臺灣股票市場異常超額報酬之預測性｜國立中央大學博碩士論文系統

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研究生：	吳俞潔 Yu-Jie Wu
論文名稱：	臺灣股票市場異常超額報酬之預測性 The Forecastability of Abnormal Returns in Taiwan’s Equity Market
指導教授：	徐之強 Chih-Chiang Hsu 廖志興 Chih-Hsing Liao
口試委員:
學位類別：	碩士 Master
系所名稱：	管理學院 - 經濟學系 Department of Economics
論文出版年：	2025
畢業學年度：	113
語文別：	中文
論文頁數：	69
中文關鍵詞：	效率市場假說、異常超額報酬、臺灣加權指數、機器學習預測模型
外文關鍵詞：	Efficient Market Hypothesis, The anomalous expected market return, Taiwan Stock Exchange Weighted Stock Index, Machine learning predictive model
相關次數：	點閱：24 下載：0
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Fama (1970) 一文提出效率市場假說，認為有效率的資本市場沒有資產錯誤定價獲利的機會，然而這與現實情況有所不同，過去諸多文獻探討股票市場屬於何種效率市場，但其文獻多使用單一變數，本文同時使用總體經濟面、市場資金面與股價技術面變數為預測因子預測臺灣股票市場超額報酬率。
本文實證結果發現，被篩選出最能有效預測臺灣股市超額報酬之變數以臺灣股票市場資金面與股價技術面預測因子為主。整體而言，預測模型中簡單組合法預測表現最為傑出，接著為組合彈性網路法與平均預測法，其中組合彈性網路法與簡單組合法之變異度表現最佳、預測累積年化報酬率以簡單組合法與主成分分析法最高。
本文預測結果，機器學習預測模型可有效捕捉股票市場超額報酬，然而當前所擁有資訊隨著時間經過不斷進行校正，使得向前預測二個月與三個月之預測報酬率最高，然而隨著預測期數越長，資訊會逐漸反應於股價上，累積超額報酬率越低，當前所擁有資訊僅能於短期內獲取額外的超額報酬。

Fama (1970) proposed the efficient market hypothesis, arguing that an efficient capital market has no opportunity to gain any profit from mispricing. However, in the past, many literatures have discussed what kind of efficient market the stock market belongs to, but many literatures used the single part of variables, we are different from the past, simultaneously uses the macroeconomic, market capital, and technical variables as predictors to predict the excess return rate of the Taiwan stock market.

The empirical results found that the selected variables that are most effective in predicting excess returns in the Taiwan stock market are mainly cash flow and technical variables. Overall, the simple combination method has the best forecasting performance among forecasting models, then the combination elastic net method and the average forecasting method, combined elastic network method especially had the best forecasting performance in recession.

The most important results this article observes is that the information is continuously corrected over time, making the two-month and three-month forecast returns the highest. However, the longer ahead forecast period we predict, the more information will gradually be reflected in the stock price. In other words, the information just can only obtain additional excess returns in the short term.

摘要                                                                                     i
ABSTRACT                                                                                ii
致  謝                                                                                  iii
目  錄                                                                                    iv
圖目錄                                                                                    vi
表目錄                                                                                   vii
第一章　緒論                                                                              1
  1-1　研究背景與研究動機                                                                     1
  1-2　研究方法概述                                                                     2
  1-3　研究架構                                                                             3
第二章　文獻回顧                                                                             4
  2-1　總體經濟面預測因子與股票報酬                                                     4
  2-2　市場資金面預測因子與股票報酬                                                     5
  2-3　股價技術面預測因子與股票報酬                                                     5
第三章　研究方法與模型                                                                     7
  3-1　變數解釋                                                                             7
    3-1-1　總體經濟面預測因子                                                             8
    3-1-2  市場資金面預測因子                                                            10
    3-1-3　股價技術面預測因子                                                            11
  3-2　超額報酬預測模型                                                                    12
  3-3　平均超額報酬預測指標 (Prevailing Mean Benchmark，PM)                            13
  3-4　機器學習模型                                                                    13
    3-4-1　普通最小平方法 (Ordinary Least Squares，OLS)                                    14
    3-4-2　最小絕對值收斂法 (Least Absolute Shrinkage and Selection Operator，LASSO)    14
    3-4-3　脊迴歸法 (Ridge Regression)                                                    15
    3-4-4　彈性網絡法 (Elastic Net，ENet)                                            15
    3-4-5　簡單組合法 (Forecast Combination)                                            16
    3-4-6　組合彈性網路法 (Combination Elastic Net，C-Enet)                            17
    3-4-7　平均預測法 (Averaging Forecasting Method)                                    17
    3-4-8　主成分分析法 (Principal Component Analysis，PCA)                            18
    3-4-9　偏最小平方法 (Partial Least Squares，PLS)                                    18
    3-4-10　向前逐步迴歸 (Forward Stepwise Regression)                                    19
  3-5　樣本外R_OS^2值與假設檢定                                                            20
第四章　實證結果                                                                            21
  4-1　市場超額報酬預測因子敘述統計                                                    21
  4-2　各機器學習方法選取重要預測因子                                                    24
  4-3　樣本外期間超額報酬預測                                                            32
    4-3-1  平均超額報酬預測指標                                                            32
    4-3-2  一階向前預測變異度                                                            33
    4-3-3  二階向前預測變異度                                                            34
    4-3-4  三階向前預測變異度                                                           36
    4-3-5  六階向前預測變異度                                                            37
    4-3-6  十二階向前預測變異度                                                        39
  4-4　機器學習模型之預測準確度分析                                                    41
    4-4-1　樣本外期間預測之R_OS^2統計值                                                41
    4-4-2　樣本外R_OS^2值為正之預測準確度                                              42
　4-5　機器學習模型之預測年化超額報酬率                                                    47
第五章　結論與建議                                                                    53
參考文獻                                                                                    54
                                

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