跳到主要內容

簡易檢索 / 詳目顯示

研究生: 邱芝螢
Chiu, Jhih-Ying
論文名稱: 是否有比歷史平均法更有效預測股票市場溢酬的方法?-以美國市場為例
Is there a way to predict the stock market risk premium better than historical average? Evidence from the US market stock market
指導教授: 顏佑銘
Yen, Yu-Min
口試委員: 顏佑銘
Yen, Yu-Min
謝淑貞
Shieh, Shwu-Jane
張子溥
Chang, Tzu-Pu
學位類別: 碩士
Master
系所名稱: 商學院 - 國際經營與貿易學系
Department of International Business
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 25
中文關鍵詞: 股票市場溢酬樣本外測試模型平均法
外文關鍵詞: Stock market premium, Out of sample test, Model averaging
DOI URL: http://doi.org/10.6814/NCCU202001021
相關次數: 點閱:248下載:2
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究乃運用Welch and Goyal(2008)所提出的1945年至2018年的十二項經濟變數資料去預測股票市場溢酬。首先重新檢驗單一變數的樣本內測試(in-sample test)及樣本外測試(out-of-sample test)的結果,接著透過組合變數模型估計,最後再以模型平均法(Model Averaging)建構新的模型。希望藉由新的建構模型方法,比較不同預測模型的預測能力。實證結果發現,以上所建構的模型,皆無法打敗歷史平均法(historical average method)。


    In this paper, I use the twelve economic variables of Welch and Goyal (2008) to predict the stock market premium. First of all, I reexamine the in-sample and out-of-sample test. After that, I establish the combination variable to predict the stock market. Finally, I use the model averaging to establish new models. Empirical results display that, the whole models fail to beat the historical average.

    論文審定書 i
    謝辭 ii
    摘要 iii
    Abstract iv

    第一章 緒論 1
    第一節 研究動機與目的 1
    第二節 研究架構(流程) 2

    第二章 文獻回顧 3

    第三章 研究方法 6
    第一節 單一變數 6
    第二節 組合變數 6
    第三節 模型平均 6
    第四節 預測結果評估 7

    第四章 實證分析 9
    第一節 樣本說明 9
    第二節 單一變數 10
    第三節 組合變數 17
    第四節 模型平均 19

    第五章 結論 23
    參考文獻 24

    中文參考文獻

    1.陳旭昇(2009),"時間序列-總體經濟與財務金融與財務金融之應用”,台北:東華書局

    英文參考文獻

    1.Ang, Andrew Bekaert, Geert. (2007) “Stock return predictability: Is it there? “, Review of Financial Studies, 651-707
    2.Baetje, FabianMenkhoff, Lukas.(2016) “Equity premium prediction: Are economic and technical indicators unstable?”, International Journal of Forecasting, 1193-1207
    3.Campbell, John Y.Thompson, Samuel B.( 2008) “Predicting excess stock returns out of sample: Can anything beat the historical average? ”, Review of Financial Studies, 1509-1531
    4.Cenesizoglu, Tolga Timmermann, Allan (2012) “Do return prediction models add economic value?”, Journal of Banking and Finance, 2974-2987
    5.Cochrane, John H.(2008) “The dog that did not bark: A defense of return predictability”, Review of Financial Studies, 1533-1575
    6.Cremers, K. J.Martijn (2002) “Stock Return Predictability: A Bayesian Model Selection Perspective”, Review of Financial Studies, 1223-1249
    7.Hjalmarsson, Erik (2010) “Predicting global stock returns”, Journal of Financial and Quantitative Analysis, 49-80
    8.Li, Jiahan Tsiakas, Ilias (2017) “Equity premium prediction: The role of economic and statistical constraints”, Journal of Financial Markets, 56-75
    9.Neely, Christopher J. Rapach, David E. Tu, Jun Zhou, Guofu (2014) “Forecasting the equity risk premium: The role of technical indicators”, Management Science, 1772-1791
    10.Pettenuzzo, Davide Timmermann, Allan Valkanov, Rossen (2014) “Forecasting stock returns under economic constraints” , Journal of Financial Economics, 517-553
    11.Paye, Bradley S. Timmermann, Allan (2006) “Instability of return prediction models”, Journal of Empirical Finance, 274-315
    12.Rapach, David E. Strauss, Jack K. Zhou, Guofu (2010) “Out-of-sample equity premium prediction: Combination forecasts and links to the real economy”, Review of Financial Studies, 821-862
    13.Welch, Goyal (2008) “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction”, The Review of Financial Studies, 1455-1508

    QR CODE
    :::