Responsive image
博碩士論文 etd-0524116-203313 詳細資訊
Title page for etd-0524116-203313
論文名稱
Title
馬爾可夫狀態轉換模型對 Smart Beta 之應用 —以台灣股票市場之交易策略研究
A study of strategy trading in Taiwan stock market-An application of Markov Switch Regression Model on Smart Beta
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
73
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-06-23
繳交日期
Date of Submission
2016-06-24
關鍵字
Keywords
狀態轉換模型、隱藏馬爾可夫模型、最大期望演算法、預測報酬、投資策略、績效回測、多重起始值
Regression Regime Switch, Hidden Markov Model, Expectation-maximization Algorithm, Return forecasting, Investment strategy, Back-test, Multiple Starting Point
統計
Statistics
本論文已被瀏覽 5802 次,被下載 24
The thesis/dissertation has been browsed 5802 times, has been downloaded 24 times.
中文摘要
在金融市場中,所有的投資者都在尋找屬於自己的投資聖杯,透過主動式 操作,打敗大盤報酬,創造年化報酬率為正的績效。本文透過 CAPM、Fama- French 三因子模型及 Smart Beta 五因子模型,運用隱藏馬爾可夫狀態轉換模型 來預測下一期市場所處在的隱狀態機率。在隱藏馬爾可夫模型中,參數估計是 以 EM 演算法(Expectation-maximization Algorithm)得到最適化參數,並利用多 重起始點來避免 EM 演算法對初始值估計過於敏感的問題。透過每期財務報表 季報及月報公布時的財報資訊作為篩股的標準,發展出一套完整的投資策略。
實際回測的結果顯示,此套投資策略應用於現實股票市場並搭配台指期貨 避險下,三種模型之投組績效皆至少達到年化報酬率 30%、夏普比率 1.91 以 上,且長期操作皆能有穩定的績效報酬。另外,本研究將傳統迴歸模型與狀態 迴歸模型相比較,發現區分出兩狀態的迴歸式有更準確預測能力,所篩選出的 投資組合績效較傳統迴歸模型更佳,且馬爾可夫狀態模型在考慮了隨機波動 下,明顯地區分出兩種模型的在選股上的不同。
Abstract
In the financial markets, all investors are looking for their Holy Grail of investing. By actively operating, they defeat the market return, and create a positive performance of the annual rate of return. In this paper, through CAPM, Fama-French three factor model and Smart-Beta five factor model, using hidden Markov model to predict the probability of hidden regime in the next period. In hidden Markov model, the EM algorithm is used to estimate the optimal parameters, and using multiple starting point to avoid EM algorithm highly relies on the initial values. Through the financial reports, released quarterly and monthly, selecting asset pool to compose investment portfolio, develop a complete investment strategies.
The empirical results show that this investment strategy applied in the actual financial market and used Taiwan stock index futures to achieve the objective of hedging. Regardless of which factor models, all of their portfolio have at least 30% annual return rate, Sharpe ratio 1.91 and the long-term performance can be stable. Furthermore, this study also compare traditional regression model and Markov regression model, and found that Markov regression model has more accurate prediction ability, the performance of its portfolio is better than traditional regression model. Because Markov regression model consider the stochastic volatility of stocks, obviously distinguishing the difference of selecting stocks.
目次 Table of Contents
目錄
誌謝辭.....................................................................................................................i
摘要........................................................................................................................ii
Abstract ................................................................................................................ iii
目錄.......................................................................................................................iv
圖次........................................................................................................................v
表次.......................................................................................................................vi
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 傳統迴歸模型............................................................................................. 4
第二節 Smart Beta 因子之解釋能力 ......................................................................4
第三節 馬爾可夫狀態轉換模型之應用.................................................................... 5
第四節 EM 演算法.................................................................................................. 5
第三章 研究方法......................................................................................................7
第一節 變數介紹..................................................................................................... 7
第二節 模型設定..................................................................................................... 9
第三節 投資策略................................................................................................... 25
第四章 實證結果....................................................................................................31
第一節 績效回測................................................................................................... 31
第二節 模型間比較................................................................................................ 61
第五章 結論與建議.................................................................................................62
第一節 結論........................................................................................................... 62
第二節 後續研究建議............................................................................................. 63
參考文獻.................................................................................................................64
中文部分 ............................................................................................................... 64
英文部分 ............................................................................................................... 64
參考文獻 References
蔡欣穎. (2015). 運用經濟金融指標之馬可夫轉換模型預測台灣加權 股價指數. 中山大學. Available from Airiti AiritiLibrary database. (2015 年)
葉千綺. (2015). 量化選股策略--以台灣市場為例. 中山大學. Available from Airiti AiritiLibrary database. (2015 年)
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, 61(1), 259-299.
Asness, C. S., Frazzini, A., Israel, R., & Moskowitz, T. J. (2015). Fact, fiction, and value investing. Available at SSRN 2595747.
Asness, C. S., Frazzini, A., & Pedersen, L. H. (2014). Quality minus junk. Available at SSRN 2312432.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Cai, J. (1994). A Markov model of unconditional variance in ARCH. Journal of Business and Economic Statistics, 12(3), 309-316.
Cappé, O., & Moulines, E. (2009). On‐line expectation–maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(3), 593-613.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), 1-38.
Engel, C., & Hamilton, J. D. (1989). Long swings in the exchange rate: are they in the data and do markets know it? : National Bureau of Economic Research.
Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. The Journal of Finance, 47(2), 427-465.
Fama, E. F., & French, K. R. (1998). Value versus growth: The international evidence. The Journal of Finance, 53(6), 1975-1999.
Fu, F. (2009). Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics, 91(1), 24-37.
Hamilton, J. D. (1994). Time series analysis (Vol. 2): Princeton university press Princeton.
Kole, E. (2010). Regime Switching Models: An Example for a Stock Market Index.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The review of economics and statistics, 13-37.
Liu, X., Margaritis, D., & Wang, P. (2012). Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors. Journal of Empirical Finance, 19(4), 483-496.
Mongillo, G., & Deneve, S. (2008). Online learning with hidden Markov models. Neural computation, 20(7), 1706-1716.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrics 34. October, 768, 83.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Smith, D. R., & Layton, A. (2007). Comparing probability forecasts in Markov regime switching business cycle models. Journal of Business Cycle Measurement and Analysis, 2007(1), 79-98.
Wu, C. J. (1983). On the convergence properties of the EM algorithm. The Annals of statistics, 95-103.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code