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研究生： 李蕙芳
Li, Huei-Fang
論文名稱： 基本面或技術面分析?
Fundamental or Technical Analysis?
指導教授： 黎明淵
Li, Ming-Yuan
學位類別： 碩士
Master
系所名稱： 管理學院 - 財務金融研究所
Graduate Institute of Finance
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 30
中文關鍵詞： 股票報酬 、基本面分析 、技術面分析
外文關鍵詞： Stock returns, Fundamental analysis, Technical analysis
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    • 有別於單一股票報酬預測模型，本篇論文建構一個結合基本面與技術面的股票報酬混合預測模型。本研究使用S&P500上市公司在2001年到2010年期間的基本面(前期本益比、股息殖利率與銷售淨額變動率)與技術面(前期股票報酬與交易量)季資料預測季股票報酬率。實證結果包含以下結論: 首先，混合預測模型較單一預測模型之預測誤差為小，也就是混合預測模型預測能力較單一預測模型佳。更進一步，我們發現當基本面分析與技術面分析的權重各為一半時，其預測誤差將比其它權重下之預測誤差小，因此我們可以得知在一般情形下，投資人應混合使用基本面與技術面分析去預測股價，而且分析之比重為各半。接下來，我們探討在三種不同情形下，投資人應著重使用基本面分析還是技術面分析，得到的結論為以下三點: (1) 如果公司存在盈餘管理(裁決性應計變數的絕對值較大)時，技術面分析比重應較大。(2) 沒有顯著結果說明當大公司或小公司時，應著重何種分析去預測股價。(3) 若股價上漲時，基本面分析比重應較大。

    This study establishes a hybrid stock returns prediction model with fundamental and technical analysis in contrast with single prediction models. The empirical data include the quarterly stock returns based on the fundamental variables (P/E ratio, dividend yield, and the change of net sales in previous period) and the technical variables (stock returns and trading volume in previous period) from S&P 500 firms (non-financial firms) during the period from 2001 to 2010. The empirical findings of this work are consistent with the following notions. First, the hybrid model with constant weights has better performance with regard to forecasting stock returns than the highbred one. Second, market investors give fundamental and technical analysis with equal weight when predicting stock returns. Third, we examine three situations, and draw the following conclusions: (1) if a company has earnings management (a high absolute value of discretionary accruals) we should weight technical analysis more heavily; (2) it is not clear whether fundamental analysis or technical analysis should be given more weight in large and small firms; and (3) if the stock price is rising, fundamental analysis should be weighted more heavily.

    Contents Abstract I 摘要 II 誌謝 III Contents IV Table of contents VI 1.Introduction 1 2.Literature review 3 3.Model specifications 6 3.1 Model I: Fundamental analysis 6 3.2 Model II: Technical analysis 6 3.3 Model III: Combined fundamental and technical analysis 7 3.4 Determining optimal loadings of the hybrid model (or Model III) 8 3.5 Optimal loadings across firms with high/low earnings management 9 3.5.1 Measures of earnings management 9 3.5.2 Sample segment 10 3.6 Optimal loadings across large/small firms 11 3.6.1 Measure of firm size 11 3.6.2 Sample segment 11 3.7 Optimal loadings across stocks with rise/fall in price 11 3.7.1 Measure of stock price change and sample segment 11 4.Forecasting performance measure 13 5.Data and empirical results 14 5.1 Data 14 5.2 Highbred versus hybrid models 14 5.3 Determination of optimal loading 15 5.3.1 Determining the optimal loading of the hybrid model 15 5.3.2 Determining optimal loading across firms with high/low earnings management 16 5.3.3 Determining optimal loading across large/small firms 17 5.3.4 Determining optimal loading across stocks with rise/fall in price 17 6.Conclusions and discussion 19 References 20 Table of contents Table 1.Variable definition 23 Table 2.Summary statistics of variables 24 Table 3.Parameter estimates of alternative model specifications for stock return rates 25 (a) Model I: Fundamental analysis 25 (b) Model II: Technical analysis 25 (c) Model III: Combined fundamental and technical analysis 25 Table 4.Performance comparisons of various model specifications for stock return rates 26 Table 5.Optimal loading of the hybrid model (i.e. Model III) 27 Table 6.Optimal loadings across firms with high/low earnings management 28 Table 7.Optimal loadings across large/small firms 29 Table 8.Optimal loadings across stocks with rise/fall in price 30

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