
表31. 各成份股之參考數列與比教數列.....................................................................46
表32. 各準則之灰關聯係數....................................................................................47
表33. 各成份股之灰關聯係度..............................................................................48
表34. 三大指數期貨原始資料數列........................................................................49
表35. 三大指數期貨之參考數列與比教數列............................................................49
表36. 各準則之灰關聯係數..............................................................................50
表37. 三大指數期貨之灰關聯度..............................................................................50
中文文獻
王嘉隆、詹淑惠,2005,「分類迴歸樹於 S&P500 指數預測之研究」,管理科學研究,第 一屆管理與決策 2005 年學術研討會特刊,141-150。
朱崇維,2007,「應用資料包絡分析法與分析層級程序作為選股決策之探討:以美國網 路股為例」,國立中興大學,碩士論文。
李怡芳,2011,「結合 DEMATEL 與 DANP 的方法來探討銀行人員的工作情境對獎勵制 度的評估與改善」,開南大學,碩士論文。
李建和,2007,「運用 RSI 技術指標於台灣指數期貨市場價差交易之實證研究」,國立高 雄應用科技大學,碩士論文。
林楷模,2011「波動率指數期貨與標準普爾 500 指數成分股之流動性共變」,國立臺灣 大學,碩士論文。
林煜城,2010,「S&P500 指數期現貨與 NASDAQ 指數期貨之關聯性及波動外溢與跳躍 現象之探討-GARCH-Jump 模型建立及避險比率與績效評估」,國立臺北大學,碩士 論文。
吳川熺,2011,「金融風暴後標準普爾 500 指數於時間序列最適模型之研究」,國立臺北 大學,碩士論文。
洪之良,2001,「台美兩地之股價與總體經濟變數關聯性研究」,國立交通大學,碩士論 文。
胡宜中、邱榆淨,2005,「使用能力集合擴展決定專案中子系統開發之優先順序」,台大 管理論叢,第 16 卷,第 1 期,21-40。
柯維喬,2011,「美國長短公債期貨價差與股匯、經濟數據之關係研究」,國立中興大學, 碩士論文。
唐宇宏,2012,「應用複合多準則決策模式探討產物保險業服務品質績效」,中原大學, 碩士論文。
郭信華,2003,「台灣美國股價指數與其總體經濟變數關聯性之研究」,國立高雄第一科 技大學,碩士論文。
黃光宇、簡春娟、張廷政、林聖博,2011,「結合灰關聯分析與粗集合以建立股票投資 組合暨買賣時機之決策模型」,計量管理期刊,第 8 卷,第 1 期,25-38。
黃永成,2011,「結合灰關聯分析與粗集合以建立股票投資組合暨買賣時機之決策模型」, 資訊管理學報,第 18 卷,第 1 期,133-153。
黃彥棠,2015,「不同時間長度技術指標對台指期貨報酬之研究:以 KD 指標為例」,國立 臺北大學,碩士論文。
陳宗敬,2012,「應用灰關聯分析、遺傳演算法與模糊神經網路預測臺灣股票加權指數 之研究」,義守大學,碩士論文。
陳惠莉,2011,「台股報酬與波動動態影響之探討-不同產業類別之分析」,國立台北商 業技術學院,碩士論文。
陳盈安,2011,「由 DMI 指標檢測時國指數基金之投資績效」,國立中正大學,碩士論 文。
陳逸穎,2015,「股票投資決策因素之探討-DEMATEL 模型之應用」,朝陽科技大學,碩 士論文。
楊淑真,2010,「標準普爾期貨指數之預測」,國立中興大學,碩士論文。 劉裕仁,2015,「國中畢業生於十二年國教下選擇高中職之關鍵因素—以桃園市為例—」,
中原大學,碩士論文。 鄧振源,2012,「多準則決策分析方法與應用」,台北:鼎茂圖書。
英文文獻
Bessembinder, H., Chan, K., and Seguin, P. J. 1996. “An empirical Examination of Information, Differences of Opinion, and Trading Activity.” Journal of Financial Econometrics, Vol. 40, No. 1, 105-134.
Chang, E., Chou, R. Y., and Nelling, E. F. 2000. “Market Volatility and the Demand for
Hedging in Stock Index Futures.” Journal of Futures Markets, Vol. 20, No. 2, 105-125.Deng, J., 1982. “Control Problems of Grey System.” System and Control Letters, Vol. 5, 288-294.
Deng, J., 1989. “Introduction to Grey System Theory.” The Journal of Grey System, Vol. 1, No.1, 1-24.
Engle, R. F. and Granger, C. W. J. 1987. “Co-integration and Error Correction: Representation, Estimation, and Testing.” Econometrica, Vol. 55, No. 2, 251-276.
Granger, C. W. J. and Newbold, P. 1974. “Spurious Regressions in Econometrics.” Journal of Econometrics, Vol. 2, No. 2, 111-120.
Gordon, T. J. and Helmer, O. 1964. Report on a Long-Range Forecasting Study, The Rand Corporation.
Hamilton, J. D. and Lin, G. 1996. “Stock Market Volatility and the Business Cycle.” Journal of Applied Econometrics, Vol. 11, No. 5, 573-593.
Hu, J. W. S., Hu, Y. C., and Yang, T. P. 2016. “Evaluating the Optimum Strategy Developed by Combing Jesse L. Livermore’s Key Price Logic and the D-ANP Methods.” In Proceeding of SBIR 2016 Kuala Lumpur Conference on Interdisciplinary Business Research, Kuala Lumpur.
Hu, Y. C., Chiu, Y. J., Hsu, C. S. and Chang Y. Y. 2015. “Identifying Key Factors of Introducing GPS-based Fleet Management Systems to the Logistics Industry.” Mathematical Problems in Engineering, Vol. 2015, No. 8, 1-14.
Johansen, S. and Juselius, K. 1990. “Maximum Likelihood Estimation and Inference of Economics and Statistics.” Oxford Bulletin of Economics and Statistics, Vol. 52, 169-210.
Koehler, A. B. and Murphree E. S. 1988. “A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order.” Journal of the Royal Statistical Society Series C (Applied Statistics), Vol. 37, No. 2, 187-195.
Kurihara, Y., 2006. “The Relationship between Exchange Rate and Stock Prices during the Quantitative Easing Policy.” International journal of business, Vol. 11, No. 4, 375-386. Kwon, K. Y. and Kish, R. 2002. “Technical Trading Strategies and Return Predictability:
NYSE.” Applied Financial Economics, Vol. 12, No. 9, 639-653.
Liu, S. and Lin, Y. 2006. Grey Information Theory and Practical Applications. London:
Springer-Verlag London Limited.
Mall, M., Pradhan, B. B., and Mishra, P. K. 2011. “The Efficiency of India’s Stock Index Futures Market: An Empirical Analysis Authors.” International Research Journal of Finance and Economics, Vol. 69, 178-184.
Meade, L. M. and Sarkis, J. 1999. “Analyzing Organizational Project Alternatives for Agile Manufacturing Processes: An Analytical Network Approach.” International Journal of Production Research, Vol. 37, No. 2, 241-261.
Michael, T. 2008. “Borda and the Maximum Likelihood Approach to Vote Aggregation.” Mathematical Social Sciences, Vol. 55, No. 1, 96-102.
Poshakwale S. and Patra, T., 2006. “Economic Variables and Stock Returns: Evidence from the Athens Stock Exchange.” Applied Financial Economics, Vol. 16, No. 13, 993-1005.
Ratanapakorn, O. and Sharma, S. C. 2007. “Dynamics Analysis between the US Stock Returnand the Macroeconomics Variables.” Applied Financial Economics, Vol. 17, No.4, 369-377.
Saaty, T. L. 1980. The Analytic Hierarchy Process, New York, NY: McGraw-Hill.
Saaty, T. L. 1996. The Analytic Network Process-Decision Making with Dependence and
Feedback. Pittsburgh, PA: RWS Publications.
Saaty, T. L. 2001. Decision Making with Dependence and Feedback: The Analytic Network
Process, Pittsburg, PA: RWS Publications.
Suganthi, L. and Samuel, A. A. 2012. “Energy Models for Demand Forecasting—A Review.”
Renewable & Sustainable Energy Reviews, Vol. 16, No.2, 1223-1240.
Sun, Q. and Tong, W. H. S. 2000. “The Effect of U.S. Trade Deficit Announcements on the Stock Price of U.S. and Japanese Automakers.” The Journal of Financial Research, Vol.
23, No. 1, 15-43.
Tzeng, G. H. and Huang, J. J. 2011. Multiple Attribute Decision Making: Methods and
Applications. (Eds.), Florida, CRC Press.
Wang, G. H. K. and Yau, J. 2000. “Trading Volume, Bid-Ask Spread, and Price Volatility in
Futures Market.” Journal of Futures Markets, Vol. 20, No. 10, 943-970.
Xu, Z. and Wei, C. 1999. “A Consistency Improving Method in the Analytic Hierarchy
Process.” European Journal of Operational Research, Vol. 116, No.2, 443-449.
Yang, Y. P. O., Shieh, H. M., Leu, J. D., and Tzeng, G. H. 2008. “A Novel Hybrid MCDM Model Combined with DEMATEL and ANP with Applications.” International Journal of
Operations Research, Vol. 5, No. 3, 160-168.
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