博碩士論文 etd-0524116-203313 詳細資訊


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姓名 廖志鴻(Chih-Hung Liao) 電子郵件信箱 E-mail 資料不公開
畢業系所 財務管理學系研究所(Finance)
畢業學位 碩士(Master) 畢業時期 104學年第2學期
論文名稱(中) 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
論文名稱(英) A study of strategy trading in Taiwan stock market-An application of Markov Switch Regression Model on Smart Beta
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    紙本論文:5 年後公開 (2021-06-24 公開)

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    摘要(中) 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。
    摘要(英) 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.
    關鍵字(中)
  • 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
  • 關鍵字(英)
  • Regression Regime Switch
  • Hidden Markov Model
  • Expectation-maximization Algorithm
  • Return forecasting
  • Investment strategy
  • Back-test
  • Multiple Starting Point
  • 論文目次 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
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    口試委員
  • 黃北豪 - 召集委員
  • 蔡維哲 - 委員
  • 王昭文 - 指導教授
  • 黃振聰 - 指導教授
  • 口試日期 2016-06-23 繳交日期 2016-06-24

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